Journal of Hydrology-Regional Studies最新文献_第5页

Significant differences in terrestrial water storage estimated by four common methods

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-20 DOI: 10.1016/j.ejrh.2025.102238

Anqi Niu , Long Sun , Ranhao Sun , Liding Chen

Study region

The Yellow River headwaters, located in the northeastern part of the Tibetan Plateau.

Study focus

Terrestrial water storage can be estimated by multiple approaches. However, the limited quantification of these methods regarding terrestrial water storage stocks limits the assessment of their applicability. Here, we quantified and compared water storage and its spatial patterns by four common methods: SWAT, InVEST, WB (based on water balance theory), and RSI (remotely sensed inversion).

New hydrological insights for the region

The results showed that SWAT, InVEST, and WB captured remarkable spatial heterogeneity of water storage, with CV (coefficient of variation) being 57.8 %, 41.2 %, and 85.2 %, respectively, whereas the CV of RSI was only 12.5 %, with WB exhibited the most spatial heterogeneity. RSI showed a pronounced distinct spatial pattern compared to the other three methods. Precipitation and NDVI (p < 0.01) are the common main drivers for all methods except RSI. The discrepancies in water storage can be attributed to the differences in models or methods response to influencing factors, e.g., the effects of topography and land use on water storage are considered to varying degrees. The biases or errors in the average water storage caused by different methods across various years range from 90.3 mm to 136.3 mm. Consequently, it is critical to consider the applicability of the methodology, especially considering different climatic, land use, soil, and topographic environments.

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引用次数: 0

Rainfall-runoff generation patterns and key influencing factors in the plain of the Taihu Lake Basin, China

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-19 DOI: 10.1016/j.ejrh.2025.102247

Pingnan Zhang , Gang Chen , Chuanhai Wang , Pengxuan Zhao , Lanlan Li , Jingyi Cao , Youlin Li

Study Region

The two field runoff experimental sites, Baitaqiao and Wangmuguan, are located in the plains of the Taihu Basin, China.

Study Focus

This study investigates the hydrological cycle mechanisms in the Taihu Basin plains, aiming to support the development of next-generation, physically-based, refined hydrological models. Two experimental sites were established to comprehensively monitor hydrometeorological variables, including rainfall, evaporation, groundwater depth, soil water content, outlet flow, and other hydrological and meteorological factors. The study analyzes runoff processes and patterns under varying rainfall amounts and intensities, and examines the impacts of rainfall, groundwater depth, and micro-topography on runoff generation. Additionally, existing Taihu Basin models were used to simulate the rainfall-runoff process, with a focus on model errors and the significant role of rainfall-runoff patterns and micro-topography in the hydrological cycle.

New Hydrological Insights for the Region

This study provides a comprehensive clarification of the parameters for the saturation-excess runoff model in humid agricultural plains, offering valuable references for the calibration of hydrological models in similar regions. While the saturation-excess runoff model predominates in the Taihu Basin, the research also identifies the occurrence of infiltration-excess and mixed runoff mechanisms under specific conditions, thus highlighting the limitations of relying solely on the saturation-excess runoff model. Furthermore, the study demonstrates the significant impacts of rainfall amount, rainfall intensity, groundwater depth, and microtopography on rainfall-runoff processes. Through a mechanistic analysis of these factors, the findings provide a theoretical foundation for the refinement of physically based, fine-scale hydrological cycle models, advancing the understanding of hydrological processes and supporting future model development in plain areas.

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引用次数: 0

Improving trans-regional hydrological modelling by combining LSTM with big hydrological data

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-19 DOI: 10.1016/j.ejrh.2025.102257

Senlin Tang , Fubao Sun , Qiang Zhang , Vijay P. Singh , Yao Feng

Study region

Lancang-Mekong River Basin (LMRB), Brazil.

Study focus

Streamflow prediction in ungauged basins is a significant challenge in hydrology. This study investigates the transferability of deep learning models for hydrological simulations in ungauged basins, focusing on how constraints like catchment attributes, meteorological forcing, and Global Hydrological Models (GHMs) improve model performance when transferring knowledge from gauged to ungauged basins. We applied the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS-BR) dataset alongside GHMs and deep learning techniques to simulate hydrological processes in the LMRB.

New hydrological insights for the region

The results demonstrate that a post-processing scheme combining deep learning, meteorological data, and GHMs significantly improves model accuracy, achieving a median Nash-Sutcliffe Efficiency (NSE) of 0.64, compared to 0.50 for the baseline Long Short-Term Memory (LSTM) model without GHMs. Key factors influencing model performance include catchment attributes, climate variations, and the length of the modelling series. A notable finding is the importance of catchment attributes in defining hydrological similarity, which enhances model migration between regions with differing data availability. Cross-regional migration was particularly successful when hydrological similarities between the Amazon Basin and LMRB were evaluated, achieving an NSE of 0.86 at the Pakse hydrological station. These insights provide a novel modelling framework for hydrological simulations in data-scarce regions, emphasizing the role of physical mechanisms and hydrological similarities in improving model transferability.

