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The natural hydrological cycle of basins has been significantly altered by climate change and human activities, leading to considerable uncertainties in attributing runoff. In this study, the impact of climate change and human activities on runoff of the Ganjiang River Basin was analyzed, and a variety of models with different spatio-temporal scales and complexities were used to evaluate the influence of model choice on runoff attribution and to reduce the uncertainties. The results show the following: (1) The potential evapotranspiration in the Ganjiang River Basin showed a significant downward trend, precipitation showed a significant upward trend, runoff showed a nonsignificant upward trend, and an abrupt change was detected in 1968; (2) The three hydrological models used with different temporal scales and complexity, GR1A, ABCD, DTVGM, can simulate the natural distribution of water resources in the Ganjiang River Basin; and (3) The impact of climate change on runoff change ranges from 60.07% to 82.88%, while human activities account for approximately 17.12% to 39.93%. The results show that climate change is the main driving factor leading to runoff variation in the Ganjiang River Basin. [ABSTRACT FROM AUTHOR]

Hydrological hybrid models have been proposed as an option to combine the enhanced performance of deep learning methods with the interpretability of process-based models. Among the various hybrid methods available, the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks has shown high potential. We explored this method further to evaluate specifically if the flexibility given by the dynamic parameterization overwrites the physical interpretability of the process-based part. We conducted our study using a subset of the CAMELS-GB dataset. First, we show that the hybrid model can reach state-of-the-art performance, comparable with LSTM, and surpassing the performance of conceptual models in the same area. We then modified the conceptual model structure to assess if the dynamic parameterization can compensate for structural deficiencies of the model. Our results demonstrated that the deep learning method can effectively compensate for these deficiencies. A model selection technique based purely on the performance to predict streamflow, for this type of hybrid model, is hence not advisable. In a second experiment, we demonstrated that if a well-tested model architecture is combined with an LSTM, the deep learning model can learn to operate the process-based model in a consistent manner, and untrained variables can be recovered. In conclusion, for our case study, we show that hybrid models cannot surpass the performance of data-driven methods, and the remaining advantage of such models is the access to untrained variables. [ABSTRACT FROM AUTHOR]

An event-based approach has been used to explore the potential effects of climate change on the spatial and temporal coherence of widespread flood events in Great Britain. Time series of daily mean river flow were generated using a gridded national-scale hydrological model (Grid-to-Grid) driven by a 12-member ensemble of regional climate projections from UK Climate Projections 2018 (UKCP18), for 30-year baseline (1980–2010) and future (2050–2080) time slices. From these, sets of widespread extreme events were extracted. The question of what defines a "widespread flood event" is discussed; here it was defined as an event exceeding an at-site 99.5th percentile (equivalent to 2 d per year) simultaneously over an area of at least 20 km 2 , with a maximum duration of 14 d. This resulted in a set of 14 400 widespread events: approximately 20 events per year, per ensemble member, per time slice. Overall, results have shown that events are more temporally concentrated in winter in the future time slice compared to the baseline. Distributions of event area were similar in both time slices, but the distribution of at-site return periods showed some heavier tails in the future time slice. Such information could be useful for adaptation planning and risk management for floods under climate change, but the potential future changes have to be interpreted in the context of some differences in event characteristics between the baseline climate-projection-driven model runs and an observation-driven model run. While the focus here is Great Britain, the methods and analyses described could be applied to other regions with hydrological models and climate projections of appropriate resolution. [ABSTRACT FROM AUTHOR]

Estimating runoff at ground-mounted solar photovoltaic (PV) installations is challenging because of the disconnected nature of impervious solar panels and the pervious ground surface underneath and between panel rows. There is a need for improved tools to estimate how low impact development practices at these solar installations affect stormwater runoff. The objective of this study was to develop an innovative spreadsheet-based runoff calculator that rapidly estimates stormwater runoff from ground-mounted solar PV sites. The calculator is built on a 2-D hydrologic model (Hydrus-2D/3D) calibrated and validated using experimental data from five commercial solar farms in Colorado, Georgia, Minnesota, New York, and Oregon. The Hydrus-2D/3D hydrologic model was then used to generate nomographs for stormwater runoff that were incorporated into an easy-to-use Excel-based solar farm runoff calculator. This calculator allows for rapid estimation of NRCS stormwater runoff curve number (CN) values at solar farms by considering several complex factors unique to PV installations including: soil and topographic characteristics, surface cover, disconnected impervious surface factors associated with various solar panel designs, and climatic factors. The solar farm runoff calculator quickly estimates runoff CN for pre- and post-construction scenarios, and can estimate actual depth of runoff based on a user-specified 24-h design storm depth. Factors that have the most significant impact on stormwater runoff include design storm return frequency, soil texture, soil bulk density, and soil depth. Ground surface cover has a moderate impact on stormwater runoff, and factors that have a lesser impact on stormwater runoff include slope and array size, spacing and orientation on the landscape. The runoff calculator allows for accurate estimates of runoff generated by disconnected impervious surfaces and low impact development practices at solar farms as affected by a wide range of site-specific conditions. [ABSTRACT FROM AUTHOR]

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km2. Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash–Sutcliffe Efïciency (NSE), Coefficient of Determination (R2), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions. [ABSTRACT FROM AUTHOR]

After a flood disaster, the question often arises: "What could have happened if the event had gone differently?" For example, what would be the effects of a flood if the path of a pressure system and the precipitation field had taken a different trajectory? In this paper, we use alternative scenarios of precipitation footprints shifted in space, the so-called "spatial counterfactuals" to generate plausible but unprecedented events. We explore the spatial counterfactuals of the deadly July 2021 flood in the Ahr Valley, Germany. We drive a hydrological model of the Ahr catchment with precipitation fields of this event systematically shifted in space. The resulting discharge is used as a boundary condition for a high-resolution two-dimensional hydrodynamic model. We simulate changes in peak flows, hydrograph volumes, maximum inundation extent and depths and affected assets and compare them to the simulations of the actual event. We show that even a slight shift of the precipitation field by 15–25 km eastwards, which does not seem implausible due to orographic conditions, causes an increase in peak flows at the gauge Altenahr of about 32 % and of up to 160 % at the individual tributaries. Also, significantly larger flood volumes of more than 25 % can be expected due to this precipitation shift. This results in significantly larger inundation extents and maximum depths at a number of analyzed focus areas. For example, in the focus area around Altenahr, the increase of mean and maximum depth of up to 1.25 m and 1.75 m, respectively, is simulated. The presented results should encourage flood risk managers as well as the general public to meet precautionary measures for extreme and unprecedented events. [ABSTRACT FROM AUTHOR]

