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The real‐time control of urban watersheds is now being enabled by a new generation of "smart" and connected technologies. By retrofitting stormwater systems with sensors and valves, it becomes possible to adapt entire watersheds dynamically to individual storms. A catchment‐scale control algorithm is introduced, which abstracts an urban watershed as a linear integrator delay dynamical system, parameterizes it using physical watershed characteristics, and then controls network flows using a Linear Quadratic Regulator. The approach is simulated on a 4‐km2 urban headwater catchment in Ann Arbor, Michigan, demonstrating the gains of a stormwater system that can adaptively balance between flood mitigation and flow reduction. We introduce an equivalence analysis and illustrate the performance of the controlled watershed across large events (30‐year storms) to show the uncontrolled passive watershed can only match it during smaller events (10‐year storm). For these smaller events, the storage volume of the controlled storage nodes (ponds, basins, and wetlands) could be reduced as much as 50% and still achieve the same performance of the controlled watershed. A controller placement analysis is also carried out, whereby all possible combinations of controlled sites are simulated across a wide spectrum of design storms. We show that the control of every storage node may not be needed in a watershed, but rather that in our case study a small subset (30%) of the overall watershed can be controlled in coordination to achieve outcomes that match a fully controlled system, even when tested across a long‐term rainfall record and under noisy sensor measurements. Plain Language Summary: Internet‐connected sensors are changing how we study and manage water systems. By adding valves, gates, and pumps to stormwater systems, it will be possible to adaptively control urban watershed in response to individual storms. Without requiring new and expensive construction, this will allow existing infrastructure to be used more effectively. In this paper, we introduce a control algorithm that can be used to control urban watersheds. We also investigate how many control valves are needed to retrofit an urban watershed and where, which may serve as tools for city managers to reduce flooding and manage flows. Key Points: A linear feedback controller is formulated for the control of urban catchments (1‐5 km2) using physical catchment propertiesSimulations show strong flow control performance when compared to uncontrolled catchmentDesired hydraulic outcomes can be achieved by retrofitting a small number of control sites [ABSTRACT FROM AUTHOR]

Hortonian overland flow, Dunne overland flow, and subsurface stormflow are the three most dominant mechanisms contributing to both the volume and timing of streamflow in headwater catchments. In this paper, guided by the Dunne diagram, we explore the impacts of climate, soil, and topography on estimated probability distributions of the travel times of each of these three runoff components. In each case, these are expressed in terms of the Connected Instantaneous Response Functions (CIRF) and account for the dynamics of their individual partial effective contributing areas that retain the connectivity to the outlet (instead of the whole catchment area). A spatially distributed hydrological model is used to derive the CIRFs numerically under multiple combinations of climate, soil, and topographic properties. The mean travel times and dimensionless forms of the CIRFs (i.e., scaled by their respective mean travel times) are used to examine both advective and dispersive aspects of catchment's runoff routing response. It is found that the CIRFs, upon nondimensionalization, collapsed to common characteristic shapes, which could be explained in terms of the relative contributions of hillslope and channel network flows, and the size of runoff contributing areas. The contributing areas, particularly for the Dunne overland flow, are themselves found to be governed by the competition between drainage of and recharge to the water table, and could be explained by a dimensionless drainage index which quantifies this competition. The study also reveals simple indicators based on landscape properties that can explain the magnitude of travel times in different catchments. [ABSTRACT FROM AUTHOR]

Bedload is notoriously challenging to measure and model; its dynamics, therefore, remains largely unknown in most fluvial systems worldwide. We present results from a global scale bedload flux model as part of the WBMsed modeling framework that well predict the distribution of water discharge, suspended sediment and bedload. The sensitivity of bedload predictions to river slope, particle size, discharge, river width, and suspended sediment were analyzed, showing the model to be most responsive to spatial dynamics in river discharge and slope. The relationship between bedload and total sediment flux is analyzed globally, and for representative longitudinal river profiles (Amazon, Mississippi, and Lena Rivers). The results show that while the proportion of bedload decreases from headwaters to the coasts, there is considerable variability between basins and along river corridors. The topographic and hydrological longitudinal profiles of rivers are shown to be the key drivers of bedload trends, with fluctuations in slope controlling its more local dynamics. Estimates of water and sediment fluxes to global oceans from 2,067 largest river outlets (draining 67% of the global continental area) are provided. Estimated water discharge at 30,579 km3/y corresponds well to past estimates; however, sediment flux is higher. Total global particulate load of 17.8 Gt/y is delivered to global oceans, 14.8 Gt/y as washload, 1.1 Gt/y as bedload, and 2.6 Gt/y as suspended bed material. The largest 25 rivers are predicted to transport more than half of the total sediment flux to global oceans. Plain Language Summary: Sediment carried by rivers varies considerably in space and time as a function of hydrological and environmental characteristics, and human‐made modifications of river systems. Understanding and predicting the sediment dynamics within river systems are important, as they have direct impacts on the functioning of riverine and coastal ecosystems, water quality and use, and chemical dynamics of the Earth System as a whole (e.g., carbon fluxes). Sediment is transported in three modes: suspension for small particles, bedload for larger particles, and suspended bed material for medium particles. Bedload and suspended bed material are difficult to measure and model and thus remain largely unknown in most river systems. In this paper, we present new components to the WBMsed global hydro‐geomorphic modeling framework that estimate bedload and suspended bed material fluxes for large rivers worldwide. We show good agreement with observations and provide an analysis of the distribution of the three sediment transport modes globally and, in greater detail, in three large river systems (Mississippi, Amazon, and Lena Rivers). An estimate of water and sediment fluxes to the global oceans is provided, the first to include all three sediment transport modes. Key Points: New global‐scale bedload and suspended bed material flux modules are introduced within the WBMsed frameworkModel performance and sensitivity analyses show strong correspondence to observations and dependence on discharge and river slope parametersThe proportion of bedload flux is shown to be highly variable between and within basins, driven by topographic and hydrological settings [ABSTRACT FROM AUTHOR]

Abstract: A river channel network is an important geomorphological feature in a catchment. A method to extract river channels in urban environments from a high‐resolution digital elevation model (DEM) is introduced in this paper. The method utilizes terrain transition lines to delineate channel networks. First, terrain transition lines are extracted from the DEM. Then, shallow road ditches are removed based on elevation from the terrain transition lines, and then, noise is removed using the region growing method. Subsequently, most of the road ditches are removed by pixel block expansion, and then, the nonchannel pixels are removed by fitting a quadratic surface. The computational efficiency and accurate localization of river channel extraction using the proposed method is demonstrated using light detection and ranging (LiDAR) data for an urban environment in Johnson County, Iowa. The proposed method is effective for extracting channels and removing road ditches in this urban environment. A potential application of this method is to provide the river channels required for riverine flood modeling and geomorphological studies. [ABSTRACT FROM AUTHOR]

Abstract: General circulation models (GCMs) have been demonstrated to produce estimates of precipitation, including the frequency of extreme precipitation, with substantial bias and uncertainty relative to their representation of other fields. Thus, while theory predicts changes in the hydrologic cycle under anthropogenic warming, there is generally low confidence in future projections of extreme precipitation frequency for specific river basins. In this paper, we explore whether a GCM simulates large‐scale atmospheric circulation indices that are associated with regional extreme precipitation (REP) days more accurately than it simulates REP days themselves, and thus whether conditional simulation of the precipitation events based on the circulation indices may improve the simulation of REP events. We show that a coupled Geophysical Fluid Dynamics Laboratory GCM simulates too many springtime REP days in the Ohio River Basin in historical (1950–2005) simulations. The GCM, however, does credibly simulate the distributional and persistence properties of several indices (which represent the large‐scale atmospheric pressure features, local atmospheric moisture content, and local vertical velocity) that are shown to modulate the likelihood of REP occurrence in the reanalysis/observational record. We show that simulation of REP events based on the GCM‐based atmospheric indices greatly reduces the bias of GCM REP frequency relative to the observed record. The simulation is conducted via a Bayesian regression model by imposing the empirical relationship between observed REP occurrence and the reanalysis‐based atmospheric indices. Application of this model to future (2006–2100) representative concentration pathway 8.5 scenario suggests an increasing trend in springtime REP incidence in the study region. The proposed approach of simulating precipitation events of interest, particularly those poorly represented in GCMs, with a statistical model based on climate indices that are reasonably simulated by GCMs could be applied to subseasonal to seasonal forecasts as well as future projections. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a socio‐hydrologic analysis of channel flows in Punjab province of the Indus River basin in Pakistan. The Indus has undergone profound transformations, from large‐scale canal irrigation in the mid‐nineteenth century to partition and development of the international river basin in the mid‐twentieth century, systems modeling in the late‐twentieth century, and new technologies for discharge measurement and data analytics in the early twenty‐first century. We address these processes through a socio‐hydrologic framework that couples historical geographic and analytical methods at three levels of flow in the Punjab. The first level assesses Indus River inflows analysis from its origins in 1922 to the present. The second level shows how river inflows translate into 10‐daily canal command deliveries that vary widely in their conformity with canal entitlements. The third level of analysis shows how new flow measurement technologies raise questions about the performance of established methods of water scheduling (warabandi) on local distributaries. We show how near real‐time measurement sheds light on the efficiency and transparency of surface water management. These local socio‐hydrologic changes have implications in turn for the larger scales of canal and river inflow management in complex river basins. [ABSTRACT FROM AUTHOR]

