Journal of Hydrology-Regional Studies最新文献_第7页

Impact of deformation response patterns to groundwater level: A post water division project operation in Beijing Plain 变形响应模式对地下水位的影响：北京平原后分水工程运行

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.ejrh.2026.103154

Xin Li , Lin Zhu , Haigang Wang , Huili Gong , Xiaojuan Li

Study region

The Beijing Plain (BJP), China.

Study focus

With the variable rise of groundwater levels in BJP from 2015 to 2023, deformation patterns and spatio-temporal features have become more complicated. To identify these deformation features and quantify the relationship between deformation and groundwater levels within multiple confined aquifer systems, this study combined K-Shape, BEAST with TFA on basis of the InSAR-derived deformation data. These findings provide scientific support for groundwater management and deformation risk assessment in different deformation pattern regions.

New hydrological insights

Four deformation patterns were identified, including subsidence (Sub), slow subsidence followed by slow rebound (SSFR), slow rebound (SR), and rapid rebound (RR) zones. The Sub zone covered the largest area of 2447 km², while the areas of RR and SSFR were comparable and small with the value of 573 km² and 533 km². In RR zone, uplift was driven by groundwater level recovery with an average increase of 23 m in the first confined aquifer, where the lithology is composed of sand-gravel with low clay content. This type of deposits facilitates elastic rebound with high coherence of 0.63 at 6-month timescale and short lag times of about 16 days. In the Sub zone, subsidence is primarily governed by the second and third confined aquifers. This reflects the legacy of intensified deep-groundwater exploitation and is characterized by high gain and significant lag times of about 104 and 109 days for the second and third confined aquifers, respectively. These phenomena are strongly associated with strata lithology and exploitation history in different deformation pattern zones.

研究区域：中国北京平原。随着2015 - 2023年印度人民党地下水位的变化，其变形模式和时空特征变得更加复杂。为了识别这些变形特征，并量化多个承压含水层系统中变形与地下水位之间的关系，本研究在insar导出的变形数据的基础上，将K-Shape、BEAST和TFA结合起来。研究结果为不同变形模式区域的地下水管理和变形风险评价提供了科学依据。确定了四种变形模式，包括沉降区（Sub）、缓慢沉降后缓慢反弹区（SSFR）、缓慢反弹区（SR）和快速反弹区（RR）。分区面积最大，为2447 km2，而RR和SSFR面积较小，分别为573 km2和533 km2。RR带主要受地下水位恢复驱动，第一承压含水层平均上升23 m，岩性为砂砾质，粘土含量较低。这种类型的沉积物有利于弹性反弹，在6个月的时间尺度上具有0.63的高相干性，滞后时间短，约为16天。在该区，下沉主要受第二和第三承压含水层控制。这反映了深层地下水开采加剧的遗留问题，其特点是高增益，第二和第三承压含水层的滞后时间分别约为104天和109天。这些现象与不同变形模式带的地层岩性和开采历史密切相关。

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引用次数: 0

How well do U.S. National Water Model short-range forecasts predict flood event timing and magnitude? 美国国家水模型短期预测对洪水事件时间和规模的预测有多好？

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-19 DOI: 10.1016/j.ejrh.2026.103108

Iman Maghami , Daniel P. Ames , Amin Aghababaei , Abin Raj Chapagain , Jacob M. Anderson , Jerson J. Garcia

Study region

Continental United States (CONUS).

Study focus

The National Water Model (NWM), operational since 2016, provides real-time, continuous hydrologic forecasts across the CONUS. Prior studies have evaluated NWM performance, but comprehensive assessments across multiple regions, lead times, and settings remain limited. This study evaluates NWM version 2.1 short-range forecasts for multiple flood events during 2021–2023, analyzing performance across varying watershed characteristics, flood magnitudes (return periods), and lead times. We used data from 306 U.S. Geological Survey gauges across 16 study areas, several with multiple floods per site, to assess forecast accuracy in terms of hydrograph skill, peak discharge, flood volume, and time-to-peak bias.

New hydrologic insights for the region

Results show that forecast accuracy improves with shorter lead times and smaller floods but varies by watershed traits and climate. Systematic underestimation of peak discharge and flood volume occurred across all basin types. Urban, regulated, arid, and low-order watersheds tend to show higher forecast errors. We qualitatively inspected hydrograph shapes, finding that while many forecasts captured rising and falling limbs, some exhibited systematic anomalies, such as consistently declining, delayed forecasts, and failure to detect sharp flood peaks, highlighting structural issues in model response. Findings provide insight into the strengths and limitations of NWM short-range flood forecasts and offer a baseline for evaluating future NWM versions and the emerging NextGen modeling framework.

