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This study introduces a simple and cost-effective method for the indirect determination of field capacity (FC) in soil, a critical parameter for soil hydrology and environmental modeling. The relationships between FC and soil moisture constants, specifically maximum capillary water capacity (MCWC) and retention water capacity (RWC), were established using undisturbed soil core samples analyzed via the pressure plate method and the "filter paper draining method". The aim was to reduce the time and costs associated with traditional FC measurement methods, as well as allowing for the use of legacy databases containing MCWC and RWC values. The results revealed the substantial potential of the "filter paper draining method" as a promising approach for indirect FC determination. FC determined as soil water content at −33 kPa can be effectively approximated by the equation FC33 = 1.0802 RWC − 0.0688 (with RMSE = 0.045 cm3/cm3 and R = 0.953). FC determined as soil water content at −5 or −10 kPa can be effectively approximated by both equations FC5 = 1.0146 MCWC − 0.0163 (with RMSE = 0.027 cm3/cm3 and R = 0.961) and FC10 = 1.0152 MCWC − 0.0275 (with RMSE = 0.033 cm3/cm3 and R = 0.958), respectively. Historical pedotransfer functions by Brežný and Váša relating FC to fine particle size fraction were also evaluated for practical application, and according to the results, they cannot be recommended for use. [ABSTRACT FROM AUTHOR]

The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model can be applied to simulate hydrological processes. This model has been widely used in recent days because it is openly accessible to the public. The search result from this study identifies 180 cases discussing the model applications in Indonesian watersheds in peer-reviewed journal articles and conference papers. The model has been used in Indonesia since 2005, but the highest application is in 2020 with 42 articles. In the context of application distribution, Java Island ranks first place with 123 scientific publications. However, the HEC-HMS model is predominantly used to simulate the "Small" and "Very Small" watershed size categories. The availability of insufficient field data is huge obstacle for hydrological modeling in Indonesia. Furthermore, the model may be extended to address the issues in Indonesia's eastern watersheds. Moreover, the model also can be used to assess the impact of climate change on streamflow. [ABSTRACT FROM AUTHOR]

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model's ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input. [ABSTRACT FROM AUTHOR]

This paper introduces and presents the Spatial Processes in HYdrology (SPHY) model (v2.0), its development background, its underlying concepts, and some typical applications. The SPHY model is developed using the best components of existing and well-tested simulation models, and is developed with the explicit aim to simulate terrestrial hydrology at flexible scales, under various land use and climate conditions. SPHY is a spatially distributed leaky bucket type of model, and is applied on a cell-by-cell basis. The model is written in the Python programming language using the PCRaster dynamic modelling framework. Compared to other hydrological models, that typically focus on the simulation of streamflow only, the SPHY model has several advantages: it (i) integrates most relevant hydrological processes, (ii) is setup modular, (iii) is easy adjustable and applicable, (iii) can easily be linked to remote sensing data, and (iv) can be applied for operational as well as strategic decision support. The most relevant hydrological processes that are integrated in the SPHY model are rainfall-runoff processes, cryosphere processes, evapotranspiration processes, the simulation of dynamic vegetational cover, lake/reservoir outflow, and the simulation of rootzone moisture contents. Studies in which the SPHY model was successfully applied and tested are described in this paper, and range from (i) real-time soil moisture predictions to support irrigation management in lowland areas, to (ii) detailed climate change impact studies in snow and glacier-fed river basins, to (iii) operational flow forecasting in mountainous catchments. [ABSTRACT FROM AUTHOR]

Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations. [ABSTRACT FROM AUTHOR]

Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts. [ABSTRACT FROM AUTHOR]

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

Widespread flooding in Africa has devastating repercussions on communities, and sometimes leading to loss of life, displacement of populations, and significant damage to infrastructure and agriculture. Despite this, there are limited studies that investigate the behaviour of high time frequency river flows for the major river systems of Africa to inform adaptation and mitigation strategies for improved resilience of society. This paper fills this gap by assessing the occurrence time of annual maximum daily discharge for five of the longest rivers of Africa using a statistical modelling approach. This is the first of such a study covering all of the five longest rivers of Africa in one paper. Annual maximum daily discharge time for each river was modeled by mixtures of von Mises distributions, fitted by a Markov chain Monte Carlo algorithm. Data on mean daily discharge was obtained from the Global Runoff Data Centre database for the Niger, Zambezi, Okavango, Limpopo and Orange rivers in Africa. Estimates were inferred for the location parameter of the major mode, location parameter of the minor mode, concentration parameter of the major mode, concentration parameter of the minor mode, mean time, mean resultant, circular variance, circular skewness, and circular kurtosis. The developed models reveal distinctive temporal patterns of peak discharge events in each river, which can have significant implications for flood management, water resource planning, hydrological modeling, risk assessment and infrastructure design. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue on recent advances in hydrological modeling. It provides an introduction to hydrological models and their role in water resources operations and management. The editorial discusses the historical development of hydrological modeling, from empirical equations to sophisticated numerical models, and highlights the advancements in computing technology and data assimilation techniques that have improved the predictive capabilities of these models. It also summarizes the papers featured in the special issue, which cover topics such as machine learning in assessing the performance of hydrological models, improved estimation of extremes using adaptive filtering methods, and the impact of climate change on snow seasonality and water supply. The editorial concludes by emphasizing the importance of continued application and validation of novel techniques, particularly those involving machine learning and data assimilation, in hydrological modeling. [Extracted from the article]

Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications. [ABSTRACT FROM AUTHOR]

As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance. [ABSTRACT FROM AUTHOR]

Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth. [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]

