Journal of Hydrology最新文献

{"title":"Assessing the performance of convection-permitting climate model in reproducing basin-scale hydrological extremes: A western Norway case study","authors":"Kun Xie ,&nbsp;Lu Li ,&nbsp;Hua Chen ,&nbsp;Chong-Yu Xu","doi":"10.1016/j.jhydrol.2025.132989","DOIUrl":"10.1016/j.jhydrol.2025.132989","url":null,"abstract":"<div><div>Convection-permitting regional climate models (CPRCMs) have been shown to improve the representation of extreme precipitation compared to coarser resolution regional climate models (RCMs). Their benefits for hydrological extremes, such as floods, remains uncertain. This study evaluates the performance of a 3-km resolution convection-permitting model (HCLIM3) against a coarser 12-km resolution climate model (HCLIM12) from the HARMONIE-Climate (HCLIM) model, focusing on precipitation, temperature, and floods in two basins over Western Norway: Røykenes basin (dominated by rainfall-generate flood) and Bulken basin (dominated by snowmelt-generate flood). In the study, we use both a physically-based, distributed Weather Research and Forecasting Model Hydrological system (WRF-Hydro) and a conceptual, lumped Hydrologiska Byråns Vattenbalansavdelning (HBV) model to assess flood simulations. The results show: (1) HCLIM3 better captures spatial variability of annual maximum 1-day and 1-hour precipitation compared to HCLIM12, but both HCLIM models exhibit cold biases which are more pronounced at lower elevation areas, particularly in HCLIM12. (2) HCLIM3-driven simulations do not show benefit in flood simulations across the two basins, except for severe flood peaks, compared to HCLIM12, the choice of hydrological model has a large impact on the results. The HBV model underestimates flood peaks and frequency, while WRF-Hydro more accurately simulates them in the Røykenes but overestimates them in the Bulken likely due to the biases of forcing data, particularly when driven by HCLIM3. The study concludes that CPRCMs improve the simulation of extreme precipitation and temperature but not show clear added value for flood simulations, especially in Bulken. This highlights the critical need for bias correction to ensure accurate flood predictions, even when driven by CPRCMs.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"656 ","pages":"Article 132989"},"PeriodicalIF":5.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local- and global-scale hydrological and sediment connectivity over grassland and shrubland hillslopes","authors":"Shubham Tiwari,&nbsp;Laura Turnbull,&nbsp;John Wainwright","doi":"10.1016/j.jhydrol.2025.132896","DOIUrl":"10.1016/j.jhydrol.2025.132896","url":null,"abstract":"<div><div>Quantifying connectivity patterns in dryland ecosystems enables us to understand how changes in the vegetation structure influence the runoff and erosion processes. This knowledge is crucial for mitigating the impacts of climate change and land use modifications. We quantify the multi-scale water-mediated connectivity within grassland and shrubland hillslopes using a weighted, directed network model. By integrating high-resolution elevation data, vegetation information, and modeled event-based hydrologic and sediment transport, we assess both structural connectivity (physical landscape layout) and functional connectivity (dynamic water and sediment movement) under varying rainfall and soil moisture conditions.</div><div>Our findings reveal a marked increase in local (patch-scale) connectivity metrics in shrublands compared to grasslands. Metrics like betweenness centrality—which measures the importance of nodes in connecting different parts of the network—and the weighted length of connected pathways increase up to tenfold in shrublands. Despite substantial local changes, global (plot-scale) properties like efficiency of water and sediment transfer show less variation, suggesting a robust network topology that sustains geomorphic functionality across different vegetation states.</div><div>We also find that the functional connectivity is more strongly correlated with structural connectivity for sediment than for water. This difference is particularly pronounced under high rainfall conditions and shows little sensitivity to variations in antecedent soil moisture, highlighting the critical role of rainfall-driven processes in shaping connectivity patterns.</div><div>The study offers a comprehensive framework for analyzing connectivity at multiple scales, which can inform targeted management strategies aimed at enhancing ecosystem resilience, such as interventions to control erosion or restore vegetation patterns.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132896"},"PeriodicalIF":5.