Hydrological Simulation Research Articles

Climate change impacts on the Chiffa basin (northern Algeria) using bias-corrected RCM data

IntroductionThis study aims to assess the efficacy of Quantile mapping (QM) and Delta change (DC) bias correction methods to improve hydrological simulations of the Chiffa basin in northern Algeria. The main issue addressed is the need for corrected climate data to provide reliable hydrological projections in semi-arid climates.MethodsHydrological simulations were conducted using the GR2M conceptual rainfall-runoff model, recognized for its robustness in Mediterranean climates. This model was coupled with precipitation simulations from the Rossby Centre regional atmospheric model RCA4 of the Coordinated Regional Climate Downscaling Experiment (Cordex-Africa) forced by two global circulation models (MPI-ESM-LR and CRNM-CM5). Hydrological projections were produced for the future period 20702099 under RCP 4.5 and RCP 8.5 scenarios, comparing raw and bias-corrected data.Results and discussionThe findings indicate that raw precipitation data are inadequate for reflecting future rainfall trends and simulating future flows. Bias correction methods significantly improved the models performance, with the coefficient of determination (R2) increasing from 0.440.53 to 0.830.97. Additionally, regional climate models project a 5 to 8% decrease in annual flows by the end of the 21st century under RCP 4.5 and RCP 8.5 scenarios. These results highlight the importance of bias correction methods for hydrological impact studies, and we recommend implementing specific adaptation measures, such as improved irrigation efficiency, development of water storage infrastructure, and adoption of drought-resistant agricultural practices. Future research should focus on employing multivariate bias correction methods, utilizing higher-resolution climate data (≤10 km), and implementing ensemble modeling approaches to better characterize uncertainties.

Investigating Spatial Heterogeneity of Karst Water Storage Capacity and Nonclosure of Underground Watersheds in Karst Hydrological Simulation

ABSTRACTKarst landforms interfere with the runoff generation and confluence process, resulting in generally poor hydrological simulation accuracy in karst watersheds. We proposed a new karst hydrological module, which has two cores. One is the karst water storage capacity distribution curve that represents the distribution of runoff generation thresholds in karst areas, and the other is the underground nonclosure coefficient that represents the nonclosure phenomenon of underground watersheds in karst areas. The new module was further coupled with the Xinanjiang rainfall–runoff (XAJ) model to establish a complete hydrological model for karst areas (referred to as XAJ‐karst model). The sensitivity of the XAJ‐karst model parameters was analysed using the Sobol method, and applied to a typical karst watershed in Guizhou Province, China, to test the model performance on daily and hourly time scales. In addition, we also explored the impact of dynamic changes in the nonclosure coefficient of underground watershed area in karst watersheds on model results. Results showed that the average value of Kling–Gupta efficiency (KGE) of the XAJ‐karst model on the daily and hourly time scales was 0.85 and 0.77, respectively. In comparison with the XAJ model, the average KGE value of the XAJ‐karst model on both daily and hourly scales improved by 10.8% and 6.4%, respectively, demonstrating better simulation accuracy. In addition, there is a underground nonclosure phenomenon in the Xiangyang watershed, and the actual area of underground watershed expands abruptly as the antecedent‐precipitation increases to the critical value. Moreover, the water storage and hysteresis effects of the karst landform result in a certain hysteresis in water exchange between the underground watershed and adjacent watersheds.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

The Analysis and the Impact of Surface Temperature Anomalies on the Health of Residents in the River Niger Basin Development Authority Area, West Africa.