When nutrient level in the soil surpasses vegetation demand, nutrient losses due to surface runoff and subsurface leaching are the major reasons for the deterioration of water quality. The Lower Mississippi river basin (LMRB) is one of the sub-basins that deliver the highest nitrogen loads to the Gulf of Mexico. Potential changes in episodic events induced by hurricanes may exacerbate water quality issue in the future. However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. While the overall performance of the calibrated ELM is satisfactory, some discrepancies in WTD remain in slope areas with low precipitation due to the missing lateral flow process in ELM. Simulations including biogeochemistry performed using ELM with and without model calibration showed important influences of soil hydrology, precipitation intensity, and runoff parameterization on the magnitude of nitrogen runoff loss and leaching pathway. Despite such sensitivities, both ELM simulations produced reduced WTD and increased runoff and accelerated nitrate-nitrogen runoff loading during Hurricane Ida in August 2021, consistent with the observations. With observations suggesting more pronounced effects of Hurricane Ida on nitrogen runoff than the simulations, we identified factors for model improvement to provide a useful tool for studying hurricane-induced nutrient losses in the LMRB region. [ABSTRACT FROM AUTHOR]

The susceptibility mapping of rainfall-induced landslides is an effective tool for predicting and locating disaster-prone zones at the regional scale. One of the most important parts of landslide susceptibility models is the hydrological model. In this context, the present study considers three pore water pressure (PWP) profiles with surface runoff to estimate the spatiotemporal variation of wetting front depth (WFD) during rainfall episodes. To reasonably simulate the inherent uncertainty and variability involved in the hydrogeomechanical properties of the surficial soil layers at the regional scale, probabilistic analysis based on the recursive first-order reliability method (FORM) is employed to calculate the probability of slope failure. The regional time-dependent landslide susceptibility mapping is realised using a newly developed model called Physically-based probabilistic modelling of Rainfall Landslides using Simplified Transient Infiltration Model (PRL-STIM). The proposed model is applied in a representative area that suffered extensive rainfall-induced landslides in July 2013 (Niangniangba Town, Gansu Province, China). The results indicate that the PRL-STIM model achieved a satisfactory prediction accuracy of 75% AUC compared to existing models like transient rainfall infiltration and grid-based regional slope-stability model (72%) and the probabilistic analysis results based on the first-order second moment method (74%). It also performed well in predicting the spatial distribution of shallow landslides, with a success rate of 81.6%. Regarding the model efficiency, the completion of a raster file for calculating the landslide probabilities of the study area (including 711,051 cells) requires only 17.1 s. It is thus hoped that the proposed calculation framework of PRL-STIM that considers various uncertainties (e.g., nonlinearity of the physical model, non-normal probability distributions, random variable cross correlations, etc.) in geotechnical parameters is better suited for landslide susceptibility mapping at the regional scale, where only limited historical event data is available. [ABSTRACT FROM AUTHOR]

Understanding the hydrological processes associated with wetland dynamics is fundamental to studying climate change impacts and the global water cycle. This study simulates the variation of water balance in the Ghorra Playa, Eastern Tunisia, over the period extending from September 2017 to August 2020. The playa is a seasonal habitat for a number of migratory bird species and its watershed is of vital agricultural importance. Water balance estimations were performed using two different approaches: remote sensing-based analyses (Sentinel-2B, Global Precipitation Measurements (GPM) and Famine Early Warning System Network (FEWS-NET)) and modelling founded on field data. The playa presents an average annual water balance of 1.1 million cubic metres. Water inflows come from direct rainfall, mostly in the fall and spring seasons. Groundwater flow into the playa significantly influenced the pattern of water flux (81%). The annual, seasonal and monthly water budget simulations show reasonably good agreement between the remote sensing-based analysis and the hydrological modelling exercise. [ABSTRACT FROM AUTHOR]

Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. This study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season, 1998–2016. Crop yield was linked with SMDI and other covariates using a random forest machine learning-based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in the years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security. [ABSTRACT FROM AUTHOR]

The hydrological model Soil Water Assessment Tool (SWAT) is widely used in water resources management worldwide. It is also used to simulate catchment hydrology in high‐mountainous regions where glaciers play an important role. However, SWAT considers glaciers in a simplistic way. Although some efforts were done to overcome this limitation, there is no official version available that considers glaciers adequately. This strongly impairs its applicability in glacierized catchments. In this technical note, we propose a novel version of the traditional SWAT, called SWAT‐GL, which introduces (1) a mass balance module and (2) a glacier evolution routine to represent dynamic glacier changes. Mass balance calculations are based on a conceptual degree‐day approach, similar to the snow routine implemented in SWAT. Glacier evolution is realized using the delta‐h (∆h) parameterization, which requires a minimum of data and is thus suitable in data‐scarce regions. The approach allows users to simulate spatially distributed glacier changes. Annual mass balance changes are translated to distributed ice thickness changes depending on the glacier elevation. We demonstrate how SWAT‐GL is technically integrated into SWAT and how glaciers are merged with the existing spatial units. Model code and test data are freely accessible to promote further model development efforts and a wide application. Ultimately, SWAT‐GL aims to make SWAT easily applicable in glacierized catchments without the need of additional tools. [ABSTRACT FROM AUTHOR]

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: 'low risk', 'moderate risk', 'high risk', and 'very high risk'. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness. [ABSTRACT FROM AUTHOR]

Rain-on-snow events can cause severe flooding in snow-dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy lead to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub-canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favoured in the sub-canopy snowpack during rain-on-snow events. We then used the multi-layer snow model SNOWPACK to simulate the sub-canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows the model to reproduce 33 % more of the observed melt–freeze layers that are induced by rain-on-snow events. This new model development also delays and reduces the snowpack runoff. In fact, it triggers the unloading of dense snow layers with small rounded grains, which in turn produces fine-over-coarse transitions that limit percolation and favour refreezing. Our results suggest that the boreal vegetation modulates the sub-canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow property measurements to improve hydrological models in forested snow-covered regions. [ABSTRACT FROM AUTHOR]

