Journal of Hydrology最新文献

{"title":"Bayesian structural decomposition of streamflow time series","authors":"Vitor Recacho, Márcio P. Laurini","doi":"10.1016/j.jhydrol.2024.132478","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132478","url":null,"abstract":"Due to the significant influence of climate change and human activities on the water cycle, accurately estimating short- and long-term water availability has become imperative. This study introduces a time series model specifically crafted to decompose river flow time series, enabling estimation of trends, seasonality, and long memory components. This decomposition is interesting as it allows to separate permanent patterns, which can be associated with climate change processes, from transient effects on flow patterns. Additionally, this decomposition is incorporated into the quantile regression in quantile regression framework using a gamma function link. The estimation of this model is based on Bayesian inference, exploring the computational efficiency and accuracy of Integrated Nested Laplace Approximations. This methodology is applied to the principal rivers within the Araguaia River basin in Brazil and compared with other alternative time series decompositions with results indicating a remarkable alignment between the model and observed data.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"31 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825423","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":"Interaction of shallow and deep groundwater with a tropical ocean: Insights from radiogenic (87Sr/86Sr) and stable isotope cycling and fluxes","authors":"Kousik Das, Sourav Ganguly, Prakrity Majumder, Ramananda Chakrabarti, Abhijit Mukherjee","doi":"10.1016/j.jhydrol.2024.132479","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132479","url":null,"abstract":"Coastal groundwater is susceptible to physico-chemical modification from interaction with seawater and other surface waters. Surface water-groundwater (SW-GW) interaction can alter the Sr concentration and radiogenic <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr signature of both seawater and groundwater from multi-depth aquifers. In this study, we document such an interaction between a tropical ocean (Bay of Bengal [BoB]) and the coastal aquifers of a large mega-deltaic system formed by the Himalayan-sourced Ganges River, at shallow (10–50 m below ground level [bgl]), and deeper (115 and 333 m bgl) depths, using radiogenic strontium isotopes (<ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr), stable isotope ratios (δ<ce:sup loc=\"post\">18</ce:sup>O and δD), salinity and dissolved solutes. The mean <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr for shallow coastal aquifers (10–50 m bgl: 0.71094) suggests that seawater mixes with the terrestrial-sourced shallow groundwater, modifying them to brackish water. This is further supported by the stable isotope signatures (14–25 m bgl: −3.63 to −0.7 ‰ and 30–50 m bgl: −3.5 to −1.2 ‰ δ<ce:sup loc=\"post\">18</ce:sup>O). The radiogenic <ce:sup loc=\"post\">87</ce:sup>Sr/<ce:sup loc=\"post\">86</ce:sup>Sr (115 m bgl: 0.71681 and 333 m bgl: 0.71995) and depleted δ<ce:sup loc=\"post\">18</ce:sup>O (115 m bgl: −5.04 to −1.61 ‰ and 333 m bgl: −4.43 to −2.38 ‰) suggest relatively less to negligible mixing between seawater and terrestrial-sourced resident groundwater at greater depths. The mixing process is additionally characterized by a significant Sr flux discharged from these coastal aquifers to the BoB, which ranges between 7.7 × 10<ce:sup loc=\"post\">4</ce:sup> and 12 × 10<ce:sup loc=\"post\">5</ce:sup> mol/year for shallow aquifers, and between 1.78 × 10<ce:sup loc=\"post\">4</ce:sup> and 8.26 × 10<ce:sup loc=\"post\">4</ce:sup> mol/year for deep aquifers, respectively. The overall contribution of Sr from old groundwater of deep aquifers is 1.43 % (115 m bgl) and 0.66 % (333 m bgl), whereas shallow aquifers show a higher contribution, ranging from 6.18 to 9.57 % of BoB Sr budget. This study suggests that the discharge of recirculated brackish water to the BoB from the shallow aquifers contributes more than 5 times higher Sr to the oceanic budget than the deep aquifer, contributing as an essential component of the global oceanic budget of Sr.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"21 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825422","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":"Modeling phosphorus dynamics in rice irrigation systems: Integrating O-Fe-P coupling and regional water cycling","authors":"Xuanye Liu, Minghong Chen, Yun Li, Lu Bai, Jiansong Guo","doi":"10.1016/j.jhydrol.2024.132405","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132405","url":null,"abstract":"The simulation of phosphorus (P) transport processes in rice irrigation areas plays a crucial role in managing eutrophication issues in downstream water bodies within the context of water conservation. Rice cultivation typically occurs in flat plains, where the soil and water environment of paddy fields undergo significant changes during the growth phase, particularly under water conservation practices. This study constructed a distributed model for the microenvironmental stratification of P transformation and transport in paddy fields, which was coupled with a hydrodynamic water quality model for river networks in irrigation areas. The model incorporated several crucial hydrological and water quality processes specific to rice irrigation areas, including water management within paddy fields, diffusion and coupled transformation processes of oxygen-iron-phosphorus in paddy soils, water partitioning-catchment processes in river networks, purification of P in rivers or drainage ditches, and other pertinent physical and biochemical processes related to P transport in irrigation areas. Application of the model in the Heping Irrigation District demonstrated that the simulation of water and P transport processes across various scales well matched the measured data. Both experimental and simulated results indicated that P loads in drainage ditches and rivers were primarily influenced by P discharge from upstream paddy fields, with the model effectively capturing the impact of hydrological fluctuations in paddy fields on P transformation and transport. Thus, the model proves highly suitable for assessing P loads in irrigation districts under varying water management practices.