This study investigates the impact of surface temperature anomalies on the health of residents within the River Niger Basin Development Authority (RIBDA) enclave, which covers Nigeria, Niger, and Mali in West Africa, with a focus on the regional implications for public health. Historical climate data from 1985 to 2014, sourced from the Climatic Research Unit Time-Series, Version 3.22 (CRU TS 3.22), was analyzed to comprehend past climate patterns and establish a baseline for future comparisons. Predictions for future climate conditions (2015-2044) were derived by adjusting the CRU data using temperature projections from the Community Climate System Model 4 under the Representative Concentration Pathway 8.5 scenario. To assess the potential impacts of these climate changes, particularly during the boreal summer season of July-August-September (JAS), the study utilized the Hydrology, Entomology, and Malaria Transmission Simulator (HYDREMATS). Findings indicate that surface temperature can intricately influence disease transmission, with varied effects on parameters such as Ro, EIR, prevalence, and immunity index. Observations revealed fluctuations in temperature anomalies over the years, with negative anomalies in 1991-1995 and positive anomalies in subsequent years. Although precise predictions for 2016-2044 are challenging based solely on data trends from 1985 to 2015, continued temperature rises could potentially lead to increased disease prevalence and decreased immunity index. Moreover, the analysis identified a notable temporal increase in mean annual temperature and mean annual maximum temperature from 1999 to 2020, suggesting a faster warming trend in maximum temperatures compared to minimum temperatures. This increase in temperature variability may alter the onset and cessation dates of the rainy season, affecting water availability, accessibility, and consumption, consequently fostering conditions conducive to health-related diseases. By incorporating predicted long-term temperature changes due to greenhouse gas emissions while maintaining current inter-annual climate patterns, this approach allows researchers to anticipate potential future health implications in the studied regions.

Construction and verification of distributed hydrothermal coupling model in the source area of the Yangtze River

Study regionThe source area of the Yangtze River, a typical catchment in the cryosphere on the Tibet Plateau, was used to develop and validate a distributed hydrothermal coupling model. Study focusClimate change has caused significant changes in hydrological processes in the cryosphere, and related research has become hot topic. The source area of the Yangtze River (SAYR) is a key catchment for studies of hydrological processes in the cryosphere, which contains widespread glacier, snow, and permafrost. However, the current hydrological modeling of the SAYR rarely depicts the process of glacier/snow and permafrost runoff from the perspective of coupled water and heat transfer, resulting in distortion of simulations of hydrological processes. Therefore, we developed a distributed hydrothermal coupling model, namely WEP-SAYR, based on the WEP-L (Water and energy transfer process in large river basins) model by introducing modules for glacier and snow melt and permafrost freezing and thawing. New hydrological insights for the regionIn the WEP-SAYR model, the soil hydrothermal transfer equations were improved, and a freezing point equation for permafrost was introduced. In addition, the glacier and snow meltwater processes were described using the temperature index model. Compared to previously applied models, the WEP-SAYR portrays in more detail glacier/snow melting, dynamic changes in permafrost water and heat coupling, and runoff dynamics, with physically meaningful and easily accessible model parameters. The model can describe the soil temperature and moisture changes in soil layers at different depths from 0 to 140cm. Moreover, the model has a good accuracy in simulating the daily/monthly runoff and evaporation. The Nash-Sutcliffe efficiency exceeded 0.75, and the relative error was controlled within ±20%. The results showed that the WEP-SAYR model balances the efficiency of hydrological simulation in large scale catchments and the accurate portrayal of the cryosphere elements, which provides a reference for hydrological analysis of other catchments in the cryosphere.

Setting priorities for floods mitigation through forest restoration: The threshold elevation hypothesis

Nature based solutions (NbS) for flood regulation (e.g., forest restoration) need to be informed by the analysis of climate change and land-use/cover change (LUCC) effects on floods, but these effects are still poorly understood. In this study, it is hypothesized that effects of climate change and LUCC on floods exhibit an abrupt change at a threshold elevation with implications for forest restoration. The study was carried out in the Tena watershed located in Ecuadorian Amazon. Hydrological simulations were run using TETIS model for different climate and LUCC scenarios. Projected precipitation from the Global Climate Models (GCMs) under the SSP5-8.5 scenario of CMIP6 was assessed. Isolated and combined effects of climate change and LUCC on floods across an altitudinal gradient were analyzed at 42 flow sites. Obtained results confirm the hypothesis showing the existence of a threshold elevation at 590 m a.s.l., where abrupt changes on floods occurred. The effects of LUCC prevailed over the effects of climate change in the upper basin, while in the lower basin, effects of climate change prevailed, especially for small and medium flood events. The results suggest that native forest is priority in the area above the threshold elevation, informing restoration as a NbS for flood regulation in humid tropical basins in a context of climate change.