Abstract: Groundwater is a critical input to agricultural production across the globe. Current groundwater pumping rates frequently exceed recharge, often by a substantial amount, leading to groundwater depletion and potential declines in agricultural profits over time. As a result, many regions reliant on irrigated agriculture have proposed policies to manage groundwater use. Even when gains from aquifer management exist, there is little information about how policies affect individual producers sharing the resource. In this paper, we investigate the variability of groundwater management policy impacts across heterogeneous agricultural producers. To measure these impacts, we develop a hydroeconomic model that captures the important role of well capacity, productivity of water, and weather uncertainty. We use the model to simulate the impacts of groundwater management policies on producers in the High Plains aquifer of eastern Colorado and compare outcomes to a no‐policy baseline. The management policies considered include a pumping fee, a quantity restriction, and an irrigated acreage fee. We find that well capacity and soil type affect policy impacts but in ways that can qualitatively differ across policy type. Model results have important implications for the distributional impacts and political acceptability of groundwater management policies. [ABSTRACT FROM AUTHOR]

Reservoir operations may alter the characteristics of Flood Frequency Curve (FFC) and challenge the basic assumption of stationarity used in flood frequency analysis. This paper presents a combined data-modeling analysis of reservoir as a nonlinear filter of runoff routing that alters the FFCs. A dimensionless Reservoir Impact Index (RII), defined as the total upstream reservoir storage capacity normalized by the annual streamflow volume, is used to quantify reservoir regulation effects. Analyses are performed for 388 river stations in the contiguous U.S. using the first two moments of the FFC, mean annual maximum flood (MAF) and coefficient of variations (CV), calculated for the pre and post-dam periods. It is found that MAF generally decreases with increasing RII but stabilizes when RII exceeds a threshold value, and CV increases with RII until a threshold value beyond which CV decreases with RII. Hence depending on the magnitude of RII, reservoir regulation acts as a filter to increase or reduce the nonlinearity of the natural runoff routing process and alters flood characteristics. The nonlinear relationships of MAF and CV with RII can be captured by three reservoir models with different levels of complexity, suggesting that they emerge from the basic flood control function of reservoirs. However, the threshold RII values in the nonlinear relationships depend on the more detailed reservoir operations and objectives that can only be captured by the more complex reservoir models. Our conceptual model may help improve flood-risk assessment and mitigation in regulated river systems at the regional scale. [ABSTRACT FROM AUTHOR]

In this paper, a four-parameter Budyko equation is derived for mean annual water balance by applying the proportionality relationship to a two-stage partitioning of precipitation. The four dimensionless parameters include the Horton index ( H, defined as the ratio of evaporation to total wetting) and λ (the ratio of initial evaporation to total wetting) for slow runoff, and β (the ratio of initial wetting to total wetting) and γ (the ratio of total wetting to its potential) for fast runoff. The derived four-parameter equation balances model parsimony and representation of dominant hydrologic processes and provides a framework to disentangle the roles of climate variability, vegetation, soil, and topography on long-term water balance in gauged watersheds. The four parameters are determined for 165 watersheds by using observations of precipitation, potential evaporation, streamflow, and soil properties. Based on the principal component regression analysis, average time interval between rainfall events, slope, normalized difference vegetation index, and wilting point are identified as the dominant controlling factors on H and λ; saturated hydraulic conductivity and the difference between field capacity and residual soil moisture are identified as the dominant controlling factors on β; and γ is controlled by effective soil water storage capacity, frequency of rainfall events, and climate seasonality. The combination of four-parameter Budyko equation and the principal component regression equations provides a model to assess the long-term responses of evaporation and runoff to climate and watershed property changes in ungauged watersheds. [ABSTRACT FROM AUTHOR]

This paper demonstrates improved retrieval of snow water equivalent (SWE) from spaceborne passive microwave measurements for the sparsely forested Upper Kern watershed (511 km2) in the southern Sierra Nevada (USA). This is accomplished by assimilating AMSR-E 36.5 GHz measurements into model predictions of SWE at 90 m spatial resolution using the Ensemble Batch Smoother (EnBS) data assimilation framework. For each water year (WY) from 2003 to 2008, SWE was estimated for the accumulation season (1 October to 1 April) with the assimilation of the measurements in the dry snow season (1 December to 28 February). The EnBS SWE estimation was validated against snow courses and snow pillows. On average, the EnBS accumulation season SWE RMSE was 77.4 mm (13.1%, relative to peak accumulation), despite deep snow (average peak SWE of 545 mm). The prior model estimate without assimilation had an accumulation season average RMSE of 119.7 mm. After assimilation, the overall bias of the accumulation season SWE estimates was reduced by 84.2%, and the RMSE reduced by 35.4%. The assimilation also reduced the bias and the RMSE of the 1 April SWE estimates by 80.9% and 45.4%, respectively. The EnBS is expected to work well above tree line and for dry snow. [ABSTRACT FROM AUTHOR]

The moisture maximization approach has a simple technique for controlling the risk of overestimating the Probable Maximum Precipitation (PMP): the maximization ratio is limited by an upper bound. The upper bound limit depends on storm records and watershed characteristics. However, it is not readily available in many watersheds. A robust scientific justification for limiting the maximization ratio is missing. In this paper, a novel approach is proposed to estimate the maximization ratio which does not impose an upper limit. The new approach, which uses regional climate model data, is based on constructing annual maximum precipitable water time series with precipitable water values for which atmospheric variables are similar to the original event to be maximized. These time series are then used to estimate the 100 year return period of the precipitable water value required to calculate the ratio. The new approach was tested in three watersheds in the province of Québec, Canada. Results showed that maximization ratio values were lower than the proposed upper bound value for these watersheds. In comparison to the approach using an upper bound, the proposed approach reduced the PMPs in these watersheds by 11%. [ABSTRACT FROM AUTHOR]

Recursive digital filters (RDFs) are widely used for estimating base flow from streamflow hydrographs, and various forms of RDFs have been developed based on different physical models. Numerical experiments have been used to objectively evaluate their performance, but they have not been sufficiently comprehensive to assess a wide range of RDFs. This paper extends these studies to understand the limitations of a generalized RDF method as a pathway for future field calibration. Two formalisms are presented to generalize most existing RDFs, allowing systematic tuning of their complexity. The RDFs with variable complexity are evaluated collectively in a synthetic setting, using modeled daily base flow produced by Li et al. (2014) from a range of synthetic catchments simulated with HydroGeoSphere. Our evaluation reveals that there are optimal RDF complexities in reproducing base flow simulations but shows that there is an inherent physical inconsistency within the RDF construction. Even under the idealized setting where true base flow data are available to calibrate the RDFs, there is persistent disagreement between true and estimated base flow over catchments with small base flow components, low saturated hydraulic conductivity of the soil and larger surface runoff. The simplest explanation is that low base flow 'signal' in the streamflow data is hard to distinguish, although more complex RDFs can improve upon the simpler Eckhardt filter at these catchments. [ABSTRACT FROM AUTHOR]

This paper examines catchments that are almost ungauged, i.e., catchments for which only a small number of point flow measurements are available. In these catchments, hydrologists may still need to simulate continuous streamflow time series using a rainfall-runoff model, and the methodology presented here allows using few point measurements for model parameterization. The method combines regional information (parameter sets of neighboring gauged stations) and local information (contributed by the point measurements) within a framework where the relative weight of each source of information is made dependent on the number of point measurements available. This approach is tested with two different hydrological models on a set of 609 catchments in France. The results show that on average a few flow measurements can significantly improve the simulation efficiency, and that 10 measurements can reduce the performance gap between the gauged and ungauged situations by more than 50%. The added value of regional information progressively decreases until being almost insignificant when sufficient flow measurements are available. Model parameters tend to come closer to the values obtained by calibration in fully gauged conditions as the number of point flow measurements increases. [ABSTRACT FROM AUTHOR]

This paper develops a method that incorporates the public value for environmental cobenefits when a conservation buyer can purchase water quality credits based on nonmarket valuation results. We demonstrate this approach through an experiment with adult students in a classroom laboratory environment. Our application contributes to the study of individual preference and willingness to pay for cobenefits associated with the production of water quality credits in relation to the Ohio River Basin Trading Project. We use three different methods to elicit individuals' willingness to pay (WTP), including (1) a hypothetical referendum, (2) a real referendum lacking incentive compatibility, and (3) a real choice with incentive compatibility. Methodologically, our WTP estimates suggest individuals are more sensitive to the cost changes and reveal the lowest value in the real choice with incentive compatibility. Practically, we find individuals value certain cobenefits and credits as public goods. Incorporating public value toward cobenefits may improve the overall efficiency of a water quality trading market. Based on our specification of a planner's welfare function, results suggest a substantial welfare improvement after identifying an optimal allocation of a buyer's budget across credits derived from agricultural management practices producing different portfolios of cobenefits. [ABSTRACT FROM AUTHOR]

