Journal of Hydrology-Regional Studies最新文献

{"title":"The effects of climate and geomorphology on bankfull conditions in subtropical Australian streams","authors":"Ryan Shojinaga ,&nbsp;David Hamilton ,&nbsp;Michele A. Burford ,&nbsp;Daniel J. Sobota","doi":"10.1016/j.ejrh.2025.102356","DOIUrl":"10.1016/j.ejrh.2025.102356","url":null,"abstract":"<div><h3>Study region</h3><div>Southeast Queensland (SEQ), Australia.</div></div><div><h3>Study focus</h3><div>Bankfull conditions are used to derive stream properties, such as bankfull discharge and interval of recurrence, that are essential for hydrologic modelling and channel restoration design. Bankfull conditions are well studied in temperature regions in the northern hemisphere, but elsewhere, data are sparse and bankfull applications are underdeveloped. We hypothesised that subtropical regions in the southern hemisphere, such as parts of eastern Australia, are different hydrologically to well-studied areas due to more variability in hydroclimate and different geophysical settings. We assessed how bankfull properties such as bankfull discharge and recurrence interval are affected by hydroclimatic and geomorphic contexts in subtropical areas in the southern hemisphere. We developed models to predict bankfull discharge based on catchment size while accounting for climatic and geomorphology. Additionally, we evaluated two analytical methods of bankfull stage identification compared to a qualitative, visual assessment.</div></div><div><h3>New hydrological insight for the region</h3><div>In general, we found that hydroclimate exerted little influence on bankfull properties in (SEQ). We also found that geomorphology significantly relates to recurrence intervals but was not an explanatory factor for bankfull discharge. Modelling of bankfull discharge demonstrated that including rainfall as a predictive variable did not substantially improve estimations, which climatic regionalization improved predictions in some areas, but not in others. Furthermore, one analytical method of bankfull identification, the bench index method, produced similar bankfull stage estimates to the qualitative bankfull identification. Overall, these results suggest bankfull discharge in rivers and streams of subtropical regions in the southern hemisphere is expressive of similar hydrologic characteristics as rivers and streams in temperate climates even with different geomorphological conditions. Our study also demonstrates the bench index method could provide an efficient and effective surrogate for identifying bankfull conditions.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102356"},"PeriodicalIF":4.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776282","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":"Water depth inversion based on ICESat-2 and Sentinel-2—A case study of Qiagui Co and Ayakekumu Lake on the Tibetan Plateau","authors":"Baojin Qiao ,&nbsp;Tianjiao Du ,&nbsp;Jianting Ju ,&nbsp;Liping Zhu","doi":"10.1016/j.ejrh.2025.102354","DOIUrl":"10.1016/j.ejrh.2025.102354","url":null,"abstract":"<div><h3>Study region</h3><div>Two lakes on the Tibetan Plateau.</div></div><div><h3>Study focus</h3><div>This study utilizes ICESat-2 data to extract water depth, integrates Sentinel-2 and various machine learning models to invert water depth, and evaluates the accuracy of these models, ensuring robust validation of the results. An improved OPTICS denoising algorithm was applied to extract lake water depths from the ICESat-2 data. The water depths from ICESat-2 and the reflectance data from Sentinel-2 were used to construct the water depth inversion with eight machine learning models, including Quadratic Polynomial, SVR, XGBoost, LightGBM, RF, MLP, Transformer and KAN.</div></div><div><h3>New hydrological insights</h3><div>The extracted maximum depth of Qiagui Co and Ayakekumu Lake was 14.14 m and 15.96 m, respectively, and the accuracy was high with low RMSE (0.356–0.369 m) by comparing with <em>in-situ</em> bathymetric data. Among the machine learning models, the KAN model exhibited the best inversion accuracy (RMSE: 0.789–0.952 m), followed by the MLP and Transformer models, and the SVR model was the poorest (RMSE: 0.893–1.253 m). Comparison of the water storage changes from the KAN model and SRTM in different periods since 2000, suggested that the accuracy of the KAN model was high with an average error of 12.8% in ∼7 m water depth. This study provides new insights into the lake depth extraction, water storage and change estimation based on the ICESat-2 and Sentinel-2 imagery data.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102354"},"PeriodicalIF":4.