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"How can SWOT derived water surface elevations help calibrating a distributed hydrological model?","authors":"Girish Patidar ,&nbsp;Adrien Paris ,&nbsp;J. Indu ,&nbsp;Subhankar Karmakar","doi":"10.1016/j.jhydrol.2025.132968","DOIUrl":"10.1016/j.jhydrol.2025.132968","url":null,"abstract":"<div><div>The launch of the Surface Water and Ocean Topography (SWOT) mission with the Ka-band Radar Interferometer (KaRIn) sensor has opened up new possibilities for river monitoring and hydrological/hydrodynamic model calibration with 2 to 4 observations per cycle (∼21 days) over the Indian region. The present study examines the potential of discharge derived from SWOT-type WSE for hydrological model calibration. Results are presented for Modelo de Grandes Bacias (MGB) hydrological/hydrodynamic model for calibration using in-situ, SWOT-like discharge and SWOT-like discharge with 30 % variance data. The results of proxy SWOT discharge indicate NSE ranging between 0.72 and 0.97 across 11 stations over the Mahanadi River basin in India, with enhanced NSE during high-flow (Monsoon) conditions compared to low-flow (non-monsoon) conditions. It is also inferred that MGB hydrological model, when calibrated against in-situ, SWOT-like discharge and SWOT-like discharge with 30 % variance, shows a close NSE value for discharge simulation with a 4.7 % and 7.9 % reduction in the NSE value at the outlet of the Mahanadi River basin. The simulated discharge after calibration for both scenarios closely matches during monsoon as well as during non-monsoon seasons, which strongly suggests that the SWOT discharge has significant potential in the river discharge monitoring and can help calibrating the hydrological model with fidelity over the large river basin like Mahanadi river basin, hence potentially improving the discharge monitoring after mission’s lifetime.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"656 ","pages":"Article 132968"},"PeriodicalIF":5.9,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics informed neural network for forward and inverse multispecies contaminant transport with variable parameters","authors":"Qingzhi Hou ,&nbsp;Xiaolong Xu ,&nbsp;Zewei Sun ,&nbsp;Jianping Wang ,&nbsp;Vijay P. Singh","doi":"10.1016/j.jhydrol.2025.132977","DOIUrl":"10.1016/j.jhydrol.2025.132977","url":null,"abstract":"<div><div>Multispecies contaminant transport occurs frequently in groundwater systems. Currently, most solutions to multispecies transport problems do not consider parameter variability which has a determinant impact on concentration distribution. In this paper, a physics-informed neural network (PINN) containing a locally adaptive residual network and a probabilistic point selection strategy referred to as RP-PINN is proposed to solve the forward and inverse problems of multispecies contaminant transport with variable parameters. The RP-PINN model solves the contaminant transport problem by embedding a system of partial differential equations (PDEs) into the loss function of the deep neural network. The effect of spatiotemporally varying dispersion coefficient and transport velocity on contaminant transport was analyzed. Three transport systems with four different temporal functions were investigated. Results showed that although the original PINN yielded reasonable solutions to multispecies contaminant transport problems with variable parameters, the RP-PINN had better fitting ability and stability. For the inverse problem of model coefficient identification, RP-PINN accurately learnt the diffusion coefficients and transport velocities varying in space and time, which dynamically helped correct the model parameters.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132977"},"PeriodicalIF":5.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geomorphological evolution in a medium macrotidal estuary across 88 years: shift from natural to human-influenced states","authors":"Jun Zheng ,&nbsp;Xiaoming Xia ,&nbsp;Hongcheng Sun ,&nbsp;Yining Chen ,&nbsp;Aldo Sottolichio ,&nbsp;Isabel Jalón-Rojas ,&nbsp;Yifei Liu ,&nbsp;Tinglu Cai ,&nbsp;Xinkai Wang ,&nbsp;Zhiguo He","doi":"10.1016/j.jhydrol.2025.132933","DOIUrl":"10.1016/j.jhydrol.2025.132933","url":null,"abstract":"<div><div>Since the 1970s, human activities such as navigational projects, land reclamations, sand mining, and upstream damming have significantly impacted the geomorphology of Oujiang River Estuary (ORE). This study utilized bathymetric surveys, river discharge data, tide records, historical current velocity and suspended sediment concentration, and historical satellite imagery to investigate the geomorphological evolution of ORE over the past 88 years. The results reveal a distinctive five-phase evolution: a period of pronounced erosion (1931–1964), followed by pronounced deposition (1964–1979), minor erosion/deposition fluctuations (1979–2007), rapid erosion (2007–2014) and fast back-siltation (2014–2018). In its natural state before the 1970s, the ORE exhibited morphodynamics characterized by wandering, braided, and meandering channels interspersed with shoals. Meanwhile, its erosion and deposition pattern featured a dynamic equilibrium: high river discharge induced erosion, while dominant flood tides facilitated net upward sediment transport and deposition during low river discharge. However, post-1970s human activities disrupted this natural equilibrium and led to various geomorphological responses. Navigational projects stabilized shoals and channels, affecting local sedimentation. Dams reduced the frequency and peak of floods, thus reducing the potential for erosion. Land reclamations narrowed channels and reshaped the coastline. Sand mining and dredging for reclamation contributed significantly to erosion, especially during 1979–2014. The Empirical Orthogonal Function analysis revealed two primary morphodynamic patterns. The first mode indicates long-term continuous erosion in the channel and siltation over the tidal flat, identifying navigational projects and sand mining as the dominant causes of the main morphological changes. The second mode describes the transition from erosion to siltation, highlighting land reclamations and reservoir dams as the key factors driving this transition. Furthermore, human activities changed hydrodynamics and sediment transport, likely enhancing tidal pumping and strengthening longitudinal circulation. Consequently, the net up-estuary sediment transport had been intensified, ultimately resulting in fast back-siltation during 2014–2018. This insight is essential for sustainably managing medium-sized macrotidal estuaries, especially as they shift from natural to human-influenced states.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132933"},"PeriodicalIF":5.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time error correction of multiple-hour-ahead flash flood forecasting based on the sliding runoff-rain data and deep learning models","authors":"Xingyu Zhou ,&nbsp;Xiaorong Huang ,&nbsp;Xue Jiang ,&nbsp;Jinming Jiang","doi":"10.1016/j.jhydrol.2025.132918","DOIUrl":"10.1016/j.jhydrol.2025.132918","url":null,"abstract":"<div><div>Real-time error correction of flood forecasting is a key method for improving forecast accuracy. However, due to the rapid and unpredictable rise of flash flood discharge and the limited availability of analysis data on short temporal scales, developing robust forecast error correction methods remains a challenge. In this study, we employed a physically-based distributed hydrological model combined with deep learning techniques to develop a real-time error correction method for continuous flash flood forecasting based on sliding runoff-rain data. Taking a typical mountainous river in southwestern China as the study area, we established three input schemes: “sliding runoff data only” (Scheme 1), “hydrological model outputs and sliding runoff data” (Scheme 2), and “hydrological model outputs and sliding runoff-rain data” (Scheme 3). We compared the real-time correction performance of three deep learning models with different architectures—CNN, LSTM, and Transformer—under different input schemes. The results indicate that: 1) LSTM performed the best and most consistently according to the three main evaluation metrics. Although the Transformer showed performance fluctuations, it demonstrated great potential in long forecast correction times, where the correlation between feature inputs and target values is relatively weak. 2) After adding sliding cumulative maximum precipitation data, CNN performance improved significantly, especially in correcting multi-peak floods. 3) The length of the forecast correction time has a significant impact on correction performance. When the forecast correction time approximates the basin’s lag time of runoff concentration, the corrected results have reached a relatively reliable level and entered a more stable phase. This method effectively improves the accuracy of real-time flash flood multiple-hour-ahead forecasting and could provide reliable references for disaster management authorities.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132918"},"PeriodicalIF":5.