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Stochastic hydrology produces ensembles of time series that represent plausible future streamflow to simulate and test the operation of water resource systems. A premise of stochastic hydrology is that ensembles should be statistically representative of what may occur in the future. In the past, the application of this premise has involved producing ensembles that are statistically equivalent to the observed or historical streamflow sequence. This requires a number of metrics or statistics that can be used to test statistical similarity. However, with climate change, the past may no longer be representative of the future. Ensembles to test future systems operations should recognize non‐stationarity and include time series representing expected changes. This poses challenges for their testing and validation. In this paper, we suggest an evidence‐based analysis in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized and assessed using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed a tree‐based classification approach to categorize both ensembles and metrics. This approach provides a way to visualize and interpret differences between streamflow ensembles. The metrics presented, along with the classification, provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles, recognizing the presence of non‐stationarity. This contributes to better planning in large river basins, such as the Colorado, facing water supply shortages. Plain Language Summary: Long‐range water supply planning in many river basins requires an assessment of ensembles of plausible future streamflow time series used to simulate and test the operation of water resource systems. With climate change, and growing recognition that hydrologic processes are changing over time, the past may no longer be representative of the future. This poses challenges when using statistical metrics to test future streamflow ensembles. In this paper, we suggest an evidence‐based approach in which streamflow ensembles, whether statistically similar to and representative of the past or a changing future, should be characterized using an extensive set of statistical metrics. We have assembled a broad set of metrics and applied them to annual streamflow in the Colorado River at Lees Ferry to illustrate the approach. We have also developed an approach to categorize both ensembles and metrics. The metrics presented and the classification provide an analytical framework for characterizing and assessing the suitability of future streamflow ensembles for water resources system planning. The metrics and classification developed advance and contribute to better planning in large river basins facing water supply shortages. Key Points: Many ensembles representing plausible future streamflow are available for the Colorado River BasinMetrics are presented to provide an evidence‐based framework for evaluating these streamflow ensemblesA classification approach was developed to group similar ensembles and assess their suitability for planning in different future scenarios [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these "gage‐constrained" discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science. Plain Language Summary: The Surface Water and Ocean Topography (SWOT) satellite mission was launched on 15 December 2022. SWOT is designed to produce estimates of river discharge on many rivers where no in situ discharge measurements are currently available. This paper describes how SWOT discharge estimates will be created, and their expected accuracy. SWOT discharge will be estimated using simple flow laws that combine SWOT measurements of river water elevation above sea level, river width, and river slope, with ancillary data such as river bathymetry. We expect that discharge uncertainty will be less than 30% for two‐thirds of global reaches and will be dominated by a systematic bias. Temporal variations in river discharge time series are expected to be estimated within 15% for nearly all reaches, thus capturing the response of river discharge to rainfall and snowmelt events, including in basins that are currently ungaged, and providing a new capability for scientists to better track the flows of freshwater water through the Earth system. Key Points: The Surface Water and Ocean Topography satellite mission is designed to enable the estimation of discharge for global rivers wider than 100 mWhen unconstrained by in situ data, discharge uncertainty is expected to be <30% for most reaches, and to be dominated by systematic biasWe expect discharge temporal variations to be estimated to within 15% for nearly all reaches globally [ABSTRACT FROM AUTHOR]

Sociohydrology was launched as the science dealing with feedbacks between coupled human and water systems. Much of the early work in sociohydrology involved studies in spatially isolated domains (e.g., river basins) dealing with phenomena that involved emergent patterns in the time domain, with a focus on formulating and testing hypotheses about how they arise. The papers collected in this Special Section "Sociohydrology: Spatial and Temporal Dynamics of Coupled Human‐Water Systems" illustrate that the scientific scope of sociohydrology has broadened over the last few years, with a rich diversity of phenomena studied and an expansion of the knowledge foundations and methodologies applied. These Special Section papers now incorporate methodologies and approaches from a wide range of social science disciplines, including anthropology, complex systems, economics, and sociology. The major themes tackled by these papers are understanding (i) water metabolism—the economic use of water; (ii) interactions between humans and droughts; (iii) interactions between humans and floods; and (iv) the role of human institutions, policy, and management. These collected papers provide a foundation for future research that strives to understand how to achieve water resources sustainability (society to water) and reduce the risk of hydrological hazards in society (water to society). Going forward, we suggest that the development of a common sociohydrology framework will be paramount for research development and student training. Additionally, increased engagement with the broader water management communities will enhance sociohydrology understanding and impact. Key Points: The foundation of sociohydrology are the many emergent phenomena that arise during the practice of water resources managementSociohydrology studies benefit from the concepts and methodologies drawn from a wide range of natural and social science disciplinesIncreased engagement with the broader water management communities and the development of common frameworks represent important issues for sociohydrology going forward [ABSTRACT FROM AUTHOR]

Hypoxic blackwater events occur worldwide, affecting inland and coastal waters. These events have been exacerbated by man‐made floodplain drainage, leading to large‐scale fish kills and ecological degradation. This paper presents a new method to identify estuarine catchment areas that are most likely to generate hypoxic conditions. The method uses established risk factors, including vegetation type, inundation extent and duration, ground‐truthed in eastern Australia. A catchment is at higher risk of hypoxic blackwater generation if (a) it is located where floodwaters are high and/or drainage is impeded, (b) the site topography includes an extensive, low‐lying floodplain; and/or (c) the land‐use and environmental characteristics have a high deoxygenation potential. Consequent impacts to an estuary are determined by the floodplain connectivity with the estuary, and the discharge characteristics of the catchment drainage system. Where multiple, proximate catchments have similar drainage conditions, compounding plumes of hypoxic blackwater can overwhelm the assimilation capacity of the estuary. Climate change may significantly increase the volume and frequency of blackwater events in estuarine environments as a result of reduced drainage due to sea level rise, higher temperatures, and more intense and sporadic rainfall events. It is recommended that management measures be introduced to mitigate the effects of climate change and avoid further widespread hypoxic blackwater events. Key Points: A methodology is presented to differentiate estuarine catchments by their potential to generate hypoxic blackwater eventsLocal topographic and hydrodynamic conditions strongly influence the potential volume of hypoxic blackwater generated over a catchmentClimate change may increase the frequency and severity and change the distribution of blackwater risk throughout an estuary [ABSTRACT FROM AUTHOR]

This study investigates how environmental flows (e‐flows) can be designed as dynamic operating policies to optimize long‐term economic and ecosystem performance in reservoir systems. The main goal is to provide e‐flow solutions that contribute to better preparedness and flexibility of hydro‐systems to face multiyear stress periods, reducing the impact of water crises. The methodology framework combines a fish‐flow model with a multi‐objective evolutionary algorithm to construct multiple environmental water demand curves and capture the opportunity cost of different levels of ecosystem preservation. The water demand curves applied to a stochastic dynamic hydro‐economic model then derive dynamic e‐flow policies that balance immediate and future water use tradeoffs. The approach, termed dynamically adaptive environmental flows (DAE‐flows), is demonstrated on the Paraná River Basin, Brazil, a large‐scale hydropower system. Results show that the approach can adjust e‐flows (coordinated with other hydro‐system releases) over the time horizon, sacrificing them at certain times at the expense of some ecosystem loss, but improving long‐term ecosystem functioning. A long‐term approach to adaptation also yields better results for the environment without imposing a hard constraint to hydropower during droughts. Even under a drier climate change scenario, this allowed maintenance and improvement of environmental performance in most years, so during severe droughts the water could still be reallocated to hydropower but at a lesser cost to the environment. Plain Language Summary: The environment provides us with important resources (e.g., clean water, fish, livelihood and recreation). However, in order to thrive, the environment also needs water, which has specific patterns of flow in time depending on the species that live in the rivers and lakes. As we divert and store water in reservoirs to meet other important economic demands (e.g., hydropower) those flow patterns are disturbed, along with the species relying on it. This paper investigates how we can better represent flow patterns necessary to sustain fish life in large river systems, so we can identify and implement new strategies to achieve those flows downstream of reservoirs while still maintaining good performance to other economic demands. Results show that those strategies can not only be created by combining different patterns of flow but can also be updated and adjusted as conditions in the river change in the future, resulting from drought events and even different climates. To implement those strategies however, we need operations that are flexible and aimed at the future, besides the present. This is a key learning that helps us find better ways to adapt to future challenges, preserving our environmental assets while also meeting other important demands. Key Points: Ecological‐flow relationships, hydrological conditions and long‐term ecosystem performance are applied as drivers of dynamic e‐flowsDynamic e‐flows conserve water in some periods at the expense of some environment loss to improve long‐term ecosystem functioningThe operation of hydro‐systems can be adapted to reconcile long‐term economic and environmental demands [ABSTRACT FROM AUTHOR]