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760361","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":"SWOT mission enables high-precision and wide-coverage lake water levels monitoring on the Tibetan Plateau","authors":"Suhui Wu ,&nbsp;Yu Cai ,&nbsp;Chang-Qing Ke ,&nbsp;Yao Xiao ,&nbsp;Haili Li ,&nbsp;Zhiyue He ,&nbsp;Zheng Duan","doi":"10.1016/j.ejrh.2025.102357","DOIUrl":"10.1016/j.ejrh.2025.102357","url":null,"abstract":"<div><h3>Study region</h3><div>The Tibetan Plateau (TP) is a high-altitude region characterized by harsh environmental conditions and limited accessibility, making the monitoring of its numerous lakes a significant challenge. This region is crucial for hydrological studies, and its numerous lakes play an important role in regional water dynamics and climate change research.</div></div><div><h3>Study focus</h3><div>This study evaluates the applicability and accuracy of Surface Water and Ocean Topography (SWOT) satellite data in monitoring lake water levels across the TP. The study compares SWOT-derived lake levels with data from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), instead of relying on in situ measurements.</div></div><div><h3>New hydrological insights for the region</h3><div>Based on SWOT data, lake water levels for 1919 lakes were successfully extracted, covering approximately 99 % of lakes larger than 0.2 km² and filling observation gaps in around 800 lakes compared to traditional altimetry satellites. Validation against ICESat-2 data demonstrated high consistency, with an average bias of −0.01 ± 0.13 m and a mean absolute error (MAE) of less than 0.1 m. SWOT outperformed other radar altimeters in monitoring lake levels when compared to ICESat-2. Furthermore, by integrating SWOT data with other altimetry-derived lake level products, we created high-frequency time series data for 12 lakes, which showed strong correlations with the DAHITI and Hydroweb datasets. These results highlight SWOT's potential for global lake monitoring, offering new opportunities for water resource management and climate change research.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102357"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748316","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":"Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques","authors":"Xuemei Wang ,&nbsp;Ronghua Liu ,&nbsp;Chaoxing Sun ,&nbsp;Xiaoyan Zhai ,&nbsp;Liuqian Ding ,&nbsp;Xiao Liu ,&nbsp;Xiaolei Zhang","doi":"10.1016/j.ejrh.2025.102345","DOIUrl":"10.1016/j.ejrh.2025.102345","url":null,"abstract":"<div><h3>Study region</h3><div>China</div></div><div><h3>Study focus</h3><div>We developed machine learning (ML) models for design flood estimation in mountainous catchments (≤ 500 km²) across China. This process considered different ML algorithms (random forest, extreme gradient boosting, and support vector regression), model scopes (nation and hydrological zones), and feature input sets (1–14 features) to optimize model development strategies.</div></div><div><h3>New hydrological insights for the region</h3><div>Based on estimation performance and hyperparameter tuning efficiency, random forest was found to be the optimal algorithm. The optimal model scope resulted in five distinct models: a single lumped model encompassing six eastern zones and four separate zonal models for the western zones. Considering both accuracy and efficiency, the optimal number of input features ranged from 5 to 14 for different models. High estimation accuracy was observed in the Qinba-Dabie North, Southeast, Southwest, and Yunnan-Tibet Zone, with average <em>RMSE</em>, <em>R</em>², <em>MQE</em>, and <em>QR</em> ranging from 55.90 to 103.97, 0.83–0.93, 45.62–65.77 %, and 55.90–60.98 %, respectively, for the test set across different return periods. The remaining zones exhibited moderate accuracy, with the Northwest Basin Zone demonstrating particularly low accuracy due to fewer catchments. Notably, catchments with areas &gt; 100 km² demonstrated higher estimation accuracy, with an average 60 % reduction in <em>MQE</em> and a 30 % increase in <em>QR</em> compared to catchments of all sizes. This study provides crucial reference and data support for national flash flood prevention efforts.