The Himalayan water tower provides crucial water resources for Asia. Permafrost degradation is deemed to exert important impacts on streamflow in the Himalayan rivers. Yet, the magnitudes of such impacts remain poorly quantified. Here, we established a robust hydrological model that incorporated active layer deepening and ground ice melt for the drainage basin of the largest river in the northern Himalayas-the Yarlung Zangbo River (YZR). We estimated that permafrost degradation led to ∼0.65 km3/yr decrease in surface runoff and ∼0.35 km3/yr increase in baseflow and ground ice melt contributed ∼0.25% to the annual streamflow in the YZR for the period 2001–2022. The "fill-and-spill" mechanism helps explain the seeming contradiction of observed increasing versus decreasing baseflow in different permafrost regions worldwide. We propose that the dilution of riverine dissolved organic carbon (DOC) concentrations by baseflow may lead to the riverine DOC hysteresis patterns. This study not only lays solid scientific basis for water resources management in the Himalayas, but also yields new insights into how to interpret measured river discharge and nutrient flux in permafrost regions over the globe. [ABSTRACT FROM AUTHOR]

Machine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of R 2 and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models. [ABSTRACT FROM AUTHOR]

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model's performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system. [ABSTRACT FROM AUTHOR]

Integrated hydrologic models can simulate coupled surface and subsurface processes but are computationally expensive to run at high resolutions over large domains. Here we develop a novel deep learning model to emulate subsurface flows simulated by the integrated ParFlow‐CLM model across the contiguous US. We compare convolutional neural networks like ResNet and UNet run autoregressively against our novel architecture called the Forced SpatioTemporal RNN (FSTR). The FSTR model incorporates separate encoding of initial conditions, static parameters, and meteorological forcings, which are fused in a recurrent loop to produce spatiotemporal predictions of groundwater. We evaluate the model architectures on their ability to reproduce 4D pressure heads, water table depths, and surface soil moisture over the contiguous US at 1 km resolution and daily time steps over the course of a full water year. The FSTR model shows superior performance to the baseline models, producing stable simulations that capture both seasonal and event‐scale dynamics across a wide array of hydroclimatic regimes. The emulators provide over 1,000× speedup compared to the original physical model, which will enable new capabilities like uncertainty quantification and data assimilation for integrated hydrologic modeling that were not previously possible. Our results demonstrate the promise of using specialized deep learning architectures like FSTR for emulating complex process‐based models without sacrificing fidelity. Plain Language Summary: Computational models are important for understanding and predicting terrestrial hydrology, but our most physically detailed models can be time‐consuming and expensive to run over large regions. In this study, we trained deep learning models to emulate a complex hydrology model that simulates groundwater flow over the contiguous US. We developed a new model architecture called FSTR that captures spatiotemporal patterns better than standard deep learning models. FSTR is over 1,000 times faster than ParFlow, the original hydrologic model. This enables new possibilities like forecasting groundwater changes and estimating uncertainties. Our results show that specialized deep learning architectures can accurately emulate complex hydrologic models while drastically reducing computation time. Key Points: Deep learning models can emulate a complex integrated hydrology model that simulates groundwater over the United StatesWe developed a new model architecture that is more robust over longer simulation periods than off‐the‐shelf neural networksDeep‐learning based emulators of complex models enable new applications such as real‐time forecasting and estimating uncertainties [ABSTRACT FROM AUTHOR]

This description outlines a Geographic Information System (GIS)-based rainfall-runoff model that simulates the flow of water in a river basin. The model operates on a daily time step and consists of 4 non-linear storage components: Interception, soil moisture, channel, and groundwater. It employs (SCS) Unit Hydrograph model to determine unit hydrograph ordinates. The model replicates the movement and storage of water in various parts of the basin, including vegetation, the soil surface, the soil profile, and groundwater layers. To address uncertainty, a Monte Carlo simulation feature is integrated into the model. Monte Carlo Simulation involves predicting outcomes by generating numerous iterations using estimated ranges of values for variables with inherent uncertainty. This feature generates required number of sample sets with random parameter values. The model is run for all these realizations during a calibration period, and performance metrics like NSE are calculated for each calibration year to assess prediction uncertainty, model parameter weights are computed by normalizing the corresponding likelihood values. These weights sum up to one and represent the probabilistic distribution of predicted variables, illustrating the impact of structural and parameter errors on model predictions. A sensitivity analysis reveals that the Muskingum constants K and X have the greatest influence on model performance, while parameters Фgw, Фsw, Фfc, and Фpc have a minimal effect on the model's performance. The outcomes presented in the findings indicate that the Soil moisture accounting Model successfully forecasted peak discharge by leveraging the existing historical data. The accuracy of both volume and timing in the predictions suggests the model's appropriateness for the examined catchments. [ABSTRACT FROM AUTHOR]

The evaluation of streamflow predictions forms an essential part of most hydrological modelling studies published in the literature. The evaluation process typically involves the computation of some evaluation metrics, but it can also involve the preliminary processing of the predictions as well as the subsequent processing of the computed metrics. In order for published hydrological studies to be reproducible, these steps need to be carefully documented by the authors. The availability of a single tool performing all of these tasks would simplify not only the documentation by the authors but also the reproducibility by the readers. However, this requires such a tool to be polyglot (i.e. usable in a variety of programming languages) and openly accessible so that it can be used by everyone in the hydrological community. To this end, we developed a new tool named evalhyd that offers metrics and functionalities for the evaluation of deterministic and probabilistic streamflow predictions. It is open source, and it can be used in Python, in R, in C++, or as a command line tool. This article describes the tool and illustrates its functionalities using Global Flood Awareness System (GloFAS) reforecasts over France as an example data set. [ABSTRACT FROM AUTHOR]

Assessing runoff changes is of great importance especially its responses to the projected future climate change on local scale basins because such analyses are generally done on global and regional scales which may lead to generalized conclusions rather than specific ones. Climate change affected the runoff variation in the past in the upper Daqinghe Basin, however, the climate was mainly considered uncertain and still needs further studies, especially its future impacts on runoff for better water resources management and planning. Integrated with a set of climate simulations, a daily conceptual hydrological model (MIKE11-NAM) was applied to assess the impact of climate change on runoff conditions in the Daomaguan, Fuping and Zijingguan basins in the upper Daqinghe Basin. Historical hydrological data (2008–2017) were used to evaluate the applicability of the MIKE11-NAM model. After bias correction, future projected climate change and its impacts on runoff (2025–2054) were analysed and compared to the baseline period (1985–2014) under three shared social economic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The MIKE-11 NAM model was applicable in all three Basins, with both R2 and Nash-Sutcliffe Efficiency coefficients greater than 0.6 at daily scale for both calibration (2009–2011) and validation (2012–2017) periods, respectively. Although uncertainties remain, temperature and precipitation are projected to increase compared to the baseline where higher increases in precipitation and temperature are projected to occur under SSP2-4.5 and SSP5-8.5 scenarios, respectively in all the basins. Precipitation changes will range between 12%–19% whereas temperature change will be 2.0°C–2.5°C under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. In addition, higher warming is projected to occur in colder months than in warmer months. Overall, the runoff of these three basins is projected to respond to projected climate changes differently because runoff is projected to only increase in the Fuping basin under SSP2-4.5 whereas decreases in both Daomaguan and Zijingguan Basins under all scenarios. This study's findings could be important when setting mitigation strategies for climate change and water resources management. [ABSTRACT FROM AUTHOR]