The transferability of conceptual hydrologic models in time is often limited by both their structural deficiencies and adopted parameterizations. Adopting a stationary set of model parameters ignores biases introduced by the data used to derive them, as well as any future changes to catchment conditions. Although time invariance of model parameters is one of the hallmarks of a high quality hydrologic model, very few (if any) models can achieve this due to their inherent limitations. It is therefore proposed to consider parameters as potentially time varying quantities, which can evolve according to signals in hydrologic observations. In this paper, we investigate the potential for Data Assimilation (DA) to detect known temporal patterns in model parameters from streamflow observations. It is shown that the success of the DA algorithm is strongly dependent on the method used to generate background (or prior) parameter ensembles (also referred to as the parameter evolution model). A range of traditional parameter evolution techniques are considered and found to be problematic when multiple parameters with complex time variations are estimated simultaneously. Two alternative methods are proposed, the first is a Multilayer approach that uses the EnKF to estimate hyperparameters of the temporal structure, based on apriori knowledge of the form of nonstationarity. The second is a Locally Linear approach that uses local linear estimation and requires no assumptions of the form of parameter nonstationarity. Both are shown to provide superior results in a range of synthetic case studies, when compared to traditional parameter evolution techniques. [ABSTRACT FROM AUTHOR]

The growing availability of field data, from information and communication technologies (ICTs) in 'smart' urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multiobjective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure. [ABSTRACT FROM AUTHOR]

This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg-a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the dominant processes associated with different landscape types, and the spatial relations of catchment processes. [ABSTRACT FROM AUTHOR]

In Part 1 of this two-part series, Hale and McDonnell (2016) showed that bedrock permeability controlled base flow mean transit times (MTTs) and MTT scaling relations across two different catchment geologies in western Oregon. This paper presents a process-based investigation of storage and release in the more permeable catchments to explain the longer MTTs and (catchment) area-dependent scaling. Our field-based study includes hydrometric, MTT, and groundwater dating to better understand the role of subsurface catchment storage in setting base flow MTTs. We show that base flow MTTs were controlled by a mixture of water from discrete storage zones: (1) soil, (2) shallow hillslope bedrock, (3) deep hillslope bedrock, (4) surficial alluvial plain, and (5) suballuvial bedrock. We hypothesize that the relative contributions from each component change with catchment area. Our results indicate that the positive MTT-area scaling relationship observed in Part 1 is a result of older, longer flow path water from the suballuvial zone becoming a larger proportion of streamflow in a downstream direction (i.e., with increasing catchment area). Our work suggests that the subsurface permeability structure represents the most basic control on how subsurface water is stored and therefore is perhaps the best direct predictor of base flow MTT (i.e., better than previously derived morphometric-based predictors). Our discrete storage zone concept is a process explanation for the observed scaling behavior of Hale and McDonnell (2016), thereby linking patterns and processes at scales from 0.1 to 100 km2. [ABSTRACT FROM AUTHOR]

Shallow Water Equations (SWE) provide a fundamental component for the quantification and mapping of hydraulic hazard. In steep mountain valleys, the use of one-dimensional SWE (also known as St. Venant Equations, SVE) is often legitimate and computationally competitive against two-dimensional solvers. However, in the same environment, the solution of SVE is hindered by the need of an accurate bathymetric reconstruction, which implies a number of cross sections which cannot be readily acquired by conventional field surveys. On the other hand, Digital Elevation Models ( DEM) with resolution adequate for studies of flood propagation are available in many areas of the world. In this paper, I propose to compute cross sections automatically by operating along the channel network derived from a valley's raster DEM, on the basis of algorithms that hitherto have been used for geomorphological and hydrological purposes. The extraction process can be refined by varying cross section inter-distance and width, in order to prevent superimpositions that might occur due to the sinuosity of the thalweg and to better capture the valley's local topography. At the end of this process, the geometric functions needed by SVE solvers can be computed for each cross section. A software tool that implements the described algorithm is provided to the scientific community. [ABSTRACT FROM AUTHOR]

In this paper we combined a multimodel ensemble based on 15 regional climate models with a multicatchment approach to explore the hydrologic sensitivity of 14 neighboring and rather similar catchments to changing climate conditions. Current (1982-2010) and future (2062-2090) streamflow was simulated with the HBV model. A diagnostic approach was used, which considered major behavioral catchment functions by using hydrologically relevant signatures related to overall water balance, flow duration curves and hydrograph attributes. Projected increases in temperature and precipitation resulted in increased total available streamflow, with lower spring and summer flows, but substantially higher winter streamflow. Furthermore, significant changes in flow durations with lower chances of both high and low flows can be expected in boreal Sweden in the future. This overall trend in projected streamflow pattern changes was comparable among the analyzed catchments but the magnitude of change differed considerably. This suggests that catchments belonging to the same region can show distinctly different degrees of hydrological responses to the same external climate change signal. We reason that differences in spatially distributed physical catchment properties within catchments are not only of great importance for current streamflow behavior, but also play a major role in the sensitivity of catchments to changing climate conditions. [ABSTRACT FROM AUTHOR]

We combine experimental and modeling results from a headwater catchment at the Hubbard Brook Experimental Forest (HBEF), New Hampshire, USA, to explore the link between stream solute dynamics and water age. A theoretical framework based on water age dynamics, which represents a general basis for characterizing solute transport at the catchment scale, is here applied to conservative and weatheringderived solutes. Based on the available information about the hydrology of the site, an integrated transport model was developed and used to compute hydrochemical fluxes. The model was designed to reproduce the deuterium content of streamflow and allowed for the estimate of catchment water storage and dynamic travel time distributions (TTDs). The innovative contribution of this paper is the simulation of dissolved silicon and sodium concentration in streamflow, achieved by implementing first-order chemical kinetics based explicitly on dynamic TTD, thus upscaling local geochemical processes to catchment scale. Our results highlight the key role of water stored within the subsoil glacial material in both the short-term and long-term solute circulation. The travel time analysis provided an estimate of streamflow age distributions and their evolution in time related to catchment wetness conditions. The use of age information to reproduce a 14 year data set of silicon and sodium stream concentration shows that, at catchment scales, the dynamics of such geogenic solutes are mostly controlled by hydrologic drivers, which determine the contact times between the water and mineral interfaces. Justifications and limitations toward a general theory of reactive solute circulation at catchment scales are discussed. [ABSTRACT FROM AUTHOR]

From ~1997 to 2009 the Murray-Darling Basin (MDB), Australia's largest water catchment and reputed "food bowl," experienced a severe drought termed the "Millennium Drought" or "Big Dry" followed by devastating floods in the austral summers of 2010/2011, 2011/2012, and 2012/2013. The magnitude and severity of these extreme events highlight the limitations associated with assessing hydroclimatic risk based on relatively short instrumental records (~100 years). An option for extending hydroclimatic records is through the use of paleoclimate records. However, there are few in situ proxies of rainfall or streamflow suitable for assessing hydroclimatic risk in Australia and none are available in the MDB. In this paper, available paleoclimate records are reviewed and those of suitable quality for hydroclimatic risk assessments are used to develop preinstrumental information for the MDB. Three different paleoclimate reconstruction techniques are assessed using two instrumental rainfall networks: (1) corresponding to rainfall at locations where rainfall-sensitive Australian paleoclimate archives currently exist and (2) corresponding to rainfall at locations identified as being optimal for explaining MDB rainfall variability. It is shown that the optimized rainfall network results in a more accurate model of MDB rainfall compared to reconstructions based on rainfall at locations where paleoclimate rainfall proxies currently exist. This highlights the importance of first identifying key locations where existing and as yet unrealized paleoclimate records will be most useful in characterizing variability. These results give crucial insight as to where future investment and research into developing paleoclimate proxies for Australia could be most beneficial, with respect to better understanding instrumental, preinstrumental and potential future variability in the MDB. [ABSTRACT FROM AUTHOR]

The notion that landscape features have coevolved over time is well known in the Earth sciences. Hydrologists have recently called for a more rigorous connection between emerging spatial patterns of landscape features and the hydrological response of catchments, and have termed this concept catchment coevolution. In this paper we review recent literature on this subject and attempt to synthesize what we have learned into a general framework that would improve predictions of hydrologic change. We first present empirical evidence of the interaction and feedback of landscape evolution and changes in hydrological response. From this review it is clear that the independent drivers of catchment coevolution are climate, geology, and tectonics. We identify common currency that allows comparing the levels of activity of these independent drivers, such that, at least conceptually, we can quantify the rate of evolution or aging. Knowing the hydrologic age of a catchment by itself is not very meaningful without linking age to hydrologic response. Two avenues of investigation have been used to understand the relationship between (differences in) age and hydrological response: (i) one that is based on relating present landscape features to runoff processes that are hypothesized to be responsible for the current fingerprints in the landscape; and (ii) one that takes advantage of an experimental design known as space-for-time substitution. Both methods have yielded significant insights in the hydrologic response of landscapes with different histories. If we want to make accurate predictions of hydrologic change, we will also need to be able to predict how the catchment will further coevolve in association with changes in the activity levels of the drivers (e.g., climate). There is ample evidence in the literature that suggests that whole-system prediction of catchment coevolution is, at least in principle, plausible. With this imperative we outline a research agenda that implements the concepts of catchment coevolution for building a holistic framework toward improving predictions of hydrologic change. [ABSTRACT FROM AUTHOR]