The need for and the use of different data assimilation techniques to improve the quality of streamflow forecast is now well established. In this paper, the goal is to demonstrate the power of a new class of methods known as the forward sensitivity method (FSM) which is based on the temporal evolution of model sensitivities with respect to the control variables consisting of initial conditions and parameters. FSM operates in two phases: the first phase provides a simple algorithm for placing observations at or near where the square of forward sensitivities attains their maximum values. Using only this selected subset of observations in a weighted least squares method, the second phase then provides an estimate of the unknown elements of the control variables. In this paper, FSM based assimilation is applied to a simple class of two parameter model in a medium‐sized agriculture dominant watershed lying in the Krishna River Basin, India. Four assimilation scenarios were tested to determine the effect of assimilating only sensitive observations as well as the impact of temporally evolving initial condition sensitivity. Sensitivity results showed that observations during the monsoon time alone are enough for assimilation purposes, which has helped in reducing the computational time greatly. Assimilation and forecast results also indicated that the scenarios which assimilated only sensitive observations are better in estimating daily streamflow. From the obtained results, it is concluded that FSM based assimilation has significant potential to improve the streamflow simulations, especially in places where data availability remains a major challenge. Key Points: The potential of assimilating only sensitive soil moisture observations on streamflow forecast was demonstrated using a novel forward sensitivity method (FSM) methodImpact of temporally evolving sensitivity of initial condition on hydrological model performance during assimilation was determinedThe sensitivity‐based soil moisture assimilation was effective up to 45 lead days during streamflow forecast [ABSTRACT FROM AUTHOR]

Previous studies have shown that regional deep learning (DL) models can improve runoff prediction by leveraging large hydrological datasets. However, training a DL regional model using all data without screening may degrade local performance. This study focuses on constructing enhanced local models through the utilization of cross‐basin data. To this end, we propose an approach that employs a novel training strategy to optimize DL model training for specific basins. The approach measures the impact of any one basin's gradient on the loss of the basin of interest, providing insights into the relationships between different basins. The approach was validated using 531 basins from the CAMELS dataset. Results suggest that local performance degradation is a common occurrence in regional models, and imbalanced data are likely to result in a specific pattern dominating the entire regional model. In comparison to a regional model simply trained with all basins, the median Nash‐Sutcliffe efficiency (NSE) for our models is 0.031 higher. In particular, the increase in NSE can exceed 0.2 for some dry basins. Our findings indicate that this novel DL strategy can significantly improve model performance in specific basins using large hydrological datasets, while mitigating local performance loss. Plain Language Summary: In the realm of deep learning, incorporating more data into the training process typically results in a more potent model. Conventionally, large datasets have been employed to train regional models with the intention of predicting rainfall‐runoff processes across all basins. However, such regional models often encounter performance degradation when applied to local basins. This degradation can be attributed to the extraction of overly general features, leading to a loss of specificity. In this paper, our objective is to harness information from a large dataset to establish a more robust local model. We have introduced a method that autonomously learns the similarities in rainfall‐runoff behavior among basins. Subsequently, we utilize this learned similarity to selectively choose data that proves advantageous for training the local model. Our results demonstrate that this strategy can significantly enhance runoff prediction, particularly in arid basins. Key Points: Training a DL regional model using all data without screening may degrade local performanceProper selection of training data is crucial for enhancing DL model training for individual basins, especially arid basinsData from different basins might act as mutual noise during the training process [ABSTRACT FROM AUTHOR]

Currently, climate change is considered as an important factor affecting nutrient loads introduced through riverine systems into the Baltic Sea. Although the prospect of a large increase in pollution has long seemed very real, it still does not translate into planning of effective remedial actions. One of the factors limiting the development of such activities is the scale of simulations, focusing generally on catchment outlet profiles. To fill this gap and enable a step forward in understanding responses toward future predictions in a higher resolution scale (subcatchment), we assessed nutrient load contribution using calculation profiles localized along a main watercourse and its tributaries. To track spatial and seasonal changes of total nitrogen and phosphorus for the Wełna River (central Poland), we used climate change data and the SWAT model. Having at our disposal a catchment model with a good performance we could follow not only total load changes in particular subcatchments, but also track localization of the pollution sources and their direct impact on load estimations. Our results showed an increase of the loads, especially from the agricultural land use type, up to 34% for TN and 85% for TP, in the most extreme scenario. Moreover, forest areas have been noted as highly reactive to climate change, and through their localisation are able to distinctly alter nutrient outflow. Finally, the contribution of urban areas should be further investigated since the dynamics of nitrogen and phosphorus release from impervious surfaces is noticeably different here than from the other diffuse sources. Plain Language Summary: This paper describes how climate change will affect the amount of nutrients in a small river catchment in the Baltic Sea region. While it is known that climate change can increase nutrient loads, effective actions to prevent them are still lacking. The "big picture" based on a whole catchment is still poor in terms of finding nutrient vulnerable areas. In this research, we looked at a more detailed scale to see where the nutrients are coming from and how they are changing over time. We used computer modeling to show that the amount of nutrients coming from agriculture, forests, and city areas will increase due to climate change. Overall, the amount of nitrogen can raise by 34% and the amount of phosphorus by 85%. Our results can be the basis for making decisions regarding actions aimed at improving the condition of surface waters and counteracting climate change effects. Key Points: Spatio‐temporal trends of nutrient loads have been tracked in a Baltic Sea region catchment under climate change scenariosClimate change suggests a potential load increase from the whole catchment by 34% for total nitrogen and 85% for total phosphorusOutputs from individual nutrient sources could grow by even 187% for total nitrogen and 302% for total phosphorus [ABSTRACT FROM AUTHOR]

Modeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for further minimizing the prediction uncertainty by inherently taking care of different sources of uncertainty. The RF algorithm inherently combines the physics of the hydrological system (i.e., process‐based variables) with machine learning‐based approach to minimize the overall prediction uncertainty. The proposed framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, through several statistical performance indices for assessing the accuracy and uncertainty of the model prediction. The framework was observed to be consistently improving the model performance minimizing the uncertainty in both watersheds. Therefore, the proposed framework can be considered to be more reliable in improving the prediction capability of hydrological models. Key Points: Development of a physics‐aware machine learning based hydrological model for streamflow simulationThe proposed framework characterizes the model prediction uncertainty, accounting different sources of uncertainty implicit/explicitlySignificant reduction in overall prediction uncertainty is achieved [ABSTRACT FROM AUTHOR]

It is not an understatement to say that a thriving and healthy river system for the Mesopotamian region means a thriving economy for all with a productive agriculture and a sustainable manufacturing base that needs reliable water supply and cheap power. When looked at from a 30,000 feet from the ground, the Mesopotamian region began experiencing a steady decline in the health of its rivers from the 1980s with a series of unfortunate man‐made events ranging from wars to uncoordinated dam development. While there have been many analyses and historical dissection of the current water situation, one question that we all need to ask now is How can we restore the Mesopotamian rivers? Plain Language Summary: We believe we need to think in radical ways and drive our multi‐year and multi‐disciplinary research agenda with the goal of driving sustainable economic development. Research just for research sake and publication of research papers is now an outdated concept that benefits a few in this region in the near term. We call on the key players of the region, such as the national governments, water ministries, US Department of State, and European Government agencies to push for win‐win solutions. It is in their best economic and sovereign interest too to see Mesopotamian rivers restore sooner rather than later. Because, if the Mesopotamian rivers fail, then we as a world fail. Key Points: Mesopotamian rivers have declined steadily over the past 40 years with a reduced flood pulse due to dams by Turkey Iraq and SyriaSuch decline has lead to an unsustainable state of affairs and livelihood for downstream nationsThere are practical solutions to restore the rivers by seeking answer to how much water is really available [ABSTRACT FROM AUTHOR]

Within the Australian southern Murray‐Darling Basin (sMDB) water markets have become a key institutional response to water scarcity and environmental problems. These markets facilitate trading of water among many users and across multiple connected regions. Increasingly, these markets also support intertemporal transfers via the carryover of user water allocations within public water storages. While trading can improve the spatial allocation of water, carryover can improve the temporal allocation by ensuring storage reserves reflect prevailing user preferences. This paper presents an economic model of water markets and irrigated agriculture in the sMDB. A reduced‐form statistical model of water demand is estimated using historical irrigation and water market data. This statistical model is placed within an economic partial‐equilibrium framework accounting for both interregional trade and interyear carryover. The model is solved numerically for a dynamic market equilibrium given assumptions over future stochastic water supplies. Out‐of‐sample tests show the model can realistically simulate water market prices and irrigation activity across the basin. The model is applied to estimate the economic benefits of interregional water trading and interyear carryover. Results show around a third of the economic benefits of water markets in the region can be attributed to interyear carryover. Plain Language Summary: The southern Murray‐Darling Basin (sMDB) is an important river system in Australia. Water from this river system is used both for irrigation farming and to support the environment. Water markets have been introduced allowing water users (farmers) in different parts of the river system to trade water among each other. These markets also allow farmers to store any unused water in dams for use in future years. This study presents a model of water markets in the sMDB. This model is developed using historical data on water supply, farm water use, and water market prices. The model can be used to simulate outcomes in the region's water market, given assumptions on the supply of water. Testing shows the model can produce results that are consistent with historical data. The model estimates that water markets generate benefits for water users of around $AUD172 m per year: around 14% of the value of water in the region. The ability to hold unused water in dams accounts for around one‐third of these benefits. Key Points: An economic model of water markets in the Australian southern Murray‐Darling Basin is developedThis model accounts for both interregional water trading and interyear water storage (carryover)Water markets are shown to generate significant economic benefits with around a third from interyear carryover [ABSTRACT FROM AUTHOR]

Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations. Plain Language Summary: The relationships between floods, physical properties of river basins, and the spatio‐temporal distribution of precipitation define the regional flood scaling laws. Improved regional flood scaling models are required for updating flood risk scoring tools to help insurers and decision‐makers in the light of anthropogenic climate changes and natural variability of Earth systems. Two scaling models are developed here to examine how the physical properties of basins and precipitation patterns can inform flood attributes across the conterminous United States (US). Using long records of streamflow and precipitation observations, we identified different scaling relationships between the catchment factors, preceding precipitation, and the duration, peaks, and volume of floods. Larger basins located at higher elevations are found to have lower scaling rates with flood peaks when interacting with the preceding precipitation patterns. Overall, low‐elevation flat basins across the Eastern, Southern, and Northwestern US have a larger positive response to precipitation in amplifying flood attributes. These new scaling laws can help advance our understanding of controlling mechanisms for flash floods and mega‐floods, which is required for a dynamic flood risk assessment in a changing environment. Key Points: Two Bayesian scaling models are developed to understand the sensitivity of floods to geomorphologic characteristics and catchment rainfallLow‐elevation and flat basins with different sizes have greater scaling to rainfall in amplifying flood duration and volumesLow‐elevation and small basins with different steepness show larger sensitivity to rainfall distribution that enhances flood peaks [ABSTRACT FROM AUTHOR]

To fully benefit from remotely sensed observations of the terrestrial water cycle, bias and random errors in these data sets need to be quantified. This paper presents a Bayesian hierarchical model that fuses monthly water balance data and estimates the corresponding data errors and error‐corrected water balance components (precipitation, evaporation, river discharge, and water storage). The model combines monthly basin‐scale water balance constraints with probabilistic data error models for each water balance variable. Each data error model includes parameters that are in turn treated as unknown random variables to reflect uncertainty in the errors. Errors in precipitation and evaporation data are parameterized as a function of multiple data sources, while errors in GRACE storage observations are described by a noisy sine wave model with parameters controlling the phase, amplitude, and randomness of the sine wave. Error parameters and water balance variables are estimated using a combination of Markov Chain Monte Carlo sampling and iterative smoothing. Application to semiarid river basins in Iran yields (a) significant reductions in evaporation uncertainty during water‐stressed summers, (b) basin‐specific timing and amplitude corrections of the GRACE water storage dynamics, and (c) posterior water balance estimates with average standard errors of 4–12 mm/month for water storage, 3.5–7 mm/month for precipitation, 2–6 mm/month for evaporation, and 0–2 mm/month for river discharge. The approach is readily extended to other data sets and other (gauged) basins around the world, possibly using customized data error models. The resulting error‐filtered and bias‐corrected water balance estimates can be used to evaluate hydrological models. Key Points: A Bayesian hierarchical model fuses water balance data containing unknown bias and random errorsThe model is solved using a combination of Markov Chain Monte Carlo sampling and iterative smoothingComputed posteriors provide hydrologically consistent data error and water balance estimates [ABSTRACT FROM AUTHOR]

Stochastic methods have been typically used for the design and operations of hydraulic infrastructure. They allow decision makers to evaluate existing or new infrastructure under different possible scenarios, giving them the flexibility and tools needed in decision making. In this paper, we present a novel stochastic streamflow simulation approach able to replicate both temporal and spatial dependencies from the original data in a multi‐site basin context. The proposed model is a multi‐site extension of the modified Fractional Gaussian Noise (mFGN) model which is well‐known to be efficient to maintain periodic correlation for several time lags, but presents shortcomings in preserving the spatial correlation. Our method, called Weighted‐mFGN (WmFGN), incorporates spatial dependency into streamflows simulated with mFGN by relying on the Cholesky decomposition of the spatial correlation matrix of the historical streamflow records. As the order in which the decomposition steps are performed (temporal then spatial, or vice‐versa) affects the performance in terms of preserving the temporal and spatial correlation, our method searches for an optimal convex combination of the resulting correlation matrices. The result is a Pareto‐curve that indicates the optimal weights of the convex combination depending on the importance given by the user to spatial and temporal correlations. The model is applied to a number of river basins in Chile, where the results show that the WmFGN approach maintains the qualities of the single‐site mFGN, while significantly improving spatial correlation. Key Points: Weighted modified Fractional Gaussian Noise model improves spatial correlations without significantly sacrificing temporal correlationThe method searches for an optimal convex combination of the spatial and temporal correlation matrices according on the user's priorityOur results in several Chilean basins demonstrate that Weighted‐modified Fractional Gaussian Noise (WmFGN) represents a significant improvement over the mFGN in preserving correlations [ABSTRACT FROM AUTHOR]

Future water availability is threatened by changes in both climate and water demand. Water rights with differing priorities are an important foundation of demand‐side tools (e.g., buyback, water pricing, and water market) to improve water use efficiency and reduce water scarcity, especially in highly regulated river systems. This paper assesses the impact of climate change on water yields from carryover storage with dual‐priority (high/low) water rights allocation systems using a simple and rapid analytical method. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and Cv). We evaluate this method against a water resource simulation model in the Goulburn River basin, Australia. In general, our analytical "dual‐priority" Gould‐Dincer model reproduces water allocation estimates from the simulation model. We further demonstrate this method across 12 Australian catchments to investigate the climate change impact on "dual‐priority" water rights yield at the catchment scale. The hydrological projections show decreasing mean annual runoff and increasing annual runoff variability, except for some catchments in northern Australia. Water yield for high‐priority water rights (HPWRs) and low‐priority water rights (LPWRs) decreases for most catchments except for some catchments in northern Australia. South Dandalup in the 2070s (RCP8.5) shows the largest percentage decrease in HPWR and LPWR yield (about −53.53% and −56.81%, respectively). Our results show that changes in mean annual inflow have a more significant influence on water yield of HPWR and LPWR than Cv. Overall, the simple method provides a rapid assessment of water yields with "dual‐priority" water rights which is applicable across multiple sites at regional or even global scale. Key Points: A simple analytical method is used to evaluate the impact of climate change on the yield of "dual‐priority" water rightsWater yield for both high‐ and low‐priority water rights generally decreases under future climate in AustraliaFuture change in mean annual inflow has a more significant influence on water yield than change in annual flow variability [ABSTRACT FROM AUTHOR]

Optimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets. Plain Language Summary: Hydrological models typically require local observations of river flow to calibrate the models and test their predictive capability. This limits the possibility for predictions in ungauged basins. This study used holdout tests to investigate the robustness of hydrological predictions for ungauged basins. Particularly we investigate how adding more basins and observed spatial patterns of evapotranspiration in the calibration of these models impact this robustness and transferability of model parameters to basins not used for calibration. Results show that transferability and spatial consistency of parameters increase when adding more basins and spatial pattern observations. Key Points: Clear improvements in simulated spatial patterns can be achieved with very limited tradeoff with discharge performanceMulti‐basin and spatial pattern‐based calibration improve parameter realism and transferability to ungauged basins [ABSTRACT FROM AUTHOR]

Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling. Plain Language Summary: This study mainly focuses on the development of an urban flood model (Dominant river tracing‐Routing Integrated with VIC Environment, DRIVE‐Urban), which couples the river‐basin and urban hydrological modeling, with a great potential for large‐scale real‐time urban flood monitoring and forecasting through the current Global Flood Monitoring System. Two inundation algorithms are developed within the DRIVE‐Urban model. One is for the river inundation caused urban flooding, and the other one is for the urban inundation caused by manhole overflow or excessive runoff drainage over the city land surface. Multi‐source observations including the closed‐circuit television video, citizen photos and municipal reports are collected for model validation. The results show that the DRIVE‐Urban model simulates well the different type of urban inundations at the 5‐m high spatial resolution with a promising hit success (i.e., POD >60%) according to three case studies. In particular, the results for the typhoon Kalmaegi case show that the model performance can be improved significantly by coupling the upstream river flow simulation with the urban runoff‐routing process, which validates our strategy of including the upstream river‐basin hydrological process in the urban flood modeling. Key Points: The Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE)‐Urban flood model is developed by coupling river basin and urban hydrological and hydraulic processTwo 2D inundation algorithms are implemented within the model to simulate the manhole inundating and river overflow flooding respectivelyThe validity of the DRIVE‐Urban model is well established through three case studies with observations during typhoon and rainfall events [ABSTRACT FROM AUTHOR]

Attributing nitrogen export to specific land use within heterogeneous catchments remains a challenge due to the spatio‐temporal variability in conditions influencing the mobilization and fate of nitrogen species. This study demonstrates that the measurement of dual stable isotopes of nitrate, taken along with routine tributary measurement of nitrogen in nitrate (NO3−‐N) and ammonium (NH4+‐N), aids in apportioning sources of the overall nitrogen load during wet periods. An inverse modeling technique was developed to estimate the land use‐specific export rates of NO3−‐N and NH4+‐N from the Caboolture River Catchment in Queensland, Australia. Measurements of nitrogen in streamflow at 51 locations during six sampling campaigns (May 2012 to April 2013) were made along with catchment geospatial data that was used to disaggregate sub‐catchments into six land use fractions. A hydrological model was applied to compute the runoff from each fraction and water routing through the stream network. This data was used within a nitrogen mixing model with inclusion δ15NNO3 and δ18ONO3. The land uses specific export rate was computed inversely as the posterior of a Bayesian interference applied to the model. During higher rainfall periods when export rates were highest, the main land use exporting nitrogen was wetland (110 g/ha/day NO3−‐N, 27 g/ha/day NH4+‐N) resulted from mineralization and nitrification of organic N, followed by urban (16 g/ha/day NO3−‐N, 2.3 g/ha/day NH4+‐N). The advantage of using the dual isotopes in conjunction with the nitrogen concentration data was demonstrated by reduced uncertainty in the computed rates during the higher rainfall periods, relative to calculations without δ15NNO3 and δ18ONO3. Plain Language Summary: Attributing nitrogen export to specific land use types within heterogeneous catchments is difficult. This paper demonstrates the use of an inverse modeling technique, dissolved inorganic nitrogen concentration data, a dual stable isotope measurement data, and geospatial data to estimate the land use‐specific export rates of dissolved inorganic nitrogen from six land use types in the Caboolture River Catchment in Queensland, Australia. The results show that during higher rainfall periods when export rates were highest, the main source of nitrogen was found to be wetland (110 g/ha/day NO3−‐N and 27 g/ha/day NH4+‐N), followed by urban (16 g/ha/day NO3‐N and 2.3 g/ha/day NH4+‐N). Key Points: An inverse modeling technique was developed to estimate the land use‐specific export rate of nitrogen using isotopes signaturesThe model was embedded within a Bayesian framework to assess the predictive uncertainty with and without inclusion of dual isotopes of nitrateDuring wet periods, when nitrogen export rates were highest, the predictive uncertainty was reduced when the model included dual isotopes of nitrate [ABSTRACT FROM AUTHOR]