Global hydrological models enhance our understanding of the Earth system and support the sustainable management of water, food and energy in a globalized world. They integrate process knowledge with a multitude of model input data (e.g., precipitation, soil properties, and the location and extent of surface waterbodies) to describe the state of the Earth. However, they do not fully utilize observations of model output variables (e.g., streamflow and water storage) to reduce and quantify model output uncertainty through processes like parameter estimation. For a pilot region, the Mississippi River basin, we assessed the suitability of three ensemble-based multi-variable approaches to amend this: Pareto-optimal calibration (POC); the generalized likelihood uncertainty estimation (GLUE); and the ensemble Kalman filter, here modified for joint calibration and data assimilation (EnCDA). The paper shows how observations of streamflow (Q) and terrestrial water storage anomaly (TWSA) can be utilized to reduce and quantify the uncertainty of model output by identifying optimal and behavioral parameter sets for individual drainage basins. The common first steps in all approaches are (1) the definition of drainage basins for which calibration parameters are uniformly adjusted (CDA units), combined with the selection of observational data; (2) the identification of potential calibration parameters and their a priori probability distributions; and (3) sensitivity analyses to select the most influential model parameters per CDA unit that will be adjusted by calibration. Data assimilation with the ensemble Kalman filter was modified, to our knowledge, for the first time for a global hydrological model to assimilate both TWSA and Q with simultaneous parameter adjustment. In the estimation of model output uncertainty, we considered the uncertainties of the Q and TWSA observations. Applying the global hydrological model WaterGAP, we found that the POC approach is best suited for identifying a single "optimal" parameter set for each CDA unit. This parameter set leads to an improved fit to the monthly time series of both Q and TWSA as compared to the standard WaterGAP variant, which is only calibrated against mean annual Q , and can be used to compute the best estimate of WaterGAP output. The GLUE approach is almost as successful as POC in increasing WaterGAP performance and also allows, with a comparable computational effort, the estimation of model output uncertainties that are due to the equifinality of parameter sets given the observation uncertainties. Our experiment reveals that the EnCDA approach performs similarly to POC and GLUE in most CDA units during the assimilation phase but is not yet competitive for calibrating global hydrological models; its potential advantages remain unrealized, likely due to its high computational burden, which severely limits the ensemble size, and the intrinsic nonlinearity in simulating Q. Partitioning the whole Mississippi River basin into five CDA units (sub-basins) instead of only one improved model performance in terms of the Nash–Sutcliffe efficiency during the calibration and validation periods. Diverse parameter sets achieved comparable fits to observations, narrowing the range for at least three parameters. Low coverage of observation uncertainty bands by GLUE-derived model output bands is attributed to model structure uncertainties, especially regarding artificial reservoir operations, the location and extent of small wetlands, and the lack of representation of rivers that may lose water to the subsurface. These uncertainties are also likely to be responsible for significant trade-offs between optimal fits to Q and TWSA. Calibration performed exclusively against TWSA in regions without Q observations may worsen the Q simulation as compared to the uncalibrated model variant. We recommend that modelers improve the realism of the output of global hydrological models by calibrating them against observations of multiple output variables, including at least Q and TWSA. Further work on improving the numerical efficiency of the EnCDA approach is necessary. [ABSTRACT FROM AUTHOR]

This paper considers what we know about the potential for disinformation in hydrological data when used for the evaluation of hydrological models. This will generally arise from epistemic uncertainties associated with hydrological observations, particularly from nonstationary or extrapolated rating curves for discharges, and poor rainfall and snowmelt information when interpolated over basin areas. Approaches based on information theory are not well suited to consideration of such epistemic uncertainties in model evaluation and an alternative approach based on setting limits of acceptability independent of any model runs is suggested. This allows for both the rejection of all models tried, and for acceptability of models across different model structures and parameter sets. The paper concludes with some suggestions for future research on defining disinformative data for both point and spatial observables, studying model failures, and defining new observations with a view to having the greatest impact on reducing model uncertainties. [ABSTRACT FROM AUTHOR]

We provide a dataset of irrigation water withdrawals by crop, county, year, and water source within the United States. We employ a framework we previously developed to establish a companion dataset to our original estimates. The main difference is that we now use the U.S. Geological Survey (USGS) variable 'irrigation — total' to partition PCR-GLOBWB 2 hydrology model estimates, instead of 'irrigation — crop' as used in previous estimates. Our findings for Surface Water Withdrawals (SWW), total Groundwater Withdrawals (GWW), and nonrenewable Groundwater Depletion (GWD) are similar to those of prior estimates but now have better spatial coverage, since several states are missing from the USGS 'irrigation — crop' variable that was originally used. Irrigation water use increases in this study, since more states are included and 'irrigation — total' includes more categories of irrigation than 'irrigation — crop'. Notably, irrigation in the Mississippi Embayment Aquifer is now captured for rice and soy. We provide nearly 2.5 million data points with this paper (3,142 counties; 13 years; 3 water sources; and 20 crops). [ABSTRACT FROM AUTHOR]

This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model. [ABSTRACT FROM AUTHOR]

Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM, the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean. [ABSTRACT FROM AUTHOR]

argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not "re-invent the water wheel" but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science. With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: 10.5281/zenodo.5119389,), we are putting our money where our mouth is and providing the hydrological community with a "FAIR by design" (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple "hello world" where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk , last access: 28 June 2022)​​​​​​​. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science. [ABSTRACT FROM AUTHOR]

The Pitt Review of the 2007 summer floods in the UK, published in 2008, commended the potential of natural flood management (NFM) for reducing flood risk. NFM is a nature‐based approach that has since gained substantial interest from both practitioners and academics. The review further highlighted the need for catchment‐based flood management (CBFM) to enhance resilience to flooding and climate change by incorporating NFM and wider nature‐based solutions into hard flood protection systems. Such integrated approaches are considered to be more sustainable and adaptable than the traditional hard‐engineered measures. More recently, the European Commission's European Green Deal also highlighted the need for greater use of nature‐based solutions including NFM for managing flood risk. Whilst there have been many attempts to quantify the effects of NFM through hydraulic and hydrological modelling, there is still no systematic review conducted for these modelling works. This review aims to summarise the current NFM modelling approaches, as well as discussing their key limitations related to data, model methods, and real‐world applications. This paper then goes further to highlight potential solutions to some of these challenges and provides guidance to assist modellers to improve future modelling and data collection process. [ABSTRACT FROM AUTHOR]