Detiding end-of-catchment flow data are an important step in determining the total volumes of freshwater (and associated pollutant loads) entering the ocean. We examine three approaches for separating freshwater and tidal flows from tidally affected data: (i) a simple low-pass Butterworth filter (BWF); (ii) a robust, harmonic analysis with Kalman smoothing (RoHAKS) which is a novel approach introduced in this paper; and (iii) dynamic harmonic regression (DHR). Using hydrographic data collected in the Logan River, Australia, over a period of 452 days, we judge the accuracy of the three methods based on three criteria: consistency of freshwater flows with upstream gauges; consistency of total discharge volumes with the raw data over the event; and minimal upstream flow. A simulation experiment shows that RoHAKS outperforms both BWF and DHR on a number of criteria. In addition, RoHAKS enjoys a computational advantage over DHR in speed and use of freely available software. [ABSTRACT FROM AUTHOR]

This paper presents a spatially explicit model for hydrothermal response simulations of Alpine catchments, accounting for advective and nonadvective energy fluxes in stream networks characterized by arbitrary degrees of geomorphological complexity. The relevance of the work stems from the increasing scientific interest concerning the impacts of the warming climate on water resources management and temperature-controlled ecological processes. The description of the advective energy fluxes is cast in a travel time formulation of water and energy transport, resulting in a closed form solution for water temperature evolution in the soil compartment. The application to Alpine catchments hinges on the boundary conditions provided by the fully distributed and physically based snow model Alpine3D. The performance of the simulations is illustrated by comparing modeled and measured hydrographs and thermographs at the outlet of the Dischma catchment (45 km2) in the Swiss Alps. The Monte Carlo calibration shows that the model is robust and that a simultaneous fitting of streamflow and stream temperature reduces the uncertainty in the hydrological parameters estimation. The calibrated model also provides a good fit to the measurements in the validation period, suggesting that it could be employed for predictive applications, both for hydrological and ecological purposes. The temperature of the subsurface flow, as described by the proposed travel time formulation, proves warmer than the stream temperature during winter and colder during summer. Finally, the spatially explicit results of the model during snowmelt show a notable hydrothermal spatial variability in the river network, owing to the small spatial correlation of infiltration and meteorological forcings in Alpine regions. [ABSTRACT FROM AUTHOR]

Irrigation with surface water enables Chilean agricultural producers to generate one of the country's most important economic exports. The Chilean water code established tradable water rights as a mechanism to allocate water amongst farmers and other water-use sectors. It remains contested whether this mechanism is effective and many authors have raised equity concerns regarding its impact on water users. For example, speculative hoarding of water rights in expectations of their increasing value has been described. This paper demonstrates how farmers can hoard water rights as a risk management strategy for variable water supply, for example, due to the cycles of El Niño or as consequence of climate change. While farmers with insufficient water rights can rely on unclaimed water during conditions of normal water availability, drought years overproportionally impact on their supply of irrigation water and thereby farm profitability. This study uses a simulation model that consists of a hydrological balance model component and a multiagent farm decision and production component. Both model components are parameterized with empirical data, while uncertain parameters are calibrated. The study demonstrates a thorough quantification of parameter uncertainty, using global sensitivity analysis and multiple behavioral parameter scenarios. [ABSTRACT FROM AUTHOR]

Transport processes and pathways through many hydrodynamic systems vary over time, often driven by variations in total water storage. This paper develops a very general approach to modeling unsteady transport through an arbitrary control volume (such as a watershed) that accounts for temporal variability in the underlying transport dynamics. Controls on the selection of discharge from stored water are encapsulated in probability distributions [ABSTRACT FROM AUTHOR]

This paper evaluates the use of a small number of generalist parameter sets as an alternative to classical calibration. Here parameter sets are considered generalist when they yield acceptable performance on a large number of catchments. We tested the genericity of an initial collection of 106 parameter sets sampled in the parameter space for the four-parameter GR4J rainfall-runoff model. A short list of 27 generalist parameter sets was obtained as a good compromise between model efficiency and length of the short list. A different data set was used for an independent evaluation of a calibration procedure, in which the search for an optimum parameter set is only allowed within this short list. In validation mode, the performance obtained is inferior to that of a classical calibration, but when the amount of data available for calibration is reduced, the generalist parameter sets become progressively more competitive, with better results for calibration series shorter than 1 year. [ABSTRACT FROM AUTHOR]

Urban catchments are characterized by a wide variety of complex juxtapositions and surface compositions that are linked to multiple overland flow paths. Their extremely high spatial heterogeneity leads to great sensitivity of hydrologic simulation to the scale variation of calculation units. Although extensive efforts have been made for investigating the scale effects and indicate its significance, less is understood of how routing features vary with spatial scales and further how the variation of routing features influences the hydrological response. In this paper, a grid‐based distributed urban hydrological model is applied to study spatial scale effects ranging from 10 to 250 m. Two parameters are proposed to quantitatively depict the routing features of overland flow specified for impervious and pervious areas. The results show that routing features are quite sensitive to spatial resolution. Large differences among simulations exist in the infiltration amounts attributed to the combined effects of the two routing parameters, which leads to opposite effects for both total flow volume and peak flow for various rainfall events. The relationship of the key model parameters at different spatial resolutions can be explicitly expressed by corresponding routing features. With this relationship, parameters transfer among different spatial scales can be realized to obtain consistent simulation results. This study further revealed the quantitative relationship between spatial scales, routing features, and the hydrologic processes and enabled accurate and efficient simulations required by real‐time flooding forecasting and land‐atmosphere coupling, while fully taking the advantages of detailed surface information. [ABSTRACT FROM AUTHOR]

The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order "n" is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2). Plain Language Summary: In hydrology, as in other endeavors, location matters. This study presents a new type of data that describes the location of a point on the landscape in the context of the network of streams that are present across the continental United States. The new data are presented as maps, and different patterns can be recognized in different areas of the United States. The patterns that can be seen also vary as one looks more or less closely at an area. The patterns that are present in these maps are shown to be useful for the purposes of mapping water resources. Key Points: Multiorder hydrologic position (lateral position and distance from stream to divide) quantifies the location of a point on the landscapeMultiorder hydrologic position is mapped for nine hydrologic orders at a 30‐m resolution for the conterminous United StatesMultiorder hydrologic position is used in machine learning models for the purposes of physiographic and hydrologic mapping [ABSTRACT FROM AUTHOR]

The changes in climate and catchment properties have altered the hydrological processes significantly at different spatiotemporal scales around the world. In particular for finer time scales, changes in water storage, which has been commonly neglected for long‐term temporal scales, may play an important role on hydrological processes. Nevertheless, few studies addressed this question in modifying the Budyko framework, with most of them remaining a steady‐state assumption on catchment characteristics. Here, we derive new analytical formulas of unsteady‐state streamflow elasticity in a modified Budyko framework, incorporating both storage change and one specific parameter for catchment properties. We study 78 humid catchments in the USA with simulation data of daily hydrological processes from a probability‐distribution‐based hydrological model (the modified HyMOD). As indicated by results, the annual storage change ratio is linearly correlated with the annual aridity index, and this relationship can be used to estimate elasticity coefficients with our formulas. The estimated elasticity coefficients perform well in simulating the annual streamflow with the power‐law model. For different catchments, variability of the unsteady‐state elasticity is higher than that of the steady‐state elasticity. Unsteady‐state streamflow coefficients show significant linear correlation with catchment properties, such as the average slope, average elevation, and catchment area. This study provides a new analytical approach to investigate the interannual stability of catchments with varying climate and catchment properties. Plain Language Summary: With the warming of the climate and shortage of water resource, the importance of studying water cycle is increasingly recognized, especially at finer time scales. Therefore, it is necessary to study the response of hydrologic variables to changes in climate and catchment properties under unsteady‐state conditions. In this study, we develop a new Budyko‐based analytical method for hydrologic elasticity analysis on catchments bearing the changes in climate and catchment properties. Case studies are undertaken for 78 humid catchments in the USA. Results show that the annual streamflow is mostly sensitive to the change in precipitation and then to changes in catchment properties and potential evapotranspiration. Key Points: Analytical formulas of unsteady‐state elasticity incorporate the aridity index, storage change, and a catchment characteristic parameterEstimated elasticity coefficients in 78 humid catchments agree well with the power‐law model for the annual streamflowVariability of unsteady‐state elasticity in different catchments is larger in comparison to the steady‐state framework [ABSTRACT FROM AUTHOR]

Soil erosion and sedimentation problems remain a major water quality concern for making watershed management policies in the Mississippi River Basin (MRB). It is unclear whether the observed decreasing trend of stream suspended sediment loading to the mouth of the MRB over the last eight decades truly reflects a decline in upland soil erosion in this large basin. Here, we improved a distributed regional land surface model, the Dynamic Land Ecosystem Model, to evaluate how climate and land use changes have impacted soil erosion and sediment yield over the entire MRB during the past century. Model results indicate that total sediment yield significantly increased during 1980–2018, despite no significant increase in annual precipitation and runoff. The increased soil erosion and sediment yield are mainly driven by intensified extreme precipitation (EP). Spatially, we found notable intensified EP events in the cropland‐dominated Midwest region, resulting in a substantial increase in soil erosion and sediment yield. Land use change played a critical role in determining sediment yield from the 1910s to the 1930s, thereafter, climate variability increasingly became the dominant driver of soil erosion, which peaked in the 2010s. This study highlights the increasing influences of extreme climate in affecting soil erosion and sedimentation, thus, water quality. Therefore, existing forest and cropland Best Management Practices should be revisited to confront the impacts of climate change on water quality in the MRB. Plain Language Summary: Climate and human activities have the potential to aggravate soil erosion and sediment yield in rivers, causing water quality degradation in the Mississippi River Basin (MRB). This study evaluated the impacts of climate and land use changes on sediment yield across the entire MRB by using a regional computer simulation model. The simulated results show that the increased extreme precipitation (EP) has amplified soil erosion and sediment yield in cropland‐dominated regions in the 2010s. Since the 1930s, climate variability has replaced land use change and become the dominant contributor to the changes in sediment yield. This study highlights the risks of EP on soil erosion and water quality in the MRB and emphasizes the need for appropriate management strategies to address these emerging challenges. Key Points: Increased extreme precipitation (EP) events have amplified soil erosion and sedimentation in the Mississippi River Basin since the 1990sA shift in the spatial pattern of EP events substantially intensified sediment yield in the Midwest in the 2010sThe major driver of soil erosion and sediment yield shifted from land use change to climate change during the past century [ABSTRACT FROM AUTHOR]