The determination of actual evapotranspiration (ET) plays a crucial role in hydrological modelling; however, it is subject to multiple sources of uncertainty. Sophisticated energy-based methods, such as METRIC, may lead to varying results based on different initial and boundary conditions. In this study, the relationship between groundwater withdrawal and the uncertainty effects of ET was explored by incorporating the uncertainty of the calculated ET values through an ensemble-based implementation of the METRIC model into the comprehensive interval-based water balance model, which includes surface and groundwater modules developed in terms of gray value model. The developed interval of ET is based on 20 members with different hot/cold pixels to provide interval-based monthly ET values. The study area is the Ghorveh–Dehgolan basin, a developed and mountainous sub-basin of the Sefidrood watershed with three alluvial aquifers in Northern Iran. The paradigm shift from deterministic hydrological structure to interval-based hydrologic structure improved the statistical metrics of the model responses, such as the streamflow KGE metric of the calibration and validation datasets, which improved from (0.5, 0.18) to (0.57, 0.49), respectively. Additionally, the proposed approach decreased the uncertainty level tied to the simulated streamflow and groundwater levels. Based on the results, normalized uncertainty efficiency (NUE) values of the simulated streamflow and groundwater level values increased as well. [ABSTRACT FROM AUTHOR]

The Storm Water Management Model (SWMM) is a hydrological model for simulating and predicting runoff. Although powerful, SWMM can be computationally demanding. Therefore, we develop machine learning (ML) models to approximate the behavior of SWMM and expedite the task of predicting runoff. We perform a case study for the First Creek watershed in Knoxville, Tennessee, USA. We train ML models using rainfall data and subcatchment characteristics and apply feature engineering and clustering to objectively compare the outputs from SWMM and ML models. The results show that random forests can predict runoff volume accurately, with a Mean Absolute Error (MAE) of 0.006 (0.001) ${10^6}$ 10 6 gallons, where predictions are made almost instantaneously. Hence, our proposed ML-based approach can accurately predict runoff while greatly reducing computational requirements, filling a critical need in the field. [ABSTRACT FROM AUTHOR]

This paper explores the use of the rain-on-grid (or direct rainfall) method for flood risk assessment at a basin scale. The method is particularly useful for rural catchments with small vertical variations and complex interactions with man-made obstacles and structures, which may be oversimplified by traditional hydrologically based estimations. The use of a hydrodynamic model solving mass and momentum conservation equations allows the simulation of runoff over the watershed at a basin scale. As a drawback, more detailed and spatially distributed data are needed, and the computational time is extended. On the other hand, a smaller number of parameters is needed compared to a hydrological model. Roughness and rainfall loss coefficients need to be calibrated only. The direct rainfall methodology was here implemented within the two-dimensional HEC-RAS model for the low-land rural, and ungauged, watershed of the Terdoppio River, Northern Italy. The resulting hydrographs at the closing section of the watershed were compared to synthetic design hydrographs evaluated through pure hydrological modelling, showing agreement on the peak discharge values for the low-probability scenarios, but not on the total volumes. The results in terms of water depth and flow velocity maps were used to create flood hazard maps using the Australian Institute for Disaster Resilience methodology. The Index of Proportional Risk model was then adopted to generate a basin-scale flood risk map, by combining flood hazard maps, damage functions for different building-use classes, and the value of reconstruction and content per unit area. [ABSTRACT FROM AUTHOR]

Counterfactuals are scenarios that describe alternative ways of how an event, in this case an extreme rainfall event, could have unfolded. In this study, we present the results of a counterfactual search for flash flood events in Germany. We used a radar-based precipitation dataset from Germany's national meteorological service (Deutscher Wetterdienst) to identify the 10 most extreme precipitation events in Germany from 2001 to 2022 and then assumed that any of these top 10 events could have happened anywhere in Germany. In other words, the events were shifted around all over Germany. For all resulting positions of the precipitation fields, we simulated the corresponding peak discharge for any affected catchment smaller than 750 km 2. From all the realizations of this simulation experiment, the maximum peak discharge was identified for each catchment. In a case study, we first focused on the devastating flood event in July 2021 in western Germany. We found that a moderate shifting of the event in space could change the event peak flow at the Altenahr gauge by a factor of 2. Compared to the peak flow of 1004 m 3 s -1 caused by the event in its original position, the worst-case counterfactual of that event led to a peak flow of 1311 m 3 s -1. Shifting another event that had occurred just 1 month earlier in eastern Germany over the Ahr River valley even effectuated a simulated peak flow of 1651 m 3 s -1. For all analysed subbasins in Germany, we found that, on average, the highest counterfactual peak exceeded the maximum original peak (between 2001 and 2022) by a factor of 5.3. For 98 % of the basins, the factor was higher than 2. We discuss various limitations of our analysis, which are important to be aware of, namely, the quantification and selection of candidate rainfall events, the hydrological model, and the design of the counterfactual search experiment. Still, we think that these results might help to expand the view of what could happen in the case that certain extreme events occurred elsewhere and thereby reduce the element of surprise in disaster risk management. [ABSTRACT FROM AUTHOR]