Measurements in forest catchments in Fukushima Prefecture indicate that cesium‐137 (137Cs) leaching from forest litter affects dissolved 137Cs levels in river water. Existing simulations have been unsuccessful in reproducing this phenomenon due to mechanistic limitations in the models connecting the forest ecosystem and the river system. This paper introduces a new coupled watershed‐geochemical model to address this. We connected a forest ecosystem compartment model to the 3D watershed model General‐purpose Terrestrial Fluid‐Flow Simulator to describe 137Cs transfer between forests and the river system. The compartment model included nonleachable and leachable 137Cs sites in the organic layer. The latter sites model how leachable 137Cs stocks increase with ambient temperature owing to decomposing organic matter. The model was tested by simulating dissolved and particulate 137Cs discharges from a forested catchment upstream of the Ohta River, Fukushima Prefecture. The results for dissolved 137Cs concentrations in river water for 1 January 2014, to 31 December 2015, correlated well with catchment measurements. The simulations reproduced dissolved 137Cs concentration peaks that occurred during three typhoon events and the seasonal variations under baseflow conditions. The results support the theory that leaching from the organic layer in forests is a primary factor affecting river water dissolved 137Cs concentrations. The wider contribution of this study is it introduces a coupled watershed‐geochemical model, applicable not only for evaluating the dynamics of 137Cs, but also those of other substances that adsorb onto sediments and soil particles, and leach from forest organic matter. Plain Language Summary: Cesium‐137 (137Cs) derived from the Fukushima Daiichi Nuclear Power Plant is a critical radioisotope remaining from the fallout owing to its high activity, long half‐life (30.1 y), and potential impacts on humans and ecosystems. Approximately 70% of all land in Fukushima Prefecture is covered with forests, where 137Cs is predominantly present. The behavior of dissolved 137Cs in the environment must be investigated because this form of 137Cs is readily absorbed by living beings. Researchers have reported that dissolved 137Cs concentrations in river water under baseflow conditions vary seasonally and rise during storms, indicating that leaching from organic matter in forests is a dissolved 137Cs source. We developed a new simulation model for the dynamics of 137Cs between the forest ecosystem and the river system. The simulations reproduced the dissolved 137Cs concentration peaks that occurred during previous typhoons and the seasonal variations that occurred under baseflow conditions. Therefore, the model's results support the theory that leaching from the organic layer in forests is a primary factor affecting dissolved 137Cs concentrations in river water. The model introduced here can not only be used for simulating 137Cs, but also has wider applicability for simulating the transport mechanisms of other contaminants through forest watersheds. Key Points: A coupled watershed‐geochemical model is introduced for simulating dissolved contaminant transport from forests into river waterThe model reproduced dissolved 137Cs concentration trends in Ohta River, Fukushima Prefecture, in base‐ and stormflow periodsThe results supported the theory that the organic layer is an important contamination source for river water [ABSTRACT FROM AUTHOR]

In mountain river basins, sediment availability on hillslopes and in channels is key to predict the sediment response to hydrological forcing. However, quantification of sediment availability and its variability in time is challenging, because sediment supply is often strongly stochastic and dominated by mass wasting. In this paper, we introduce a variable landslide sediment supply as a function of topography, hydrology, and hillslope activity in the hydrology‐sediment model TOPKAPI‐ETH. We use the model to analyze the dynamics of sediment storage in a mesoscale pre‐Alpine basin. We simulate a range of transport‐ and supply‐limited conditions to quantify the variability of suspended sediment concentrations and load, and the seasonal dynamics of sediment storage. We show that supply limitation dampens the natural variability of the hydrological and sediment transport processes, and therefore reduces the scatter of the suspended sediment rating curve. By comparing the model results with observations, we demonstrate that alternation of low and high sediment availability favors sediment load variability at the outlet. The temporal dynamics of sediment storage depends on the hillslope activity, and the balance between sediment supply by landslides and evacuation by runoff. When the export flux is comparable or greater than the recharge flux, the storage shows seasonal fluctuations with a maximum in late winter or spring, and sediment starvation in summer. By representing the dynamics of both inputs and outputs of the sediment storage, the proposed model provides a physically based tool to isolate the effect of transport‐ and supply‐limited conditions in basin sediment response. Key Points: We introduce a variable landslide sediment supply in a hydrology‐sediment model to simulate supply and transport limitation in Alpine basinsVariability in suspended sediment concentrations and load is dampened by supply limitation and favored by the stochasticity of soil erosionThe model predicts recharge of the sediment reservoir in winter by high sediment supply and emptying in spring and summer by flood events [ABSTRACT FROM AUTHOR]

Models of water resources systems are conceived to capture the underlying environmental dynamics occurring within watersheds. All such models can be regarded as working hypotheses, differing in the aspects of process representation and conceptualization. Most of the associated efforts in the water resources research community is dedicated to development of new models that perform well under specific atmospheric conditions and catchment properties. In this context, flexible modeling frameworks are gaining importance as they facilitate the model building process by providing the model building blocks, whereby the hydrologist is free to assemble the model for task at hand. Such flexible models have high degree of transferability, which in turn aid in progressing toward a unified hydrological theory at catchment scale. However, in cases without sufficient insights regarding a catchment characteristics and/or lack of expert's knowledge, one may have to try a large number of model configurations based on available model building blocks to construct an appropriate model for the catchment of interest. Undoubtedly, this may be time consuming and computationally intensive. This paper proposes a novel model building algorithm, which uses the full potential of flexible modeling frameworks by searching the model space and inferring suitable model configurations relying on machine learning. Proposed machine learning algorithm is based on evolutionary computation approach using genetic programming (GP). State‐of‐art GP applications in rainfall‐runoff modeling so far used the algorithm as a short‐term forecasting tool that generates an expected future time series very similar to neural networks application. In this case, the proposed algorithm develops a physically meaningful rainfall‐runoff model. Although at the moment we learn models using two flexible modeling frameworks (SUPERFLEX and FUSE), the model induction toolkit can be armed with any internal coherence building blocks. The model induction capabilities of the proposed framework have been evaluated on the Blackwater River basin, Alabama, United States. The model configurations evolved through the model induction toolkit are consistent with the fieldwork investigations and previously reported research findings. Key Points: This paper presents a novel machine learning algorithm, which is guided through the incorporation of existing hydrological knowledgeProposed machine learning algorithm is based on evolutionary computation approach using genetic programmingIn the present case, the building blocks of flexible hydrological modeling frameworks represent elements of the background knowledge [ABSTRACT FROM AUTHOR]

In semidistributed hydrological modeling, sequential calibration usually refers to the calibration of a model by considering not only the flows observed at the outlet of a catchment but also the different gauging points inside the catchment from upstream to downstream. While sequential calibration aims to optimize the performance at these interior gauged points, we show that it generally fails to improve performance at ungauged points. In this paper, we propose a regularization approach for the sequential calibration of semidistributed hydrological models. It consists in adding a priori information on optimal parameter sets for each modeling unit of the semidistributed model. Calibration iterations are then performed by jointly maximizing simulation performance and minimizing drifts from the a priori parameter sets. The combination of these two sources of information is handled by a parameter k to which the method is quite sensitive. The method is applied to 1,305 catchments in France over 30 years. The leave‐one‐out validation shows that, at locations considered as ungauged, model simulations are significantly improved (over all the catchments, the median KGE criterion is increased from 0.75 to 0.83 and the first quartile from 0.35 to 0.66), while model performance at gauged points is not significantly impacted by the use of the regularization approach. Small catchments benefit most from this calibration strategy. These performances are, however, very similar to the performances obtained with a lumped model based on similar conceptualization. Key Points: Sequential calibration without regularization yields low performance in small ungauged catchmentsSequential calibration constrained by regularization strategy helps improving model regionalizationRegularization benefits from a priori parameter values of neighboring catchments [ABSTRACT FROM AUTHOR]