We use the five landscape ecology metrics of aggregation index, percentage of like adjacencies, interspersion and juxtaposition index, patch density, and Shannon's evenness index to assess spatial heterogeneity at 15 floodplains in the continental United States. Assessments are based on floodplain classes and patches delineated remotely using topography and vegetation. Floodplain reaches examined here represent diverse drainage areas, flow regimes, valley geometries, channel planforms, and biomes. We selected sites with minimal direct human alteration. Our objectives are to quantify floodplain spatial heterogeneity; evaluate whether statistically significant patterns are present; and interpret the statistical analyses with respect to the influence of lateral channel mobility and valley‐floor space available. We develop a conceptual model of the influences on lateral mobility and space available, and then test specific hypotheses derived from this conceptual model. These natural floodplains have a median aggregation index of 58.8%, median percentage of like adjacencies of 58.5%, median interspersion and juxtaposition index of 74.9%, median density of 1,241 patches/ha, and median Shannon's evenness index of 0.934 (n = 15). In other words, natural floodplains have moderate aggregation of classes, high evenness and intermixing of classes, and a wide range of patch densities. Drainage area, the ratio of floodplain/channel width, elevation, precipitation, total sinuosity, large wood volume, planform, and flow regime emerge as important variables to floodplain heterogeneity. These results highlight the influence of biotic‐abiotic interactions in shaping floodplain heterogeneity across diverse river corridors. Plain Language Summary: As river channels move across their valley floors, they create diverse topography including natural levees, cutoff meanders, secondary channels, and floodplain wetlands. These features increase the spatial variability of the floodplain. Many studies indicate that this variability strongly influences habitat abundance and diversity, storage of flood waters and excess nitrate or phosphate, and other floodplain functions. However, no one has systematically measured floodplain variability across multiple rivers and different geographic regions. We measured the variability of natural floodplains at 15 sites in the continental United States using metrics developed by landscape ecologists. We find that natural floodplains have distinctive variability signatures that relate to drainage area, the size of the floodplain, and other characteristics. Key Points: Natural floodplains have moderate aggregation of classes, high evenness and intermixing of classes, and a wide range of patch densitiesFloodplain heterogeneity reflects space available on the valley floor and lateral channel mobility [ABSTRACT FROM AUTHOR]

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e., burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought‐prone, wildfire‐adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub‐watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre‐and‐post wildfire hydrology with a rainfall‐runoff hydrological model. Even though these successive wildfires burned 1%–50% of each sub‐watershed (1%–30% at moderate/high severity), we found little evidence of wildfire‐related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations—model simulations). Our findings that post‐wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions. Plain Language Summary: Western United States water resources are vulnerable to changes caused by wildfires. While many studies indicate that streamflow may increase during post‐wildfire years, the compounding factors of drought and wildfire‐adapted landscapes challenge our ability to predict and prepare for coming changes in streamflow. Our current knowledge cannot answer the question "How much wildfire is required for river water flows to change?" Our study results show that drought can buffer the hydrological response to wildfire. While the drought can mitigate post‐wildfire flood potential, effects of the wildfire could emerge years later when high intensity rain events return. Key Points: Little evidence of wildfire‐related shifts in hydrology in drought‐prone Northern California coastal region having a Mediterranean climateWhen the percent of burned area increased beyond 30% of the watershed, the magnitude of the runoff response asymptotesPost‐wildfire hydrological variability did not extend outside of pre‐wildfire streamflow conditions [ABSTRACT FROM AUTHOR]

A changing climate and often unregulated water extractions have exposed over 2 billion people to water stress worldwide. While water managers have explored a portfolio of options to reduce this stress, supply augmentation through reuse of treated municipal wastewater is becoming increasingly attractive. Wastewater treatment plants protect water quality and prevent sewage from contaminating waterways. Increasingly, this resource is utilized for numerous human (e.g., irrigation, drinking water, groundwater recharge) and conservation (e.g., stream and river recharge) needs in water stressed regions. To understand the role treated municipal wastewater plays in impacting conservation objectives we identified the intersection of wastewater treatment plant locations and occurrences of threatened and endangered (T&E) species in California and compared the permitted contribution of effluent to baseflow quantities of the receiving waterbody to assess the degree to which changes in effluent could affect instream waterbodies. We found a positive correlation between the presence of treatment plants and T&E species in California watersheds—a quarter of species have 100% of their range in watersheds with at least one treatment plant. This correlation is greatest for species associated with terraces and riparian habitat, followed by aquatic habitat and aquatic emergent vegetation. One‐third of watersheds in our analysis can receive most of their cumulative watershed baseflow from effluent and are characterized by dense urbanization or agriculture. Our analysis demonstrates that the fates of T&E species and effluent are interconnected in ways important for water policy, suggesting that species conservation goals should be considered when making decisions about effluent reuse. Key Points: A positive correlation exists between the presence of treatment plants and threatened and endangered species in California watershedsOne‐third of watersheds, characterized by dense urbanization or agriculture, can receive most of their baseflow from effluentThe fates of threatened and endangered species and effluent are interconnected in ways that are important for water policy [ABSTRACT FROM AUTHOR]

River flows change on timescales ranging from minutes to millennia. These vibrations in flow are tuned by diverse factors globally, for example, by dams suppressing multi‐day variability or vegetation attenuating flood peaks in some ecosystems. The relative importance of the physical, biological, and human factors influencing flow is an active area of research, as is the related question of finding a common language for describing overall flow regime. Here, we addressed both topics using a daily river discharge data set for over 3,000 stations across the globe from 1988 to 2016. We first studied similarities between common flow regime quantification methods, including traditional flow metrics, wavelets, and Fourier analysis. Across all these methods, the flow data showed low‐dimensional structure (i.e., simple and consistent patterns), suggesting that fundamental mechanisms are constraining flow regime. One such pattern was that day‐to‐day variability was negatively correlated with year‐to‐year variability. Additionally, the low‐dimensional structure in river flow data correlated closely with only a small number of catchment characteristics, including catchment area, precipitation, and temperature—but notably not biome, dam surface area, or number of dams. We discuss these findings in a framework intended to be accessible to the many communities engaged in river research and management, while stressing the importance of letting structure in data guide both mechanistic inference and interdisciplinary discussion. Key Points: We compared tools for describing streamflow timeseries, including streamflow metrics, wavelet, and Fourier analysisEach method indicated streamflow data are structured: variability at short timescales is negatively correlated with long timescalesGlobally, dams were less correlated with streamflow regime than catchment size and climate were [ABSTRACT FROM AUTHOR]

To better understand the changes in the atmospheric evaporative demand (AED) in the context of global warming, the anomaly contribution analysis is presented to estimate mid‐long term contributions of meteorological factors to the AED. The Pearson correlation coefficient (RP) between the total contribution (ψ) of meteorological factors and the relative variation (ϕ) of the AED, and the Sen's trend slope (βS) of (ϕ, ψ) scatters are used to evaluate the applicability of the method. The smaller the values of |1 − RP| and |1 − βS|, the more applicable the method is. To validate the method, the reference crop evapotranspiration is employed as a proxy for the AED. The multi‐year contribution analysis is used as a comparison approach, which can only investigate the dominant meteorological factors of the AED in long term. Moreover, the Huaihe River basin of China is taken as a case study. Results show that (a) the values of |1 − RP| and |1 − βS| in mid‐long term are less than 0.1 in most cases when applying the anomaly contribution analysis, and the mid‐long term contribution processes of meteorological factors to the AED are clearly demonstrated; and (b) the wind speed and sunshine hours are the two most dominant factors (the total absolute contribution exceeds 60%) in long term, but they are not always the dominant factors in mid‐long term (e.g., wind speed in summer, and sunshine hours in winter). Therefore, the anomaly contribution analysis is a reasonable and effective method, which can help to gain insights into the changes in the AED. Key Points: Anomaly contribution analysis can identify the dominant meteorological factorsAnomaly contribution analysis is better than multi‐year contribution analysisDominant meteorological factors may not be consistent across time scales [ABSTRACT FROM AUTHOR]