Predicting flood event classes aids in the comprehensive investigation of flood behavior dynamics and supports flood early warning and emergency plan development. Existing studies have mainly focused on historical flood event classification and the prediction of flood hydrographs or certain metrics (e.g., magnitude and timing) but have not focused on predicting flood event classes. Our study proposes a new approach for predicting flood event classes based on the class membership functions of flood regime metrics and hydrological modeling. The approach is validated using 1446 unimpacted flood events in 68 headstream catchments widely distributed across China. The new approach performs well, with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Furthermore, it performs better in the basins of northern China than in those of southern China, particularly for the small spike flood event class in the Songliao and Yellow River Basins, with hit rates of 80.0% ± 3.2% and 78.8% ± 3.2%, respectively. Our results indicate that the new approach will help improve the prediction performance of flood events and their corresponding classes, and provide deep insights into the comprehensive dynamic patterns of flood events for early warning and control management. Plain Language Summary: The prediction of flood event classes is more effective and informative than that of individual events for obtaining comprehensive dynamic characteristics of flood events for early warning and development of emergency plans. However, this is challenging owing to massive flood events with remarkable spatiotemporal heterogeneity and unclear class membership functions. We propose a class prediction approach for flood events using the class membership functions of flood behavior metrics based on frequency analysis and catchment hydrological modeling. This approach deeply mines the behavior characteristics of historical flood events with a strong mathematical basis and cooperates with hydrological models to predict flood classes. Over thousand unimpacted flood events in 68 headstream catchments across China were selected to validate the robustness of the prediction scheme. Our results show that the proposed approach well predicts the flood class with class hit rates of 68.3% ± 0.4% for all events; 65.8% ± 0.6%, 56.8% ± 0.9%, and 69.5% ± 0.9% for the small, moderate and high spike flood event classes, respectively; and 82.5% ± 1.2% and 75.4% ± 1.1% for the moderate and high dumpy flood event classes, respectively. Our study has strong implications for scientific flood warning research and flood mitigation management. Key Points: A prediction approach for flood event classes is developed using class membership functions of flood regime metrics with hydrological modelClass membership degree is estimated using the frequency distribution function, which fits well with statistical significanceAverage class hit rates are 68.3% ± 0.4% for all the events and predictions of dumpy flood class are better than those of spike flood classes [ABSTRACT FROM AUTHOR]

Residual fractures are an essential structural feature preserved in completely decomposed granite (CDG). However, their role in regulating rainwater infiltration and runoff remains unclear. In this study, we developed a model test device to investigate the infiltration behavior of CDG slopes with various residual fracture orientations. Our results reveal significant differences in infiltration dynamics, with slopes containing residual fractures exhibiting slower infiltration rates and finger flow characteristics. The influence of residual fractures on infiltration was found to be highly dependent on their orientation and number, with the inclined-type orientation having the most significant effect. Specifically, fractures inclined at 77° exhibited the most pronounced fingering effect, while those at 34° allowed for the fastest infiltration. The presence of gas in the fractures led to retardation, diversion, and eventually enhancement of rainfall infiltration, with the transition occurring at approximately T/4 (T is the total time from the beginning of rainfall to complete infiltration). Our findings underscore the importance of considering residual fractures in hydrological modeling of CDG slopes. [ABSTRACT FROM AUTHOR]

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models. [ABSTRACT FROM AUTHOR]

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This study aims to identify suitable sites for Corrigiola litoralis (Strapwort) using hydrological and hydrodynamic models and geographical information techniques. It was conducted on the lower Elbe River in Czechia for 1981–2020 at six research sites. Hydrological models BILAN, GR4J and TUW were used together with HEC‐RAS hydrodynamic model. In addition to hydrological model simulations, we also used observed flows. We addressed identifying sites suitable for Corrigiola litoralis by introducing conditions reflecting the ecological requirements of this species. We compared the results with the findings recorded in the literature. The accuracy of this approach was 0.750 when the source of hydrological data was the observations, and the accuracy was, on average, 0.776 when using model simulations. The determined size of a suitable area had a significant variance. The closest to the size determined from the observed data were results based on the simulation of the TUW model. The GR4J model and BILAN model overestimated the size of suitable areas. The presented approach proved to be reasonable for modelling spatio‐temporal dynamics of sites suitable for Corrigiola litoralis. [ABSTRACT FROM AUTHOR]

The paper investigates the effects of climate change on riverine ecosystems in the Upper Arno River basin, adopting a hybrid methodology relating the changes in streamflow to its related water ecosystem services. Twenty hydrological models were run using the Soil and Water Assessment Tool (SWAT), under their respective climate model and emission scenario. The streamflow variable obtained was analysed according to the method of Indicators of Hydrologic Alteration (IHA). The development of spatial maps of IHA allowed us to represent the spatial of the results according to the three river subbasins studied, with a focus on the presence of hotspots. Using IHA allowed to capture of the streamflow quantity and dynamics, through groups of magnitude, timing and rates, where the potential effect could be then linked to the concept of water ecosystem services. Finally, the results obtained were presented as a proxy that can be integrated into the existing Driver, Pressure, State, Impact, Response (DPSIR) management framework to eventually move towards an ecosystem‐based management methodology. The main results, induced from the Mann–Kendall trend analysis of the IHA, suggested that provisioning and regulating services are to be affected by climate change where the quantity of flows shows a decreasing trend under several emission scenarios, in addition to an intensification of the extreme events. This shift is accompanied by a change in the dynamics of flow, which holds the potential of affecting habitat suitability. With the emergence of studies directed towards ecosystem‐based management, the study refers to well‐established hydrological modelling to evaluate climate change as a driver and provide screening results to ensure a proactive response planning that converges with the idea of ecosystems. [ABSTRACT FROM AUTHOR]

This paper proposes a new multi-step approach for sensitivity assessment of surface runoff parameters. The procedure has been tested on a peri-urban basin in southern Italy, interested by intense urbanization. The basin has limited data about land characteristics, and nearby precipitation measurements are not available. Accordingly, rainfall events are defined based on depth-duration-frequency curve valid for the area. The main novelties of the work are to provide a general framework for assessing the influence of runoff parameters (i.e. depression storage and surface roughness) for a basin model in SWMM in relation to rain events of various intensity/duration, and to provide a ranking of crucial parameters significantly affecting peak discharge and total volume of the hydrograph, for an ungauged basin, by means the Fourier Amplitude Sensitivity Test (FAST). Results indicate the dependence on rainfall characteristics of the relative importance of the parameters describing the pervious and impervious areas. Notably, the peak discharge of the shortest considered event is influenced only by the two parameters of the impervious area, while the opposite holds for the longest rain event. The total runoff volume is mostly influenced by the depression storage of impervious areas, with the parameters of pervious areas becoming more influential for longer rain events. Results allow a clear interpretation of the modelled physical processes variability within the basin and their relationship with rainfall/areas features, thus providing useful insights for key parameter definition in other contexts and for other models. [ABSTRACT FROM AUTHOR]