研究区域美国大陆（CONUS）。国家水模型（NWM）自2016年开始运行，为整个CONUS提供实时、连续的水文预测。先前的研究已经评估了NWM的性能，但对多个地区、交货时间和环境的综合评估仍然有限。本研究评估了NWM 2.1版本对2021-2023年多个洪水事件的短期预测，分析了不同流域特征、洪水规模（回归期）和提前期的表现。我们使用的数据来自306 U.S.地质调查局测量了16个研究区域，每个站点有多个洪水，以评估水文技术、峰值流量、洪水量和峰值时间偏差方面的预测准确性。结果表明，预报精度随着提前时间的缩短和洪水的减少而提高，但因流域特征和气候而异。所有流域类型均存在对洪峰流量和洪量的系统性低估。城市、管制、干旱和低阶流域往往表现出较高的预测误差。我们定性地检查了水文曲线的形状，发现虽然许多预测捕获了上升和下降的分支，但有些预测显示出系统性异常，例如持续下降、延迟预测和未能检测到尖锐的洪峰，突出了模型响应中的结构性问题。研究结果有助于深入了解NWM短期洪水预报的优势和局限性，并为评估未来NWM版本和新兴的NextGen建模框架提供基线。

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引用次数: 0

Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions 中国北方城市污水管网水位预测的可解释深度学习：对增强系统运行条件实时评估的启示

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.ejrh.2026.103116

Zhiwen Yi , Wenchao Sun , Haozheng Wang , Guanyu Han , Jinqiang Wang

Study region

Changchun City in North China.

Study focus

Water level prediction within sewer networks ‌plays a crucial role in‌ urban water system real-time control. With‌ advanced automated monitoring technologies, the generation of vast amounts of observational data ‌makes it possible‌ to predict water levels using deep learning models. However, a knowledge gap remains regarding how input feature dimensionality and temporal sequence length influence performances of such models. To address this issue, we developed an ‌explainable deep learning framework for predicting the influent water level at the terminal wastewater treatment plant of a typical sewer system by integrating Long Short-Term Memory (LSTM) with SHapley Additive exPlanations (SHAP) for interpretable analysis.

New hydrological insights

Utilizing monitored precipitation data and upstream pipeline water level time-series data as input yields satisfactory one-step-ahead prediction performance. Contrary to expectations, incorporating additional upstream sensors did not improve model accuracy. Model performance improved progressively with longer input sequences, reaching optimal performance at 48-time steps before deteriorating with further temporal extension. SHAP analysis revealed that manual control operations exerted stronger influence on model outputs than the spatial proximity of monitoring sites to prediction points. Furthermore, sensor data anomalies and deficiencies in monitoring network configurations could be identified, confirming its utility for diagnosing behavioral patterns in modeled sewer network. These findings provided new hydrological insights and practical guidance for sewer system real-time control.

研究区域为华北长春市。研究重点污水管网水位预测在城市供水系统实时控制中起着至关重要的作用。借助先进的自动化监测技术，大量观测数据的生成使得使用深度学习模型预测水位成为可能。然而，关于输入特征维度和时间序列长度如何影响这些模型的性能，知识差距仍然存在。为了解决这个问题，我们开发了一个可解释的深度学习框架，通过将长短期记忆（LSTM）与SHapley加性解释（SHAP）相结合进行可解释分析，用于预测典型下水道系统终端污水处理厂的进水水位。利用监测降水数据和上游管道水位时间序列数据作为输入，可获得令人满意的一步超前预测性能。与预期相反，加入额外的上游传感器并没有提高模型的准确性。随着输入序列的增加，模型的性能逐渐提高，在48个时间步长时达到最优，然后随着时间的进一步扩展而退化。SHAP分析显示，人工控制操作对模型输出的影响大于监测点与预测点的空间接近程度。此外，可以识别传感器数据异常和监测网络配置的缺陷，确认其在诊断模型下水道网络行为模式方面的实用性。这些发现为下水道系统的实时控制提供了新的水文见解和实用指导。

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引用次数: 0

Meteoric water δ18O across the Dinarides: Role of topography, air-mass mixing, and precipitation seasonality 穿越Dinarides的大气水δ18O：地形、气团混合和降水季节性的作用

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ejrh.2026.103214

Gabriela Sanchez Ortiz , Marlene Löberbauer , Nevena Andrić-Tomašević , Oleg Mandic , Davor Pavelić , Vedad Demir , Patrick W. Keys , Maud J.M. Meijers , Jeremy K.C. Rugenstein

Study region

Streams in the Dinarides ranging from coastal Croatia across the high-elevation basins of Bosnia and Herzegovina to the lee of the Dinarides.