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102345"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748318","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":"Winter discharge fluctuations due to ice formation in a Swiss alpine catchment","authors":"Nina C. Nagel ,&nbsp;Michael Margreth ,&nbsp;Florian Lustenberger ,&nbsp;David F. Vetsch","doi":"10.1016/j.ejrh.2025.102352","DOIUrl":"10.1016/j.ejrh.2025.102352","url":null,"abstract":"<div><h3>Study region</h3><div>Alpine catchment of the Dischmà stream in canton of Graubünden, Switzerland</div></div><div><h3>Study focus</h3><div>The formation, presence, and melting of ice in alpine streams during winter significantly impacts the dynamics of discharge and ecosystems. This study observed the discharges using wildlife cameras and employed temperature measurements at potential sites in the Dischmà stream to better understand the controls of ice formation and its impact on winter discharge fluctuations. A new algorithm was developed for detection of icing events in the stream and quantification of water retention volumes based on discharge and temperature data whereas the observations were used as ground truth for verification and calibration of the algorithm.</div></div><div><h3>New hydrological insights for the region</h3><div>The discharge data show distinct daily fluctuations ascribed to formation of nocturnal ice, which temporarily impedes or reduces water flow. The specific morphology at the pools with moderate water depth, and step-pool formation facilitated the formation of anchor ice, especially during freezing nights. The proposed event analysis contributes to bridging a gap in hydrological literature for alpine studies, by identifying previously neglected winter stream dynamics, including temporary ice formations. The suggested algorithm can serve as basis for the analysis of such phenomena in alpine streams, offering insights that support data cleaning efforts and further investigations into winter discharge fluctuations, discharge recession, and their ecological impacts.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102352"},"PeriodicalIF":4.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748317","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":"Regionalization of hydrological cycle changes in 31 source catchments of Yellow River Basin considering multiple hydrological variables","authors":"Can Cao ,&nbsp;Yang Bai ,&nbsp;Kun Peng ,&nbsp;Bing Han ,&nbsp;Yongyong Zhang","doi":"10.1016/j.ejrh.2025.102340","DOIUrl":"10.1016/j.ejrh.2025.102340","url":null,"abstract":"<div><h3>Study region</h3><div>The 31 source catchments in the Yellow River Basin (YRB).</div></div><div><h3>Study focus</h3><div>Hydrological cycle changes showed remarkable regional heterogeneities, but existing studies usually focused on individual hydrological variable, which were insufficient to reflect comprehensive changes of entire hydrological cycles. In our study, different frequency metrics of runoff (R), evapotranspiration (ET) and water storage (WS) are adopted, and data source combinations are evaluated by water balance restriction. The hydrological change types are identified by classifying Sen’s slopes with statistical significance using cluster analysis.</div></div><div><h3>New hydrological insights for the region</h3><div>The water balance errors of the optimal data source combinations were −9.75–9.78 %. All the hydrological variables showed significant changes (-0.16–4.28 mm/a) in 31 source catchments from 2000 to 2020. The average slope was the largest in WS (1.65 mm/a), followed by ET (0.004 mm/a) and R (0.004 mm/a). The comprehensive changes were divided into three hydrological change types, namely significant intensification of R, ET and WS, significant intensification of ET and WS, and significant intensification of ET and WS but decreased in R, respectively. These three change types mainly occurred in the Yellow River Source Region (42 %), the north tributaries of Weihe River (10 %), and most of the Weihe and Yiluo rivers (48 %), respectively. This study is expected to provide better insights into the study of hydrological cycle in the watersheds under environmental changes.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102340"},"PeriodicalIF":4.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747790","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":"Comparative analysis of rainfall event characteristics and rainfall erosivity between two experimental plots in Austria and Slovenia","authors":"Borbala Szeles ,&nbsp;Juraj Parajka ,&nbsp;Mojca Šraj ,&nbsp;Günter Blöschl ,&nbsp;Dušan Marjanović ,&nbsp;Nejc Bezak ,&nbsp;Klaudija Lebar ,&nbsp;Andrej Vidmar ,&nbsp;Peter Strauss ,&nbsp;Carmen Krammer ,&nbsp;Elmar Schmaltz ,&nbsp;Patrick Hogan ,&nbsp;Gerhard Rab ,&nbsp;Katarina Zabret","doi":"10.1016/j.ejrh.2025.102353","DOIUrl":"10.1016/j.ejrh.2025.102353","url":null,"abstract":"<div><h3>Study region</h3><div>This study investigated rainfall at two locations in the Danube Basin, the Hydrological Open Air Laboratory (HOAL) agricultural catchment in Petzenkirchen, Austria and an urban experimental plot in Ljubljana, Slovenia.