Climate and land use change in the Mediterranean region is expected to affect natural and agricultural ecosystems by decreases in precipitation, increases in temperature as well as biodiversity loss and anthropogenic degradation of natural resources. Demographic growth in the Eastern and Southern shores will require increases in food production and put additional pressure on agro-ecosystems and water resources. Coping with these challenges requires informed decisions that, in turn, require assessments by means of a comprehensive agro-ecosystem and hydrological model. This study presents the inclusion of 10 Mediterranean agricultural plants, mainly perennial crops, in an agro-ecosystem model (LPJmL): nut trees, date palms, citrus trees, orchards, olive trees, grapes, cotton, potatoes, vegetables and fodder grasses. The model was successfully tested in three model outputs: agricultural yields, irrigation requirements and soil carbon density. With the development presented in this study, LPJmL is now able to simulate in good detail and mechanistically the functioning of Mediterranean agriculture with a comprehensive representation of ecophysiological processes for all vegetation types (natural and agricultural) and in a consistent framework that produces estimates of carbon, agricultural and hydrological variables for the entire Mediterranean basin. This development pave the way for further model extensions aiming at the representation of alternative agro-ecosystems (e.g. agroforestry), and opens the door for a large number of applications in the Mediterranean region, for example assessments on the consequences of land use transitions, the influence of management practices and climate change impacts. [ABSTRACT FROM AUTHOR]

The allocation of points in a river network to pixels of a coarse-resolution hydrological modelling grid is a well-known issue, especially for hydrologists who use measurements at gauging stations to calibrate and validate distributed hydrological models. To address this issue, the traditional approach involves examining grid cells surrounding the considered river point and selecting the best candidate, based on distance and upstream drainage area as decision criteria. However, recent studies have suggested that focusing on basin boundaries rather than basin areas could prevent many allocation errors, even though the performance gain is rarely assessed. This paper compares different allocation methods and examines their relative performances. Three methods representing various families of methods have been designed: area-based, topology-based and contour-based methods. These methods are implemented to allocate 2580 river points to a 1 km hydrological modelling grid. These points are distributed along the entire hydrographic network of the French southeastern Mediterranean region, covering upstream drainage areas ranging from 5 to 3000 km 2. The results indicate that the differences between the methods can be significant, especially for small upstream catchment areas. [ABSTRACT FROM AUTHOR]

A series of numerical experiments were conducted to test the connection between streamflow hydrographs at the outlet of large watersheds and the time series of hillslope-scale runoff yield. We used a distributed hydrological routing model that discretizes a large watershed (∼ 17 000 km 2) into small hillslope units (∼ 0.1 km 2) and applied distinct surface runoff time series to each unit that deliver the same volume of water into the river network. The numerical simulations show that distinct runoff delivery time series at the hillslope scale result in indistinguishable streamflow hydrographs at large scales. This limitation is imposed by space-time averaging of input flows into the river network that are draining the landscape. The results of the simulations presented in this paper show that, under very general conditions of streamflow routing (i.e., nonlinear variable velocities in space and time), the streamflow hydrographs at the outlet of basins with Horton–Strahler (H–S) order 5 or above (larger than 100 km 2 in our setup) contain very little information about the temporal variability of runoff production at the hillslope scale and therefore the processes from which they originate. In addition, our results indicate that the rate of convergence to a common hydrograph shape at larger scales (above H–S order 5) is directly proportional to how different the input signals are to each other at the hillslope scale. We conclude that the ability of a hydrological model to replicate outlet hydrographs does not imply that a correct and meaningful description of small-scale rainfall–runoff processes has been provided. Furthermore, our results provide context for other studies that demonstrate how the physics of runoff generation cannot be inferred from output signals in commonly used hydrological models. [ABSTRACT FROM AUTHOR]

This paper optimized the hydrological postprocessing of CLM5 using CaMa-Flood, combining multi-source meteorological forcing datasets and a dynamically changing surface dataset containing 16 PFTs (plant functional types) to simulate the high-resolution runoff process in the SRB from 1996 to 2014, specifically by integrating discharge with flooded area. Additionally, we evaluated the spatiotemporal variations of precipitation data from meteorological forcing datasets and discharge to validate the accuracy of model improvements. Both the discharge and the flooded area simulated by the coupled model exhibit pronounced seasonality, accurately capturing the discharge increase during the warm season and the river recession process in the cold season, along with corresponding changes in the flooded area. This highlights the model's capability for hydrological process monitoring. The simulated discharge shows a high correlation coefficient (0.65–0.80) with the observed discharge in the SRB, reaching a significance level of 0.01, and the Nash–Sutcliffe efficiency ranges from 0.66 to 0.78. Leveraging the offline coupling of CLM and CaMa-Flood, we present a method with a robust physical mechanism for monitoring and providing a more intuitive representation of hydrological events in the SRB. [ABSTRACT FROM AUTHOR]

Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km2 Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change. [ABSTRACT FROM AUTHOR]

Hydrological modeling is a crucial component in hydrology research, particularly for projecting future scenarios. However, achieving reproducibility and automation in distributed hydrological modeling research for modeling, simulation, and analysis is challenging. This paper introduces rSHUD v2.0, an innovative, open-source toolkit developed in the R environment to enhance the deployment and analysis of the Simulator for Hydrologic Unstructured Domains (SHUD). The SHUD is an integrated surface–subsurface hydrological model that employs a finite-volume method to simulate hydrological processes at various scales. The rSHUD toolkit includes pre- and post-processing tools, facilitating reproducibility and automation in hydrological modeling. The utility of rSHUD is demonstrated through case studies of the Shale Hills Critical Zone Observatory in the USA and the Waerma watershed in China. The rSHUD toolkit's ability to quickly and automatically deploy models while ensuring reproducibility has facilitated the implementation of the Global Hydrological Data Cloud (https://ghdc.ac.cn , last access: 1 September 2023), a platform for automatic data processing and model deployment. This work represents a significant advancement in hydrological modeling, with implications for future scenario projections and spatial analysis. [ABSTRACT FROM AUTHOR]