Hydrological data‐driven models require the time series of several hydrological events with different time resolutions. The interpretation of any time series event is generally difficult without some sort of filtering or converting it to a single index value. The simultaneous analysis of two or more hydrological events over a definite time span may be more informative about the region of interest. For this purpose, a new index, referred to as the successive coincidence deficit index (SCDI), was introduced to identify sinkhole‐prone regions using the persistent water deficit concept. In this study, monthly integrated multi‐satellite retrievals for GPM based precipitation (P) and gravity recovery and climate experiment‐based groundwater storage (GWS) datasets over Konya Closed Basin (KCB) in Türkiye were used to analyse the sinkhole occurrence. The main finding of this study is that SCDI distribution with high index values, concentrated on the southwestern part of KCB, is in line with the sinkholes occurred mainly after 2010. The proposed SCDI could also serve as a kind of drought index, which enables practitioners to quantify the relationship between drought and sinkhole occurrence. Moreover, the event coincidence analysis was utilized to detect deficiency in GWS over the KCB, which was associated with a rate of 0.8 for P deficiency, and this rate reaches up to 0.9 in the sinkhole region analysed in this study. As a conclusion, the proposed methodology can detect sinkhole‐prone regions to construct risk maps for stakeholders, policymakers, and end users. [ABSTRACT FROM AUTHOR]

Separating existing historical precipitation data into solid and liquid precipitation remains a challenge in the study of climate change, extreme precipitation, and hydrological modelling. Based on historical daily air temperature and precipitation data, as well as visual observations of precipitation phase (weather phenomena records) in China mainland, this study proposed a snow‐day direct definition method (SDDM) to determine the threshold air temperature (TAT) of rainfall and snowfall, and analysed the spatial pattern and its influential factors. The main findings include: (1) the TAT based on the SDDM varied from −1.2 to 6.3°C, with a mean value of 2.8°C for the entire study region; (2) TAT was generally higher and more variable in the low‐latitude areas, and the Qinghai‐Tibet Plateau was characterized by an abnormally high average TAT of 5.2°C, almost twice as large as that of the eastern monsoon region; (3) TAT exhibited a significant positive correlation with altitude and negative correlation with precipitation and relative humidity. The results presented in this paper have potential application for studies of large‐scale snowfall climatology and climate change, weather forecasting techniques, and hydrological model parameterization in areas with complex and diverse geographical and climatic conditions. [ABSTRACT FROM AUTHOR]

Under global climate changes, the temperature and precipitation on the Tibetan Plateau changed significantly, causing accelerated melting of glaciers and snow and changes in runoff. The response of runoff and its components to climate change is important for water resource management and ecology conservation in the region. Therefore, the Spatial Processes in Hydrology model, a distributed cold‐zone hydrological model that contains a glacial ablation module, was used to simulate runoff in the Yangtze River source located in the middle of the Tibetan Plateau during 2000–2050, and the Nash‐Sutcliffe efficiency coefficient, relative error and coefficient of determination (R2) for the calibration period and validation period revealed that the model performed well in most years. The results showed that rainfall runoff contributed the most to the total runoff (61%), followed by baseflow (23%), snowmelt runoff (12%) and glacier runoff (4%) in the Yangtze River source during 2000–2020. The runoff amounts of the three source rivers, Dangqu River, Tuotuo River and Chumar River, accounted for approximately 53% of the total runoff in the Yangtze River source basin, with rainfall runoff (52%) contributing the most and glacier runoff (6%) contributing the least. Compared to the contributions of glacial runoff in the Dangqu River (9%) and Tuotuo River (7%), the proportion of glacial runoff in the Chumar River is small (1%). Under the CMIP6 climate model, the mean runoff depth is predicted to increase by approximately 13.5 mm from 2020 to 2050 compared with that from 2000 to 2020. [ABSTRACT FROM AUTHOR]

We assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models. Plain Language Summary: Reservoir operations greatly influence streamflow in heavily dammed watersheds, and hence incorporating a realistic reservoir component in watershed hydrological models to simulate the impacts is important. Recent efforts have greatly advanced generic reservoir operation model development. We couple various generic reservoir operation models with a rainfall‐runoff model to develop watershed hydrological models. We use a comprehensive evaluation method with state‐of‐the‐art metrics to examine the performance of the coupled watershed models in terms of simulated watershed outflows, model parameterization, and simulated internal variables. Fully coupled watershed models based on recently developed reservoir operation models obtain significantly improved representations of the watershed system (i.e., reservoir operation + natural rainfall‐runoff processes) compared to models that ignore reservoir operations or use simplified representations of reservoirs. Key Points: Generic reservoir models with transparent structures are fully coupled with rainfall‐runoff models for streamflow simulationsFully coupled models may be reliably used in large‐scale hydrological models without losing physical significanceCoupled models are evaluated based on ability to represent distributional properties of observed flows using state‐of‐the art metrics [ABSTRACT FROM AUTHOR]

The effects of forest treatments on watershed hydrology have often been studied in isolation from climate change. Consequently, under a warming climate, it is unclear how forest thinning will impact snowpacks, evapotranspiration, and streamflow availability. In this study, we used a distributed hydrologic model to provide insight into the effects of warming and forest treatment on the hydrologic response of the Beaver Creek watershed (∼1,100 km2) of central Arizona. Prior to the numerical experiments, confidence in the hydrologic model performance was established by comparisons to long‐term observations (2003–2018) of snow water equivalent and streamflow using station observations and through spatially distributed estimates. Results indicated that warming during the 21st century could increase mean annual streamflow by 1.5% for warming levels up to +1°C, followed by a −29% decrease for continued warming up to +6°C, due to the varying effects of warming on snow sublimation, soil evaporation, and plant transpiration. On average, forest thinning increased streamflow by +12% (or 7 mm/yr) through lower plant transpiration by −19% (or −18 mm/yr), while also increasing the change in soil water storage by +42% (or 11 mm/yr). Forest thinning delayed the detrimental effects of warming on streamflow until +4°C, as compared to +2°C without forest treatment. Furthermore, model results suggested that forest cover reductions laterally displace water availability and evapotranspiration to downstream sites. These model‐derived mechanisms provide insights on the potential for water resilience toward warming effects afforded through treatment projects in southwestern US ponderosa pine forests. Plain Language Summary: The effects of forest thinning on watershed hydrology have often been studied in isolation from warming. Thus, it is unclear if forest thinning conducted under different warming levels will have hydrologic impacts on snow and streamflow conditions. Here, we used a hydrologic model to study these interactions within the Beaver Creek watershed in Arizona. First, we built confidence in the hydrologic model through comparisons to snow and streamflow observations. Then, we conducted a range of different warming scenarios with and without forest thinning. We found that warming could increase streamflow up to +1°C, but then led to larger decreases in streamflow up to +6°C. In contrast, forest thinning increased streamflow and delayed the negative effects of warming up to +4°C. The application of the hydrologic model with the warming and forest thinning scenarios provides new insights on how warming effects could be reduced through management projects in ponderosa pine forests. Key Points: Distributed simulations in mountainous watershed show good match with warm and cold season hydrologic data over 16‐year periodIndependent and combined scenarios showed that forest thinning reduces the streamflow impact of warming up to a 4°C increaseForest treatment effects propagated downstream by displacing evapotranspiration from forested uplands to near channel regions [ABSTRACT FROM AUTHOR]

