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

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

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

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

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

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]

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]

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 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]

Runoff prediction in ungauged catchments is a significant hydrological challenge. The common approach is to calibrate hydrological models against streamflow data from gauged catchments, and then regionalize or transfer parameter values from the gauged calibration to predict runoff in the ungauged catchments. This paper explores the potential for using parameter values from hydrological models calibrated solely against readily available remotely sensed evapotranspiration data to estimate runoff time series. The advantage of this approach is that it does not require observed streamflow data for model calibration and is therefore particularly useful for runoff prediction in poorly gauged or ungauged regions. The modeling experiments are carried out using data from 222 catchments across Australia. The results from the remotely sensed evapotranspiration runoff‐free calibration are encouraging, particularly in simulating monthly runoff and mean annual runoff in the wetter catchments. However, results for daily runoff and in the drier regions are relatively poor, and further developments are needed to realize the benefit of direct model calibration against remotely sensed data to predict runoff in ungauged catchments. Key Points: Direct calibration of hydrological models solely against remotely sensed ET (RS‐ET) data is used to predict runoff in ungauged regionsThe RS‐ET runoff‐free calibration approach is tested on 222 catchments across AustraliaThe RS‐ET runoff‐free calibration can estimate mean annual runoff and monthly runoff series in wet catchments reasonably well [ABSTRACT FROM AUTHOR]

Inspired by the Dunne diagram, the climatic and landscape controls on the partitioning of annual runoff into its various components (Hortonian and Dunne overland flow and subsurface stormflow) are assessed quantitatively, from a purely theoretical perspective. A simple distributed hydrologic model has been built sufficient to simulate the effects of different combinations of climate, soil, and topography on the runoff generation processes. The model is driven by a sequence of simple hypothetical precipitation events, for a large combination of climate and landscape properties, and hydrologic responses at the catchment scale are obtained through aggregation of grid-scale responses. It is found, first, that the water balance responses, including relative contributions of different runoff generation mechanisms, could be related to a small set of dimensionless similarity parameters. These capture the competition between the wetting, drying, storage, and drainage functions underlying the catchment responses, and in this way, provide a quantitative approximation of the conceptual Dunne diagram. Second, only a subset of all hypothetical catchment/climate combinations is found to be 'behavioral,' in terms of falling sufficiently close to the Budyko curve, describing mean annual runoff as a function of climate aridity. Furthermore, these behavioral combinations are mostly consistent with the qualitative picture presented in the Dunne diagram, indicating clearly the commonality between the Budyko curve and the Dunne diagram. These analyses also suggest clear interrelationships amongst the 'behavioral' climate, soil, and topography parameter combinations, implying these catchment properties may be constrained to be codependent in order to satisfy the Budyko curve. [ABSTRACT FROM AUTHOR]

Afforestation to control soil erosion has been implemented throughout China over the past few decades. The long‐term hydrological effects, such as total water yield and baseflow, of this large‐scale anthropogenic activity remain unclear. Using six decades of hydrologic observations and remote sensing data, we explore the hydrological responses to forest expansion in four basins with contrasting climates across China. No significant change in runoff was found for the period 1970–2012 for the cold and dry Hailar River Basin in northeastern China. However, both forest expansion and reduced precipitation contributed to the runoff reduction after afforestation since the late 1990s. Similarly, afforestation and drying climate since the mid‐1990s induced a significant decrease in runoff for the Weihe River Basin in semi‐arid northwestern China. In contrast, the two wet basins in the humid southern China, Ganjiang River Basin and Dongjiang River Basin, showed insignificant changes in total runoff during their study periods. However, the baseflow in the winter dry seasons in these two watersheds significantly increased since the 1950s. Our results highlight the long‐term variable effects of forest expansion and local climatic variability on basin hydrology in different climatic regions. This study suggests that landuse change in the humid study watersheds did not cause dramatic change in river flow and that region‐specific afforestation policy should be considered to deal with forestation‐water quantity trade‐off. Conclusions from this study can help improve decision‐making for ecological restoration policies and water resource management in China and other countries where intensive afforestation efforts are taking place. Key Points: Both precipitation and afforestation explained reduction in runoff for dry basinsForest recovery did not result in significant changes in total runoff for two wet basinsDry season baseflow in two wet basins had an upward trend [ABSTRACT FROM AUTHOR]