Addressing post-fire impacts largely depends on burn "severity." A singular severity classification that encompasses the holistic effects of fire on all ecosystem processes does not currently exist. Lumping vegetation burn severity and soil burn severity into one metric, or using them interchangeably, can induce large inaccuracies and uncertainties in the intended ecosystem response to forcing. Often, burn "severity" reflects fire impacts on vegetation, which can be measured through remote sensing. Vegetation burn severity is likely more apropos for ecological research, whereas soil burn severity is more relevant for hydrological analyses. This paper reviews different remotely sensed vegetation severity products currently (mis)used for hydrological modeling, provides examples of when vegetation burn severity may (not) match soil burn severity, and summarizes the potential synergistic future of remote sensing with in situ severity metrics. While the focus in this paper is on the western United States, the lessons and principles apply universally. [ABSTRACT FROM AUTHOR]

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R² and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken. [ABSTRACT FROM AUTHOR]

Hydrological modelling of small mountainous catchments is particularly challenging because of the high spatio-temporal resolution required for the meteorological forcings. In situ measurements of precipitation are typically scarce in these remote areas, particularly at high elevations. Precipitation reanalyses propose different alternative forcings for the simulation of streamflow using hydrological models. In this paper, we evaluate the performances of two hydrological models representing some of the key processes for small mountainous catchments (< 300 km 2), using different meteorological products with a fine spatial and temporal resolution. The evaluation is performed on 55 small catchments of the northern French Alps. While the simulated streamflows are adequately reproduced for most of the configurations, these evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events. However, these better performances are only obtained because the hydrological models correct the underestimations of accumulated amounts (e.g. annual) from the radar data in high-elevation areas. [ABSTRACT FROM AUTHOR]

Parameter sensitivity analysis plays a critical role in efficiently determining main parameters, enhancing the effectiveness of the estimation of parameters and uncertainty quantification in hydrologic modeling. In this paper, we demonstrate an uncertainty and sensitivity analysis technique for the holistic Soil and Water Assessment Tool (SWAT +) model coupled with new gwflow module, spatially distributed, physically based groundwater flow modeling. The main calculated groundwater inflows and outflows include boundary exchange, pumping, saturation excess flow, groundwater–surface water exchange, recharge, groundwater–lake exchange and tile drainage outflow. We present the method for four watersheds located in different areas of the United States for 16 years (2000–2015), emphasizing regions of extensive tile drainage (Winnebago River, Minnesota, Iowa), intensive surface–groundwater interactions (Nanticoke River, Delaware, Maryland), groundwater pumping for irrigation (Cache River, Missouri, Arkansas) and mountain snowmelt (Arkansas Headwaters, Colorado). The main parameters of the coupled SWAT + gwflow model are estimated utilizing the parameter estimation software PEST. The monthly streamflow of holistic SWAT + gwflow is evaluated based on the Nash–Sutcliffe efficiency index (NSE), percentage bias (PBIAS), determination coefficient (R2) and Kling–Gupta efficiency coefficient (KGE), whereas groundwater head is evaluated using mean absolute error (MAE). The Morris method is employed to identify the key parameters influencing hydrological fluxes. Furthermore, the iterative ensemble smoother (iES) is utilized as a technique for uncertainty quantification (UQ) and parameter estimation (PE) and to decrease the computational cost owing to the large number of parameters. Depending on the watershed, key identified selected parameters include aquifer specific yield, aquifer hydraulic conductivity, recharge delay, streambed thickness, streambed hydraulic conductivity, area of groundwater inflow to tile, depth of tiles below ground surface, hydraulic conductivity of the drain perimeter, river depth (for groundwater flow processes), runoff curve number (for surface runoff processes), plant uptake compensation factor, soil evaporation compensation factor (for potential and actual evapotranspiration processes), soil available water capacity and percolation coefficient (for soil water processes). The presence of gwflow parameters permits the recognition of all key parameters in the surface and/or subsurface flow processes, with results substantially differing if the base SWAT + models are utilized. [ABSTRACT FROM AUTHOR]

The terrestrial biosphere regulates climate through carbon, water, and energy exchanges with the atmosphere. Land surface models estimate plant transpiration, which is actively regulated by stomatal pores, and provide projections essential for understanding Earth's carbon and water resources. Empirical evidence from 204 species suggests that significant amounts of water are lost through leaves at night, though land surface models typically reduce stomatal conductance to nearly zero at night. Here, we apply observed nighttime stomatal conductance values to a global land surface model, to better constrain carbon and water budgets. We find that our modifications increase transpiration up to 5% globally, reduce modeled available soil moisture by up to 50% in semi-arid regions, and increase the importance of the land surface on modulating energy fluxes. Carbon gain declines up to ~ 4% globally and > 25% in semi-arid regions. We advocate for realistic constraints of minimum stomatal conductance in future climate simulations, and widespread field observations to improve parameterizations. [ABSTRACT FROM AUTHOR]

This paper illustrates the intimate coupling of a hydrologic model with an economic input–output model. A realistic watershed and a simple hypothetical economy are used to illustrate the trade-off between water use and water availability. This approach provides two key benefits for water management. First, it directly links the supply side (the hydrologic model is used to estimate water availability) to the demand side (the economic model is used to estimate water use by sector) using a common framework that accounts for the interdependence of the two models. This link allows us to analyze water allocation and calculate the intensity of water scarcity. Second, it enables us to consider the effect of spatial distribution of economic activity on the hydrologic model and prevents either under or over estimating water scarcity. Without this spatial disaggregation, a shortfall in one sub-watershed may be offset by an abundance in another sub-watershed. The framework is sufficiently flexible to assess more complex situations, including varied spatial disaggregation and feedbacks. The coupled model is much faster and can be applied to watersheds with different characteristics. We use system dynamics to develop the integrated hydrologic-economic modelling framework and analyze three scenarios: a baseline situation, a spatially-resolved coupled model, and a temporally-resolved coupled model. The paper concludes with recommendations for implementation and future research. [ABSTRACT FROM AUTHOR]