Study focus

The topographic evolution of the Dinarides is poorly-constrained and its controlling geodynamic mechanisms remain unclear. The oxygen-isotope composition (δ¹⁸O) of authigenic minerals is a common paleo-altimeter for reconstructing past topography, proper interpretation requires thorough constraints on mechanisms modifying modern meteoric-water δ¹⁸O. To constrain modern δ¹⁸O patterns across the Dinarides, we collected new stream samples and integrated them with published water stable isotope data.

New hydrological insights for the region

Meteoric-water data show δ¹⁸O is higher at the coast (∼-6 ‰) and lower at the peak (∼-11 ‰). We use moisture trajectory models to show isotopic patterns across the Dinarides reflect two distinct moisture sources. The dominant source of moisture on the windward side originates from the Mediterranean and the leeward side has a continental source. This difference in moisture sources is reflected in d‐excess values, which are high along the windward margin—reflective of Mediterranean moisture—and low in the lee, reflective of summertime, continental-sourced moisture. We interpret orographic rainout as the primary-driver of modern precipitation and surface water δ¹⁸O with secondary influences from moisture sources and precipitation seasonality. Our findings have implications for understanding the climatic processes that deliver moisture as well as our understanding of the past topography of the Dinarides.

研究区域：迪纳里德斯山脉的河流从克罗地亚沿海地区穿过波斯尼亚和黑塞哥维那的高海拔盆地，一直延伸到迪纳里德斯山脉的背风处。研究重点Dinarides的地形演化约束较差，其控制地球动力学机制尚不清楚。自生矿物的氧同位素组成（δ18O）是重建过去地形的常用古高度计，正确的解释需要彻底限制现代大气水δ18O的改变机制。为了约束整个Dinarides的现代δ18O模式，我们收集了新的溪流样本，并将其与已发表的水稳定同位素数据进行了整合。区域大气水数据的新水文见解表明，δ18O在海岸较高（~ -6 ‰），在峰值较低（~ -11 ‰）。我们使用水分轨迹模型来显示横跨迪纳里德斯的同位素模式反映了两种不同的水分来源。迎风面主要的水汽来源来自地中海，背风面则有大陆来源。水分来源的差异反映在d -过剩值上，在迎风边缘高，反映地中海的水分，在背风边缘低，反映夏季大陆的水分。我们认为地形降水是现代降水和地表水δ18O的主要驱动因素，其次是水汽来源和降水季节性的影响。我们的发现对理解提供水分的气候过程以及我们对Dinarides过去地形的理解具有重要意义。

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引用次数: 0

Diving deep: Trends and gaps in Philippine groundwater research (1982–2024) 深潜：菲律宾地下水研究的趋势和差距（1982-2024）

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ejrh.2026.103198

John Kenneth R. Fraga , Sedney S. Mendoza , Ashemir B. Velasco , Jacquilyn L. Estrada , Daniel Edison M. Husana , Francis S. Magbanua

Study Region

Groundwater in the Philippines covers approximately 50,000 km² and contains an estimated 251 km³ of water, serving as a vital resource for domestic use, agriculture, and industry.

Study Focus

This study reviews groundwater research in the Philippines from 1982 to 2024 using data from Scopus and Web of Science. It highlights research trends, key institutions, and knowledge gaps to help guide sustainable management as overextraction and pollution become bigger concerns.

New Hydrological Insights for the Region

The study's results offer a comprehensive overview of the research landscape, highlighting key themes in groundwater research in the Philippines. Over the past four decades, only 79 relevant publications have been identified, underscoring persistent research gaps in addressing groundwater challenges. While international collaborations and local institutions play a significant role in shaping research priorities, there is a notable lack of local expertise and dedicated groundwater research programs. Groundwater contamination has emerged as the most frequently studied topic, with growing interest in submarine groundwater discharge (SGD). However, studies on SGD often treat groundwater as a secondary concern rather than a primary focus, particularly in marine or coastal ecology. Despite a recent uptick in research output, the overall volume remains limited, underscoring the need for a more proactive, interdisciplinary research agenda. These findings highlight the importance of adopting a comprehensive approach to groundwater research and management tailored to the Philippine context.