Descriptions of runoff generation processes continue to grow, helping to reveal complexities and hydrologic behavior across a wide range of environments and scales. But to date, there has been little grouping of these process facts. Here, we discuss how the "fill‐and‐spill" concept can provide a framework to group event‐based runoff generation processes. The fill‐and‐spill concept describes where vertical and lateral additions of water to a landscape unit are placed into storage (the fill)—and only when this storage reaches a critical level (the spill), and other storages are filled and become connected, does a previously infeasible (but subsequently important) outflow pathway become activated. We show that fill‐and‐spill can be observed at a range of scales and propose that future fieldwork should first define the scale of interest and then evaluate what is filling‐and‐spilling at that scale. Such an approach may be helpful for those instrumenting and modeling new hillslopes or catchments because it provides a structured way to develop perceptual models for runoff generation and to group behaviors at different sites and scales. Key Points: Runoff is at the scale of the beholder. We need to move from a notion of uniqueness of place to uniqueness of scaleFill‐and‐spill, together with its components is common to all event runoff systemsFill‐and‐spill as a framework is perhaps a guide for field hydrologists on what to measure, in what order and why [ABSTRACT FROM AUTHOR]

Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of 15 catchments of the U.S. Critical Zone Observatory (CZO) network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning. The CZOs collect data sets that characterize the physical, chemical, and biological architecture of the subsurface, while also monitoring hydrologic fluxes such as streamflow, precipitation, and evapotranspiration. For the first time, we collate time series of hydrologic variables across the CZO network and begin the process of examining hydrologic signatures across sites. We find that catchments with low baseflow indices and high runoff sensitivity to storage receive most of their precipitation as rain and contain clay‐rich regolith profiles, prominent argillic horizons, and/or anthropogenic modifications. In contrast, sites with high baseflow indices and low runoff sensitivity to storage receive the majority of precipitation as snow and have more permeable regolith profiles. The seasonal variability of water balance components is a key control on the dynamic range of hydraulically connected water in the critical zone. These findings lead us to posit that water balance partitioning and streamflow hydraulics are linked through the coevolution of critical zone architecture but that much work remains to parse these controls out quantitatively. Key Points: Quantitative signatures of hydrologic function show variations across the U.S. Critical Zone Observatory networkDynamic water storage range is correlated with hydroclimatic seasonality of water inputsVariations in catchment sensitivity and baseflow index point toward underlying structural controls [ABSTRACT FROM AUTHOR]

River runoff is estimated as a water budget residual using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage time series, ERA5 reanalysis data, and precipitation observations for January 2003 through December 2015 for the Obidos upstream drainage basin and for the entire Amazon basin. Estimated runoff based on the water budget agrees remarkably well with in situ gauge observations at Obidos, especially at seasonal time scales, with nearly perfect phase agreement but slightly larger seasonal amplitude. The discrepancy in the seasonal amplitude may be attributed to underestimation of river gauge runoff during the wet season when water overflows the riverbanks. The ERA5 model appears to overestimate long‐term mean evapotranspiration in the Amazon by ~2 cm/month based on comparisons with precipitation and runoff observations. Using precipitation data based on satellites and gauge observations relative to gauge observations alone improved agreement between water budget runoff estimates and in situ runoff observations. Seasonal variations in ERA5‐simulated runoff are about twice as large as those from in situ observations and show a large phase lag as well. Water budget‐based runoff for the entire Amazon (~7,200 km3 averaged over the 13‐year period) is significantly larger than observed runoff (~5,700 km3) at Obidos and notably larger than previous estimates for the entire Amazon. These differences may be partly related to submarine runoff from the Amazon basin that cannot be captured by surface gauges. Key Points: We provide a new estimate of seasonal and yearly river runoff changes for the Amazon basin using the water budget closure methodThe water budget closure estimates remarkably agree well with in situ observations in the Amazon basin at seasonal scalesWe have demonstrated that water budget closure method has the potentials to detect submarine runoff from the Amazon basin [ABSTRACT FROM AUTHOR]

