Journal of Hydrology-Regional Studies最新文献

{"title":"Improving trans-regional hydrological modelling by combining LSTM with big hydrological data","authors":"Senlin Tang ,&nbsp;Fubao Sun ,&nbsp;Qiang Zhang ,&nbsp;Vijay P. Singh ,&nbsp;Yao Feng","doi":"10.1016/j.ejrh.2025.102257","DOIUrl":"10.1016/j.ejrh.2025.102257","url":null,"abstract":"<div><h3>Study region</h3><div>Lancang-Mekong River Basin (LMRB), Brazil.</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrological insights for the region</h3><div>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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102257"},"PeriodicalIF":4.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445726","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}

{"title":"Evaluation of the performance of ERA5, ERA5-Land and MERRA-2 reanalysis to estimate snow depth over a mountainous semi-arid region in Iran","authors":"Faezehsadat Majidi,&nbsp;Samaneh Sabetghadam,&nbsp;Maryam Gharaylou,&nbsp;Reza Rezaian","doi":"10.1016/j.ejrh.2025.102246","DOIUrl":"10.1016/j.ejrh.2025.102246","url":null,"abstract":"<div><h3>Study region</h3><div>Mountainous semi-arid region, Iran.</div></div><div><h3>Study focus</h3><div>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).</div></div><div><h3>New hydrological insights for the region</h3><div>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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102246"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428070","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}

{"title":"Adaptive rolling runoff forecasting model: Combining multi-source correlated sequences and extreme value encoding","authors":"Tao Wang ,&nbsp;Jingzhe Liu ,&nbsp;Yongming Cheng ,&nbsp;Jingjing Duan ,&nbsp;Yifei Zhao ,&nbsp;Jing Zhao ,&nbsp;Peiling Wang ,&nbsp;Jiaqi Zhai","doi":"10.1016/j.ejrh.2025.102241","DOIUrl":"10.1016/j.ejrh.2025.102241","url":null,"abstract":"<div><h3>Study region</h3><div>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.</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrological insights for the region</h3><div>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 &gt; 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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102241"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428154","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}

{"title":"Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer","authors":"El Bouazzaoui Imane ,&nbsp;Ait Elbaz Aicha ,&nbsp;Ait Brahim Yassine ,&nbsp;Machay Hicham ,&nbsp;Bougadir Blaid","doi":"10.1016/j.ejrh.2025.102249","DOIUrl":"10.1016/j.ejrh.2025.102249","url":null,"abstract":"<div><h3>Study region</h3><div>The Haouz aquifer, situated in central Morocco, a data-scarce region.</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New Hydrological Insights for the Region</h3><div>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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102249"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428069","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}

{"title":"Modeling framework for coordinated operation of series-parallel reservoir groups considering storage-discharge regulation mechanisms","authors":"Jing Huang ,&nbsp;Chao Tan ,&nbsp;Xiaohong Chen ,&nbsp;Jiqing Li ,&nbsp;Bikui Zhao ,&nbsp;Xiongpeng Tang ,&nbsp;Yu Li ,&nbsp;Chao Gao","doi":"10.1016/j.ejrh.2025.102245","DOIUrl":"10.1016/j.ejrh.2025.102245","url":null,"abstract":"<div><h3>Study region</h3><div>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.</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrological insights for the region</h3><div>When encountering floods with design frequency <em>P</em> ≥ 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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102245"},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437194","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}

{"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 ,&nbsp;Jinhang Li ,&nbsp;Mengdie Zhao ,&nbsp;Hui Guo ,&nbsp;Chunying Wang ,&nbsp;Huiping Huang ,&nbsp;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}

{"title":"Nitrate source and transformation processes in river water and groundwater in seasonal freezing and thawing region","authors":"Xiaole Kong ,&nbsp;Yan-jun Shen ,&nbsp;Bo Cao","doi":"10.1016/j.ejrh.2025.102254","DOIUrl":"10.1016/j.ejrh.2025.102254","url":null,"abstract":"<div><h3>Study region</h3><div>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.</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrological insights for the region</h3><div>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&amp;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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102254"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428068","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}

{"title":"Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis","authors":"Zhan Xie ,&nbsp;Weiting Liu ,&nbsp;Si Chen ,&nbsp;Rongwen Yao ,&nbsp;Chang Yang ,&nbsp;Xingjun Zhang ,&nbsp;Junyi Li ,&nbsp;Yangshuang Wang ,&nbsp;Yunhui Zhang","doi":"10.1016/j.ejrh.2025.102227","DOIUrl":"10.1016/j.ejrh.2025.102227","url":null,"abstract":"<div><h3>Study region</h3><div>The study area is located in the urban area of Chongqing City, the largest metropolis in southwestern China.</div></div><div><h3>Study focus</h3><div>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.</div></div><div><h3>New hydrological insights for the region</h3><div>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.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102227"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422183","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}