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引用次数: 0

Evaluation of the performance of ERA5, ERA5-Land and MERRA-2 reanalysis to estimate snow depth over a mountainous semi-arid region in Iran

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-18 DOI: 10.1016/j.ejrh.2025.102246

Faezehsadat Majidi, Samaneh Sabetghadam, Maryam Gharaylou, Reza Rezaian

Study region

Mountainous semi-arid region, Iran.

Study focus

Snow is a critical component of the cryosphere, with significant seasonal and annual variability that impacts global water circulation and energy balance. While ground-based observations provide the most reliable snow depth (SND) data, their sparse distribution in remote regions necessitates the use of alternative datasets for monitoring snow depth. This study evaluates the ability of three reanalysis datasets—ECMWF's ERA5, ERA5-Land and the Modern-Era Retrospective Analysis (MERRA-2)—for estimating snow depth across Iran from 1980 to 2020. A comparison was conducted using SND data from synoptic stations within the study area. The evaluation was performed on both temporal and spatial scales, employing statistical indicators such as correlation coefficients, bias, and root mean square error (RMSE).

New hydrological insights for the region

This study provides critical new insights into the hydrology of the region, particularly in understanding the limitations of existing datasets in mountainous areas. Our findings indicate that all datasets can approximate observations, although their performance varies considerably across different regions. All datasets report maximum snow depth in the mountainous regions of Iran, particularly in the Alborz and Zagros Mountain ranges. Despite the higher correlation and lower RMSE of ERA5 and ERA5-Land compared to MERRA-2, all datasets exhibit common weaknesses in accurately estimating SND in complex terrains. The superior performance of ERA5-Land in this study can be attributed to its fine horizontal resolution, advanced data assimilation techniques and improved physical modeling, which enhance its ability to capture snow dynamics accurately. Additionally, the study highlights the challenges MERRA-2 faces in capturing snow depth in mountainous regions. Future research could benefit from integrating additional datasets and employing machine learning algorithms to improve snow depth assessments, as these approaches may reduce estimation uncertainties and enhance the understanding of snow dynamics across various regions, ultimately contributing to more reliable hydrological assessments.

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引用次数: 0

Adaptive rolling runoff forecasting model: Combining multi-source correlated sequences and extreme value encoding

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-18 DOI: 10.1016/j.ejrh.2025.102241

Tao Wang , Jingzhe Liu , Yongming Cheng , Jingjing Duan , Yifei Zhao , Jing Zhao , Peiling Wang , Jiaqi Zhai

Study region

The research subject of this study is the control watershed at the inlet cross-section of the Linjiacun Reservoir in the Baoji Gorge Irrigation Area, China.

Study focus

This study proposes MEN, a neural network integrating LSTM and CNN architectures to model multi-source runoff sequences and address extreme value challenges. By synergizing dynamic sequence refinement, Kruskal-Wallis sampling for extreme data imbalance, and gating-controlled extreme value encoding, MEN enhances both general runoff prediction and extreme event accuracy. The framework effectively captures long-term hydrological dependencies while mitigating uncertainty in complex forecasting scenarios.

New hydrological insights for the region

This study applies the MEN model to real-time runoff forecasting for the Linjiacun Reservoir inflow section in the Baoji Gorge Irrigation District, using historical reservoir runoff data and upstream rainfall data for model training. Compared to SARIMAX and LSTM benchmarks, MEN achieves the lowest average relative error and maintains R² > 0.8 across extended lead times, demonstrating robustness. By synergizing multi-source data learning and extreme value encoding, the framework offers enhanced technical support for real-time predictions in complex watersheds.

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引用次数: 0

Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-18 DOI: 10.1016/j.ejrh.2025.102249

El Bouazzaoui Imane , Ait Elbaz Aicha , Ait Brahim Yassine , Machay Hicham , Bougadir Blaid

Study region

The Haouz aquifer, situated in central Morocco, a data-scarce region.

Study focus

Groundwater resources in semi-arid regions face increasing threats from climate change, particularly due to warming and overexploitation. However, data scarcity limits the ability to monitor and predict groundwater changes accurately. This study addresses this challenge by predicting future drought conditions in the Haouz aquifer using SPI and SPEI climatic drought indices, Machine Learning models, and Med-CORDEX regional climate models under RCP 4.5 and 8.5 scenarios.