Sediment connectivity plays a fundamental role in sustaining ecosystem goods and services in fluvial systems, including hydropower production. Dams alter the natural processes of sediment transport by trapping sediment and reshaping downstream hydrology and geomorphology. Due to these processes' interconnected nature, dams' impacts extend in time and space beyond the dam site to the entire river system. System‐scale approaches to reduce dam impacts commonly only consider dam siting, overlooking the potential of sediment management strategies integrated into the dam operations to offer more flexible solutions for mitigation. Herein, we contribute a sediment routing model (D‐CASCADE) to assess the impacts of reservoirs and their management strategies on river sediment connectivity. D‐CASCADE is applied to the 3S river system, a tributary of the Mekong River, a hotspot of potential dams in the Lower Mekong. We analyze three dam development portfolios. The effect of reservoir management is examined by assessing daily sediment delivery with specific dam release strategies. Model results predict sediment yield to the Mekong to reduce by 31%–60%. Finally, we explore trade‐offs between hydropower generation and sediment connectivity across cascades of multiple reservoirs. Results show that repeated flushing operations during the early wet season could significantly increase sediment delivery with minimal (max 6%) hydropower losses. While poor trade‐offs between sediment and hydropower have been locked‐in in the Mekong, our results highlight the potential of including sediment connectivity models in multi‐objective decision‐making frameworks to devise integrated water and sediment management strategies that mitigate connectivity disruptions while minimizing losses in other sectors. Key Points: The D‐CASCADE model can reproduce robust, basin‐wide patterns of river sediment connectivity in the Se Kong‐Se San‐Sre Pok (3S) river basinWe quantify the impact on sediment transport for different grain sizes of three dam development scenarios on the lower 3S systemWe optimize the design of drawdown sediment flushing operations considering both sediment connectivity and hydropower generation [ABSTRACT FROM AUTHOR]

Climate change has rapidly degraded the cryosphere in alpine headwaters, altering the runoff regime of alpine watersheds and threatening water security worldwide. To precisely simulate runoff response to climate change in alpine catchments, we took the source region of the Yellow River as a case to develop a physics‐informed deep learning (DL) model that tightly hybridizes DL with the physics of dominant hydrological processes, including the neural network‐based coupling of soil freeze‐thaw physics inspired by the Stefan equation. A physics‐informed DL model without soil freeze‐thaw representation was also established as a benchmark. Results demonstrated the former model's superiority in capturing streamflow's inter‐annual dynamics and reproducing baseflow recession properties caused by soil freeze‐thaw when average temperature rises by 1°C. This emphasizes the importance of the physics of dominant hydrological processes for DL to make credible runoff projections under rapid climate change. Furthermore, we comprehensively compared the physics‐informed DL model with a lumped model, physically‐based distributed model, and standard DL model, showing that the physics‐informed DL model performs best in simulating streamflow at multi‐timescales and the impacts of soil freeze‐thaw on runoff, and is more capable of simulating runoff relative change and trend in response to climate change. In summary, our findings demonstrate physics‐informed DL's credibility in runoff projections under drastic climate change when infused with the physics of dominant hydrological processes, and illustrate its superior ability to learn complex hydrological processes, providing a new approach for runoff simulation in permafrost‐affected alpine catchments under climate change. Plain Language Summary: The changing climate greatly affects permafrost regions in mountain headwaters, leading to the shift of the runoff regime. Projecting future runoff under climate change in such catchments is crucial for global water security. However, traditional distributed hydrological modeling falls short due to the complex and unclear underlying mechanisms, while deep learning (DL) models' unreliability in out‐of‐sample predictions might be amplified by significant climate change. To address this gap, we focused on an alpine permafrost catchment, the source region of the Yellow River, and developed a model that combines DL techniques with the fundamental physics of hydrological processes. By introducing a neural network‐based coupling of soil freeze‐thaw, inspired by a well‐known physical equation, our model outperforms the benchmark model without soil freeze‐thaw representation in simulating annual runoff variations in response to a 1°C/year temperature increase. Furthermore, our model excels in simulating runoff changes and trends caused by climate change, surpassing the lumped model, distributed hydrological model, and standard DL model. These indicate that the infused physics can help DL better capture the soil freeze‐thaw process and make more credible projections under climate change. Our study offers a valuable approach for assessing climate change impacts on runoff in permafrost‐affected mountain headwaters. Key Points: A model hybridizing deep learning and physics of dominant hydrological processes was developed for runoff simulation in alpine catchmentsPhysics‐informed deep learning model can well simulate runoff change under warming of 1°C, while the standard deep learning model can notPhysics‐informed deep learning outperforms physically‐based models in simulating runoff response to climate change in alpine catchments [ABSTRACT FROM AUTHOR]

The variability of the Indian Summer Monsoon Rainfall (ISMR), lasting from June to September, has a major impact on the Indian economy and ecosystem. An attempt to assess the temporal fluctuation of ISMR with the long‐distance interaction indicators has been presented here. This also corroborates the argued statement of a weakening relationship between ISMR and Indian Summer Monsoon Index (ISMI) due to significant temporal fluctuation. Monsoon indices: the ISMI, East Asian Summer Monsoon Index (EASMI), South Asian Summer Monsoon Index (SASMI), and Western‐North Pacific Monsoon Index (WNPMI) have been studied to create a correlation, coherency, and teleconnection with the ISMR over the Mahanadi River basin (MRB) during 1948–2017. High‐resolution gridded precipitation data were employed to assess the statistical properties of the monsoon and to address the correlation, coherency, and teleconnection with the ISMR. Spearman's rank‐based correlation test, a non‐parametric Mann‐Kendall trend test, and Sen's slope have been used to assess the statistical properties of observed precipitation and the monsoon indices. Furthermore, wavelet transform coherence is used to detect transient correlations between ISMR and monsoon indices that have previously been uncorrelated. SASMI (ρ = 0.440) dominates ISMI (ρ = 0.422) as the most significant and influential long‐distance indicator with ISMR. The findings were reconfirmed on a sub‐basin scale, with the middle Mahanadi River Basin (MMRB) being the most significant and influential, whereas the upper Mahanadi River Basin and lower Mahanadi River Basin were the second and third most influential, respectively. Plain Language Summary: A study on precipitation, where its variability, seasonal shift, and long‐distance interaction with climate have been investigated. In the face of climate change, a substantiated assessment of the Indian Summer Monsoon Rainfall (ISMR)'s relationship with the Indian Summer Monsoon Index (ISMI) and a reclassification of the dominant monsoon index are presented. The study investigates rainfall over the Mahanadi River Basin (MRB) and the large‐scale monsoon indicators to identify the most influential long‐distance interactions for spatial and temporal variability and trends in seasonal precipitation throughout the MRB from 1948 to 2017. The study revealed that MRB experiences a monsoonal precipitation drop of 0.905 mm/year, which is driven by a significant drying trend in middle Mahanadi River Basin (MMRB) (−1.717 mm/year, −0.184%/year) and a wetting trend in lower Mahanadi River Basin (LMRB) (0.439 mm/year). The pattern interpretation of monsoon precipitation over LMRB creates positive centers and a relatively negative center at upper Mahanadi River Basin (UMRB) and north of MMRB, suggesting relatively high precipitation over the coastal plains of LMRB and feeble precipitation over the MMRB and UMRB. The long‐distance interaction of ISMR with monsoon indices suggests the most influential SASMI interaction, and ISMI was undervalued by ρ = 0.018. The result agrees with the time‐frequency analysis, where a period from 1971 to 2006 experienced weak coherence, suggesting a feeble condition and a weakening relationship between ISMR and ISMI. Key Points: The drying trends of middle Mahanadi River Basin (−2.185 mm/year) and upper Mahanadi River Basin (−1.717 mm/year) appear statistically significant at 98% and 90% CI, respectivelySouth Asian Summer Monsoon Index dominates on the basin scale (Mahanadi River Basin) for long‐distance interaction with ρ = 0.440, while Indian Summer Monsoon Index (ISMI) (ρ = 0.422) has been undervaluedISMI has experienced 36 years (1971–2006) of weak coherence and is in a condition where its relationship with ISMR has become feeble [ABSTRACT FROM AUTHOR]

Understanding the nexus between food, energy, and water systems (FEW) is critical for basins with intensive agricultural water use as they face significant challenges under changing climate and regional development. We investigate the food, energy, and water nexus through a regional hydroeconomic optimization (RHEO) modeling framework. The crop production in RHEO is estimated through a hierarchical regression model developed using a biophysical model, AquaCropOS, forced with daily climatic inputs. Incorporating the hierarchical model within the RHEO also reduces the computation time by enabling parallel programming within the AquaCropOS and facilitates mixed irrigation—rainfed, fully irrigated and deficit irrigation—strategies. To demonstrate the RHEO framework, we considered a groundwater‐dominated basin, South Flint River Basin, Georgia, for developing mixed irrigation strategies over 31 years. Our analyses show that optimal deficit irrigation is economically better than full irrigation, which increases the groundwater pumping cost. Thus, considering deficit irrigation in a groundwater‐dominated basin reduces the water, carbon, and energy footprints, thereby reducing FEW vulnerability. The RHEO also could be employed for analyzing FEW nexus under potential climate change and future regional development scenarios. Key Points: A new regional hydroeconomic optimization modeling framework is proposed for mixed irrigation regimes using AquaCropOSOptimal deficit irrigation strategy is economically better than full irrigation by reducing the cost and energy for groundwater pumpingCrop simulation module and optimization module are linked using Bayesian Hierarchical Model to reduce computation time [ABSTRACT FROM AUTHOR]