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic segmentation of urban flood extent in video image with DSS-YOLOv8n","authors":"Jiaquan Wan ,&nbsp;Fengchang Xue ,&nbsp;Yufang Shen ,&nbsp;Hao Song ,&nbsp;Pengfei Shi ,&nbsp;Youwei Qin ,&nbsp;Tao Yang ,&nbsp;Quan J. Wang","doi":"10.1016/j.jhydrol.2025.132974","DOIUrl":"10.1016/j.jhydrol.2025.132974","url":null,"abstract":"<div><div>With the impact of global climate change, floods triggered by extreme rainfall events seriously threaten the operation of urban systems in recent years. Real time and accurate urban flood inundation information is critical for disaster management and emergency response. With emergent technologies and citizen sensing, video image has become a core data source of urban system and exhibits great potential for flood management. Some research studies have made progress in video image flood extent extraction, but still face challenges such as non-universal datasets, outdated technology, and lack of support for multi-terminal deployment. In this study, an advanced approach is proposed to address the common challenges facing previous video image-based flood segmentation, by compiling a specialized dataset and training an enhanced flood segmentation model. Initially, a flood inundation dataset containing 2819 samples and 6048 labeled water instances is compiled based on urban flood video images searched from public platforms. Subsequently, Distributed Shift Convolution (DSConv) is introduced to enhance the performance of You Only Look Once version 8 for segmentation (YOLOv8n-seg) model for flood segmentation, and an optimal model is obtained, called DSS-YOLOv8n. Various cases prove that DSS-YOLOv8n has superior performance in flood extent segmentation. Compared to the baseline YOLOv8n-seg, the DSS-YOLOv8n has advanced performance with the Box mAP50 (mean Average Precision at 50 % Recall) value of 77.5 % (a 1.6 % enhancement), the Mask mAP50 value of 76.5 % (a 1.7 % enhancement), and a reduction of the floating-point operations by 0.6 G. Besides, the behavior of DSS-YOLOv8n for flood segmentation in complex scenarios and the comparison results with typical flood segmentation systems demonstrate its robustness and generality in urban flood segmentation. In brief, this study successfully demonstrates the advancement of the proposed approach in urban flood segmentation and further promotes the use of video images for urban flood management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132974"},"PeriodicalIF":5.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applicability of three remote sensing based soil moisture variables for mapping soil organic matter in areas with different vegetation densities","authors":"Chenconghai Yang ,&nbsp;Lin Yang ,&nbsp;Lei Zhang ,&nbsp;Feixue Shen ,&nbsp;Di Fu ,&nbsp;Shengfeng Li ,&nbsp;Zhiqiang Chen ,&nbsp;Chenghu Zhou","doi":"10.1016/j.jhydrol.2025.132980","DOIUrl":"10.1016/j.jhydrol.2025.132980","url":null,"abstract":"<div><div>Obtaining accurate spatial information on soil organic matter (SOM) is crucial for understanding global carbon cycle. Digital soil mapping (DSM) has become an effective method for mapping SOM, in which selection of influential environmental covariates plays an important role. Soil moisture (SM) can serve as a potential covariate, especially it can be estimated at large spatial scales thanks to remote sensing. The normalized shortwave-infrared difference bare soil moisture indices (NSDSIs) based on Landsat SWIR bands generated at bare soil period has been employed in SOM mapping previously. However, soil is usually covered by vegetation, it is thus necessary to develop new SM indices applicable to areas covered with vegetation, and examine how SM indices perform in areas with different vegetation densities. In this paper, we developed a new SM index by introducing NSDSIs to the Optical TRApezoid Model (OPTRAM-NSDSI), and compared it with the original OPTRAM with the shortwave infrared transformed reflectance (OPTRAM-STR), as well as NSDSIs. SM indices were generated across two study areas, i.e. Zhuxi, Fujian (104 samples and 43.93 km² with forestland and farmland as main land uses) and Heshan, Heilongjiang (106 samples and 60 km² with primarily farmland) in China. The Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation approach was utilized as the SOM prediction model. The results suggest that adding SM variables into the commonly-used environmental covariates improves the prediction accuracies. The highest accuracy improvement of 26.8% in terms of Lin’s concordance correlation coefficient in Zhuxi is obtained by NSDSIs, and the highest improvement of 56.7% in Heshan is obtained by OPTRAM-NSDSI. This may indicate that OPTRAM-NSDSI is more effective in areas with higher vegetation densities while NSDSIs in areas with lower densities. Furthermore, the optimal image dates for SM estimation are probably at the vegetation “green-up” stage. This study provides a reference for using SM information to improve SOM mapping in areas covered with vegetation.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132980"},"PeriodicalIF":5.