The processes of overland flow, infiltration through an unsaturated zone (UZ) and groundwater flow discharge to a surface-water network are simulated by a synthetic flow and transport watershed model under a 30-year warming signal. A promising strategy is to use both methods as complementary to each other: machine learning techniques can improve the calibration of numerical models whereas process-based numerical models are suitable to understand the physical system and, on turn, select proper input variables for machine learning models. In general, the study highlights that choosing the modelling approach based on the specific aquifer features is fundamental to obtaining a modelling tool efficient in supporting groundwater management actions aimed at mitigation of the effects of climate change. [Extracted from the article]

Climate change refers to a statistically significant change in the average state of the climate or a climate alteration that lasts for a long period of time. Runoff (R) is as a measure of the interaction between climate change and human activities and plays an important role in the hydrological cycle, as it is directly related to the development of agricultural water management. Therefore, it is a requirement to correctly simulate R and have the ability to separate the impacts due to climate change and human activities. In this paper, five single-type simulation models (Back Propagation Neural Network (BP), Non-Autoregressive (NAR), Radial Basis Function (RBF), Support Vector Machine (SVM) and TOPMODEL Hydrological Model (TOPMODEL)) were adopted to simulate the R to analyze the simulating quality by comparing the evaluation indexes like relative error (RE), relative mean squared error (RMSE) and Nash–Sutcliff Efficiency (NSE) with the combined hierarchical structure hydrological (CHSH) simulation model. In traditional studies, only the relative contribution of the impacts of human activities and climate change on R are considered; however, in this study, the relative contribution of each meteorological factor affecting R is included. To quantitatively analyze the impact of human activities and climate change on R, we used a CHSH simulation model to calculate runoff values for the Lancang River of China for a period of nine years (2005–2013). Our objective was to use this type of model to improve both the accuracy and stability of calculated values of R. For example, the RE, RMSE and NSE of simulated monthly R calculated with the CHSH model were 6.41%, 6.67 × 108 m3 and 0.94, respectively. These values substantiate the improved accuracy and stability of calculated values of R obtained with single-type simulation models (the SVM model, for instance, widely used in runoff simulations, and the RE, RMSE and NSE were 14.1%, 12.19 × 108 m3 and 0.87, respectively). The total contribution of human activities and climate change to R, respectively, accounted for 34% and 66% for the nine-year period based on the CHSH model. Furthermore, we adopted a vector autoregressive (VAR) model to analyze the impacts of the meteorological factors on R. The results from this analysis showed that R has a strong fluctuation response to the changes in precipitation (P) and potential water evaporation (Ep). The contribution rates of Ep, P and air temperature (Ta) to R were 15%, 14% and 2%, respectively. Based on the total climate change contribution, the corresponding contribution rates of Ep, Ta and P in the Lancang River of China were 32%, 30% and 5%, respectively. The values of R calculated with the CHSH model are more accurate and stable compared to values obtained with single-type simulation model. Further, they have the advantage of avoiding drawbacks associated when using a single-type simulation model. Moreover, moving away from the traditional method of separating the impact of meteorological factors on R, the vector autoregressive model proposed in this paper can describe the contribution of different meteorological factors on R in more detail and with precision. [ABSTRACT FROM AUTHOR]

This paper presents a fractional linear reservoir model as the elementary response function of hydrologic systems corresponding to the classical linear reservoir model and tests its applicability to rainfall–runoff modeling. To this end, we formulate a fractional linear reservoir model in terms of fractional calculus following the same procedure as the classical linear reservoir model and, at the simplest level, compare its performance of rainfall–runoff modeling with the linear and nonlinear reservoir models. The impulse response function of a fractional linear reservoir model, a probability density function (PDF) following the Mittag–Leffler distribution, shows nonlinearity due to its time-variant behavior compared to that of a linear reservoir model. In traditional linear hydrologic system theory, the lag and route version of a fractional linear reservoir model produces the fast-rising and slow-recession of runoff hydrographs, implying the mixed response of linear and nonlinear reservoir models to rainfall. So, a fractional linear reservoir model could be considered a fundamental tool to effectively reflect the nonlinearity of rainfall–runoff phenomena within the framework of the linear hydrologic system theory. In this respect, the fractional order of the storage relationship specifying a fractional linear reservoir model can be viewed as a kind of parameter to quantify the heterogeneity of runoff generation within a river basin. [ABSTRACT FROM AUTHOR]

Water resources engineers and geospatial analysts often face the challenge of spatially estimating parameters such as the Soil Conservation Service (SCS) Curve Number (CN) and Manning's roughness number (n), which are critical for predicting runoff and streamflow in hydrologic studies. Addressing the above challenge, this paper presents an innovative ArcMap tool developed using Python. This tool streamlines the SCS-CN and Manning's n spatial calculations and is designed to handle large datasets, even at the scale of the entire US. Additionally, it offers the unique capability of geoprocessing mixed soil types and seamlessly integrating data if the watershed spans over different states. Our tool automates the integration of land cover data, hydrologic soil group data, and hydrologic boundaries. The tool reads watershed boundaries and uses the National Land Cover Database (NLCD) and the Gridded Soil Survey Geographic Database (gSSURGO) to develop SCS-CN and Manning's n spatial layers. The tool also offers users the unique flexibility to add any desired values for CN or Manning's n in the form of a so-called lookup table, which is a great help with the iterative process of calibrating hydrologic or hydraulic models. Our tool addressed one of the major limitations of its predecessors, acknowledging the existence of mixed hydrologic soil groups, e.g., B/C or C/D, and allowing for user adjustments to address hydrologic or hydraulic models' calibration needs. The tool was developed with a flexible framework to incorporate additional spatial parameters soon, such as the spatial green-ampt parameters. With a user-friendly interface and integration capabilities, the tool is invaluable for hydrologic and hydraulic studies at local, regional, and global scales. [ABSTRACT FROM AUTHOR]