Dominant processes in a watershed are those that most strongly control hydrologic function and response. Estimating dominant processes enables hydrologists to design physically realistic streamflow generation models, design management interventions, and understand how climate and landscape features control hydrologic function. A recent approach to estimating dominant processes is through their link to hydrologic signatures, which are metrics that characterize the streamflow timeseries. Previous authors have used results from experimental watersheds to link signature values to underlying processes, but these links have not been tested on large scales. This paper fills that gap by testing signatures in large sample data sets from the U.S., Great Britain, Australia, and Brazil, and in Critical Zone Observatory (CZO) watersheds. We found that most inter‐signature correlations are consistent with process interpretations, that is, signatures that are supposed to represent the same process are correlated, and most signature values are consistent with process knowledge in CZO watersheds. Some exceptions occurred, such as infiltration and saturation excess processes that were often misidentified by signatures. Signature distributions vary by country, emphasizing the importance of regional context in understanding signature‐process links and in classifying signature values as "high" or "low." Not all signatures were easily transferable from single, small watersheds to large sample studies, showing that visual or process‐based assessment of signatures is important before large‐scale use. We provide a summary table with information on the reliability of each signature for process identification. Overall, our results provide a reference for future studies that seek to use signatures to identify hydrological processes. Key Points: Large sample signature distributions enabled us to put signature values into context as high or low, but differ by countryMost signatures agreed with the processes they are supposed to represent, except for infiltration and saturation excess signaturesWe provide a table of signatures with recommendations on their reliability and use for process interpretation [ABSTRACT FROM AUTHOR]

To represent the physical processes at hillslope scales for hyper‐resolution land surface modeling, we propose a hierarchical, catchment‐based spatial tessellation method. The land surface is divided into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. This catchment‐based structure explicitly represents hillslope drainage networks and can be applied at various resolutions determined by a pre‐defined maximum height band size. The proposed tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneity, resulting in a higher aggregation skill through the height band representation. The spatial variations in air temperature, leaf area index, saturated soil hydraulic conductivity, and soil porosity are generally lower within a height band than those in a conventional rectangular grid, reflecting the nature of topographic control on climate, vegetation, and soil distribution. The improvement in aggregation skill depends on resolutions and terrain slope angle, more pronounced at 1/6° model resolution and over steeper terrains. Finally, we demonstrate that our proposed catchment‐based structure performs better than the grid‐based structure through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2° using the ILAMB model evaluation metrics. Plain Language Summary: This paper develops a catchment‐based spacing approach for high‐resolution land surface modeling. The land surface is divided into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. We demonstrates that the catchment‐based approach can better represent the heterogeneous distributions of water, soils, plants, and climates, especially over mountainous regions than does the conventional, rectangular grid‐based approach. When used in a land surface model, the catchment‐based approach also performs better than the grid‐based approach through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2°. Key Points: A catchment‐based hierarchical spatial tessellation: catchments, height bands, and land cover patches, is proposedThe catchment‐based tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneitiesThe proposed catchment‐based structure performs better than the grid‐based structure in land surface modeling [ABSTRACT FROM AUTHOR]

Understanding transit times (TT) and residence times (RT) distributions of water in catchments has recently received a great deal of attention in hydrologic research since it can inform about important processes relevant to the quality of water delivered by streams and landscape resilience to anthropogenic inputs. The theory of transit time distributions (TTD) is a practical framework for understanding TT of water in natural landscapes but, due to its lumped nature, it can only hint at the possible internal processes taking place in the subsurface. While allowing for the direct observation of water movement, Electrical Resistivity Imaging (ERI) can be leveraged to better understand the internal variability of water ages within the subsurface, thus enabling the investigation of the physical processes controlling the time‐variability of TTD. In this study, we estimated time‐variable TTD of a bench‐scale bare‐soil sloping soil lysimeter through the StorAge Selection (SAS) framework, a traditional lumped‐systems method, based on sampling of output tracer concentrations, as well as through an ERI SAS one, based on spatially distributed images of water ages. We compared the ERI‐based SAS results with the output‐based estimates to discuss the viability of ERI at laboratory experiments for understanding TTD. The ERI‐derived images of the internal evolution of water ages were able to elucidate the internal mechanisms driving the time‐variability of ages of water being discharged by the system, which was characterized by a delayed discharge of younger water starting at the highest storage level and continuing throughout the water table recession. Plain Language Summary: Understanding how long water stays in the earth's surface (from the moment it lands as precipitation until it reaches a stream or evaporates back to the atmosphere) is quite important for many earth science investigations. However, its estimation is still in its infancy, and not many methods exist that allow for the direct observation of water ages within the subsurface. This paper reports on an experiment that tries to do so by combining two different methods: the first method use information from chemically labeled water from influxes and outfluxes, and the second one used electrical resistivity of the soil, allowing for the imaging of the internal distribution of ages. We learned that our system tends to discharge older water from storage until it reaches its highest levels of storage, which is when greater portions of younger water start being released. This behavior can be explained by an upward movement of the water table that is able to reach the regions where younger water is located, allowing for its mobilization out of the system. Key Points: Electrical resistivity imaging was used to derive spatial distributions of water ages in a model hillslopeAnalysis of time‐varying SAS functions indicated increasing release of younger water with decreasing storageDownward and upward soil saturation mechanisms were identified as causing the observed behavior [ABSTRACT FROM AUTHOR]

It remains an open question whether the state of knowledge on bend hydrodynamics applies to sharp bends in the largest river systems on Earth. This paper reports a field investigation into the hydrodynamics in two consecutive sharp bends of the Middle Yangtze River (MYR), and demonstrates that the reduced‐order model of Blanckaert and de Vriend is able to resolve the secondary flow strength and continuous flow redistribution in rivers of this size to high accuracy. Consequently, the model has been validated over the entire range of scales. These two bends show similarities to those of smaller size in terms of bathymetry, flow redistribution, and secondary flow evolution. The transverse location of the core of maximum velocity along the bends lags behind the thalweg by approximately half a width. A center‐region cell of secondary flow develops over the outer bank near the apex, and gradually decays downstream until it completely vanishes at the bend exit. Both bends are mildly curved from the perspective of hydrodynamic modeling, with a weak interaction between the secondary flow and primary flow, although they are sharply curved from the perspective of geomorphology (width/radius > 0.5). A term‐by‐term analysis of the model indicates that the flow redistribution is primarily controlled by planimetric changes in curvature and by topographic steering, and is slightly influenced by the secondary flow. However, the secondary flow may indirectly affect the flow redistribution by conditioning the bed topography. Important knowledge gaps remain with respect to the coupling between the hydrodynamic and morphological processes. Key Points: Field measurements of 3D flow field in very large sharp bendsPatterns of mean and secondary flows are similar as in smaller systemsThe reduced‐order model can resolve the flow field over the entire range of scales [ABSTRACT FROM AUTHOR]

The paper presents a thermodynamic basis for water balance partitioning at the catchment scale, through formulation of flux-force relationships for the constituent hydrological processes, leading to the derivation of optimality conditions that satisfy the principle of Maximum Entropy Production (MEP). Application of these optimality principles at three different time scales leads to the derivation of water balance equations that mimic widely used, empirical models, i.e., Budyko-type model over long-term scale, the 'abcd' model at monthly scale, and the SCS model at the event scale. The applicability of MEP in each case helps to draw connections between the water balances at the three different time scales, and to demonstrate a common thermodynamic basis for the otherwise empirical water balance models. In particular, it is concluded that the long time scale Budyko-type model and the event scale SCS model are both special cases of the monthly 'abcd' model. [ABSTRACT FROM AUTHOR]

This paper presents a two‐step framework to identify key water resource vulnerabilities in transboundary river basins where data availability on both hydrological fluxes and the operation of man‐made facilities is either limited or nonexistent. In a first step, it combines two state‐of‐the‐art modeling tools to overcome data limitations and build a model that provides a lower bound on risks estimated in that basin. Land data assimilation (process‐based hydrological modeling taking remote‐sensed products as inputs) is needed to evaluate hydrological fluxes, that is, streamflow data and consumptive use in irrigated agriculture—a lower‐end estimate of demand. Hydroeconomic modeling provides cooperative water allocation policies that reflect the best‐case management of storage capacity under hydrological uncertainty at a monthly time step for competing uses—hydropower, irrigation. In a second step, the framework uses additional scenarios to proceed with the in‐depth analysis of the vulnerabilities identified despite the use of what is by definition a best‐case model. We implement this approach to the Tigris‐Euphrates river basin, a politically unstable region where water scarcity has been hypothesized to serve as a trigger for the Syrian revolution and ensuing war. Results suggest that even under the framework's best‐case assumptions, the Euphrates part of the basin is close to a threshold where it becomes reliant on transfers of saline water from other parts of the basin to ensure irrigation demands are met. This Tigris‐Euphrates river basin application demonstrates how the proposed framework quantifies vulnerabilities that have been hitherto discussed in a mostly qualitative, speculative way. Key Points: This paper presents a two‐step framework to identify key vulnerabilities in data‐scarce transboundary river basinsThe framework brings together land data assimilation and hydroeconomic analysis to identify these vulnerabilities in complex river networksApplication to the Tigris‐Euphrates basin shows vulnerability of Euphrates portion of basin to additional to irrigation developments [ABSTRACT FROM AUTHOR]