A comprehensive approach to building a continuous hydrologic model with Soil Moisture Accounting using Earth Observation data

ABSTRACT In recent years, both availability and interest in Earth Observations (EO) have increased due to their ability to provide information with extensive spatial coverage, which is valuable for data-scarce regions. This study provides a roadmap to exploit EO/satellite data (EO-based model) for continuous hydrological simulation using the Hydrologic Engineering Center–Hydrologic Modelling System model with a soil moisture accounting (SMA) component. As a case study, we consider the Boeotikos Kephisos River basin, in Greece. The SMA component is calibrated using the HiHydroSoils dataset, to map its parameters to the regionally varying soil hydraulic properties and a comparison is made to an alternative parameterization, following the standard literature approach (literature-based model). The effectiveness of satellite data in enhancing model performance is further assessed, by comparing three different satellite precipitation datasets, as model drivers, and by using satellite-based soil moisture for model initialization. The discussion extends to the potential for integration of EO/satellite data at the operational level, by simulating a significant precipitation event. Yet, the most promising result pertains to the opportunity to exploit satellite-derived estimates of soil hydraulic properties to base the calibration of the data-intensive SMA scheme, with the EO-based model significantly outperforming the literature-based model parameterization.

Spatial Prediction Method for Digital Soil Mapping

The spatial distribution of soil information is the basic data required for ecological hydrological simulation, global change research, and resource and environmental management. Digital soil mapping (DSM) is an efficient way to obtain and express soil spatial distribution information. This paper introduces the theories and principles of DSM, compares the principles, characteristics, and applicable conditions of spatial prediction methods widely used in DSM, and proposes a selection strategy for spatial prediction methods for DSM, which provides a reference for the promotion of DSM.

Integrating machine learning and zoning-based techniques for bias correction in gridded precipitation data to improve hydrological estimation in the data-scarce region

Gridded precipitation products are valuable for hydrological assessments due to their wide spatial coverage and improved spatiotemporal resolution compared to the use of rain gauges. They have been extensively used in hydrology studies over the past decade. However, it’s important to recognize that these products come with inherent uncertainties, which can impact hydrological simulations.To address this limitation, our study introduces a novel bias correction technique that combines machine learning and zoning-based approaches. We applied this technique in a case study to correct bias in the gauge-based gridded precipitation product in a data-scarce region, such as the Mekong Basin. To evaluate the effectiveness of this proposed method, we compared it against conventional bias correction methods, including mean-based and distribution-based approaches, using various statistical indices and hydrological simulations.Our findings consistently reveal a bias pattern within the gridded precipitation product across the study area, which affects streamflow estimations. All correction methods show promise in reducing precipitation biases and improving streamflow predictions. Among these methods, one particularly promising approach involves integrating machine learning techniques and taking into account climate zones. This integrated approach has emerged as a superior strategy, offering significant advantages in terms of both correcting precipitation data and improving broader hydrological assessments.This investigation underscores the crucial role of this integrated method in optimizing hydrological simulations across diverse geographical areas, especially in regions where data availability is limited.

Comprehensive evaluation of nine evapotranspiration products from remote sensing, gauge upscaling and land surface model over China.

Benefiting from the advancements in monitoring and measuring terrestrial evapotranspiration (ET), diverse ET products have been proliferated. This study evaluated nine ET products from three types, namely remote sensing-based retrievals (GLEAM, PML and PLSH), gauge-based upscaling (FCCRU, FCGSW and FCWFD) and land surface model-based reanalysis (ERA5-Land, GLDAS and MERRA) over China and its seven climate zones. Both spatial and temporal change trends in ET were investigated, and period feature were analyzed. Then, in-situ ET observations were used for validating the performances of ET products. The results demonstrate that all products show comparable performances in spatial distribution over China, but the mean ET values present evident discrepancies (433-563 mm/a). Among them, reanalysis ET products reproduce higher ET, but with less difference. In terms of climate sub-regions, the most significant discrepancies are located in QT. In addition, PLSH, MERRA and GLDAS present substantial increasing trends, while all three gauge-based upscaling ET products display decreasing trends. Regionally, all the ET products show positive trends in QT. Moreover, most of ET products present apparent periodic oscillation ranging from 2.0-5.5 year scales. At point scale, most ET products perform well at NMG and CBS sites (CC > 0.80, RMSE < 20 mm/month). However, general underestimations appear in northwestern China sites (HB and DX), and systematical overestimation exist in southern China sites (DHS and XSBN). By comparison, remote sensing-based ET products performs best, followed by gauge-based upscaling ET, comparatively, reanalysis-based ET products have poorest performances against in-situ ET observations. This study can provide valuable reference information for the selection of proper ET datasets for the hydrological simulation and analysis over China.