This paper presents an efficient flood early warning system developed for the city of Mandra, Greece which experienced a devastating flood event in November 2017 resulting in significant loss of life. The location is of particular interest due to both its small-sized water basin (20 km 2 upstream of the studied cross-section), necessitating a rapid response time for effective flood warning calculations, and the lack of hydrometric data. To address the first issue, a database of pre-simulated flooding events with a 2D hydrodynamic model corresponding to synthetic precipitations with different return periods was established. To address the latter issue, the hydrological model was calibrated using qualitative information collected after the catastrophic event, compensating for the lack of hydrometric data. The case study demonstrates the establishment of a hybrid (online–offline) flood early warning system in data-scarce environments. By utilizing pre-simulated events and qualitative information, the system provides valuable insights for flood forecasting and aids in decision-making processes. This approach can be applied to other similar locations with limited data availability, contributing to improved flood management strategies and enhanced community resilience. [ABSTRACT FROM AUTHOR]

The combined use of weather generators (WG) and hydrological models (HM) in what is called synthetic continuous simulation (SCS) has become a common practice for carrying out flood studies. However, flood quantile estimations are far from presenting relatively high confidence levels, which mostly relate to the uncertainty of models' input data. The main objective of this paper is to assess how different precipitation regimes, climate extremality, and basin hydrological characteristics impact the uncertainty of daily flood quantile estimates obtained by SCS. A Monte Carlo simulation from 18 synthetic populations encompassing all these scenarios was performed, evaluating the uncertainty of the simulated quantiles. Additionally, the uncertainty propagation of the quantile estimates from the WG to the HM was analyzed. General findings show that integrating the regional precipitation quantile ( X T , P)   in the WG model calibration clearly reduces the uncertainty of flood quantile estimates, especially those near the regional X T , P . Basin size, climate extremality, and the hydrological characteristics of the basin have been proven not to affect flood quantiles' uncertainty substantially. Furthermore, it has been found that uncertainty clearly increases with the aridity of the climate and that the HM is not capable of buffering the uncertainty of flood quantiles, but rather increases it. [ABSTRACT FROM AUTHOR]

The Xin'anjiang hydrological model (XHM) is the practical tool for runoff simulation and flood forecasting in most regions in China, but it still presents some challenges when applied to Northwest China, where the river runoff mostly comes from high-temperature snowmelt, as the model lacks such a functional module. In this study, the improved XHM coupling snowmelt module is presented to complete the existing XHM for better suitability for flood simulation in areas dominated by snowmelt. The improved model includes four sub-models: evapotranspiration, runoff yield, runoff separation, and runoff routing, where the snowmelt runoff module is introduced in both the runoff yield and separation sub-models. The watershed is divided into two types, non-snow areas with lower altitudes and snow-covered areas with higher altitudes, to study the mechanism of runoff production and separation. The evaluation index, determination coefficients (R2), mean square error (MSE), and Nash efficiency coefficients (NSE) are used to assess the improved XHM's effect by comparing it with the traditional model. Results show that the R2 of the improved XHM coupled with snowmelt are around 0.7 and 0.8 at the Zamashk and Yingluoxia stations, respectively, while the MSE and NSE are also under 0.4 and above 0.6, respectively. The absolute value of error of both flood peaks in the Yingluoxia station simulated by improved XHM is only 10% and 6%, and that of traditional XHM is 32% and 40%, indicating that the peak flow and flood process can be well simulated and showing that the improved XHM coupled with snowmelt constructed in this paper can be applied to the flood forecasting of the Heihe River Basin. The critical temperature of snow melting and degree-day factor of snow are more sensitive compared with other parameters related to snow melting, and the increasing trend of peak flow caused by both decreased critical temperature and increased degree-day factor occurs only when the value of the model's state (snow reserve) is higher. These results can expand the application scope in snow-dominated areas of the XHM, providing certain technical references for flood forecasting and early warning of other snowmelt-dominated river basins. [ABSTRACT FROM AUTHOR]

Estimating peak flow for a catchment is commonly undertaken using the design event method; however, this method does not allow for the understanding of uncertainty in the result. This research first presents a simplified method of fragments approach to rainfall disaggregation that ignores the need to consider seasonality, offering a greater diversity in storm patterns within the resulting sub-daily rainfall. By simulating 20 iterations of the disaggregated sub-daily rainfall within a calibrated continuous simulation hydrologic model, we were able to produce multiple long series of streamflow at the outlet of the catchment. With these data, we investigated the use of both the annual maximum and peaks over threshold approaches to flood frequency analysis and found that for a 1-in-100-year annual exceedance probability peak flow, the peaks over threshold method (333 m3/s ± 50 m3/s) was significantly less uncertain than the annual maximum method (427 m3/s ± 100 m3/s). For the 1-in-100-year annual exceedance probability, the median peak flow from the peaks over threshold method (333 m3/s) produced an outcome comparable to the design event method peak flow (328 m3/s), indicating that this research offers an alternative approach to estimating peak flow, with the additional benefit of understanding the uncertainty in the estimation. Finally, this paper highlighted the impact that length and period of streamflow has on peak flow estimation and noted that previous assumptions around the minimum length of gauged streamflow required for flood frequency analysis may not be appropriate in particular catchments. [ABSTRACT FROM AUTHOR]

In the process of applying the hydrological model to hydrological forecasting, there are many simplifications and assumptions in the structure of the basin hydrological model, which will bring errors to the hydrological forecasting results. Therefore, error correction technology is needed to improve the accuracy of the hydrological model in real-time forecasting. The system differential response method uses the system response function to modify the forecast results by modifying the inputs, state variables and parameters of the hydrological model. This method has a solid theoretical foundation, simple structure, and the advantages of not losing the forecast period. It has been widely used in multiple watershed hydrological model with good results. VIC model is a large-scale distributed hydrological model, which divides the watershed into multiple grids of the same size to simulate runoff generation and concentration results within the grid. This paper takes the Jianyang Basin of the Minjiang River as the research area, constructs a basin VIC model for runoff simulation, applies the system differential response error correction method to the VIC model, corrects the forecast results by modifying the rainfall in the model input, and compares and analyzes it with the traditional autoregressive model error correction method (AR model). The results show that the system differential response method has a good correction effect in the VIC model, with the Nash efficiency coefficient increasing from 0.752 to 0.895 before correction, and the relative error of runoff decreasing from 7.89% to 5.71%. The Nash efficiency coefficient of the AR model correction result is 0.807, and the relative error of runoff is 6.77%. The results of these two correction methods indicate that the system differential response method is superior to the AR model error correction method. The system differential response correction method has good applicability in the VIC model, which can significantly improve the accuracy of daily runoff simulation in the basin and can be applied to future hydrological forecasting processes. [ABSTRACT FROM AUTHOR]