This paper is motivated to understand the multi‐temporal scale characteristics of Horton Index (HI), an indicator of catchment‐scale plant water use defined as the ratio of catchment vaporization (total evapotranspiration) to catchment wetting (precipitation minus surface runoff). Based on the generalized proportionality hypothesis, we first develop an analytical framework of HI applicable at multi‐temporal scales as a single function of ecological aridity index (EAI), defined as the ratio of potential evapotranspiration to catchment wetting. We successfully validate this framework at the long‐term, annual, and monthly scales at 343 natural catchments over the conterminous United States under varying climate and vegetation regimes. We show that vegetation plays an important role in regulating not only the partitioning of catchment wetting into vertical vaporization and horizontal baseflow, but also further partitioning of vaporization into initial and continuing components. Aided by this framework, we reveal emergent space‐time similarity patterns between HI's spatial (inter‐catchment, regional) and temporal (within‐catchment, inter and intra‐annual) variability. On the one hand, HI increases with EAI, both spatially and temporally, following similar trends predicted by the analytical framework. On the other hand, the slopes of HI ∼ EAI relationships, denoted as d (HI)/d (EAI), decreased with EAI both spatially and temporally and, again, following similar trends suggested by the analytical framework. These findings suggest the promising potential of Horton Index as a conceptual yet quantitative framework for improving understanding and modeling of multi‐scale ecological and hydrological processes and their interactions. Key Points: Derivation and validation of an analytical framework of Horton Index applicable over long‐term, annual, and monthly scales across biomesVegetation regulates catchment storage partitioning into vaporization and baseflow, then vaporization into initial and continuing componentsEmergent space‐time similarity patterns detected empirically and explained theoretically using the analytical framework [ABSTRACT FROM AUTHOR]

In multiple watershed planning and design problems, such as conservation planning, quantitative estimates of costs, and environmental benefits of proposed conservation decisions may not be the only criteria that influence stakeholders' preferences for those decisions. Their preferences may also be influenced by the conservation decision itself—specifically, the type of practice, where it is being proposed, existing biases, and previous experiences with the practice. While human‐in‐the‐loop type search techniques, such as Interactive Genetic Algorithms (IGA), provide opportunities for stakeholders to incorporate their preferences in the design of alternatives, examination of user‐preferred conservation design alternatives for patterns in Decision Space can provide insights into which local decisions have higher or lower agreement among stakeholders. In this paper, we explore and compare spatial patterns in conservation decisions (specifically involving cover crops and filter strips) within design alternatives generated by IGA and noninteractive GA. Methods for comparing patterns include nonvisual as well as visualization approaches, including a novel visual analytics technique. Results for the study site show that user‐preferred designs generated by all participants had strong bias for cover crops in a majority (50%–83%) of the subbasins. Further, exploration with heat maps visualization indicate that IGA‐based search yielded very different spatial patterns of user‐preferred decisions in subbasins in comparison to decisions within design alternatives that were generated without the human‐in‐the‐loop. Finally, the proposed coincident‐nodes, multiedge graph visualization was helpful in visualizing disagreement among participants in local subbasin scale decisions, and for visualizing spatial patterns in local subbasin scale costs and benefits. Key Points: Watershed stakeholders may have additional reasons for preferring watershed conservation decisions beyond quantified costs and benefitsDesign of watershed plans using interactive optimization necessitate postanalysis of spatial patterns in stakeholder‐preferred decisionsInformation Visualization techniques can help provide insights on agreements and disagreements among stakeholders on local decisions [ABSTRACT FROM AUTHOR]

Interactive Genetic Algorithms (IGA) are advanced human-in-the-loop optimization methods that enable humans to give feedback, based on their subjective and unquantified preferences and knowledge, during the algorithm's search process. While these methods are gaining popularity in multiple fields, there is a critical lack of data and analyses on (a) the nature of interactions of different humans with interfaces of decision support systems (DSS) that employ IGA in water resources planning problems and on (b) the effect of human feedback on the algorithm's ability to search for design alternatives desirable to end-users. In this paper, we present results and analyses of observational experiments in which different human participants ( surrogates and stakeholders) interacted with an IGA-based, watershed DSS called WRESTORE to identify plans of conservation practices in a watershed. The main goal of this paper is to evaluate how the IGA adapts its search process in the objective space to a user's feedback, and identify whether any similarities exist in the objective space of plans found by different participants. Some participants focused on the entire watershed, while others focused only on specific local subbasins. Additionally, two different hydrology models were used to identify any potential differences in interactive search outcomes that could arise from differences in the numerical values of benefits displayed to participants. Results indicate that stakeholders, in comparison to their surrogates, were more likely to use multiple features of the DSS interface to collect information before giving feedback, and dissimilarities existed among participants in the objective space of design alternatives. [ABSTRACT FROM AUTHOR]

In this paper, we test the hypothesis that natural catchments utilize hedging strategy for evapotranspiration and water storage carryover with uncertain future precipitation. The hedging strategy for evapotranspiration in catchments under different levels of water availability is analytically derived with marginal utility principle. It is found that there exists hedging between evapotranspiration for present and future only with a portion of water availability. Observation data sets of 160 catchments in the United States covering the period from 1983 to 2003 demonstrate the existence of hedging in catchment hydrology and validate the proposed hedging strategy. We also find that more water is allocated to carryover storage for hedging against the future evapotranspiration deficit in the catchments with larger aridity indexes or with larger variability in future precipitation, i.e., long-term climate and precipitation variability control the degree of hedging. [ABSTRACT FROM AUTHOR]

The projection of runoff in a large‐scale basin under climate change and human activities is crucial for the future management of water supplies. The impacts of climate change on runoff projections are associated with large uncertainties. In this study, using the optimization and selection of climate models, as well as an uncertainty analysis via the global Bayesian model averaging (BMA) approach, an ensemble projection framework was established to significantly improve the reliability of runoff projections in the future. The global BMA algorithm proposed in this study is an improvement on the BMA algorithm for large basins and is designed to reflect the differences among various models across the basin. In this algorithm, comprehensive BMA weights are obtained by considering the number (N) of stations in a basin. To verify the feasibility and improvement of this method, the runoff in 2050 and 2070 was projected with data from the Yellow River Basin, China. The runoff and its 90% confidence intervals at the six main stations in the Yellow River Basin were obtained. The increased evapotranspiration will exceed the increase in runoff generated by increase in precipitation in the future. The runoff in the upper and middle reaches of the Yellow River in 2050 and 2070 are projected to be 9% and 7% lower, respectively, than those in the reference period. The ensemble projection method proposed in this paper can be used as a widely applicable process in hydrometeorological ensemble projection and provides a basis for water resource management planning. Key Points: An ensemble projection framework was established to significantly improve the reliability of runoff in the futureThe global BMA algorithm is proposed to obtain comprehensive BMA weights by considering number of stations in a basinThe runoff of the Yellow River Basin in 2050 and 2070 will decrease by 9% and 7%, respectively [ABSTRACT FROM AUTHOR]

The efficacy of low‐impact development (LID) toward the attenuation of urban flood at the catchment scale is still an unsolved question. This work aims at providing insight into the effectiveness of LID at the catchment scale by a simple hydrological modeling approach that allows for an analytical solution. The paper focuses on LID design, investigating the peak discharge at the catchment outlet as a function of the return period of the rainfall event under unaltered and retrofitted conditions. The model captures the most important effects of LID on flood, that is, the temporal shift and attenuation of water discharge and the decrease of water volume conveyed by the drainage network due to increased hydrological losses. It is found that LID is likely more effective in reducing peak discharge at the local scale, while the effectiveness decreases with increasing catchment size. For some configurations, for example, when retrofit is performed in downstream areas, closer to the outlet, LID may even worsen the flood risk. The effectiveness of LID depends on the combination of several factors, including the fractional area covered by the LID infrastructures, their distribution within the catchment, and their hydraulic properties, as compared with the general hydraulic features of the catchment (network dispersion, detention, lag time, etc.). As a consequence, the LID design should be carried out considering all the relevant scales of the problem, from the local one, at which the LID infrastructure is implemented, to the catchment scale, and the discharge at the outlet in particular, in a comprehensive perspective. Key Points: We develop a simple hydrological model, based on the IUH framework, to model LID effects on peak discharge quantiles at the catchment scaleWe found that LID are effective in reducing urban flooding at the local scale, while effectiveness decreases with increasing catchment sizeWhen retrofit is performed through LID implementation in areas close to the catchment outlet, LID may even worsen the flood risk [ABSTRACT FROM AUTHOR]

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual "bucket" rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the "Millennium Drought" in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model "buckets" are already "emptying" on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate. Key Points: Environmental state variables such as groundwater display multiyear trends in response to sustained climate anomaliesWe show that five commonly used conceptual "bucket" rainfall runoff models are unable to replicate such trendsBecause of this, the models fail to extrapolate realistically to different climatic conditions, compromising runoff projections Plain Language Summary: It is common in science to use a mental picture or metaphor that simplifies a complex phenomenon. A common metaphor used for a water supply catchment is that of a leaky bucket. When it rains, the bucket fills up; when it does not rain for a while, the bucket empties due to evaporation and water used by trees; and leaking water is like river flow. Computer models based on variants of this metaphor are common and can provide predictions of how much streamflow might occur under future scenarios. This paper explores limitations of the bucket metaphor and associated models. Recently, during a 13‐year drought in Australia, river catchments gradually started to dry up. With each passing year, the depth to groundwater increased gradually as the water used by trees was not replenished by rainfall. We compare this long, slow behavior to that of five commonly used "bucket" models. The models do not show the long, slow drying up—they only show the seasonal ups and downs, and their predictions of streamflow over the drought are poor. This is surprising, and it means we should choose our models carefully and seek out models that can simulate this behavior and its impact on streamflow. [ABSTRACT FROM AUTHOR]

River floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space‐time structure of catchment rainfall, which is determined by large‐scale, or even global‐scale, atmospheric processes. The identification of coherent, large‐scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall‐induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany. Key Points: Atmospheric circulation patterns that generate high streamflow events in Germany are identifiedEvent synchronization is used to determine synchronization and delay between atmospheric circulation patterns and high streamflow eventsThe occurrence probability of high streamflow events is conditioned on atmospheric circulation patterns and integrated water vapor transport [ABSTRACT FROM AUTHOR]

Snow surveys in two Arctic watersheds located in Alaska, USA, provide 32 years of spatially distributed snow water equivalent (SWE) and snow depth observations. Annual snow surveys from the Imnavait Creek (20,036 measurements from 1985 to 2017) and Upper Kuparuk River (5,804 measurements from 1997 to 2017) watersheds were conducted to capture end‐of‐winter snow accumulation. The average end‐of‐winter SWE in the Upper Kuparuk River watershed (102 ± 29 mm) is consistently less than the Imnavait Creek watershed (130 ± 34 mm) during the common period of record (1997–2017). The average end‐of‐winter SWE in both watersheds indicates a positive trend. Comparison of SWE records with cumulative solid precipitation measured at the Imnaviat [sic] SNOTEL site highlights the undercatch of gauge precipitation and difference in long‐term trends. In this paper, we present a historic overview of data collection, discuss data accuracy, and point out advantages and limitations associated with ground‐based snow measurements in remote Arctic locations. As new methods and techniques of measuring SWE and solid precipitation become available, the presented data set will provide a historic perspective for new observations and will quantitatively relate current or future snow conditions to those that have occurred since the late twentieth century. Key Points: Repeated snow surveys (25,840 measurements over 32 years) are conducted in two Arctic watersheds for hydrologic applicationsAnnual snow water equivalent range is 73–207 mm, with average SWE of 124 ± 34 mm in the Imnavait Creek watershed (1985–2017)Annual snow water equivalent range is 51–169 mm with average SWE of 102 ± 29 mm in the Upper Kuparuk River watershed (1997–2017) [ABSTRACT FROM AUTHOR]

The estimation of extreme flood quantiles is challenging due to the relative scarcity of extreme data compared to typical target return periods. Several approaches have been developed over the years to face this challenge, including regional estimation and the use of historical flood data. This paper investigates the combination of both approaches using a multivariate peaks-over-threshold model that allows estimating altogether the intersite dependence structure and the marginal distributions at each site. The joint distribution of extremes at several sites is constructed using a semiparametric Dirichlet Mixture model. The existence of partially missing and censored observations (historical data) is accounted for within a data augmentation scheme. This model is applied to a case study involving four catchments in Southern France, for which historical data are available since 1604. The comparison of marginal estimates from four versions of the model (with or without regionalizing the shape parameter; using or ignoring historical floods) highlights significant differences in terms of return level estimates. Moreover, the availability of historical data on several nearby catchments allows investigating the asymptotic dependence properties of extreme floods. Catchments display a significant amount of asymptotic dependence, calling for adapted multivariate statistical models. [ABSTRACT FROM AUTHOR]

Many watershed models used within the hydrologic research community assume (by default) stationary conditions, that is, the key watershed properties that control water flow are considered to be time invariant. This assumption is rather convenient and pragmatic and opens up the wide arsenal of (multivariate) statistical and nonlinear optimization methods for inference of the (temporally fixed) model parameters. Several contributions to the hydrologic literature have brought into question the continued usefulness of this stationary paradigm for hydrologic modeling. This paper builds on the likelihood-free diagnostics approach of Vrugt and Sadegh (2013) and uses a diverse set of hydrologic summary metrics to test the stationary hypothesis and detect changes in the watersheds response to hydroclimatic forcing. Models with fixed parameter values cannot simulate adequately temporal variations in the summary statistics of the observed catchment data, and consequently, the DREAM(ABC) algorithm cannot find solutions that sufficiently honor the observed metrics. We demonstrate that the presented methodology is able to differentiate successfully between watersheds that are classified as stationary and those that have undergone significant changes in land use, urbanization, and/or hydroclimatic conditions, and thus are deemed nonstationary. [ABSTRACT FROM AUTHOR]

Estimates of hydrological risk are crucial to enable adequate planning and preparation for extreme events. However, the accurate estimation of hydrological risk is hampered by relatively short instrumental records in many parts of the world. Information derived from climate-sensitive paleoclimate proxies provide an opportunity to resolve hydroclimatic variability, but many regions, such as Australia's Murray-Darling Basin (MDB), currently lack the suitable in situ proxies necessary to do this. Here new MDB rainfall reconstructions are presented based on a novel method using paleoclimate rainfall proxies in the Australasian region spanning from 749 B.C.E. to 1980 C.E. Our results emphasize the need to develop additional reconstructions and, with the companion paper, demonstrate how this information can be used to benefit water resource management. This study shows that prior to the twentieth century, both dry and wet epochs have persisted for longer periods than observed in the instrumental record--with the probability of both dry and wet periods exceeding a decade at least 10 times more likely prior to 1883 than suggested by the instrumental records. Some reconstructed rainfalls exceeded the instrumental range (i.e., drier dry epochs and wetter wet spells) despite a systematic underestimation of extremes due to a combination of proxy quality and model bias. Importantly, the results demonstrate that the instrumental record does not cover the full range of hydroclimatic variability possible in the MDB. Therefore, hydroclimatic risk assessments based on the instrumental record likely underestimate, or at least misinterpret, the frequency, duration, and magnitude of wet and dry epochs. [ABSTRACT FROM AUTHOR]

This paper introduces a novel measure to assess similarity between event hydrographs. It is based on cross recurrence plots (CRP) and recurrence quantification analysis (RQA), which have recently gained attention in a range of disciplines when dealing with complex systems. The method attempts to quantify the event runoff dynamics and is based on the time delay embedded phase space representation of discharge hydrographs. A phase space trajectory is reconstructed from the event hydrograph, and pairs of hydrographs are compared to each other based on the distance of their phase space trajectories. Time delay embedding allows considering the multidimensional relationships between different points in time within the event. Hence, the temporal succession of discharge values is taken into account, such as the impact of the initial conditions on the runoff event. We provide an introduction to cross recurrence plots and discuss their parameterization. An application example based on flood time series demonstrates how the method can be used to measure the similarity or dissimilarity of events, and how it can be used to detect events with rare runoff dynamics. It is argued that this methods provides a more comprehensive approach to quantify hydrograph similarity compared to conventional hydrological signatures. Key Points: This manuscript presents the first application of recurrence plots in catchment hydrologyCRP is used as a novel method for quantifying comprehensive hydrograph similarity based on runoff dynamicsTime delay embedded phase space trajectory allows to consider relationships between magnitudes of different point in time [ABSTRACT FROM AUTHOR]

Observatory‐scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation‐funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes‐Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south‐central Minnesota, USA, a 43,400‐km2 tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change, allowing for transdisciplinary research into the complex cascade of environmental changes that occur due to hydrology and land use alterations from intensive agricultural management and climate change. Data sets collected include water chemistry and biogeochemical data, geochemical fingerprinting of major sediment sources, high‐resolution monitoring of river bluff erosion, and repeat channel cross‐sectional and bathymetry data following major floods. The data collection efforts led to development of a series of integrative reduced complexity models that provide deeper insight into how water, sediment, and nutrients route and transform through a large channel network and respond to change. These models represent the culmination of efforts to integrate interdisciplinary data sets and science to gain new insights into watershed‐scale processes in order to advance management and decision making. The purpose of this paper is to present a synthesis of the data sets and models, disseminate them to the community for further research, and identify mechanisms used to expand the temporal and spatial extent of short‐term observatory‐scale data collection efforts. Key Points: Observatory‐scale study of MN River Basin created integrative data sets and reduced complexity models to understand environmental changeData sets include water biogeochemistry, sediment sources and sinks, sediment fingerprinting, geomorphic change detection, and bathymetryModeling efforts focus on basin‐scale environmental transport to inform land management, reduce pollutant export, and improve river biota [ABSTRACT FROM AUTHOR]

Integrated, process-based numerical models in hydrology are rapidly evolving, spurred by novel theories in mathematical physics, advances in computational methods, insights from laboratory and field experiments, and the need to better understand and predict the potential impacts of population, land use, and climate change on our water resources. At the catchment scale, these simulation models are commonly based on conservation principles for surface and subsurface water flow and solute transport (e.g., the Richards, shallow water, and advection-dispersion equations), and they require robust numerical techniques for their resolution. Traditional (and still open) challenges in developing reliable and efficient models are associated with heterogeneity and variability in parameters and state variables; nonlinearities and scale effects in process dynamics; and complex or poorly known boundary conditions and initial system states. As catchment modeling enters a highly interdisciplinary era, new challenges arise from the need to maintain physical and numerical consistency in the description of multiple processes that interact over a range of scales and across different compartments of an overall system. This paper first gives an historical overview (past 50 years) of some of the key developments in physically based hydrological modeling, emphasizing how the interplay between theory, experiments, and modeling has contributed to advancing the state of the art. The second part of the paper examines some outstanding problems in integrated catchment modeling from the perspective of recent developments in mathematical and computational science. [ABSTRACT FROM AUTHOR]