New Hydrological Insights for the Region

This study is the first in the region to predict groundwater drought based on precipitation and temperature data, relying on the principle of drought propagation. The comparative analysis of the machine learning models shows that Random Forest stands out for its superior predictive performance, influenced by annual trends and long-term climatic indices, with significant contributions from geographical variables. The results indicate a combined influence of land use and natural characteristics on the drought of the Haouz aquifer, following a longitudinal variation and showing a trend towards decreasing variability from the mid- to long-term. Additionally, extreme drought conditions are expected to intensify in most study points particularly under RCP 8.5. The eastern area of the aquifer remains the least impacted by this future trend, continuing to reflect normal drought conditions even in the long term under RCP 8.5.

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引用次数: 0

Modeling framework for coordinated operation of series-parallel reservoir groups considering storage-discharge regulation mechanisms

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-18 DOI: 10.1016/j.ejrh.2025.102245

Jing Huang , Chao Tan , Xiaohong Chen , Jiqing Li , Bikui Zhao , Xiongpeng Tang , Yu Li , Chao Gao

Study region

The core reservoir group of the main stream from the lower Jinsha River to the middle Yangtze River in China includes Wudongde, Baihetan, Xiluodu, Xiangjiaba cascade reservoirs (abbreviated as Jinxia Reservoir Group), and the Three Gorges Reservoir.

Study focus

A coordinated operational modeling framework that simultaneously performs multi-objective optimization and storage-discharge regulation, as well as a multi-level watershed generalization method for extracting operation objects, relationships, and periods, have been proposed.

New hydrological insights for the region

When encountering floods with design frequency P ≥ 1 %, the Jinxia Reservoir Group should prioritize synchronous regulation order, providing 37.27 × 10⁸ m³ storage capacity (24.06 % of the total flood control storage capacity) to the Three Gorges Reservoir for coordinated flood control pressure management in the middle and lower Yangtze River. Furthermore, several notable findings were obtained: (a) The response intensity of reservoirs to regulation mechanisms is positively correlated with storage capacity. (b) Differences in the regulation order can lead to differences of 1.3–2.6 times storage capacity consumption. The findings of this study contribute to the advancement of operational modeling theory and the development of refined coordinated operation schemes for reservoir groups.

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引用次数: 0

Analysis of rainfall abundance and drought occurrence and probability of flood and drought occurrence in Yellow River Basin based on Copula function family

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-17 DOI: 10.1016/j.ejrh.2025.102242

Yuping Han , Jinhang Li , Mengdie Zhao , Hui Guo , Chunying Wang , Huiping Huang , Runxiang Cao

Study region

The Yellow River Basin, China.

Study focus

Using Copula joint distribution models, this study delves into the analysis of wetness-dryness encounters and their evolving patterns in the all three reaches. The research specifically explores the probability of asynchronous and synchronous wetness-dryness encounters, providing valuable insights into the hydrological dynamics of the region. Additionally, the study constructs and simulates a Bayesian network model for flood and drought management based on the observed wetness-dryness patterns.

New hydrological insights for the region

The findings of this study reveal several noteworthy insights. Firstly, there is no significant trend in rainfall in the all three reaches, but periodic cycles of 5 years, 4 years, and 16 years are identified. Secondly, the probability of asynchronous wetness-dryness encounters is higher than synchronous encounters, with annual asynchronous probabilities of 54.46 %, 80.65 %, and 62.9 % in the upper, middle, and lower reaches, respectively. Thirdly, the overall probability of synchronous wetness-dryness encounters is relatively low, with concurrent dryness having the highest probability. Lastly, the study indicates an overall 50 % probability of floods and droughts in the Yellow River. The simulation results further highlight a 91 % probability of floods during wet years and an equal probability of droughts during dry years. These findings contribute to a theoretical foundation for optimizing and allocating water resources in the Yellow River Basin.