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. Key Points: Using bias‐corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchmentsCompared to raw PML‐AET, bias‐corrected PML‐AET improves runoff prediction noticeably and adding GRACE shows limited benefitGridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML‐AET data [ABSTRACT FROM AUTHOR]

This study proposes a new process‐based framework to characterize and classify runoff events of various magnitudes occurring in a wide range of catchments. The framework uses dimensionless indicators that characterize space–time dynamics of precipitation events and their spatial interaction with antecedent catchment states, described as snow cover, distribution of frozen soils, and soil moisture content. A rigorous uncertainty analysis showed that the developed indicators are robust and regionally consistent. Relying on covariance‐ and ratio‐based indicators leads to reduced classification uncertainty compared to commonly used (event‐based) indicators based on absolute values of metrics such as duration, volume, and intensity of precipitation events. The event typology derived from the proposed framework is able to stratify events that exhibit distinct hydrograph dynamics even if streamflow is not directly used for classification. The derived typology is therefore able to capture first‐order controls of event runoff response in a wide variety of catchments. Application of this typology to about 180,000 runoff events observed in 392 German catchments revealed six distinct regions with homogeneous event type frequency that match well regions with similar behavior in terms of runoff response identified in Germany. The detected seasonal pattern of event type occurrence is regionally consistent and agrees well with the seasonality of hydroclimatic conditions. The proposed framework can be a useful tool for comparative analyses of regional differences and similarities of runoff generation processes at catchment scale and their possible spatial and temporal evolution. Key Points: A new process‐based framework for characterizing runoff events of diverse sizes is proposed and applied to a wide set of catchmentsNovel indicators combining the space–time dynamics of event precipitation and catchment state are used for classificationThe derived event typology captures distinct shapes of event hydrographs even if streamflow data are not directly used for classification [ABSTRACT FROM AUTHOR]

Arctic coastal watersheds, though rarely monitored, are expected to have increased runoff, as climate models predict more precipitation in the Arctic. This study provides a synthesis of streamflow changes in watersheds of the Alaska Arctic Coastal Plain (AACP) based on available historic discharge data and water balance analysis. A comparison of annual runoff from the Putuligayuk River watershed (471 km²) from the period 1970-1986 (78±24.1 mm/yr) to the period 1999-2015 (122649.6 mm/yr) shows increasing discharge and interannual variability. From this discontinuous record of 32 years, the three lowest runoff years occurred in 1979, 2007, and 2008, and the three highest runoff years occurred in 2003, 2014, and 2015. Other studied AACP watersheds with shorter discharge records demonstrate similar patterns of dry (2007-2008) and wet (2014-2015) years during common periods of observation. A combination of favorable antecedent surface storage conditions and above-average precipitation is required to generate large volumes of surface runoff. A strong relationship between climate, surface storage, and runoff inherent to AACP watersheds makes these systems highly responsive to sea ice retreat and hydrological intensification. Our new estimates of freshwater flux from the AACP to the Beaufort Sea and Chukchi Sea account for an observed range of runoff variability and provide baseline data for modeling arctic hydrologic systems. [ABSTRACT FROM AUTHOR]