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-asymmetry on residual sediment transport in the branching channels of the Yangtze Estuary","authors":"Simin Zhou ,&nbsp;Chunyan Zhu ,&nbsp;Jianliang Lin ,&nbsp;Weiming Xie ,&nbsp;Naiyu Zhang ,&nbsp;Leicheng Guo ,&nbsp;Qing He","doi":"10.1016/j.jhydrol.2025.132947","DOIUrl":"10.1016/j.jhydrol.2025.132947","url":null,"abstract":"<div><div>Tidal asymmetry plays a crucial role in sediment transport and morphological evolution in estuarine environments. While there is more than one tidal asymmetry, their individual contributions to residual sediment transport remain insufficiently quantified. In this study, we introduce a multi-asymmetry approach, utilizing short-term field data to quantify the contributions of flood-ebb asymmetries in water depth, flow velocity, duration, and suspended sediment concentration (SSC) on residual sediment transport. The approach is implemented in the branching channels-North Channel, North Passage, and South Passage, located in the turbidity maximum (TM) of the Yangtze Estuary. The results reveal that in the North Channel, seaward residual sediment transport is primarily driven by asymmetries in current velocity (44 %) and duration (33 %) due to strong river flow. In the North Passage, velocity asymmetry accounts for 39 % in the spring-neap tidal cycle, with a significant contribution from SSC asymmetry (33 %) at neap tides. The asymmetries result in the seaward residual sediment transport due to intensified ebb currents, amplified by human-induced channel modifications. Conversely, the South Passage exhibits landward residual sediment transport with a dominance of SSC asymmetry particularly at neap tides, suggesting offshore sediment supply. The residual sediment transport pattern indicates that the North Channel and North Passage primarily export water and sediment, respectively, whereas the South Passage mainly imports sediment. The inter-channel residual sediment transport circulation plays an important role in sediment trapping and tidal flat accretion in the mouth zone. The findings provide valuable insights into understanding sediment dynamics and help managing branching channels in estuarine systems.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132947"},"PeriodicalIF":5.9,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the field-scale crop water condition over an intensive agricultural plain using UAV-based thermal and multispectral imagery","authors":"Saroj Kumar Dash ,&nbsp;Harjinder Sembhi ,&nbsp;Mary Langsdale ,&nbsp;Martin Wooster ,&nbsp;Emma Dodd ,&nbsp;Darren Ghent ,&nbsp;Rajiv Sinha","doi":"10.1016/j.jhydrol.2025.132966","DOIUrl":"10.1016/j.jhydrol.2025.132966","url":null,"abstract":"<div><div>The ever-increasing food demand globally exerts a pressing need to develop efficient water resource management to meet crop water requirements. While satellite remote sensing has proven invaluable for assessing basin-scale crop water conditions (CWC), foreseeable challenges still exist to evaluate field-scale water status in crop-intensive regions. In this study, we present a systematic framework to assess the field-scale CWC using an unmanned aerial vehicle (UAV) based thermal and multispectral imageries at two (upstream and downstream) instrumented sites of an agro-intensive Ganga basin Critical Zone Observatory (CZO), India. We first estimated the land surface temperature (LST) by incorporating emissivity and atmospheric radiance components. We then used the LST and normalised difference vegetation index (NDVI) to compute the vegetation temperature condition index (VTCI) for each site as a proxy for field scale CWC. Our results reveal a significant correlation between aerial LST and <em>in-situ</em> radiometer observations with an absolute difference of ≤±1K. The aerial LST estimation also showed high accuracy and consistently low error (≤3K) in predicting synchronous ground-based surface temperature with a slightly warm bias of 1–2 K. While the variability in mean VTCI across both windows is minimal, the downstream window reveals a sharp increase in drier CWC from 22 % to 51 % in comparison to the upstream region (20–27 %). Further, the field-scale VTCI demonstrated a significant positive correlation (r = 0.6 to 0.81, Kendall’s τ = 0.5 to 0.82) to concurrent soil moisture conditions during all acquisitions. Our study highlights the potential of UAV remote sensing for assessing of high-resolution CWC and designing strategies for regulating agricultural water resources and improving water use efficiency.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"655 ","pages":"Article 132966"},"PeriodicalIF":5.9,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}