Many Victorian catchments experienced a rainfall‐runoff relationship (RRR) shift during the Millennium Drought (1997–2009). Less annual streamflow was generated from annual rainfall during the drought than would have been expected for the same rainfall before the drought. Changes in weather systems, such as cyclones, fronts, and thunderstorms, were also observed during this period. In this study, we assess the role that changes in weather systems played in the RRR shift. We find that catchments that experienced a RRR shift tended to receive less of their rainfall from frontal weather systems, and more of their rainfall from thunderstorm‐enhanced cyclone and cyclone‐front hybrid systems during the drought than catchments that showed little change in RRR. Overall, changes in the proportion of rainfall from weather systems alone explained up to 43% of the variance in the RRR shift between catchments. Including parameters that quantify catchment processes and physiographic features indicated that weather system changes are not the primary indicator of a RRR shift. Catchment mean slope, aridity and valley confinement, together with proportional rainfall change of cyclone‐thunderstorms and cyclone‐front‐thunderstorms, explained 60% of the variance in the RRR shift between catchments. Focusing on streamflow availability, we find the importance of different weather systems and catchment characteristics varies depending on warm or cool season, eastern or western Victoria, and before or during the drought. Following the drought, some catchments largely maintained the RRR shift that occurred during the drought, and also experienced less of their rainfall from fronts and cyclones compared to the other catchments. Key Points: Weather system changes in Victoria, Australia are partly related to a change in rainfall‐runoff relationship (RRR) during a decade‐long droughtLess rainfall from fronts and more rainfall from thunderstorm enhanced systems were an indicator of RRR changeInternal catchment characteristics were a greater indicator of RRR change than changes in weather systems [ABSTRACT FROM AUTHOR]

Seasonally snow‐covered catchments in the western United States supply water to growing populations as both annual snowmelt‐driven streamflow and multi‐year groundwater recharge. Although interannual variability in streamflow is driven largely by precipitation, runoff efficiency (the ratio of streamflow to precipitation) in individual catchments varies by 50% or more. Recent work suggests that interannual variability in groundwater storage, inferred from winter baseflow, is a primary control on runoff efficiency, highlighting a need to quantify both the time scales on which groundwater storage varies and the hydro‐climatic drivers of storage. Using over a century of daily stream discharge data from 10 seasonally snow‐covered catchments in northern Utah, we find that temporal variability in winter baseflow, an index of groundwater storage, measured from mean daily January discharge, exhibits a 2–5‐ and 12–15‐year periodicity, driven by regional precipitation patterns and snowmelt dynamics. Specifically, multiple linear regression (MLR) modeling using antecedent hydro‐climatic variables demonstrates that winter baseflow (groundwater storage) was positively related to 3–4 years of antecedent annual precipitation, negatively related to the previous year's mean annual temperature, and positively related to 1–4 antecedent years of snowmelt rate and duration. Because antecedent baseflow (groundwater storage) is strongly related to runoff efficiency, these results suggest that more frequent and longer droughts in a future climate will reduce surface water supplies faster than otherwise expected. More broadly, these results highlight the importance of including the influence of antecedent climate on groundwater storage when modeling and managing water supplies from seasonally snow‐covered catchments. Key Points: Interannual changes in groundwater storage inferred from baseflow in 10 headwater catchments exhibit coherent 2–5 and 12–15 year periodicityInterannual variability in groundwater storage is related to 1–4 years of antecedent precipitation (+) snowmelt rate (+) and temperature (−)Groundwater storage in warmer/drier catchments is related to longer periods of antecedent climate than cooler/wetter catchments [ABSTRACT FROM AUTHOR]

Regression models have consistently identified discharge as a suitable variable for capturing the systematic trends in channel geometry. Using a discharge‐based relation, however, does not reflect the more fundamental dependence of channel geometry on basic watershed properties and precipitation characteristics. To explore relations between bankfull channel geometry and watershed properties, we consider two sets of dimensionless regression models: (a) with bankfull discharge as an independent variable, and (b) with bankfull discharge as a dependent variable. While bankfull discharge is used as an input variable in the first set of regression analyses, average annual precipitation has been used in the second set. Other watershed and channel variables considered include drainage area, median sediment grain size, and valley slope. A data set of 538 sandy and gravel channel reaches from UK, and various physiographic provinces of the USA is considered. By representing bankfull discharge, width, depth and watershed variables in the form of dimensionless parameters, we specify the relationship between channel morphology and watershed and precipitation characteristics using an approach that is applicable to sandy and gravel streams from a range of climatic and physiographic regions. The formulation presented here is an effort toward the development of models that will be capable of predicting bankfull channel dimensions and discharge under modified average annual precipitation due to change in climate. Key Points: Regression analyses of bankfull hydraulic geometry (BHG) for both gravel and sandy streams and conducted using watershed variables and precipitationEvaluation of bankfull discharge indicates dependence on watershed and climatic characteristicsThe formulation presented here allows for the prediction of BHG subject to changes in average annual precipitation [ABSTRACT FROM AUTHOR]

Physical interactions of microplastics within vegetation and turbulent flows of freshwater systems are poorly understood. An experimental study was conducted to investigate the underlying physical transport mechanisms of microplastics over submerged canopies across a range of flow conditions common in the natural environment. The effects of changing canopy heights were investigated by testing two model canopies of varying stem heights, simulating seasonal variation. This study determined and compared the mixing and dispersion processes for microplastics and solutes utilizing fluorometric tracing techniques. A hydrodynamic model was developed based on the advection‐dispersion equation for quantifying microplastic mixing in submerged canopies. Longitudinal dispersion coefficients for neutrally buoyant microplastics (polyethylene) and solutes were significantly correlated within submerged model vegetation irrespective of the complexity of the flow regime. Hydrodynamic and solute transport models were shown to be capable of robust predictions of mixing for neutrally buoyant microplastics in environmental flows over a canopy, facilitating a new approach to quantify microplastic transport and fate. We compare the mixing processes for microplastics and solutes then propose a hydrodynamic model for quantifying the mixing in submerged canopies. Plain Language Summary: Microplastic movement and fate within vegetation and turbulent flows of freshwater systems is poorly understood. A study was conducted within a laboratory flume, scaled for real‐world river systems, to investigate the transport of microplastics over submerged canopies across a range of flow conditions common in the natural environment. The effects of changing vegetation heights were investigated by testing two model canopies of varying stem heights, simulating seasonal variation. To measure microplastic movement in real‐time this study determined and compared the mixing of microplastics and solutes using fluorometric techniques that measure fluorescence at specific wavelengths of light produced by the stained microplastics and Rhodamine WT dye (solute). Models utilizing velocities over depth and solute dispersion were adapted to quantify microplastic mixing in submerged canopies. The dispersion of neutrally buoyant microplastics (polyethylene), microplastics of a similar buoyancy to water, and solutes were significantly correlated within submerged vegetation irrespective of the complexity of the flow regime. The models were shown to be capable of reliable predictions of mixing for neutrally buoyant microplastics in environmental flows over a canopy, facilitating a new approach to measure microplastic transport and fate. Key Points: Neutrally buoyant microplastics disperse analogous to solutes within the water column of fluvial environments with submerged vegetationA novel fluorometric tracing and particle staining technique is proven to accurately trace stained microplastics within vegetated flowsA robust hydrodynamic model is proven to predict mixing of neutrally buoyant microplastics [ABSTRACT FROM AUTHOR]

Reservoirs play a vital role in the supply and management of water resources and their operation can significantly alter downstream flow. Despite this, reservoirs are frequently excluded or poorly represented in large‐scale hydrological models, which can be partly attributed to a lack of open‐access data describing reservoir operations, inflow and storage. To help inform the development of reservoir operation schemes, we collate a suite of hydrological signatures designed to detect the impacts of reservoirs on the flow regime at large‐scales from downstream flow records only. By removing the need for pre‐and‐post‐reservoir flow timeseries (a requirement of many pre‐existing techniques), these signatures facilitate the assessment of flow alteration across a much wider range of catchments. To demonstrate their application, we calculate the signatures across Great Britain in 111 benchmark (i.e., near‐natural) catchments and 186 reservoir catchments (where at least one upstream reservoir is present). We find that abstractions from water resource reservoirs induce deficits in the water balance, and that pre‐defined flow releases (e.g., the compensation flow) reduce variability in the downstream flow duration curve and in intra‐annual low flows. By comparing signatures in benchmark and reservoir catchments, we define thresholds above which the influence of reservoirs can be distinguished from natural variability and identify 40 catchments significantly impacted by the presence of reservoirs. The signatures also provide insights into local reservoir operations, which can inform the development of tailored reservoir operation schemes, and identify locations where current modeling practices (which lack reservoir representation) will be insufficient. Plain Language Summary: Reservoirs play a vital role in the supply and management of water resources. However, reservoirs are frequently either excluded, or poorly represented in large‐scale river flow simulations due to a lack of open‐access data describing their operation. To help inform the representation of reservoirs, we use a suite of simple metrics to detect where and how rivers are being impacted by upstream reservoirs from records of downstream flow. We apply these metrics over hundreds of gauges across Great Britain and find that abstractions for water supply reduce the volume of water in rivers downstream of reservoirs, and that controlled water releases reduce downstream flow variability. The application of the metrics defined in this study enable us to gain insights into how upstream reservoirs are operated and will help to improve our ability to simulate and predict river flow under changing climate and water demand. Key Points: Hydrological signatures can be used to identify reservoir‐induced hydrological alteration using only downstream flow timeseriesApplication to 186 catchments across Great Britain finds reservoirs often induce losses in the water balance and reduce flow variabilitySignatures provide insights into reservoir operations which will help to design and evaluate new operation schemes in hydrological models [ABSTRACT FROM AUTHOR]