Spatially distributed hydrology and land surface models are typically applied in combination with river routing schemes that convert instantaneous runoff into streamflow. Nevertheless, the development of such schemes has been somehow disconnected from hydrologic model calibration research, although both seek to achieve more realistic streamflow simulations. In this paper, we seek to bridge this gap to understand the extent to which the configuration of routing schemes affects hydrologic model parameter searches in water resources applications. To this end, we configure the Variable Infiltration Capacity (VIC) model coupled with the mizuRoute routing model in the Cautín River basin (2770 km2), Chile. We use the Latin hypercube sampling (LHS) method to generate 3500 different model parameters sets, for which basin-averaged runoff estimates are obtained directly (no routing or instantaneous runoff case) and are subsequently compared against outputs from four routing schemes (unit hydrograph, Lagrangian kinematic wave, Muskingum–Cunge, and diffusive wave) applied with five different routing time steps (1, 2, 3, 4, and 6 h). The results show that incorporating routing schemes may alter streamflow simulations at sub-daily, daily, and even monthly timescales. The maximum Kling–Gupta efficiency (KGE) obtained for daily streamflow increases from 0.64 (instantaneous runoff) to 0.81 (for the best routing scheme), and such improvements do not depend on the routing time step. Moreover, the optimal parameter sets may differ depending on the routing scheme configuration, affecting the baseflow contribution to total runoff. Including routing models decreases streamflow values in flood frequency curves and may alter the probabilistic distribution of the medium- and low-flow segments of the flow duration curve considerably (compared to the case without routing). More generally, the results presented here highlight the potential impacts of river routing implementations on water resources applications that involve hydrologic models and, in particular, parameter calibration. [ABSTRACT FROM AUTHOR]

Hydrological applications such as storm-water management usually deal with region-specific reference rainfall regulations based on intensity–duration–frequency (IDF) curves. Such curves are usually obtained via frequency analysis of rainfall and exceedance probability estimation of rain intensity for different durations. It is also common for reference rainfall to be expressed in terms of precipitation P , accumulated in a duration D , with a return period T. Meteorological modules of hydro-meteorological models used for the aforementioned applications therefore need to be capable of simulating such reference rainfall scenarios. This paper aims to address three research gaps: (i) the discrepancy between standard methods for defining reference precipitation and the strong multi-scale intermittency of precipitation, (ii) a lack of procedures to adapt multi-fractal precipitation modelling to specified partial statistical references, and (iii) scarcity of proper multi-scale tools to quantitatively estimate the effectiveness of such simulation procedures. Therefore, it proposes (i) a procedure based on extreme non-Gaussian statistics in two scaling regimes due to earth's finite size to tackle multi-scale intermittency head on, (ii) a renormalization technique to make simulations comply with the aforementioned partial statistical references, and (iii) multi-scale metrics to compare simulated rainfall time series with those observed. While the first two proposals are utilized to simulate reference rainfall scenarios for three regions (Paris, Nantes, and Aix-en-Provence) in France that are characterized by different climates, the last one is used to validate them. The scope of this paper is that the baseline precipitation scenarios simulated here can be used as realistic inputs into hydrological models for applications such as the optimal design of storm-water management infrastructure, especially green roofs. Although only purely temporal simulations are considered, this approach could possibly be generalized to space–time as well. [ABSTRACT FROM AUTHOR]

Empirical evidence continues to show that climate change remains a threat to the stability of the hydrologic system. As the climate system interacts with the hydrologic cycle, one significant repercussion of global warming includes changes in water availability at both regional and local scales. Climate change adaptation is intrinsically difficult to attain due to the dynamic earth system and lack of a comprehensive understanding of future climate and its associated uncertainties. Mostly in developing countries, climate adaptation is hampered by scarcity of good quality and adequate hydro-meteorological data. This article provides a synopsis of the modelling chain applied to investigate the response of the hydrologic system under changing climate, which includes choosing the appropriate global climate models, downscaling techniques, emission scenarios, and the approach to be used in hydrologic modelling. The conventional criteria for choosing a suitable hydrological model are discussed. The advancement of emission scenarios including the latest Shared Socioeconomic Pathways and their role in climate modelling, impact assessment, and adaptation, are also highlighted. This paper also discusses the uncertainties associated with modelling the hydrological impacts of climate change and the plausible approaches for reducing such uncertainties. Among the outcomes of this review include highlights of studies on the commonly used hydrological models for assessing the impact of climate change particularly in the sub-Saharan Africa region and some specific reviews in southern Africa. Further, the reviews show that as human systems keep on dominating within the earth system in several ways, effective modelling should involve coupling earth and human systems models as these may truly represent the bidirectional feedback experienced in the modern world. The paper concludes that adequate hydro-meteorological data is key to having a robust model and effective climate adaptation measures, hence in poorly gauged basins use of artificial neural networks and satellite datasets have shown to be successful tools, including for model calibration and validation. [ABSTRACT FROM AUTHOR]