</div></div><div><h3>Study focus</h3><div>The variability of rainfall characteristics and rainfall erosivity were explored using measurements of precipitation and drop size distributions in the period 2014–2018. Annual and seasonal rainfall event characteristics were analyzed for each site and comparatively assessed using hierarchical clustering. Annual and seasonal variability of rainfall erosivity was compared between the plots.</div></div><div><h3>New hydrological insights for the region</h3><div>Despite having the same Köppen-Geiger climate classification, differences were found between the sites. The long-term average annual precipitation in Ljubljana was almost twice as high as in the HOAL. According to the clustering analysis, larger and more intense rainfall events occurred in Ljubljana than in the HOAL. The average drop sizes and velocities tended to be lower in Ljubljana but the range of drop size distributions was larger in Ljubljana than in the HOAL. The seasonalities of rainfall event characteristics and rainfall erosivity were similar at the sites. Rainfall intensities tended to peak in summer when rainfall durations were shorter, and larger and faster drops were observed. Rainfall erosivity was between 2 and 7 times larger in Ljubljana than in the HOAL because of the more intense rainfall and single faster and larger drops during events.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102353"},"PeriodicalIF":4.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735287","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":"Enhancing drought monitoring through regional adaptation: Performance and calibration of drought indices across varied climatic zones of Iran","authors":"Saeed Sharafi,&nbsp;Fatemeh Omidvari,&nbsp;Fatemeh Mottaghi","doi":"10.1016/j.ejrh.2025.102350","DOIUrl":"10.1016/j.ejrh.2025.102350","url":null,"abstract":"<div><h3>Study region</h3><div>Iran.</div></div><div><h3>Study focus</h3><div>This study evaluates the performance of various drought indices, including SPEI (Standardized Precipitation Evapotranspiration Index), Standardized Soil Moisture Index of the top two layers (SSI₁ and SSI₂), and the Multivariate Standardized Drought Indices (MSDI₁ (P&amp;ET_ref), MSDI₂ (P&amp;SM₁), and MSDI₃ (P&amp;SM₂)) models, across six distinct climatic zones using data from 30 basins with 621 gridded points (1979–2022). The analysis covers three time scales—1, 3, and 12 ∼ months—and assesses the drought characteristics and criteria in diverse climate regions.</div></div><div><h3>New hydrological insights for the region</h3><div>The MSDI models exhibited superior performance across all climatic zones, achieving an overall precision rate of 85 % and consistently outperforming the SPEI and SSI models in both short-term (1- and 3-month) and long-term (12-month) drought predictions. In coastal wet and mountain regions, the MSDI models demonstrated exceptional precision rates of 90 % and 85 %, respectively, with robust Taylor skill scores of 0.92 and 0.89, significantly surpassing the accuracy of the SPEI and SSI models. In semi desert and desert regions, the MSDI models maintained a precision rate of 77 %, with a slight decline at the 12-month scale. Despite this decrease, they continued to outperform the SPEI and SSI models, particularly in short-term (3-month) drought assessments. These findings underscore the necessity of selecting and calibrating drought indices to enhance monitoring accuracy, with the MSDI models proving particularly reliable in semi-desert and mountainous regions. The study advocates for region-specific drought indices to better capture local climatic variations and emphasizes the importance of improved model calibration in regions exhibiting lower performance. Policymakers are urged to implement tailored drought management strategies to enhance water resource sustainability, strengthen agricultural resilience, and mitigate the adverse impacts of drought. Further research is essential to refine these models and integrate advanced methodologies, such as machine learning (ML), to enhance drought prediction accuracy and support climate adaptation efforts.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102350"},"PeriodicalIF":4.