Many rivers and streams are ungauged or poorly gauged and predicting streamflow in such watersheds is challenging. Although streamflow signals result from processes with different frequencies, they can be "sparse" or have a "lower‐dimensional" representation in a transformed feature space. In such cases, if this appropriate feature space can be identified from streamflow data in gauged watersheds by dimensionality reduction, streamflow in poorly gauged watersheds can be predicted with a few measurements taken. This study utilized this framework, named data‐driven sparse sensing (DSS), to predict daily‐scale streamflow in 543 watersheds across the contiguous United States. A tailored library of features was extracted from streamflow training data in watersheds within the same climatic region, and this feature space was used to reconstruct streamflow in poorly gauged watersheds and identify the optimal timings for measurement. Among different regions, streamflow in snowmelt‐dominated and baseflow‐dominated watersheds (e.g., Rocky Mountains) was more effectively predicted with fewer streamflow measurements taken. The prediction efficiency in some rainfall‐dominated regions, for example, New England and the Pacific coast, increased significantly with an increasing number of measurements. The spatial variability of prediction efficiency can be attributed to the process‐driven mechanisms and the dimensionality of watershed dynamics. Storage‐dominated systems are lower‐dimensional and more predictable than rainfall‐dominated systems. Measurements taken during periods with large streamflow magnitudes and/or variances are more informative and lead to better predictions. This study demonstrates that DSS can be an especially useful technique to integrate ground‐based measurements with remotely sensed data for streamflow prediction, sensor placement, and watershed classification. Plain Language Summary: Many rivers and stream reaches are ungauged or poorly gauged because streamflow measurement is costly and resource intensive. Predicting the streamflow time‐series in these ungauged or poorly gauged watersheds is still challenging. Here, we use a signal processing technique called data‐driven sparse sensing on a national‐scale streamflow data set across the contiguous United States. We predict streamflow time‐series in each watershed based on existing streamflow data in watersheds nearby, and explore the best times during the year for measuring streamflow. Our analysis shows that data‐driven sparse sensing is an effective tool to predict streamflow time‐series in poorly gauged watersheds based on very few streamflow measurements. The streamflow in watersheds with high snowmelt and high baseflow can be more easily predicted than in other watersheds. Our analysis also shows that the streamflow measurements taken during periods with large streamflow peaks and variances contain more information and are beneficial for making predictions. We conclude that data‐driven sparse sensing can be further used to classify watersheds and to identify the best locations for streamflow gauging. Key Points: We utilize data‐driven sparse sensing to predict daily streamflow and identify the optimal times for streamflow measurement across the contiguous United StatesStreamflow was more effectively predicted in watersheds dominated by snowmelt and baseflow than those dominated by rainfall and quickflowThe optimal sampling times for streamflow prediction by data‐driven sparse sensing are periods with large flow magnitudes and variances [ABSTRACT FROM AUTHOR]

Despite plentiful evidence of frozen ground effects on snowmelt infiltration from lab experiments at pedon scales, streamflow observations show a weaker or no effect in terms of timing and magnitude at larger scales. We aim to understand this conflicting phenomenon through modeling using the Noah land surface model with multi‐physics (MP; Noah‐MP) options and the Routing Application for Parallel computatIon of Discharge (RAPID) over the Mississippi River Basin. We conduct 16 experiments with combinations of two supercooled liquid water (SLW) parameterization schemes and four soil hydraulic property schemes in Noah‐MP driven by two gridded precipitation products of the North American Land Data Assimilation System (NLDAS) and the Integrated Multi‐satellitE Retrievals for GPM (IMERG) Final. We then use RAPID to route Noah‐MP modeled surface runoff and groundwater discharge to predict daily streamflow at 52 United States Geological Survey gauges from 2015 to 2019. A model with the highest permeability performs better than other schemes on daily streamflow predictions by 20%–57% throughout a water year and 29%–113% for the spring as measured by the mean Kling‐Gupta Efficiency of the 52 gauges. Different SLW schemes demonstrate negligible effects on streamflow predictions. Models forced by IMERG show a better prediction skill compared with those forced by NLDAS at most of the gauges. Both precipitation products confirm that a scheme of higher permeability yields more accurate streamflow predictions over frozen ground. Future models should represent preferential flows through macropore networks to improve the understanding of frozen soil effects on infiltration and discharge across scales. Plain Language Summary: Frozen ground presumably affects the discharge of snowmelt water into rivers during winter and spring due to the apparent effects of ice "blockage." The presence of ice in the soil affects the release of soil liquid water and the time to release the water to local streams and rivers through the effects of soil ice on water flow and capacity to hold snowmelt water. At present, it is not fully understood how the soil ice affects the soil's capability of holding and releasing liquid water to rivers at river‐basin to continental scales. We use a computer model to test competing hypotheses through combinations of optional schemes of water holding capacity and water flow. The modeling results over major sub‐basins in the Mississippi River show that a model with higher permeable frozen soil results in higher skill in streamflow predictions at river basin scales. This study highlights the need to represent water flow through macropores that may be formed due to ice expansion during freezing/thawing cycles. Key Points: Streamflow predictions are substantially sensitive to the choice of frozen soil hydraulic property parameterizationsIrrespective of precipitation product used, a scheme of higher frozen soil permeability yields more skillful streamflow predictionsStreamflow prediction improvement with a scheme of higher frozen soil permeability is pronounced in basins dominated by frozen ground [ABSTRACT FROM AUTHOR]

Stochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as precipitation, are needed to build alternative but equally plausible inputs for water‐related design and management, and to estimate uncertainty and assess risks. However, while existing stochastic simulation methods are mature enough to deal with relatively small domains and coarse spatiotemporal scales, additional work is required to develop simulation tools for large‐domain analyses, which are more and more common in an increasingly interconnected world. This study proposes a methodological advancement in the CoSMoS framework, which is a flexible simulation framework preserving arbitrary marginal distributions and correlations, to dramatically decrease the computational burden and make the algorithm fast enough to perform large‐domain simulations in short time. The proposed approach focuses on correlated processes with mixed (zero‐inflated) Uniform marginal distributions. These correlated processes act as intermediates between the target process to simulate (precipitation) and parent Gaussian processes that are the core of the simulation algorithm. Working in the mixed‐Uniform space enables a substantial simplification of the so‐called correlation transformation functions, which represent a computational bottle neck in the original CoSMoS formulation. As a proof of concept, we simulate 40 years of daily precipitation records from 1,000 gauging stations in the Mississippi River basin. Moreover, we extend CoSMoS incorporating parent non‐Gaussian processes with different degrees of tail dependence and suggest potential improvements including the separate simulation of occurrence and intensity processes, and the use of advection, anisotropy, and nonstationary spatiotemporal correlation functions. Key Points: Introducing the mixed‐Uniform CoSMoS allowing simulation in thousands of stationsDimensionality reduction and fast simulation through mixed‐Uniform transformationsPreserving marginal distributions, probability of zero, and correlations in all stations [ABSTRACT FROM AUTHOR]

The estimation of reservoirs impact on flood peak reduction at the catchment scale is of fundamental importance for risk assessment and planning purposes. It is generally addressed using detailed hydrologic‐hydrodynamic simulations or simple, empirically based indices. The former provide detailed results, at the cost of a large amount of information and high computational efforts; the latter are cost‐effective and simple, yet they generally provide approximate results. A promising compromise between the two is the physically based attenuation index R $\mathcal{R}$, based on the concept of equivalent reservoir; R $\mathcal{R}$ was recently proposed in the literature to estimate the impact of multiple reservoirs located in series along the main channel, based on the assumption of rectangular catchment. In this work, we extend the equivalent reservoir approach to a generic catchment, with any shape and without restrictions on the location of the reservoirs. For an effective assessment of the method, we also introduce the novel concept of Reservoir‐influenced Instantaneous Unit Hydrograph (RIUH); the RIUH can be derived using a Monte Carlo procedure to include the effects of reservoirs on the Instantaneous Unit Hydrograph. Simulations of numerous fictitious reservoir configurations in a real basin demonstrate the potential of the methods in reproducing the attenuation effect. Remarkably, an average error of 3%–5% exists between R $\mathcal{R}$ and the peak reduction of a less simplified reality (RIUH). Finally, we provide a global, catchment‐scale application of the attenuation index, which constitutes an effective tool for the evaluation of each reservoir in managed catchment system and the design or planning of new hydraulic infrastructures. Key Points: A simplified reservoir attenuation index R $\mathcal{R}$, applicable to a generic catchment and any reservoir location, is proposedWe introduce the novel concept of Reservoir‐influenced Instantaneous Unit Hydrograph to easily include reservoirs effects in Instantaneous Unit HydrographWe provide a global, catchment‐scale application of R $\mathcal{R}$ that constitutes an effective tool for risk assessment and reservoir design [ABSTRACT FROM AUTHOR]