Hydrological Simulation Research Articles

Impacts of vegetation dynamics on hydrological simulations under drought conditions in a humid river basin in Southern China

AbstractVegetation plays an essential role in the atmospheric and hydrological processes, and vegetation responds differently to climate change in various regions, especially in extreme climates. Therefore, the use of static prescribed vegetation information from past years in numerical models can be a source of biases in hydrological simulations. However, previous studies have mainly focused on the effects of vegetation dynamics on hydrological processes in arid and semi‐arid regions. It remains unclear how static or dynamic vegetation affects hydrological simulations in humid regions, especially under drought conditions. In this study, the Weather Research and Forecasting (WRF) model coupled with Noah‐MP was used to assess the impact of vegetation dynamics on hydrological simulations in the East River basin (ERb) of China, which is a major water source for several major cities in the Pearl River Delta. The model was run with prescribed and dynamic vegetation conditions, respectively. Our model validation based on observed 2‐m temperature (T2) and Leaf Area Index (LAI) showed that the model performance was improved when vegetation dynamics were considered. Our simulations with static or dynamic vegetation showed the impacts of vegetation dynamics on hydrological simulations under droughts. The model with vegetation dynamics simulated a wetter condition with higher soil moisture and runoff and lower T2, compared with the simulations of static vegetation. The results suggested that ignoring vegetation dynamics may overestimate the severity of drought in this humid basin, unlike arid and semi‐arid regions. Therefore, consideration of vegetation dynamics in this humid basin will deepen our research on different types of zones and serve as a reference for other humid regions.

Suitability of ERA5-Land reanalysis dataset for hydrological modelling in the Alpine region

Study region: The region of interest is the South-Tyrol in the southeastern Alps, Italy. A comparison of meteorological forcing is performed with reference to this region while hydrological simulations are conducted in the Passirio river basin.Study focus: The objective of this work is to evaluate the suitability of ERA5-Land reanalysis product as a reference dataset for hydrological modelling in topographically complex Alpine regions. ERA5-Land is compared with observational gridded dataset, obtained by means of the Kriging interpolation, available at two contrasting spatial resolutions a coarse grid size of 11 × 8 kilometres and a high-resolution grid size 1 × 1 kilometres. The hydrological coherence of the three datasets (ERA5-Land and the two observational datasets) with observed streamflow time series is then assessed with refer the Passirio/Passer river basin and to its nested and snow-dominated sub-catchment Plan/Pfelders.New hydrological insights for the region: The study assesses ERA5-Land’s suitability for hydrological simulations in the South-Tyrol region by comparing streamflow outcomes. At the annual scale, an overestimation of precipitation of about 40mm/month and a temperature underestimation of about 2.1 °C is reported. These discrepancies influence hydrological simulations, resulting in an overestimation of both streamflow and snow water equivalent. It is thus crucial to consider the limitations of ERA5-Land when using it as forcing for hydrological application in watersheds characterized by complex terrains, such as Alpine region.

Modelling the future climate impacts on hydraulic infrastructure development in tropical (peri-)urban region: Case of Kigali, Rwanda

The current global climate has shown a significant change, mostly resulting from human-induced activities. Frequent experiences of extreme rainstorms, deadly landslides, and floods followed by the destruction of roads, bridges, drainage, buildings, agriculture, and other infrastructures have been appearing across the globe along with extensive socio-economic effects including human lives losses whereby tropical Africa is among the greatly affected regions. Several studies in the region acclaim the increase of climate-related extremes due to a gradual climate variation. Hence, this study aimed to evaluate how existing water structures might respond to the future climate, which is getting more severe and frequent in the region. The study was conducted on the Nyabugogo River catchment (NRC), covering a huge part of the Kigali metropolitan area. It was carried out through a downscaled global climate model (CMIP6 GCM) projection coupled with a joint SWAT + hydrological model and HEC-RAS hydrodynamic simulation. The study showed that the annual precipitation in Kigali might keep increasing, resulting in increased risks of extreme weather events. The study identified up to 38% (+514.9 mm) annual precipitation increment, which resulted in more than a doubled flow rate (+28.0 m3/s) increment by the end of the century under a high greenhouse gas emission scenario (ssp585). As a result, hydrodynamic simulations revealed that the Bridge-1 in NRC might fail to accommodate the 50-year return peak storm under ssp585. Henceforth, there is a need to adopt high GHG emission scenarios in critical infrastructure development. Further, enforcing green-grey infrastructures in flood risk-low resilient areas is recommended to improve climate resilience. Thus, the results of this study might prove useful in climate-resilient infrastructure development and other pre-emptive adaptation practices, most importantly building anticipated resilience against climate-related hazards.

Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

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.

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China.

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

Refined analysis of flood-regional composition under changing environment in the middle reach of Hanjiang River

To investigate the flood-regional composition under changing environmental conditions in the middle reach of Hanjiang River (MHR), a data-driven hydrological simulation model and its related quantitative methods were developed. The flood-regional composition of Huangzhuang(HZ) in the MHR was quantitatively analyzed, and the influence of environmental changes on river flood routing was discussed. The primary research findings are as follows: ① A hydrological simulation model based on support vector regression machine (SVRM) is constructed to simulate the daily average flow process of HZ from 1965 to 2021. The Nash-Sutcliffe Efficiency (NSE) coefficient achieved values above 0.95, and the overall relative error (RE) was within ± 1 %, indicating excellent simulation performance. ② A quantitative analysis method has been proposed to identify the composition of flood areas. The results indicate that the upper reach of Hanjiang River (UHR) is the primary contributor to floods in the MHR, accounting for 60.62 % to 78.05 % of the total. The Tangbai River (TR) contributed between 14.1 % and 27.4 %, whereas the Nan River had a smaller contribution of only 6.83 % to 8.85 %. ③ Trend analysis indicates that the proportion of floods originating from the UHR increases in the summer flood season and decreases in the autumn flood season, while those changes of TR and Nanhe River (NR) are coincidental, especially in the autumn flood season, the proportion of floods in the TR increases significantly. The impact of floods from the UHR and TR cannot be ignored when implementing flood control measures. ④ A comprehensive analysis method has been proposed to quantify the integrated impacts of environmental changes. The results show that environmental changes had a relatively minor impact on flood routing and its flood-regional composition. However, they did affect the flood propagation process, resulting in earlier occurrences in peak flow, increased in peak discharge, and rapid rise and fall of floodwaters for floods exceeding 12,000 m3/s. These research findings provide strong foundational support for designing flood-regional, as well as flood control and disaster reduction systems in the MHR.

Satellite-based precipitation error propagation in the hydrological modeling chain across China

Satellite-based precipitation products have great potential to promote hydrological modeling skill owing to the spatially homogeneous measurements they provide. However, satellite-based precipitation uncertainties and their hydrological impacts are poorly understood for different products and hydroclimate regions in China. This study comprehensively evaluates the error properties of ten satellite-based precipitation products and the precipitation error propagation characteristics in the hydrological modeling chain during the period 2001–2018. The results indicate that precipitation error tends to become smaller during the routing process for most satellite precipitation products; e.g., αRMAE, an error propagation ratio based on relative mean absolute error, decreased about 26% (from 0.58 to 0.43) when considering routing processes during hydrological simulation by using the PERSIANN-CDR satellite precipitation product. The linear slope of simulated streamflow against precipitation error greatly decreases (compared to that of simulated runoff) after flow routing of 366 catchments across China. This study also found that satellite-based precipitation product errors in hydrological simulations for drier and water-limited small catchments are more likely for amplify in the streamflow simulations. These findings are essential to improving understanding of the interactions between satellite-derived precipitation forcing and hydrological processes, and thus for improving the data accuracy of precipitation forcing for hydrological simulation in China.

Hydrologic simulation of a neotropical alpine catchment influenced by conductive topsoils in the Ecuadorian Andes

Highly conductive topsoils in neotropical high-elevation grassland-dominated ecosystems, or so-called paramos in the Andean region, influence the local rainfall-runoff processes predominated by saturation-excess overland flow as the primary source of freshwater. The Soil and Water Assessment Tool (SWAT) model has shown limitations when applied to mountainous catchments with highly conductive soils that generate surface runoff as saturation-excess overland flow. In this study, we enhanced SWAT to simulate runoff as saturation-excess overland flow and examined the hydrological responses of an intensively monitored paramo catchment in Ecuador. The model setup considered a detailed representation of the hydro-physical properties of the soils at different depths, including high infiltration and lateral flow rates in the hillslopes and restricted groundwater interactions, a characteristic of the páramo catchments. SWAT reasonably reproduced the daily discharge during dry and wet periods and the cumulative occurrence of high and low flows. The performance metrics NSE, RSR, and PBIAS values during calibration/validation period were 0.86/0.84, 0.31/0.4, and −11.2/-7.58, respectively. The runoff ratio and partitioning of the total runoff into the lateral flow and surface runoff were physically meaningful. More significantly, SWAT was able to simulate saturation-excess overland flow, which is dominant compared to infiltration excess, and it is a distinctive characteristic of páramo catchments. Nevertheless, the model showed limitations in simulating low flows.