Climate change has exacerbated water stress and water‐related disasters, necessitating more precise streamflow simulations. However, in the majority of global regions, a deficiency of streamflow data constitutes a significant constraint on modeling endeavors. Traditional distributed hydrological models and regionalization approaches have shown suboptimal performance. While current deep learning (DL)‐related models trained on large data sets excel in spatial generalization, the direct applicability of these models in certain regions with unique hydrological processes can be challenging due to the limited representativeness within the training data set. Furthermore, transfer learning DL models pre‐trained on large data sets still necessitate local data for retraining, thereby constraining their applicability. To address these challenges, we present a physics‐informed DL model based on a distributed framework. It involves spatial discretization and the establishment of differentiable hydrological models for discrete sub‐basins, coupled with a differentiable Muskingum method for channel routing. By introducing upstream‐downstream relationships, model errors in sub‐basins propagate through the river network to the watershed outlet, enabling the optimization using limited downstream streamflow data, thereby achieving spatial simulation of ungauged internal sub‐basins. The model, when trained solely on the downstream‐most station, outperforms the distributed hydrological model in streamflow simulation at both the training station and upstream held‐out stations. Additionally, in comparison to transfer learning models, our model requires fewer gauge stations for training, but achieves higher precision in simulating streamflow on spatially held‐out stations, indicating better spatial generalization ability. Consequently, this model offers a novel approach to hydrological simulation in data‐scarce regions, especially those with poor hydrological representativeness. Plain Language Summary: Climate change leads to more water shortages and disasters, requiring better streamflow predictions. Yet, a big hurdle in dealing with this issue is the lack of streamflow data across many parts of the world. Traditional physics‐based distributed hydrological models and current deep learning (DL) models have their limitations, especially for regions with unique hydrological processes and limited observations. To address these challenges, we developed a new tool combining physics‐informed DL and a traditional river routing model based on the distributed framework. The model divides the region into sub‐basins, where a physics‐informed DL rainfall‐runoff model calculates runoff generation, and a physics‐informed DL routing model computes the movement of water within each subunit toward the river. Model errors propagate downstream through the river network, thus requiring only a small amount of downstream data to optimize all sub‐basin models and effectively simulate internal unmonitored sub‐basins. When solely using the downstream‐most discharge stations for training, our model outperforms the traditional physics‐based distributed hydrological model. In addition, our approach requires less training data than transfer learning, while achieving higher spatial generalization accuracy. In summary, our model provides a new way to simulate streamflow in data‐scarce regions with unique processes. Key Points: A distributed physics‐informed deep learning hydrological model was proposed for data‐scarce regionsThe new model outperforms the traditional distributed hydrologic model in simulating streamflow in upstream held‐out stationsOur model requires less data for training but performs better than the transfer learning model in spatial generalization [ABSTRACT FROM AUTHOR]

Flood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide. Key Points: Global hydrological models can be used to drive a large‐scale stochastic flood inundation model in Southeast AsiaA reanalysis‐based stochastic flood model generates realistic flood eventsThe computed flood exposure exceedance curve for Southeast Asia compares well to the EM‐DAT database [ABSTRACT FROM AUTHOR]

In agriculture‐dominated watersheds where natural drainage is poor, agricultural ditches (narrow engineered channels) and tile drains (perforated pipes) are widely employed to enhance surface and subsurface drainage, respectively. Despite their relatively small scale, these features exert substantial control over the hydro‐biogeochemical function of watersheds and their effects need to be represented in the models. We introduce a novel strategy to incorporate the effects of artificial agricultural drainage into a fully distributed basin‐scale integrated surface‐subsurface hydrology models. In our approach, narrow agriculture ditches for surface drainage are resolved efficiently using ditch‐aligned computational meshes that are hydrologically conditioned to ensure connectivity in the stream/ditch network. For tile drainage in the subsurface, we use the physically based Hooghoudt's drainage equation as a subgrid model and route the water drained through tiles to the nearest ditch. Without site‐specific calibration, this model reproduced observed streamflow in the Portage River Watershed (>1,000 km2) as recorded by a USGS gauge with good accuracy (normalized KGE = 0.81) and outperformed a calibrated SWAT model (normalized KGE = 0.68). Numerical experiments confirm that artificial drainage reduces surface inundations and effectively controls the water table. At the watershed scale, artificial drainage increases baseflow but has little effect on watershed discharges above the 90th percentile. The strong physical underpinnings and reduced need for calibration allow us to study the impacts of artificial drainage on distributed hydrological response in terms of fluxes and states and provide a platform for investigating watershed‐scale nutrient transport. Key Points: Novel strategy is developed to incorporate effects of artificial drainage into fully distributed basin‐scale integrated hydrology modelWithout site‐specific calibration, our model reproduced observed streamflow well and outperformed calibrated SWAT modelNumerical experiments reveal the effects of surface and surface drainage on various hydrological states and fluxes [ABSTRACT FROM AUTHOR]

Distribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility. Plain Language Summary: The past decades have witnessed an unbridled growth in goodness‐of‐fit metrics of hydrologic models. These metrics may satisfy the needs of hydrologists but lack conforming theory and principles. This state of affairs (a) elicits improper model training and evaluation, (b) provokes and supports misguided inferences, (c) impedes statistically‐principled uncertainty quantification, metric standardization and development of universal model benchmarks and (d) obfuscates determination of whether the model has finished learning. What is more, most hydrologic model evaluation metrics in use today are rather exclusive in how they quantify model performance using only single‐valued simulated output of the quantities of interest. Predictive distributions derived from (quasi)‐Bayesian methods or ensembles are usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures (scoring functions) such as the mean absolute error. This implies a large loss of information. This paper develops a distribution‐based approach to hydrologic model evaluation and diagnostics. Distribution evaluation opens the necessary theory and guidelines for development of robust information‐theoretically principled metrics of watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. The hydrograph functionals offer a statistically principled means for hydrologic model evaluation, diagnostics and selection. Key Points: Scoring rules of hydrograph functionals provide an information‐theoretically principled means for watershed model training, evaluation, and diagnosticsWe present strictly proper (divergence) scores for flood frequency analysis, flow duration, and recession curvesPropriety and elicitability offer useful working paradigms for metric development of hydrograph functionals [ABSTRACT FROM AUTHOR]