研究区域菲律宾的地下水面积约为50,000 平方公里，其中估计含有251 立方公里 的水，是家庭、农业和工业的重要资源。本研究使用Scopus和Web of Science的数据回顾了1982年至2024年菲律宾的地下水研究。它强调了研究趋势、关键机构和知识差距，以帮助指导可持续管理，因为过度开采和污染成为更大的问题。该研究的结果提供了对研究前景的全面概述，突出了菲律宾地下水研究的关键主题。在过去的四十年中，只发现了79份相关的出版物，这突显了在应对地下水挑战方面持续存在的研究差距。虽然国际合作和地方机构在确定研究重点方面发挥了重要作用，但当地明显缺乏专业知识和专门的地下水研究项目。地下水污染已成为最常见的研究课题，海底地下水排放（SGD）日益受到关注。然而，关于SGD的研究往往将地下水作为次要问题而不是主要焦点，特别是在海洋或沿海生态学中。尽管最近研究产出有所增加，但总体数量仍然有限，这强调需要一个更积极主动的跨学科研究议程。这些发现突出了采用适合菲律宾国情的地下水研究和管理综合方法的重要性。

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引用次数: 0

Rainfall intensity estimation at night using deep learning and urban surveillance cameras in Jiangsu Province, China 基于深度学习和城市监控摄像机的江苏省夜间降雨强度估算

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1016/j.ejrh.2026.103112

Xing Wang , Haiqin Chen , Ang Zhou , Ye Chen

Study region

This study was conducted in the Yangtze River Delta of eastern China, focusing on the highly urbanized corridor of Nanjing, Yangzhou, and Wuxi (Jiangsu Province), where the nighttime surveillance videos were collected during 2022–2025.

Study focus

Nighttime rainfall measurement from surveillance video remains challenging due to low visibility, uneven illumination, and complex background noise. To address these issues, this study proposes NightRAIN-Net (Nighttime Rainfall Adaptive and Integrated Network), a novel deep learning (DL) framework tailored for nighttime rainfall estimation. The framework integrates two key modules: Rain-Adaptive Channel Enhancement, which adapts to nighttime lighting variations to enhance raindrop visibility, and Selective Raindrop Localization, which captures raindrop’s shape and structure, mitigating interference from complex backgrounds. Furthermore, NightRAIN-Net integrates LSTM for modeling short-term fluctuations in rainfall intensity and Transformer for learning long-range dependencies, enabling robust performance across diverse precipitation types, from light drizzle to extreme rainfall.

New hydrological insights

Real-world experimental results demonstrate that NightRAIN-Net achieves a Mean Absolute Error (MAE) of 3.22 mm/h and a Root Mean Squared Error (RMSE) of 3.88 mm/h, while remaining stable across different scenarios and varying camera parameters. It exhibits stable performance across different rainfall scenarios, outperforming state-of-the-art methods. These findings indicate that camera networks can provide scalable, near-continuous (24-hour) high-frequency rainfall information in Yangtze River Delta, supporting urban hydrological monitoring, rapid flood/urban waterlogging early warning, and disaster risk mitigation.

研究区域本研究在中国东部的长江三角洲进行，重点关注南京、扬州和无锡（江苏省）高度城市化的走廊，收集了2022-2025年期间的夜间监控视频。研究重点：由于能见度低、光照不均匀和复杂的背景噪声，从监控视频中测量夜间降雨量仍然具有挑战性。为了解决这些问题，本研究提出了NightRAIN-Net（夜间降雨自适应和集成网络），这是一种为夜间降雨量估计量身定制的新型深度学习（DL）框架。该框架集成了两个关键模块：雨水自适应通道增强，可适应夜间照明变化以增强雨滴的能见度；选择性雨滴定位，可捕获雨滴的形状和结构，减轻复杂背景的干扰。此外，NightRAIN-Net集成了用于模拟降雨强度短期波动的LSTM和用于学习长期依赖关系的Transformer，从而实现了从小雨到极端降雨等不同降水类型的稳健性能。现实世界的实验结果表明，NightRAIN-Net的平均绝对误差（MAE）为3.22 mm/h，均方根误差（RMSE）为3.88 mm/h，同时在不同的场景和不同的相机参数下保持稳定。它在不同的降雨情况下表现稳定，优于最先进的方法。这些发现表明，摄像机网络可以在长三角地区提供可扩展的、近连续的（24小时）高频降雨信息，支持城市水文监测、快速洪水/城市内涝预警和灾害风险缓解。