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.

Advancing isotope‐enabled hydrological modelling for ungauged calibration of data‐scarce humid tropical catchments

AbstractRealistic projections of the future climate and how this translates to water availability is crucial for sustainable water resource management. However, data availability constrains the capacity to simulate streamflow and corresponding hydrological processes. Developing more robust hydrological models and methods that can circumvent the need for large amounts of hydro‐climatic data is crucial to support water‐related decisions, particularly in developing countries. In this study, we use natural isotope tracers in addition to hydro‐climate data within a newly developed version of the spatially‐distributed J2000iso as an isotope‐enabled rainfall‐runoff model simulating both water and stable isotope (δ2H) fluxes. We pilot the model for the humid tropical San Carlos catchment (2500 km2) in northeastern Costa Rica, which has limited time series, but spatially distributed data. The added benefit of simulating stable isotopes was assessed by comparing different amounts of observation data using three model calibration strategies (i) three streamflow gauges, (ii) three gauges with stream isotopes and (iii) isotopes only. The J2000iso achieved a streamflow Kling–Gupta efficiency (KGE) of 0.55–0.70 across all the models and gauges, but differences in hydrological process simulations emerged when including stable water isotopes in the rainfall‐runoff calibration. Hydrological process simulation varied between the standard J2000 rainfall‐runoff model with a high simulated surface runoff proportion of 37% as opposed to the isotope version with 84%–89% simulated baseflow or interflow. The model solutions that used only isotope data for calibration exhibited differences in simulated interflow, baseflow and model performance but captured bulk water balances with a reasonable match between the simulated and observed hydrographs. We conclude that J2000iso has shown the potential to support water balance modelling for ungauged catchments using stable isotope, satellite and global reanalysis data sets.

Uncertainty of the Simulated Mid-Pliocene Changes of Sahel Summer Rainfall in the PlioMIP2 Multimodel Ensemble

Abstract The mid-Pliocene (approximately 3.3–3.0 Ma) was one of the past geological warm periods. Since this past warmer climate was in many respects comparable to near future warming projections, how the regional monsoonal rainfall changed during the mid-Pliocene is an important scientific and socioeconomic concern. Based on phase 2 of the Pliocene Model Intercomparison Project (PlioMIP2), this work examines the simulated changes of Sahel summer rainfall during the mid-Pliocene. The results show the considerable intermodel uncertainty of the simulated mid-Pliocene Sahel rainfall changes in the PlioMIP2 multimodel ensemble, which is due to the uncertainties of both the simulated dynamic and thermodynamic responses to this past warmer climate. In particular, we find that the intermodel spread in the simulated northern North American warming is a major source of the uncertainty of the mid-Pliocene Sahel summer rainfall changes through two processes. One is a direct dynamic process: a stronger warming over northern North America could enhance the meridional temperature gradient between the extratropical and tropical regions, inducing an interhemisphere energy imbalance of the atmosphere. This could lead to a northward shift of the intertropical convergence zone, strengthening the Western African summer monsoon (WASM) circulation and Sahel summer rainfall. Another is an indirect thermodynamic process: the strengthened WASM circulation could further induce anomalous moisture convergence over the Sahel region, increasing local atmospheric moisture at the low-level troposphere, in favor of a wetter Sahel. Our results suggest that an improved warming simulation over northern North America is essential for the hydrological cycle simulation around the Sahel in the mid-Pliocene warmer climate. Significance Statement The Sahel summer rainfall change in a global warmer climate is a widespread scientific and socioeconomic issue because of its important impacts on regional agriculture, ecosystems, food security, water resources, and even cultural environment. However, the present study identifies a nonnegligible intermodel uncertainty of the simulated Sahel rainfall changes during the mid-Pliocene (one of the most recent geological warm periods in Earth’s history) in the Pliocene Model Intercomparison Project phase 2 (PlioMIP2) multimodel ensemble. In particular, such an intermodel uncertainty of the simulated Sahel rainfall changes during the mid-Pliocene is attributed to that of the simulated northern North America warming via both the direct dynamic process and the indirect thermodynamic process. This is quite different from the uncertainty source of near-future projections of Sahel summer rainfall changes. The present results improve our understanding of the underlying physics of the hydrological cycle change around the Sahel region in the mid-Pliocene warmer climate, with implications for the future projections of regional monsoonal rainfall.