Regional-scale seasonal forecast of surface water availability in a semi-arid environment: The case of Ceará State in Northeast of Brazil

Study region:Ceará (Brazil). Study focus:Considerable intra- and inter-annual variability of rainfall in this semi-arid region lead to strong temporal variations in water availability. To store and supply water in times of water scarcity, tens of thousands of freshwater reservoirs have been built over time, most of which are unmonitored. Here, we develop a hydrological forecasting system for the entire state of Ceará which integrates satellite-based monitoring of reservoir water storage, bias-corrected seasonal weather forecasts and hydrological modeling of freshwater availability. We test and demonstrate the applicability of this system by conducting experiments with historic data, hindcasts and forecasts. New hydrological insights for the region:The assimilation of in-situ and Sentinel-1 based observations of reservoir fillings into the hydrological model WASA-SED proved to be feasible and an important step in the modeling of available water resources dynamics. Hydrological simulations for January to June from 1990–2019 based on meteorological observations resulted in a median average NRMSE between observed and modeled reservoir fillings of strategic reservoirs of 29.51%. The comparison of observed and predicted precipitation from two different seasonal forecasting systems were in the same order of magnitude (i.e. 19.51% and 24.52%). Hindcast experiments suggested the superposition of uncertainties of different model components. Efforts are currently being made to further test and improve the developed integrated framework as part of the operational service.

Hydrological Simulation and Parameter Optimization Based on the Distributed Xin’anjiang Model and the Particle Swarm Optimization Algorithm: A Case Study of Xunhe Watershed in Shandong, China

Hydrological models serve as essential tools in hydrological research, allowing us to address practical hydrological issues. This study focuses on the Xunhe Watershed in Shandong Province, China, constructing a distributed Xin’anjiang hydrological model. Furthermore, traditional manual calibration and automatic calibration using the Particle Swarm Optimization (PSO) algorithm were employed to determine model parameters, followed by hydrological simulations, with the aim of investigating the applicability of the distributed Xin’anjiang model in this watershed. The research findings indicate that the distributed Xin’anjiang model accurately simulates the hydrological processes in the Xunhe Watershed. There is a high level of agreement between the observed data and the simulated results, including key indicators such as peak discharge, runoff volume, and peak time. After optimizing the model parameters using the PSO algorithm, the distributed Xin’anjiang model demonstrates improved simulation performance in the Xunhe Watershed. During the calibration period, the mean relative peak discharge error (RPE) is 4.1%, the mean relative runoff error (RRE) is 4.34%, and the average Nash–Sutcliffe efficiency (NSE) for simulating the flood events is 0.89. During the validation period, the mean RPE is 3.82%, the mean RRE is 6.1%, and the average NSE for the process is 0.83. This indicates that the distributed Xin’anjiang model has good applicability in this watershed, providing a reliable reference for flood control and disaster reduction in the Xunhe Watershed.

Quantification of streamflow response to climate change and human activities within upstream mountainous areas of the Daqing River Basin, Northern China

The Daqinghe River Basin is located in the North China Plain. In recent years, however, climate warming, drying, and intense human activities have led to declining ecosystem functions and shrinking wetlands in the region. Understanding streamflow changes in the upstream mountainous areas of the Daqinghe River Basin in this changing environment and identifying the driving factors can provide a scientific basis for water resources management and optimization in these areas. This study focuses on the Beihedian River watershed, the Xidayang Reservoir watershed, and the Wangkuai Reservoir watershed in the upstream mountainous areas of the Daqinghe River. It is based on hydro-meteorological data collected between 1963 and 2019. The methods used in the study include the linear tendency estimation method, the non-parametric Mann-Kendall trend test, the elasticity coefficient method, and hydrological simulation methods. The results of this study suggest that the streamflow, precipitation, and potential evapotranspiration (PET) in the three watersheds showed an overall decreasing trend. The minimum precipitation decrease rate ranged from −1.09 to −0.55 mm/a, and the minimum streamflow decreasing rate at the Beihedian Hydrological Station was −1.32 mm/a, with a minimum range of 0–176.03 mm. Change-point analysis revealed that the streamflow in the Beihedian River and Xidayang Reservoir watersheds experienced a significant change point around 1999, with a significant level of α=0.05. As for the Wangkuai Reservoir watershed, a significant change point was observed around 1980, which is likely attributable to land system reforms and protective forest projects. The attribution analysis which combined both climate change and human activities using the elasticity coefficient method and hydrological simulation methods indicated that climate change contributed an average of 32.93%, 34.50%, and 35.12% to the reduction in streamflow in the three watersheds, respectively. Human activities accounted for an average contribution of 67.07%, 65.50%, and 64.88%, respectively. Water conservancy projects, afforestation, and other human activities were identified as the primary factors contributing to streamflow decreases.

Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer’s advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

Impacts of Different Satellite‐Based Precipitation Signature Errors on Hydrological Modeling Performance Across China

AbstractThe quasi‐global availability of satellite‐based precipitation products (SPPs) holds significant potential for improving hydrological modeling skill. However, limited knowledge exists concerning the impacts of different SPP error type on hydrological modeling skill and their sensitivity across different climate zones. In this study, forcing data sets from 10 SPPs were collected to drive hydrological models during the period 2001–2018 for 366 catchments across China. Here, we analyze the impact of the SPP errors associated with different precipitation intensities (light, moderate, and heavy) and different precipitation signatures (magnitude, variance, and occurrence) on the performance of hydrological simulations, and rank the sensitivities of SPPs errors for four major Köppen‐Geiger climate zones. The results show that heavy precipitation in SPPs is generally associated with higher errors than light and moderate precipitation when compared to gauge‐based precipitation observations, but hydrological model skill is more sensitive to errors from moderate precipitation than from heavy precipitation. The probability of moderate precipitation detection was identified as the most sensitive metric in determining hydrological model performance, with sensitivities of 0.58, 0.39, 0.59, and 0.47 in the temperate, boreal, arid, and highland climate zones, respectively. The variance error and magnitude error for heavy precipitation from SPPs were also identified as sensitive factors for hydrological modeling in the temperate and arid climate zones, respectively. These findings are crucial for enhancing the understanding of interactions between SPPs uncertainty and hydrological simulations, leading to improved data accuracy of precipitation forcing and the identification of appropriate SPPs for hydrological simulation in China.

Geostatistical Interpolation Approach for Improving Flood Simulation Within a Data‐Scarce Region in the Tibetan Plateau

ABSTRACTThe complex orography of the Tibetan plateau (TP) and the scarcity and uneven spatial distribution of meteorological stations present significant challenges in accurately estimating meteorological variables for hydrological simulations. This study aims to enhance the accuracy of daily precipitation and temperature interpolation for hydrological simulations in the Lhasa River Basin (LRB), particularly during flood events. We evaluate and compare the performance of deterministic Inverse Distance Weighting—IDW and geostatistical (Ordinary Kriging—OK and Kriging with External Drift—KED) interpolation methods for estimating precipitation and temperature patterns. Subsequently, we investigate the influence of different interpolation methods on hydrological simulations by using the interpolated meteorological data as input for the Water Balance Simulation Model (WaSiM) to simulate daily discharge in the LRB. Our results revealed that geostatistical methods, specifically OK and KED, are more effective in capturing the spatial variability and anisotropy inherent in precipitation patterns influenced by the Indian summer monsoons. In addition, the KED method effectively captured the daily variation of the temperature lapse rate, indicating the inadequacy of using a constant lapse rate for hydrological modelling in high‐elevation regions like the TP. The geostatistical technique outperformed the Deterministic method, with KED realising the best temperature and precipitation interpolation performance based on cross‐validation results. However, although KED provides superior results based on cross‐validation performance, applying its precipitation interpolation as input into WaSiM led to the poorest discharge simulation. The combination of OK for precipitation and KED for temperature produced the most accurate discharge simulations in the LRB, highlighting the importance of not solely relying on cross‐validation results but also considering the practical implications of interpolation methods on hydrological model outputs. Our study offers a robust framework for improving flood simulations and water resource management in a data‐scarce, high‐elevation region like the TP.