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引用次数: 0

A machine learning framework for runoff simulation in small catchments: Integrating atmosphere–ocean–land large-scale indices and catchment variables 小流域径流模拟的机器学习框架：整合大气-海洋-陆地大尺度指数和流域变量

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ejrh.2026.103181

Chenzhi Ma , Jing Chen , Junqiang Yao , Zhi Li

Study region

Cherchen River catchment.

Study focus

This study presents a machine learning framework that integrates Extra Trees with Shapley Additive Explanations for variable selection, and adopts an Extended Long Short-Term Memory network as the core model for monthly runoff simulation. The framework considers multiple variables including atmosphere–ocean–land large-scale indices and catchment variables, and effectively identifies the top 10 key variables for runoff simulation from 756 variables. This study focuses on developing a runoff simulation framework for small catchments that generalizes across diverse hydrological conditions, while remaining effective under high dimensional inputs and limited local hydrological data.

New hydrologic Insights

In the Cherchen River catchment, annual runoff exhibited a significant increasing trend of 7.29 m³ s⁻¹ a⁻¹ from 1991 to 2021. Runoff variation is strongly influenced by local meteorological and hydrothermal conditions, such as wind speed and air temperature, and is also significantly associated with sea surface temperature variability in the Indian Ocean Warm Pool. In addition, large-scale circulation patterns, including the East Asian trough, the Northern Hemisphere polar vortex, and the mid-tropospheric geopotential height field over the Tibetan Plateau, play a secondary role relative to these factors. The framework proposed in this study achieves robust performance on the annual (MAE = 1.27 ×10⁷m³, RMSE = 1.34 ×10⁷m³, and R² = 0.99) and monthly (MAE = 7.03 ×10⁶m³, RMSE = 8.83 ×10⁶m3, and R² = 0.95) scales.

研究区域cherchen河集水区。本研究提出了一个机器学习框架，该框架集成了额外树和Shapley加性解释进行变量选择，并采用扩展长短期记忆网络作为月径流模拟的核心模型。该框架考虑了大气-海洋-陆地大尺度指数和流域变量等多个变量，并从756个变量中有效识别出径流模拟的前10个关键变量。本研究的重点是为小流域开发一个径流模拟框架，该框架适用于不同的水文条件，同时在高维输入和有限的当地水文数据下保持有效。在切尔陈河流域，从1991年到2021年，年径流量呈现出7.29 m³ s a⁻¹ 的显著增长趋势。径流变化受当地气象和热液条件（如风速和气温）的强烈影响，也与印度洋暖池的海表温度变化密切相关。东亚低槽、北半球极涡和青藏高原对流层中位势高度场等大尺度环流模式的作用相对次要。本研究提出的框架在年度（MAE = 1.27 ×107m3, RMSE = 1.34 ×107m3, R2 = 0.99）和月度（MAE = 7.03 ×106m3, RMSE = 8.83 ×106m3, R2 = 0.95）量表上均具有稳健性。

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引用次数: 0

Evapotranspiration dynamics and climatic-land-use controls in the Hanjiang River Basin, 2000-2018 2000-2018年汉江流域蒸散发动态与气候-土地利用控制

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-15 DOI: 10.1016/j.ejrh.2026.103125

Rui Li , Zhijie Zhang , Wanchang Zhang , Ping Rao

Study region

Hanjiang River Basin, China.

Study focus

This study investigates spatiotemporal dynamics of total evapotranspiration (ET) and its four components: dry canopy transpiration (ET_Canopy), wet canopy evaporation (E_Canopy), saturated soil evaporation (E_Soil), and moist soil evaporation (ET_Soil) from 2000 to 2018, using the physically-based ESSI-3 model with vertical transpiration partitioning. A multi-method framework (Random Forest, SHapley Additive exPlanations, Structural Equation Modeling) was employed to identify dominant drivers and causal pathways for total ET and ET_Canopy.