Hydrological response to land use land cover and climate variability, and simulation of sediment export and water yield in the catchment Kotri Sindh Pakistan using Soil and Water Assessment tool model.

The water availability concerns have been increasing due to significant impacts of land use land cover change, and climate variability. In terms of developing countries, it is one of the biggest challenges to overcome and manage sustainability in the present and future. This study aims to evaluate the change in hydrological components and simulation of sediment yield and water yield on the large-scale basin of Kotri barrage with a change in runoff due to a change in land use land cover. This study has been done on the watershed as well as the sub-watershed level to have an accurate estimation and simulation by finding the response of hydrological components toward its natural and human-induced factors using the Soil and Water Assessment tool with high-resolution geospatial-temporal inputs over the Kotri catchment. The sediment and water yield were quantified using 42 years of simulation (1981-2022) on the sub-basin level, projected to land use land cover 1990, 2000, 2010, and 2022. The increase in deforestation, agriculture, and settlement areas resulted increase in sediment load in the catchment. The sub-basins 14, 11, 12, and 13, with a high elevation and slope and with less vegetation showed higher sediment load and water yield than the sub-basins with gentle slope and with high natural vegetation cover. The sub-basins 10, 4, and 1 showed high water yield availability compared to basins 2, 3, 5, 6, 7, 8, 9. This may be the result of vegetation differences. However, contained less sediment load than basins 14, 11, 12, and 13. The main objective was to quantify the significant changes affecting catchment and sub-catchment areas, to have a better understanding of the management plan regarding land use land cover. The simulated data was further projected to prediction using machine algorithms (autoregressive integrated moving average) model for precipitation prediction, and (seasonal autoregressive integrated moving average with exogenous factors) model to predict the sediment yield and water yield in the catchment to 2060.

Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms

Total water storage anomalies (TWSAs) describe the variations of the terrestrial water cycle, which is essential for understanding our climate system. This study proposes a self-supervised data assimilation model with a new loss function to provide global TWSAs with a spatial resolution of 0.5°. The model combines hydrological simulations as well as measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite missions. The efficiency of the high-resolution information is proved by closing the water balance equation in small basins while preserving large-scale accuracy inherited from the GRACE(-FO) measurements. The product contributes to monitoring natural hazards locally and shows potential for better understanding the impacts of natural and anthropogenic activities on the water cycle. We anticipate our approach to be generally applicable to other TWSA data sources and the resulting products to be valuable for the geoscience community and society.

Impact-based flood forecasting in the Greater Horn of Africa

Abstract. Every year Africa is hit by extreme floods which, combined with high levels of vulnerability and increasing population exposure, often result in humanitarian crises and population displacement. Impact-based forecasting and early warning for natural hazards is recognized as a step forward in disaster risk reduction, thanks to its focus on people, livelihoods, and assets at risk. Yet, the majority of the African population is not covered by any sort of early warning system. This article describes the setup and the methodological approach of Flood-PROOFS East Africa, an impact-based riverine flood forecasting and early warning system for the Greater Horn of Africa (GHA), with a forecast range of 5 d. The system is based on a modeling cascade relying on distributed hydrological simulations forced by ensemble weather forecasts, link to inundation maps for specific return period, and application of a risk assessment framework to estimate population and assets exposed to upcoming floods. The system is operational and supports the African Union Commission and the Disaster Operation Center of the Intergovernmental Authority on Development (IGAD) in the daily monitoring and early warning from hydro-meteorological disasters in eastern Africa. Results show a first evaluation of the hydrological reanalysis at 78 river gauging stations and a semi-quantitative assessment of the impact forecasts for the catastrophic floods in Sudan and in the Nile River basin in summer 2020. More extensive quantitative evaluation of the system performance is envisaged to provide its users with information on the model reliability in forecasting extreme events and their impacts.

Projected increase in widespread riverine floods in India under a warming climate

Widespread floods simultaneously affect several subbasins in a large river basin and pose severe challenges to disaster management and flood mitigation efforts. India is highly susceptible to widespread flooding as the country receives more than 70% of annual rainfall in the four-month period from June to September. However, the potential impacts of climate change on the drivers and frequency of widespread floods in India remain unexplored. We use observations, climate model simulations, and a hydrological model to examine the drivers and changes in widespread floods under a projected future climate. The observed increase in extreme precipitation does not translate to flooding in most river basins in India due to its fragmented nature. Translation of extreme precipitation to flood depends on the catchment area. We examined the changes in the widespread floods in Indian river basins using a hydrological model and climate projections. Robust increases in intense precipitation area and frequency are projected under the warming climate. Furthermore, hydrological model simulations indicate a profound increase in widespread floods in Indian river basins under a warming climate. The increase in widespread floods in a warming climate can pose challenges for flood mitigation and management in the future. Consideration of spatial extent of extreme precipitation and catchment area can enhance flood monitoring and prediction efforts. In addition, increases in extreme precipitation may not always lead to rise in flooding.

Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Accurate rainfall-runoff (RR) modeling is crucial for effective Mekong River Basin (MRB) water resource management. Satellite precipitation products (SPPs) can offer valuable data for such modeling; however, these products often exhibit biases that may adversely affect hydrological simulations. This study aimed to improve RR modeling using bias-corrected SPPs and the Soil and Water Assessment Tool (SWAT) model for MRB. A convolutional neural network-based deep learning framework was employed to correct biases in four SPPs (TRMM, PERSIANN-CDR, CHIRPS, and CMORPH), resulting in four respective bias-corrected SPPs (ADJ_TRMM, ADJ_CDR, ADJ_CHIR, and ADJ_CMOR). The bias-corrected products were compared against a gauge-based dataset in terms of rainfall analysis, and their performance within the SWAT model was assessed over calibration (2004–2013) and validation (2014–2015). Bias-corrected products demonstrated superior performance in rainfall analysis, with ADJ_TRMM outperforming other products. The SWAT model calibration results showed satisfactory performance across all stations, with a Nash-Sutcliffe Efficiency (NSE) ranging from [0.76–0.87]. Integrating bias-corrected SPPs into the SWAT model significantly increased the RR simulations in the MRB, indicated by higher NSE values [0.72–0.85] compared to uncorrected SPPs [-0.37 to 0.85] at the Kratie station. Besides, the inconsistent performance of bias-corrected products between rainfall analysis and RR modeling was observed, with ADJ_CDR outperforming ADJ_TRMM in the SWAT model. These results highlight the significance of using bias-corrected SPPs in hydrological modeling applications, especially in areas with limited ground-based precipitation data, and highlight the need for further research to refine bias correction methods and address the limitations of the SWAT model.

Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP

Hydrological simulations have seen extensive use of machine learning (ML) models. However, the existing ML models face challenges in effectively handling temporal and spatial heterogeneity of the driving features and transparency of features. To improve the runoff prediction ability of ML and the confusion of runoff generation mechanism interpreted by ML, this study combined K-means with XGBoost (Extreme gradient boosting,) and SHAP (Shapely additive explanations,) to develop an interpretable ML-based hydrological model (KXGBoost) across the Continental United States as a case study. The results show that K-means clustering based on the interpretation of SHAP can effectively capture the temporal and spatial heterogeneity of runoff-driven features. And the performance and interpretability of data-driven hydrological models in runoff simulation can be significantly improved by KXGBoost. KXGBoost yields an NSE (Nash-Sutcliffe Efficiency) of 0.803 and 0.596 during the training and testing, respectively, representing an improvement of 0.089 and 0.029 as compared to the XGBoost model. KXGBoost has demonstrated its ability to identify multiple runoff generation mechanisms under multiple spatio-temporal perspectives. This study provided a novel perspective for understanding hydrological processes and improving runoff simulation and demonstrates the potential of ML-based hydrological models in water resources management.

A Scientometric Review for Uncertainties in Integrated Simulation–Optimization Modeling System

Water resources management is a challenging task caused by huge uncertainties and complexities in hydrological processes and human activities. Over the last three decades, various scholars have carried out the study on hydrological simulation under complex conditions and quantitatively characterized the associated uncertainties for water resources systems. To keep abreast of the development of the collective knowledge in this field, a scientometric review and metasynthesis of the existing uncertainty analysis research for supporting hydrological modeling and water resources management has been conducted. A total of 2020 publications from 1991 to 2018 were acquired from the Web of Science. The scientific structure, cooperation, and frontiers of the related domain were explored using the science mapping software CiteSpace V5.4.R3. Through co–citation, collaboration, and co–occurrence network study, the results present the leading contributors among all countries and hotspots in the research domain. In addition, synthetical uncertainty management for hydrological models and water resource systems under climatic and land use change will continue to be focused on. This study comprehensively evaluates various aspects of uncertainty analysis in hydrologic simulation–optimization systems, showcasing advanced data analysis and artificial intelligence technologies. It focuses on current research frontiers, aiding decision–makers in better understanding and managing the complexity and uncertainties of water resource systems, thereby enhancing the sustainability and efficiency of responses to environmental changes.

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.