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726051","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":"Water-rights enforcement in Abshar Irrigation District, Central Iran: Leveraging water accounting for improved standard operating procedures","authors":"Amir Reza Khakpour,&nbsp;S. Mehdy Hashemy Shahdany,&nbsp;Jaber Soltani","doi":"10.1016/j.ejrh.2025.102349","DOIUrl":"10.1016/j.ejrh.2025.102349","url":null,"abstract":"<div><h3>Study Region</h3><div>The Abshar irrigation district, located in a central arid plateau in Iran, faces drought challenges, and the current operating system managing surface water distribution among stakeholders is at risk of water supply shortages.</div></div><div><h3>Study Focus</h3><div>The research aims to develop a more accurate and effective framework for assessing water rights and tailoring Standard Operating Procedures (SOP). This involved quantifying surface and groundwater contributions and adapting the System of Environmental-Economic Accounting (SEEA)'s Physical Supply Use Tables (PSUTs).</div></div><div><h3>New Hydrological Insights for the Region</h3><div>Hydrological assessments, including surface and groundwater resource allocation and hydraulic analysis, were conducted. Surface water distribution was simulated for a one-year operation under various drought-induced scenarios, and groundwater extraction was quantified for individual farmer cooperatives. The modified PSUTs were populated with data from different drought scenarios to identify discrepancies between historical and scrutinized water rights, leading to refined SOPs. The findings highlight the limitations of existing manual-based operating systems in meeting water rights demands during droughts. The proposed methodology offers practical implications for enhancing water resource management efficiency and sustainability in irrigation districts worldwide.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102349"},"PeriodicalIF":4.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726114","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":"Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data","authors":"Ali Asghar Zolfaghari ,&nbsp;Maryam Raeesi ,&nbsp;Giuseppe Longo-Minnolo ,&nbsp;Simona Consoli ,&nbsp;Miles Dyck","doi":"10.1016/j.ejrh.2025.102343","DOIUrl":"10.1016/j.ejrh.2025.102343","url":null,"abstract":"<div><h3>Study region</h3><div>Iran, characterized by diverse climatic conditions, including arid, semi-arid, and humid subtropical regions, where ET₀ dynamics vary significantly due to climatic differences.</div></div><div><h3>Study focus</h3><div>Reference evapotranspiration (ET₀) is a fundamental component of hydrological modelling and plays a critical role in agricultural water management. Reliable ET₀ predictions are essential for optimizing irrigation systems and estimating water demand. This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET₀ across these diverse climatic conditions. Daily ET₀ values were calculated using the FAO-56 Penman-Monteith model and validated against ground-based observations from 50 weather stations (2008–2017). The RF model was trained using ERA5-Land climatic variables (air temperature, relative humidity, and ET₀ from ERA5-Land) along with the day of the year (DOY).</div></div><div><h3>New hydrological insights for the region</h3><div>Results demonstrated a high correlation between ERA5-Land temperature estimates and observed station data (Pearson correlation coefficient, r = 0.97; Root Mean Square Error, RMSE = 2.77°C), while relative humidity showed a weaker agreement (Normalized Root Mean Square Error, NRMSE = 21 %). The RF model outperformed traditional approaches in arid and semi-arid regions, achieving NRMSE values of 25 % and 28 %, respectively, with a 60 % improvement over humid regions. At the 8-day scale, predictive accuracy improved further (RMSE = 6.05 mm/8 days, r = 0.99). Beyond model performance, this study provides new insights into the spatiotemporal variability of ET₀ across different climatic zones. The findings indicate that temperature is the dominant climatic factor driving ET₀ variability, while relative humidity exhibits higher uncertainty, particularly in humid regions. Seasonal trends highlight notable summer ET₀ peaks exceeding 30 mm/day in arid zones, emphasizing the need for climate-adaptive irrigation strategies. The proposed methodology is computationally efficient, requiring minimal input variables, and demonstrates robust and scalable performance for large-scale ET₀ estimation. These findings provide a cost-effective solution for water resource management, drought monitoring, and climate change adaptation, particularly in data-scarce regions.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"59 ","pages":"Article 102343"},"PeriodicalIF":4.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714631","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}