New hydrological insights for the region

Results reveal strong spatial heterogeneity and seasonal shifts in ET dominance over the study period: ET_Canopy prevails during the growing season, while E_Soil dominates in winter.Vegetation-related components (ET_Canopy, E_Canopy) exhibited increasing trends, while soil-related components (ET_Soil, E_Soil) declined, resulting in a net slight increase in total ET. Temperature, solar radiation, and leaf area index (LAI) are the primary drivers of these dynamics, exhibiting nonlinear and interactive effects on ET. In contrast, precipitation and soil water showed limited direct influence on the daily scale but contributed through lagged effects. Crucially, the transpiration attributable to plant root uptake from the second soil layer (5–30 cm) constituted the largest fraction (72.1 %) of total canopy transpiration, underscoring the role of mid-depth root zones in sustaining ET. This study provides a replicable framework for diagnosing ecohydrological processes and informs adaptive water management in monsoon-sensitive basins.

研究区域：中国汉江流域。利用基于物理的垂直蒸腾划分的esi -3模型，研究2000 - 2018年中国土壤总蒸散发（ET）及其4个组分：干冠层蒸腾（ET_Canopy）、湿冠层蒸腾（E_Canopy）、饱和土壤蒸腾（E_Soil）和湿土壤蒸腾（ET_Soil）的时空动态变化。采用随机森林（Random Forest）、SHapley加性解释（Additive exPlanations）、结构方程模型（Structural Equation Modeling）等多方法框架，确定了总蒸散量和ET_Canopy的主导驱动因素和因果通路。结果表明，研究期内ET优势表现出较强的空间异质性和季节变化特征：生长季以ET_Canopy为主，冬季以E_Soil为主。植被相关分量（ET_Canopy, E_Canopy）呈增加趋势，土壤相关分量（ET_Soil, E_Soil）呈下降趋势，导致总蒸散发呈轻微净增加趋势。温度、太阳辐射和叶面积指数（LAI）是这些动态的主要驱动因素，对蒸散发表现出非线性和交互作用。相比之下，降水和土壤水分在日尺度上的直接影响有限，但存在滞后效应。至关重要的是，第二层土壤（5-30 cm）的植物根系吸收蒸腾占总冠层蒸腾的最大比例（72.1 %），强调了中深根区在维持ET中的作用。该研究为诊断生态水文过程提供了一个可复制的框架，并为季风敏感流域的适应性水管理提供了信息。

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引用次数: 0

A rapid flood inundation integrated surrogate model based on a CNN-LSTM-ATT deep learning framework 基于CNN-LSTM-ATT深度学习框架的快速洪水淹没综合代理模型

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.ejrh.2026.103166

Xingyu Feng , Kairong Lin , Luwen Zhuang , Yuanhao Xu , Tongfang Li

Study region:

The Pajiang River Basin, China

Study focus:

The Pajiang River Basin is characterized by scarce hydrological data and frequent flash flood events, requiring efficient simulation for timely prediction and effective mitigation. To address these challenges, this study develops a rapid flood inundation integrated surrogate model (ISM) that incorporates physical-based hydrological-hydrodynamic models with deep learning algorithms. The ISM includes an improved TOPMODEL (3S-TOPMODEL), the LISFLOOD-FP hydrodynamic model, and a CNN-LSTM-Attention (CNN-LSTM-ATT) deep learning framework.

New hydrological insights for the region:

The 3S-TOPMODEL simulates interactions between surface water, soil water, and groundwater. It demonstrates better performance than the traditional TOPMODEL in calibration and validation, particularly for peak flows. The hydrological-hydrodynamic module of the ISM, which couples 3S-TOPMODEL with LISFLOOD-FP, achieves high accuracy in predicting flood depth and extent in the Pajiang River Basin. The CNN-LSTM-ATT algorithm is embedded within the ISM, accelerating flood simulations by reducing computational time from approximately one hour to just 5.4–6.79 s. Compared with baseline models (CNN-LSTM and 3D-CNN), CNN-LSTM-ATT provides higher accuracy, improved spatial generalization, and greater robustness, maintaining superior performance even with limited training data. Overall, the ISM provides both mechanistic hydrological understanding and a high-speed, reliable tool for real-time flood forecasting, supporting effective flood risk management and decision-making in the Pajiang River Basin.