Quantifying Streamflow Prediction Uncertainty Through Process‐Aware Data‐Driven Models

ABSTRACTThe hydrological model simulation accompanied with uncertainty quantification helps enhance their overall reliability. Since uncertainty quantification including all the sources (input, model structure and parameter) is challenging, it is often limited to only addressing model parametric uncertainty, neglecting other uncertainty sources. This paper focuses on exploiting the potential of state‐of‐the‐art data‐driven models (or DDMs) in quantifying the prediction uncertainty of process‐based hydrological models. This is achieved by integrating the robust predictive ability of the DDMs with the process understanding ability of the hydrological models. The Bayesian‐based data assimilation (DA) technique is used to quantify uncertainty in process‐based hydrological models. This is accomplished by choosing two DDMs, random forest algorithm (RF) and support vector machine (SVM), which are distinctly integrated with two process‐based hydrological models: HBV and HyMOD. Particle filter algorithm (PF) is chosen for uncertainty quantification. All these combinations led to four different process‐aware DDMs: HBV‐PF‐RF, HBV‐PF‐SVM, HyMOD‐PF‐RF and HyMOD‐PF‐SVM. The assessment of these models on the Baitarani, Beas and Sunkoshi river basins exemplified an improvement in the accuracy of the daily streamflow simulations with a reduction in the prediction uncertainty across all the models for all the basins. For example, the nash‐sutcliffe efficiency improved by 54.69% and 10.61% in calibration and validation of the Baitarani river basin, respectively. Equivalently, average bandwidth improved by 79.37% and 71.59%, respectively. This signified the (a) potential of the DDMs in quantifying and reducing the prediction uncertainty of the hydrological model simulations, (b) transferability of the model with an appreciable performance irrespective of the choice of basins having varying topography and climatology and (c) ability to perform significantly irrespective of different process‐based and DDMs being involved, thereby ensuring generalizability. Thus, the framework is expected to assist in effective decision‐making, including various environmental management and disaster preparedness.

Impact of Refined Boundary Conditions of Land Objects on Urban Hydrological Process Simulation

Urbanization has led to an increase in impervious areas and, consequently, an increase in the surface runoff volume and runoff rate. This has exacerbated urban flooding and highlighted the importance of modeling urban hydrological processes. The Waterview Community of Hangzhou City (WCHC) was taken as the study area, and three scenarios were developed: the original scenario, the rough description scenario, and the fine description scenario. The urban hydrological processes were simulated through a coupled model incorporating actual measurements and four design precipitation events (1-year, 5-year, 10-year, and 20-year return periods). The results show the following: (1) The refined depiction scenario has the highest accuracy in terms of measured precipitation, with an average error of 0.54 cm. (2) During different precipitation return periods, the refined depiction scenario shows the smallest range of accumulated water, with a more realistic distribution. On average, it differed from the original scenario by 21.45% and from the rough depiction scenario by 32.18%. (3) The simulation results after the refinement of the feature boundaries are more reasonable in terms of the flow rate and flow direction, indicating that the simulation results have better dynamics. The results showed that refined boundary conditions improved the accuracy and dynamics of urban hydrological simulations, especially in terms of their reflection of actual water accumulation under varying precipitation conditions.

Block-level spatial integration of population density, social vulnerability, and heavy precipitation reveals intensified urban flooding risk

Under the context of global warming and rapid urbanization, cities worldwide confront the pressing problem of urban waterlogging, hindering progress towards Sustainable Development Goals. Effective planning and mitigation of urban flooding require a comprehensive understanding of the spatial and temporal patterns of rainfall and risk heterogeneity. However, evaluating urban water-logging risk is challenged by the need for city-scale hydrological simulation and generally lacks comprehensive metrics integrating fine-scale datasets. To address these gaps, we developed a simulation method for urban flood hazards by integrating hydrological models and Random Forest algorithms. We then took Shenzhen, a megacity in China, as a case study, and investigated the spatial patterns of urban flooding risk and its determinants at the block level based on the risk assessment framework represented by Hazards-Exposure-Vulnerability (H-E-V) dimensions. We found that socio-economic indicators exhibited spatial clustering, while hazard-related indicators displayed more dispersed patterns. High-risk areas exhibited a highly heterogeneous spatial pattern, predominantly influenced by vulnerability and exposure factors, as well as the spatial mismatch among the three dimensions. Our results emphasize the importance of integrating spatial heterogeneity of exposure and vulnerability into climate adaptation resource allocation, addressing both current and future demands for effective climate mitigation.

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.