The relative importance of catchment's water provenance and flow paths varies in space and time, complicating the conceptualization of the rainfall-runoff responses. We assessed the temporal dynamics in source areas, flow paths, and age by End Member Mixing Analysis (EMMA), hydrograph separation, and Inverse Transit Time Proxies (ITTPs) estimation within a headwater catchment in the Ecuadorian Andes. Twenty-two solutes, stable isotopes, pH, and electrical conductivity from a stream and 12 potential sources were analyzed. Four end-members were required to satisfactorily represent the hydrological system, i.e., rainfall, spring water, and water from the bottom layers of Histosols and Andosols. Water from Histosols in and near the riparian zone was the highest source contributor to runoff throughout the year (39% for the drier season, 45% for the wetter season), highlighting the importance of the water that is stored in the riparian zone. Spring water contributions to streamflow tripled during the drier season, as evidenced by geochemical signatures that are consistent with deeper flow paths rather than shallow interflow through Andosols. Rainfall exhibited low seasonal variation in this contribution. Hydrograph separation revealed that 94% and 84% is preevent water in the drier and wetter seasons, respectively. From low-flow to high-flow conditions, all the sources increased their contribution except spring water. The relative age of stream water decreased during wetter periods, when the contributing area of the riparian zone expands. The multimethod and multitracer approach enabled to closely study the interchanging importance of flow processes and water source dynamics from an interannual perspective. [ABSTRACT FROM AUTHOR]

Since its introduction in 1954, the Soil Conservation Service curve number (SCS-CN) method has become the standard tool, in practice, for estimating an event-based rainfall-runoff response. However, because of its empirical origins, the SCS-CN method is restricted to certain geographic regions and land use types. Moreover, it does not describe the spatial variability of runoff. To move beyond these limitations, we present a new theoretical framework for spatially lumped, event-based rainfall-runoff modeling. In this framework, we describe the spatially lumped runoff model as a point description of runoff that is upscaled to a watershed area based on probability distributions that are representative of watershed heterogeneities. The framework accommodates different runoff concepts and distributions of heterogeneities, and in doing so, it provides an implicit spatial description of runoff variability. Heterogeneity in storage capacity and soil moisture are the basis for upscaling a point runoff response and linking ecohydrological processes to runoff modeling. For the framework, we consider two different runoff responses for fractions of the watershed area: 'prethreshold' and 'threshold-excess' runoff. These occur before and after infiltration exceeds a storage capacity threshold. Our application of the framework results in a new model (called SCS-CNx) that extends the SCS-CN method with the prethreshold and threshold-excess runoff mechanisms and an implicit spatial description of runoff. We show proof of concept in four forested watersheds and further that the resulting model may better represent geographic regions and site types that previously have been beyond the scope of the traditional SCS-CN method. [ABSTRACT FROM AUTHOR]

Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow ( Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments ( [ABSTRACT FROM AUTHOR]

The rainfall-runoff response of watersheds is affected by the legacy of past hydroclimatic conditions. We examined how variability in precipitation affected streamflow using 21 years of daily streamflow and precipitation data from five watersheds at the Coweeta Hydrologic Laboratory in southwestern North Carolina, USA. The gauged watersheds contained both coniferous and deciduous vegetation, dominant north and south aspects, and differing precipitation magnitudes. Lag-correlations between precipitation and runoff ratios across a range of temporal resolutions indicated strong influence of past precipitation (i.e., watershed memory). At all time-scales, runoff ratios strongly depended on the precipitation of previous time steps. At monthly time scales, the influence of past precipitation was detectable for up to 7 months. At seasonal time scales, the previous season had a greater effect on a season's runoff ratio than the same season's precipitation. At annual time scales, the previous year was equally important for a year's runoff ratio than the same year's precipitation. Estimated watershed storage through time and specifically the previous year's storage state was strongly correlated with the residuals of a regression between annual precipitation and annual runoff, partially explaining observed variability in annual runoff in watersheds with deep soils. This effect was less pronounced in the steepest watershed that also contained shallow soils. We suggest that the location of a watershed on a nonlinear watershed-scale storage-release curve can explain differences in runoff during growing and dormant season between watersheds with different annual evapotranspiration. [ABSTRACT FROM AUTHOR]