Infiltration-based sustainable urban drainage systems (i-SUDS) often turn out to be simple and effective solutions for on-site runoff and pollution control. Their ability to limit the discharge to sewer networks or receiving waters can be broadly assessed in terms of (pluri)annual stormwater volume reduction. Although accepted as a relevant efficiency metric, this long-term volume reduction does not integrate well in design practices that have traditionally relied on event-based approaches. This article introduces a modeling framework, involving a hydrological model and machine-learning emulation, from which a web app was developed to allow practitioners to investigate the relation between i-SUDS design and pluriannual volume reduction efficiencies. The theoretical basis for modeling and a description of the web app are first provided. A diagnosis of the hydrological model is then conducted. The uncertainty caused by model parameters that do not directly relate to i-SUDS design is evaluated through a sensitivity analysis performed over multiple design scenarios. The latter is found to be highly variable and potentially significant, thereby justifying its explicit consideration in the web app. As part of this diagnosis, the impact of a shallow groundwater or a low-permeability layer on simulated volume reduction efficiencies is later evaluated to clarify the validity domain of the model. Practical recommendations on the minimum distance to shallow groundwater or low permeability layer, for the rainfall conditions considered in the web app, are given as a function of project size and the permeability of the soil media. The applicability of the web app is later illustrated from a selection of outputs. Its outcomes are finally compared to those of a simple design rule based on the combination permanent storage (as rainfall depths) and drawdown duration targets. Results confirm the inability of such simple design rules to fully capture pluriannual volume reduction efficiency and point out the risk of oversizing i-SUDS. Stormwater infiltration in small vegetated systems can effectively reduce runoff and pollutant discharge to surface waters. A well-accepted performance objective for such systems is to achieve a significant reduction of the rainfall volume at the annual scale. However, integrating (pluri)annual volume reduction targets in design practices remains difficult as they do not accommodate well with the back-of-the-napkin, event-based calculations traditionally used by the stormwater profession. This paper introduces a web app that allows practitioners to easily investigate the relation between the design characteristics of infiltration-based solutions and pluriannual volume reduction efficiencies. The approach shows how machine learning can be used to replicate at low computational cost the outputs of specialized hydrological models and be incorporated in larger-audience tools. Through an analysis of the validity domain of the app, the study also points out the potential reduction of efficiency that may result from the presence of a shallow groundwater or a low-permeability layer, an aspect often overlooked in the design of infiltration-based systems. The applicability of the app is illustrated from different usage situations. The relevance of the proposed approach is finally demonstrated through a comparison to a simpler, event-based design method, which proves unable to adequately capture pluriannual volume reduction efficiency. [ABSTRACT FROM AUTHOR]

Predicting the response of hydrologic systems to modified driving forces beyond patterns that have occurred in the past is of high importance for estimating climate change impacts or the effect of management measures. This kind of prediction requires a model, but the impossibility of testing such predictions against observed data makes it difficult to estimate their reliability. Metamorphic testing offers a methodology for assessing models beyond validation with real data. It consists of defining input changes for which the expected responses are assumed to be known, at least qualitatively, and testing model behavior for consistency with these expectations. To increase the gain of information and reduce the subjectivity of this approach, we extend this methodology to a multi-model approach and include a sensitivity analysis of the predictions to training or calibration options. This allows us to quantitatively analyze differences in predictions between different model structures and calibration options in addition to the qualitative test of the expectations. In our case study, we apply this approach to selected conceptual and machine learning hydrological models calibrated for basins from the CAMELS data set. Our results confirm the superiority of the machine learning models over the conceptual hydrologic models regarding the quality of fit during calibration and validation periods. However, we also find that the response of machine learning models to modified inputs can deviate from the expectations and the magnitude, and even the sign of the response can depend on the training data. In addition, even in cases in which all models passed the metamorphic test, there are cases in which the quantitative response is different for different model structures. This demonstrates the importance of this kind of testing beyond and in addition to the usual calibration–validation analysis to identify potential problems and stimulate the development of improved models. [ABSTRACT FROM AUTHOR]

A hydrological model should be ideally calibrated and evaluated regarding both the representation of the streamflow and the intermediate hydrological processes. A challenging aspect is the poor spatial and temporal representativeness of in-situ measurements of these processes. This research evaluated the benefit of incorporating actual evapotranspiration (AET) estimates from two remote sensing (RS) data sources for calibrating a large-scale hydrologic model applied to a Brazilian Semi-Arid basin. Both spatially averaged and spatially distributed approaches were tested, and single—(only streamflow or only AET) and multi-variable calibration (streamflow and AET) procedures were used. Incorporating AET from MOD16 (MODerate-resolution Imaging Spectroradiometer product 16) did not help improve hydrological model performance independently of the calibration configuration. The introduction of AET estimates from GLEAM (Global Land Evaporation Amsterdam Model) as a calibration variable led the hydrological model to reproduce these AET values closer and simultaneously better represent the observed streamflow. The findings of this research highlight the benefit of incorporating AET as a calibration variable for improving the overall hydrologic model performance, which can even outperform the calibration based solely on streamflow. However, there is the need to test different calibration configurations and distinct RS data sources, which may result in very contrasting results. [ABSTRACT FROM AUTHOR]

Inland waters, especially wetlands, play a crucial role in biodiversity, water resources and climate, and contribute significantly to global methane emissions. This study investigates the seasonal and inter-annual variability of the 0.25° x 0.25° surface water extent (SWE) from the Global Inundation Extent from Multi-Satellites (GIEMS-2) extended to a 30-year time series (1992--2020). Comparison with MODIS-derived SWE, CYGNSS-derived SWE and the Global Lakes and Wetlands Database (GLWD) shows consistent spatial patterns globally and over 10 different basins, although there are discrepancies in extent, partly due to different resolutions of the initial satellite observations. Strong crosscorrelation ( > 0.8) in seasonal variability is observed when comparing GIEMS-2 with MODIS, CYGNSS and river discharge in most of the basins studied. Encouraging similarities were found in the inter-annual variability in most basins (cross-correlation > 0.6) between GIEMS-2 and MODIS over 20 years, and between GIEMS-2 and river discharge over long time series, including over the Amazon and the Congo basins. These results highlight the reliability of GIEMS-2 in detecting changes in SWE in different environments, especially under dense vegetation, making it a valuable resource for calibrating hydrological models and studying global methane emissions. [ABSTRACT FROM AUTHOR]

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling. [ABSTRACT FROM AUTHOR]

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R2 values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m3 s−1, and the average discharge was 20611 m3 s−1. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping. [ABSTRACT FROM AUTHOR]