{"title":"Analysis of rainfall abundance and drought occurrence and probability of flood and drought occurrence in Yellow River Basin based on Copula function family","authors":"Yuping Han , Jinhang Li , Mengdie Zhao , Hui Guo , Chunying Wang , Huiping Huang , Runxiang Cao","doi":"10.1016/j.ejrh.2025.102242","DOIUrl":"10.1016/j.ejrh.2025.102242","url":null,"abstract":"<div><h3>Study region</h3><div>The Yellow River Basin, China.</div></div><div><h3>Study focus</h3><div>Using Copula joint distribution models, this study delves into the analysis of wetness-dryness encounters and their evolving patterns in the all three reaches. The research specifically explores the probability of asynchronous and synchronous wetness-dryness encounters, providing valuable insights into the hydrological dynamics of the region. Additionally, the study constructs and simulates a Bayesian network model for flood and drought management based on the observed wetness-dryness patterns.</div></div><div><h3>New hydrological insights for the region</h3><div>The findings of this study reveal several noteworthy insights. Firstly, there is no significant trend in rainfall in the all three reaches, but periodic cycles of 5 years, 4 years, and 16 years are identified. Secondly, the probability of asynchronous wetness-dryness encounters is higher than synchronous encounters, with annual asynchronous probabilities of 54.46 %, 80.65 %, and 62.9 % in the upper, middle, and lower reaches, respectively. Thirdly, the overall probability of synchronous wetness-dryness encounters is relatively low, with concurrent dryness having the highest probability. Lastly, the study indicates an overall 50 % probability of floods and droughts in the Yellow River. The simulation results further highlight a 91 % probability of floods during wet years and an equal probability of droughts during dry years. These findings contribute to a theoretical foundation for optimizing and allocating water resources in the Yellow River Basin.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102242"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nitrate source and transformation processes in river water and groundwater in seasonal freezing and thawing region

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-17 DOI: 10.1016/j.ejrh.2025.102254

Xiaole Kong , Yan-jun Shen , Bo Cao

Study region

The upper reaches of the Luan River Basin, located in the seasonal freezing and thawing regions, play a crucial role in the synergistic development of Beijing-Tianjin-Hebei.

Study focus

The intensification of human activities has led to the accumulation of nitrates in both river water and groundwater in the upper reaches of the Luan River. Clarifying the spatiotemporal characteristics and source of nitrate in river water and groundwater will be useful in developing seasonal nitrate protection strategies. Stable isotopes (δ¹⁵N-NO₃^- and δ¹⁸O-NO₃^-, δD-H₂O and δ¹⁸O-H₂O), water chemistry, and statistical analysis were employed to investigate nitrate sources and transformation processes in river water and groundwater.

New hydrological insights for the region

There were 3.44 % of river water samples and 26.44 % of groundwater samples exceeded the World Health Organization threshold for drinking water (50 mg/L). Nitrate pollution in river water was mainly concentrated during the freezing and thawing season, while in groundwater, it was predominantly concentrated during the rainy season and thawing period. The primary factors influencing nitrate levels in river water and groundwater were water chemistry and human activities, respectively. During the rainy season, nitrification was the predominant process contributing to nitrate levels in river water and groundwater, whereas denitrification processes were negligible. The mean contributions of manure and sewage (M&S) were highest in river water (51.9 %) and groundwater (71.6 %). Nitrate in precipitation (NP) and soil nitrogen (SN) constituted secondary sources for nitrate in river water and groundwater, with mean contributions of 22.5 % and 25.3 %, respectively. This study comprehensively investigates the impact mechanisms of freezing and thawing on the spatiotemporal patterns of nitrate in river water and groundwater. It refines the theoretical framework for nitrate migration and transformation in regional river and groundwater systems, thereby enhancing our understanding of the sources and processes of nitrate migration and transformation in areas with seasonal freezing and thawing.

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引用次数: 0

Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-17 DOI: 10.1016/j.ejrh.2025.102227

Zhan Xie , Weiting Liu , Si Chen , Rongwen Yao , Chang Yang , Xingjun Zhang , Junyi Li , Yangshuang Wang , Yunhui Zhang

Study region

The study area is located in the urban area of Chongqing City, the largest metropolis in southwestern China.

Study focus

Various hydrochemical processes and water quality prediction are unknown, hampering the sustainable development of metropolis. In this study, geochemical model, entropy-weighted water quality index (EWQI), and machine learning (ML) methods were applied to explore the hydrochemical processes and predict the groundwater quality for drinking purposes.

New hydrological insights for the region

The self-organizing map classifies the groundwater samples into 2 clusters. Cluster 1, predominantly located along ridge areas, exhibited HCO₃–Ca as the primary hydrochemical facie. Carbonate dissolution, cation exchange processes, and agricultural activities dominated the groundwater chemistry of Cluster 1. HCO₃–Ca and HCO₃–Na types were the dominant hydrochemical types of Cluster 2 in valley areas. Silicate weathering, cation exchange processes, and domestic sewage were the driving factors controlling the hydrochemistry of Cluster 2. EWQI results showed that 59.48 %, 31.90 % and 8.62 % of samples were excellent, good and medium for drinking, respectively. Four supervised machine learning methods were conducted to predict drinking water quality. Linear regression demonstrated the best correlation of 0.9999. The findings offer invaluable insights into groundwater suitability and evolution processes in a typical population density area and ensure a secure and sustainable domestic water supply worldwide.

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引用次数: 0