Data Assimilation (DA) of time-variable satellite gravity observations, e.g., from the Gravity Recovery and Climate Experiment (GRACE), GRACE-Follow On (GRACE-FO) and future gravity missions, can be applied to constrain the vertical sum of water storage simulations of Global Hydrological Models (GHMs). However, the state-of-the-art DA of these measured Terrestrial Water Storage (TWS) changes into models is often performed regionally, and if globally, at low spatial resolution. This choice is made to handle the considerably high computational demands of DA, and to avoid numerical problems, e.g., instabilities related to the inversion of covariance matrices. To fully exploit the potential of satellite gravity observations and the high spatial resolution of GHMs, we developed a Python-based open-source PyGLDA system that allows performing DA globally at a fine scale with high numerical efficiency. The main novelties of the system include (i) implementing a globe-scale patch-wise DA via domain localization and neighbouring-weighted global aggregation and (2) its great compatibility between basin-scale and grid-scale DAs. This PyGLDA system represents a considerable functional advancement on previous implementations with wide and flexible options offered to allow for various user-specific studies. The modular structure of PyGLDA provides users with various possibilities to interact with (and add/remove) individual water storage compartments, change the representation of observations, and, therefore, the ability to choose different GHMs. In this paper, we present a full description of this system and its application for the Danube River Basin as a regional case study and through a global DA. The DA demonstrations are performed using the monthly TWS fields of GRACE (2002–2010) and the W3RA water balance model at 0.1-degree/daily spatial-temporal resolution. [ABSTRACT FROM AUTHOR]

In a previous study, the topsoil and root zone ASCAT satellite soil moisture data were implemented into three multi-objective calibration approaches of the TUW hydrological model in 209 Austrian catchments. This paper examines the model parametrization in those catchments, which in the validation of the dual-layer conceptual semi-distributed model showed improvement in the runoff simulation efficiency compared to the single objective runoff calibration. The runoff simulation efficiency of the three multi-objective approaches was separately considered. Inferences about the specific location and the physiographic properties of the catchments where the inclusion of ASCAT data proved beneficial were made. Improvements were primarily observed in the watersheds with lower slopes (median of the catchment slope less than 15 per cent) and a higher proportion of farming land use (median of the proportion of agricultural land above 20 per cent), as well as in catchments where the runoff is not significantly influenced by snowmelt and glacier runoff. Changes in the mean and variability of the field capacity parameter FC of the soil moisture regime were analysed. The values of FC decreased by 20 per cent on average. Consequently, the catchments' water balance closure generally improved by the increase in catchment evapotranspiration during the validation period. Improvements in model efficiency could be attributed to better runoff simulation in the spring and autumn month. The findings refine recommendations regarding when hydrological modelling could consider satellite soil moisture data added to runoff signatures in calibration useful. [ABSTRACT FROM AUTHOR]

Featured Application: The application provides a new tool for reducing the error in the simulated runoff of a hydro-logical model by integrating time series models with hydrological models. Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated runoff has limitations that cannot be eliminated. This paper proposes a method that integrates the hydrological and time series models to improve the reliability and accuracy of simulated runoffs. Specifically, the block-wise use of TOPMODEL (BTOP) is integrated with three time series models to improve the simulated runoff from a hydrological model of the Zhou River Basin, China. Unlike most previous research that has not addressed the influence of runoff patterns while correcting the runoff, this study manually adds the hydrologic cycle to the machine learning-based time series model. This also incorporates scenario-specific knowledge from the researcher's area of expertise into the prediction model. The results show that the improved Prophet model proposed in this study, that is, by adjusting its holiday function to a flow function, significantly improved the Nash–Sutcliffe efficiency (NSE) of the simulated runoff by 53.47% (highest) and 23.93% (average). The autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) improved the runoff but performed less well than the improved Prophet model. This paper presents an effective method to improve the runoff simulation by integrating the hydrological and time series models. [ABSTRACT FROM AUTHOR]

This paper proposes a general methodological approach to hydrological modeling for determining the areas of flooded land in the plain part of the Dniester riverbed, the second largest river in Ukraine. The purpose of the study is the selection of parameters for modeling flooded zones in the plain section of the Dniester riverbed, taking into account the rising water level caused by the freshet that occurred in the western part of Ukraine on 22–24 June 2020. The proposed study examines the workflow of hydrological modeling of the flooded land zone and the main components of this scheme: the construction of the DTM, considering the roughness of the riparian territory by Manning's coefficients, and indications of water rise. In the experiment, the influence of DTM reproduction resolution and Manning coefficients was analyzed, and their optimal values were selected, which allowed obtaining the parameters of hydrological modeling with a higher probability. The identified flooding areas were tested using a high-resolution space image during the flood in June 2020. The distance between the profiles affects not only the value of the modeling area but also their detail. The accuracy of the modeled flooded area is 5.1% for a 5 m interval between the profiles, 6.9% for 50 m, 8.2% for 100 m, and 10.8% for 200 m. These results allow determining the degree of influence of the distance between intervals on the modeling accuracy. Using different values of Manning's coefficients for individual sections with different bedding surfaces and the selected spacing between profiles, which was 50 m, the accuracy of the modeling was investigated. After the modelling, the simulated flood areas were obtained in vector form, which allows for determining their areas and comparing them with the test flooded area. In the presented research, the RMSE of determining the flooded areas is about 5%. The test area of 600.6 hectares was determined with an accuracy of 0.8%. [ABSTRACT FROM AUTHOR]

The frequency of extreme events is increasing as the consequences of climate change. In steep terrains, flash floods with high‐flow velocities induce erosion and sedimentation with potentially disastrous changes of flood path. Hence, the analysis of flash floods in steep terrains in terms of inundation area and flow‐velocity to identify critical points becomes more important. The output of a flood simulation with a traditional hydrologic model provides the flood hydrograph which must be combined with a hydraulic model for downstream consequences. In small and steep catchments, the inflow contribution from every section of the water course can be important to determine where critical conditions may arise. In this study, rain‐on‐grid technique in the hydraulic model Telemac‐2D is used to simulate flash‐flood peaks with spatially distributed precipitation as input in a small and steep catchment in western Norway. Seven events were simulated and sensitivity tests on parameters were conducted. A 200‐year design flood was simulated to show the potential consequences in the catchment. The results show that calibrated models can satisfactorily reproduce peak flows and produce relevant information about water velocities and inundation which decision makers can use for mitigation measures. The paper explores the benefits and limitations through a description of model construction, calibration, and test of sensitivities. [ABSTRACT FROM AUTHOR]