The challenge of sustainable freshwater management requires identification and characterization of the underlying components and dynamic interactions within the coupled human-hydrology system. This paper builds a model that captures the dynamic water balance evolution and coupled human response within the Lake Toolibin catchment in West Australia's wheatbelt region. Two subcatchments in different parts of the landscape were selected to examine the key emergent properties of the coupled socio-hydrology system over a 100 year period, by analyzing the two-way feedbacks of land use management (human system feedback) and land degradation (natural system feedback). Using a relatively simple parameterization of community sensitivity to land degradation within the model, we identified positive and negative feedbacks, the presence of threshold behavior, time scale differences between fast and slow moving variables, differences in time lags resulting from disparate resistance levels of the natural system, and the degree of adaptive learning inherent in the human system. Specifically, the valley floor subcatchment transitioned through four phases-expansion, contraction, recession, and recovery-demonstrating a threshold shift in the human feedback after 60 years, while the upslope subcatchment appears to still be in the contraction phase, with no sign of reaching a threshold shift in 100 years. These results demonstrate that the model is capable of isolating the two-way feedbacks of the coupled system and has implications for resilience theory, suggesting that greater resistance in the underlying natural system counteracts the onset of a negative feedback loop and instigation of adaptive behaviors in the human system. [ABSTRACT FROM AUTHOR]

The hyporheic zone, defined by shallow subsurface pathways through river beds and banks beginning and ending at the river, is an integral and unique component of fluvial systems. It hosts myriad hydrologically controlled processes that are potentially coupled in complex ways. Understanding these processes and the connections between them is critical since these processes are not only important locally but integrate to impact increasingly larger scale biogeochemical functioning of the river corridor up to the river network scale. Thus, the hyporheic zone continues to be a growing research focus for many hydrologists for more than half the history of Water Resources Research. This manuscript partly summarizes the historical development of hyporheic zone hydrologic science as gleaned from papers published in Water Resources Research, from the birth of the concept of the hyporheic zone as a hydrologic black box (sometimes referred to as transient storage zone), to its adolescent years of being torn between occasionally competing research perspectives of interrogating the hyporheic zone from a surface or subsurface view, to its mature emergence as an interdisciplinary research field that employs the wide array of state-of-the-art tools available to the modern hydrologist. The field is vibrant and moving in the right direction of addressing critical fundamental and applied questions with no clear end in sight in its growth. There are exciting opportunities for scientists that are able to tightly link the allied fields of geology, geomorphology, hydrology, geochemistry, and ecology to tackle the many open problems in hyporheic zone science. [ABSTRACT FROM AUTHOR]

This paper describes an economic model that links the demand for recreational stream fishing to fish biomass. Useful measures of fishing quality are often difficult to obtain. In the past, economists have linked the demand for fishing sites to species presence-absence indicators or average self-reported catch rates. The demand model presented here takes advantage of a unique data set of statewide biomass estimates for several popular game fish species in Michigan, including trout, bass and walleye. These data are combined with fishing trip information from a 2008-2010 survey of Michigan anglers in order to estimate a demand model. Fishing sites are defined by hydrologic unit boundaries and information on fish assemblages so that each site corresponds to the area of a small subwatershed, about 100-200 square miles in size. The random utility model choice set includes nearly all fishable streams in the state. The results indicate a significant relationship between the site choice behavior of anglers and the biomass of certain species. Anglers are more likely to visit streams in watersheds high in fish abundance, particularly for brook trout and walleye. The paper includes estimates of the economic value of several quality change and site loss scenarios. [ABSTRACT FROM AUTHOR]

Rainfall thresholds for streamflow generation are commonly mentioned in the literature, but studies rarely include methods for quantifying and comparing thresholds. This paper quantifies thresholds in ephemeral streams and evaluates how they are affected by rainfall and watershed properties. The study sites are in southern Arizona, USA; one is hyperarid and the other is semiarid. At both sites rainfall and streamflow were monitored in watersheds ranging from 10−3 to 102 km2. Streams flowed an average of 0–5 times per year in hyperarid watersheds and 3–11 times per year in semiarid watersheds. Although hyperarid sites had fewer flow events, their flow frequency (fraction of rain events causing flow) was higher than in semiarid sites for small (<1 km2) watersheds. At both locations flow frequency decreased with drainage area, but the decrease was steeper in hyperarid watersheds. Watershed mean 60‐min intensity thresholds ranged from 3–13 mm/hr in hyperarid watersheds and 7–16 mm/hr in semiarid watersheds. Higher runoff thresholds and lower flow frequencies in small semiarid watersheds likely relate to greater ground cover and soil development compared to the desert pavement and bedrock surfaces in hyperarid sites. The choice of rain data strongly influenced threshold values; single rain gauges were only adequate for threshold prediction in <1‐km2 watersheds, and incomplete rainfall data led to increases in thresholds with drainage area. We recommend using mean rainfall intensity over the drainage area for threshold analysis because this reduces apparent scale dependence in thresholds caused by incomplete rainfall information. Plain Language Summary: Ephemeral streams in deserts are usually dry, flowing only after heavy rains. Our goal was to determine how much rain is needed for these streams to flow. We studied streams in dry southwestern and in wetter southeastern Arizona, USA. The dry site has mostly bare rock across the landscape, whereas the wetter site has more vegetation, including shrubs, grasses, and oak. In the dry area, small streams flowed 3–5 times per year, and larger streams flowed 0–2 times per year. Streams required 3–13 mm of rain over 1 hr to trigger flow. The small streams flowed more frequently because rain falling on bare rock could rapidly reach the stream. Water was lost into the channel bed before reaching larger streams. In the wetter area, both small and large streams flowed 3–11 times per year. These streams required 7–16 mm of rain over 1 hr to trigger flow. This range of rainfall is slightly higher than for streams in the dry study area, likely because vegetation cover and soil development allow more rainfall to infiltrate into soil before reaching streams. Information on the amount of rainfall needed to trigger streamflow can help with issuing flash flood warnings for ephemeral streams. Key Points: Rainfall thresholds predict streamflow responses with high accuracy in small hyperarid and semiarid watershedsUsing insufficient rain data usually increases threshold values for larger watersheds, leading to apparent scale dependence in thresholdsDeclines in flow frequency and increases in thresholds with drainage area are steeper in hyperarid than in semiarid watersheds [ABSTRACT FROM AUTHOR]

Rainfall‐runoff models are frequently used for the prediction of flood discharges from extreme rainfall data in poorly gauged basins. However, the conventional methods used to estimate the frequency of flood discharges from precipitation data do not incorporate a realistic representation of the spatial and temporal structure of rainfall at the study site nor of the antecedent soil moisture content. Temporally and spatially idealized rainfall patterns that do not represent properly the intraevent variability of rainfall are in general assumed. In this paper, we propose a methodology for flood frequency analysis based on the hydrological simulation of potentially hazardous real storm events. The aim of the proposed method is to incorporate the impact of the spatial and temporal intraevent variability of rainfall on flood discharge estimates without making strong assumptions about the spatial and temporal patterns of storms. It also includes a calibrated relation between the antecedent rainfall depth and the soil infiltration capacity, in order to consider the combined effect of antecedent soil moisture content and rainfall intensity. The flood frequency estimates obtained with the proposed methodology are compared to those estimated with a conventional method based on the definition of synthetic design hyetographs from local intensity‐duration‐frequency curves. The analysis is presented in two watersheds of 84 and 353 km2 located in the NW of Spain. Results highlight the role of the antecedent soil moisture in flood frequency analysis and show that the conventional methodology tends to overestimate the flood discharges when compared to the proposed approach. Key Points: The methodology presented incorporates the observed spatial and temporal variability of rainfall in flood frequency analysisThe proposed method accounts for the combined effect of antecedent soil moisture and rainfall intensity in flood frequency estimatesWith the proposed approach, there is no need to define synthetic design hyetographs for a given return period [ABSTRACT FROM AUTHOR]

This paper studies how streamflow predictability varies with basin characteristics. We introduce an index of basin complexity that is based on a model of least statistical complexity that is needed to reliably predict daily streamflow of the basin. We then relate it with climate, vegetation and soil characteristics of the basin. Daily streamflow is modeled using k nearest neighbor model of lagged streamflow that predicts next time step streamflow based on the occurrences of similar streamflow events from the past. In order to calculate basin complexity, we identify difficult streamflow events of the basin and then use Vapnik‐Chervonenkis generalization theory, which trades off model performance with Vapnik‐Chervonenkis dimension (i.e., a measure of model complexity), to find a k nearest neighbor model of appropriate complexity for predicting a difficult streamflow event of the basin. The average of selected model complexities corresponding to difficult events is then defined as the basin's complexity. Basin complexity of 412 Model Parameter Estimation Experiment basins from continental United States are then related with its six basin characteristics. All the characteristics have been derived from the Model Parameter Estimation Experiment database to represent climate, vegetation and soil characteristics of the basins in a concise manner. Results find that more complex basins that are drier have less seasonal rainfall, vegetation with more storage capacity (i.e., smaller 5‐week Normalized Difference Vegetation Index gradient), and faster responsive soils. The results reaffirm prior observations that minimum complexity that is required to model a basin depends on its climate and landscape characteristics (e.g., complex models do not perform well in dry basins). Key Points: Basin complexity is defined in terms of difficulty in learning statistical patterns of daily streamflowRelationship between complexity of MOPEX basins and hydrological characteristics is analyzedMore complex basins that are drier have less seasonal rainfall, vegetation with more storage capacity and faster responsive soils [ABSTRACT FROM AUTHOR]