We describe a new approach that couples hydrograph separation with high-frequency nitrate data to quantify time-variable groundwater and runoff loading of nitrate to streams, and the net in-stream fate of nitrate at the watershed scale. The approach was applied at three sites spanning gradients in watershed size and land use in the Chesapeake Bay watershed. Results indicate that 58-73% of the annual nitrate load to the streams was groundwater-discharged nitrate. Average annual first-order nitrate loss rate constants (k) were similar to those reported in both modeling and in-stream process-based studies, and were greater at the small streams (0.06 and 0.22 day−1) than at the large river (0.05 day−1), but 11% of the annual loads were retained/lost in the small streams, compared with 23% in the large river. Larger streambed area to water volume ratios in small streams results in greater loss rates, but shorter residence times in small streams result in a smaller fraction of nitrate loads being removed than in larger streams. A seasonal evaluation of k values suggests that nitrate was retained/lost at varying rates during the growing season. Consistent with previous studies, streamflow and nitrate concentrations were inversely related to k. This new approach for interpreting high-frequency nitrate data and the associated findings furthers our ability to understand, predict, and mitigate nitrate impacts on streams and receiving waters by providing insights into temporal nitrate dynamics that would be difficult to obtain using traditional field-based studies. [ABSTRACT FROM AUTHOR]

Combinations of runoff characteristics are commonly used to represent distinct conceptual models of stormflow generation. In this study, three runoff characteristics: hydrograph response, time source of runoff water, and flow path are used to classify catchments. Published data from the scientific literature are used to provide evidence from small, forested catchments. Each catchment was assigned to one of the eight conceptual models, depending on the combination of quick/slow response, old/new water, and overland/subsurface flow. A standard procedure was developed to objectively diagnose the predominant conceptual model of stormflow generation for each catchment and assess its temporal and spatial support. The literature survey yielded 42 catchments, of which 30 catchments provide a complete set of qualitative runoff characteristics resulting in one of the eight conceptual models. The majority of these catchments classify as subsurface flow path dominated. No catchments were found for conceptual models representing combinations of quick response-new water-subsurface flow (SSF), slow-new-SSF, slow-old-overland flow (OF) nor new-slow-OF. Of the 30 qualitatively classified catchments, 24 provide a complete set of quantitative measures. In summary, the field support is strong for 19 subsurface-dominated catchments and is weak for 5 surface flow path dominated catchments (six catchments had insufficient quantitative data). Two alternative explanations exist for the imbalance of field support between the two flow path classes: (1) the selection of research catchments in past field studies was mainly to explain quick hydrograph response in subsurface dominated catchments; (2) catchments with prevailing subsurface flow paths are more common in nature. We conclude that the selection of research catchments needs to cover a wider variety of environmental conditions which should lead to a broader, and more widely applicable, spectrum of resulting conceptual models and process mechanisms. This is a prerequisite in studies where catchment organization and similarity approaches are used to develop catchment classification systems in order to regionalize stormflow. [ABSTRACT FROM AUTHOR]

A physically based model of the 14 km2 Dead Run watershed in Baltimore County, MD was created to test the impacts of detention basin storage and soil storage on the hydrologic response of a small urban watershed during flood events. The Dead Run model was created using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) algorithms and validated using U.S. Geological Survey stream gaging observations for the Dead Run watershed and 5 subbasins over the largest 21 warm season flood events during 2008-2012. Removal of the model detention basins resulted in a median peak discharge increase of 11% and a detention efficiency of 0.5, which was defined as the percent decrease in peak discharge divided by percent detention controlled area. Detention efficiencies generally decreased with increasing basin size. We tested the efficiency of detention basin networks by focusing on the 'drainage network order,' akin to the stream order but including storm drains, streams, and culverts. The detention efficiency increased dramatically between first-order detention and second-order detention but was similar for second and third-order detention scenarios. Removal of the soil compacted layer, a common feature in urban soils, resulted in a 7% decrease in flood peak discharges. This decrease was statistically similar to the flood peak decrease caused by existing detention. Current soil storage within the Dead Run watershed decreased flood peak discharges by a median of 60%. Numerical experiment results suggested that detention basin storage and increased soil storage have the potential to substantially decrease flood peak discharges. [ABSTRACT FROM AUTHOR]