研究区域：中国巴江流域研究重点：巴江流域水文资料匮乏，山洪灾害频发，需要高效模拟，及时预测和有效减灾。为了应对这些挑战，本研究开发了一种快速洪水淹没综合代理模型（ISM），该模型将基于物理的水文-水动力模型与深度学习算法相结合。ISM包括改进的TOPMODEL （5s -TOPMODEL）、LISFLOOD-FP水动力模型和CNN-LSTM-Attention （CNN-LSTM-ATT）深度学习框架。对该地区水文的新认识：3S-TOPMODEL模拟了地表水、土壤水和地下水之间的相互作用。它在校准和验证方面表现出比传统的TOPMODEL更好的性能，特别是在峰值流量方面。ISM的水文-水动力模块将3S-TOPMODEL与LISFLOOD-FP相结合，实现了对巴江流域洪水深度和范围的高精度预测。CNN-LSTM-ATT算法嵌入在ISM中，通过将计算时间从大约一个小时减少到5.4-6.79 s来加速洪水模拟。与基线模型（CNN-LSTM和3D-CNN）相比，CNN-LSTM- att提供了更高的精度、改进的空间泛化和更强的鲁棒性，即使在有限的训练数据下也能保持优异的性能。总体而言，ISM提供了对水文机制的理解和快速、可靠的实时洪水预报工具，支持了巴江流域有效的洪水风险管理和决策。

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引用次数: 0

Integrated multi-objective model predictive control framework for cascaded open-channel systems with multi-source lateral inflows 具有多源横向流入的级联明渠系统集成多目标模型预测控制框架

IF 5 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ejrh.2026.103164

Xiaohua Li , Guanghua Guan , Yufeng Luo , Raziyeh Farmani

Study region

Terminal ten cascaded pools of the Middle Route of the South-to-North Water Diversion Project (MR-SNWD), China, with lateral inflows from along-route reservoirs.

Study focus

Reliable water delivery in terminal canal reaches is challenged by demand fluctuations and lateral inflows acting under wave-propagation delays. To address timing and allocation mismatches, we propose a control-oriented Multi-source Lateral Inflow Integrator–Delay (MLI-ID) model that treats the main inflow and each lateral inflow as independent delayed inputs. Building on MLI-ID, a Lateral Inflow Feedforward Guided MPC (LIFG-MPC) framework embeds multi-objective lateral inflow allocation—operation performance, demand satisfaction, and source allocation—into rolling-horizon control, supported by a radar chart–based integration index for real-time compromise selection.

New hydrological insights for the regions

Coordinated regulation of multi-source lateral inflows markedly improves water-level regulation and increases supply reliability across varying demand scenarios in the terminal reach. The results reveal a capacity–delay controlled mechanism: along-route reservoirs function as fast-response buffers that can absorb incremental demand within their combined supply capacity, whereas exceedance of this capacity shifts the system’s dependence toward upstream releases whose longer propagation delays restrict timely compensation and define a practical reliability boundary for regional water delivery. These findings underscore that explicitly accounting for multi-source inflow delays and allocation–control coupling is essential for stable operation of cascaded open-channel systems in the MR-SNWD and similar regulated transfer canals.

南水北调中线10号终端梯级池沿线水库横向流入研究区域研究重点终端运河河段的可靠输水受到需求波动和波浪传播延迟作用下的侧向流入的挑战。为了解决时间和分配不匹配问题，我们提出了一个面向控制的多源横向流入积分器-延迟（MLI-ID）模型，该模型将主流入和每个横向流入视为独立的延迟输入。在MLI-ID的基础上，横向流入前馈制导MPC （LIFG-MPC）框架将多目标横向流入分配（操作性能、需求满意度和资源分配）嵌入滚动水平控制中，并由基于雷达图的实时折衷选择集成指标提供支持。多源横向流入的协调调节显著改善了水位调节，并提高了终端地区不同需求情景下的供应可靠性。研究结果揭示了一种能力-延迟控制机制：沿线水库作为快速响应缓冲区，可以在其联合供应能力范围内吸收增量需求，而超过这一能力则使系统转向上游放水，上游放水的较长传播延迟限制了及时补偿，并为区域供水确定了实际的可靠性边界。这些发现强调，明确考虑多源流入延迟和分配-控制耦合对于MR-SNWD和类似调节转运渠道级联明渠系统的稳定运行至关重要。

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引用次数: 0