{"title":"Assessing the impacts of extreme precipitation projections on Haihe Basin hydrology using an enhanced SWAT model","authors":"Lili Tan ,&nbsp;Junyu Qi ,&nbsp;Gary W. Marek ,&nbsp;Xueliang Zhang ,&nbsp;Jianing Ge ,&nbsp;Danfeng Sun ,&nbsp;Baogui Li ,&nbsp;Puyu Feng ,&nbsp;De Li Liu ,&nbsp;Baoguo Li ,&nbsp;Raghavan Srinivasan ,&nbsp;Yong Chen","doi":"10.1016/j.ejrh.2025.102235","DOIUrl":"10.1016/j.ejrh.2025.102235","url":null,"abstract":"<div><h3>Study region</h3><div>Haihe Basin (HB), North China.</div></div><div><h3>Study focus</h3><div>Studying the impact of extreme precipitation on watershed hydrological factors plays a crucial role in water resource management, climate adaptation, and disaster resilience. An improved Soil and Water Assessment Tool (SWAT) was employed to assess the impact of extreme precipitation indices (EPIs) on temporal and spatial variations in hydrological factors in the HB, China. Five EPIs were identified in this study, including R10 (moderate rain), R20 (heavy rain), R50 (torrential rain), R95p (95th percentile of precipitation), and R99p (99th percentile of precipitation).</div></div><div><h3>New hydrological insights for the region</h3><div>The EPIs with the greatest contribution rates to precipitation, water yield, and percolation in the historical period were R20 (32.1 %), R50 (14.3 %), and R20 (29.0 %), respectively, for the entire basin. During the historical period, there were more occurrences of extreme precipitation events in the plain area compared to the mountainous area. In the plain area, rainfall was beneficial for replenishing groundwater when daily precipitation exceeded 50 mm. Over the entire future period (2041–2100), R50 contributed the greatest water yield (18.4 %) and percolation (36.3 %) in the HB. Furthermore, the number of days with rainfall from 20 to 50 mm d⁻¹ and those exceeding 50 mm d⁻¹ increased in the future period relative to the historical period. The results of this study provide a reference for understanding the spatiotemporal distribution pattern of extreme precipitation in the HB and for relevant departments to formulate response strategies.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102235"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422182","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}

{"title":"Impact of the potential evapotranspiration models on drought monitoring","authors":"Weiqi Liu ,&nbsp;Shaoxiu Ma ,&nbsp;Haiyang Xi ,&nbsp;Linhao Liang ,&nbsp;Kun Feng ,&nbsp;Atsushi Tsunekawa","doi":"10.1016/j.ejrh.2025.102236","DOIUrl":"10.1016/j.ejrh.2025.102236","url":null,"abstract":"<div><h3>Study region</h3><div>China</div></div><div><h3>Study focus</h3><div>Potential evapotranspiration (PET) refers to the atmosphere evaporative demand, is a main factor in drought monitoring and prediction. Although over 100 PET models are available, the impact of the model choice on drought monitoring remains unclear. Thus, in this study, 35 typical PET models were selected, aiming to quantify the uncertainty of PET estimation and drought monitoring due to the PET model choice.</div></div><div><h3>New hydrological insights for the region</h3><div>The PET estimated by different PET models were different significantly, with the uncertainty (expressed as standard deviation) of the PET values ranging from 132 to 651 mm/yr. In the arid and humid regions, the PET trends estimated by some models might be opposite. These differences further contributed to the increased uncertainty in drought monitoring. In the arid and semi-arid regions, the meteorological drought (SPEI) and agricultural drought (PDSI) trends, characteristics, and area monitored by some PET models were significantly too high or too low, which resulted in a maximum difference of 2.1-fold in the meteorological drought severity and 2.2-fold in the agricultural drought severity. Drought characteristics monitored by models based on temperature and mass-transfer were always more severe. This study quantified the uncertainty in PET estimation and drought monitoring arising from the PET model choice, emphasizing the importance of selecting a suitable PET model in reducing uncertainty in drought monitoring.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"58 ","pages":"Article 102236"},"PeriodicalIF":4.7,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422184","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}