Subsurface lateral preferential flow (LPF) has been observed to contribute substantially to hillslope and catchment runoff. However, the complex nature of LPF and the lack of an appropriate investigation method have hindered direct LPF observation in the field. Thus, the initiation, persistence, and dynamics of LPF networks remain poorly understood. This study explored the application of time-lapse ground-penetrating radar (GPR) together with an artificial infiltration to shed light on the nature of LPF and its dynamics in a hillslope. Based on our enhanced field experimental setup and carefully refined GPR data postprocessing algorithms, we developed a new protocol to reconstruct LPF networks with centimeter resolution. This is the first time that a detailed LPF network and its dynamics have been revealed noninvasively along a hillslope. Real-time soil water monitoring and field soil investigation confirmed the locations of LPF mapped by time-lapse GPR surveys. Our results indicated the following: (1) Increased spatial variations of radar signals after infiltration suggested heterogeneous soil water changes within the studied soil, which reflected the generation and dynamics of LPF; (2) Two types of LPF networks were identified, the network at the location of soil permeability contrasts and that formed via a series of connected preferential flow paths; and (3) The formation and distribution of LPF networks were influenced by antecedent soil water condition. Overall, this study demonstrates clearly that carefully designed time-lapse GPR surveys with enhanced data postprocessing offer a practical and nondestructive way of mapping LPF networks in the field, thereby providing a potentially significant enhancement in our ability to study complex subsurface flow processes across the landscape. [ABSTRACT FROM AUTHOR]

In forested catchments, the exceedance of rainfall and antecedent water storage thresholds is often required for runoff generation, yet to our knowledge these threshold relationships remain undescribed in tropical dry forest catchments. We, therefore, identified the controls of streamflow activation and the timing and magnitude of runoff in a tropical dry forest catchment near the Pacific coast of central Mexico. During a 52 day transition phase from the dry to wet season, soil water movement was dominated by vertical flow which continued until a threshold soil moisture content of 26% was reached at 100 cm below the surface. This satisfied a 162 mm storage deficit and activated streamflow, likely through lateral subsurface flow pathways. High antecedent soil water conditions were maintained during the wet phase but had a weak influence on stormflow. We identified a threshold value of 289 mm of summed rainfall and antecedent soil water needed to generate >4 mm of stormflow per event. Above this threshold, stormflow response and magnitude was almost entirely governed by rainfall event characteristics and not antecedent soil moisture conditions. Our results show that over the course of the wet season in tropical dry forests the dominant controls on runoff generation changed from antecedent soil water and storage to the depth of rainfall. [ABSTRACT FROM AUTHOR]

Boreal soils in permafrost regions contain vast quantities of frozen organic material that is released to terrestrial and aquatic environments via subsurface flow paths as permafrost thaws. Longer flow paths may allow chemical reduction of solutes, nutrients, and contaminants, with implications for greenhouse gas emissions and aqueous export. Predicting boreal catchment runoff is complicated by soil heterogeneities related to variability in active layer thickness, soil type, fire history, and preferential flow potential. By coupling measurements of permeability, infiltration potential, and water chemistry with a stream chemistry end-member mixing model, we tested the hypothesis that organic soils and burned slopes are the primary sources of runoff, and that runoff from burned soils is greater due to increased hydraulic connectivity. Organic soils were more permeable than mineral soils, and 25% of infiltration moved laterally upon reaching the organic-mineral soil boundary on unburned hillslopes. A large portion of the remaining water infiltrated into deeper, less permeable soils. In contrast, burned hillslopes displayed poorly defined soil horizons, allowing rapid, mineral-rich runoff through preferential pathways at various depths. On the catchment scale, mineral/organic runoff ratios averaged 1.6 and were as high as 5.2 for an individual storm. Our results suggest that burned soils are the dominant source of water and solutes reaching the stream in summer, whereas unburned soils may provide longer term storage and residence times necessary for production of anaerobic compounds. These results are relevant to predicting how boreal catchment drainage networks and stream export will evolve given continued warming and altered fire regimes. [ABSTRACT FROM AUTHOR]