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"219 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790090","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":"High-risk driving factors of rain-induced flooding hazard events on the Loess Plateau and its ecological subregions","authors":"Wenting Zhao, Xinhan Zhang, Juying Jiao, Bo Yang, Xiaowu Ma, Qian Xu, Xiqin Yan, Qi Ling, Jinshi Jian","doi":"10.1016/j.jhydrol.2024.132475","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132475","url":null,"abstract":"Rain-induced flooding hazards are prevalent on the Loess Plateau (LP). Descriptive statistics, kernel density estimation, and geographical detector methods were used to explore the spatial and temporal distribution, driving factors, and their high-risk intervals of rain-induced flooding hazard events (RFHEs) on the LP and whether they differ across the entire LP and its ecological subregions. The findings showed that 91 RFHEs occurred mainly in the south-central LP during 2004–2020. The daily rainfall, surface relief amplitude (SRA), elevation, normalized difference vegetation index (NDVI), soil texture, and population were identified as the driving factors of RFHEs on the LP. However, the driving factors of RFHEs in the Sandy and Agricultural Irrigation Regions (Subregion C), and Earth-rocky Mountainous Region and River Valley Plain Region (Subregion D) all had an added soil texture and population factor compared to the entire LP, but they lacked NDVI and SRA factors, respectively. The driving factors for the Loess Plateau Gully Region (Subregion A) lacked SRA and soil texture factors. The Loess Hilly and Gully Region (Subregion B) lacked NDVI, soil texture, and population factors. There were also differences between high-risk intervals on the LP and its subregions. The high-risk daily rainfall for the entire LP was 64.5 mm, while it was 64.5, 82.3, 14.7, and 50.0 mm for subregions A, B, C, and D, respectively. Therefore, adopting uniform standards on the LP may over-estimate or under-estimate RFHE occurrence in ecological subregions. These findings contribute to guiding decision-makers involved in ecosystem management and hazard prevention.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"10 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790091","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":"Groundwater storage anomalies projection by optimized deep learning refines groundwater management in typical arid basins","authors":"Xiaoya Deng, Guangyan Wang, Feifei Han, Yanming Gong, Xingming Hao, Guangpeng Zhang, Pei Zhang, Qianjuan Shan","doi":"10.1016/j.jhydrol.2024.132452","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132452","url":null,"abstract":"The GRACE satellite provides tools for accurately characterizing the spatiotemporal variations of regional groundwater storage anomalies (GWSA) under the background of climate change and anthropogenic disturbances. However, its low spatial resolution restricts the refined management of groundwater. Multi-scale geographically weighted regression (MGWR) residuals are innovatively introduced for bias correction, which improves the GRACE-based GWSA downscaling accuracy (average R<ce:sup loc=\"post\">2</ce:sup> = 0.98). Further application of the K-means identifies four spatial distribution patterns of GWSA in the Tarim River mainstream (TRM), which showed a downward trend from 2003 to 2020. However, under effective groundwater management (such as ecological water transfer, ecological gate water diversion, etc.), the decline rate is gradually decreasing. Feature contribution analysis demonstrates that soil moisture storage (SMS), land surface temperature (LST), and normalized difference vegetation index (NDVI) are the primary driving factors of GWSA changes. Using the long short-term memory (LSTM) deep learning model optimized by multi-strategy gray wolf optimization algorithm (MSGWO), the GWSA of four spatial patterns is predicted under two shared socioeconomic pathways (SSPs, including SSP245 and SSP585). The model achieved a maximum R/NSE of 0.95/0.91 on the train dataset and 0.88/0.71 on the test dataset, outperforming similar models. The future groundwater reserves of TRM will show an improving trend, indicating that groundwater management has achieved significant benefits. Notably, high emissions without government intervention (SSP585) have exacerbated the risk of groundwater resource shortages, and refined groundwater management needs to be further strengthened in the future. Overall, the proposed GRACE-based GWSA downscaling framework and MSGWO-LSTM predictive model provide tools for the refined scientific management of groundwater in arid basins.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"88 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790092","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":"Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning","authors":"Miao He, Shanhu Jiang, Liliang Ren, Hao Cui, Shuping Du, Yongwei Zhu, Tianling Qin, Xiaoli Yang, Xiuqin Fang, Chong-Yu Xu","doi":"10.1016/j.jhydrol.2024.132440","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132440","url":null,"abstract":"Recently, differentiable modeling techniques have emerged as a promising approach to bidirectionally integrating neural networks and hydrologic models, achieving performance levels close to deep learning models while preserving the ability to output physical states and fluxes. However, there remains a lack of systematic exploration into the performance and physical interpretability of hybrid models that use neural networks to replace the runoff generation and routing processes in regionalized modeling. This research developed 12 regionalized hybrid models based on a differentiable parameter learning (DPL) framework, utilizing the Hydrologiska Byråns Vattenbalansavdelning (HBV) model as the foundational backbone. These hybrid models incorporate neural networks to replace the various physical processes within the runoff generation and routing modules. The publicly available CAMELS dataset is employed to evaluate the performance and interpretability of these hybrid models. The results show that while the median Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) coefficients for all hybrid models are lower than those of the purely data-driven regionalized long short-term memory neural network (LSTM) model (median NSE: 0.742, median KGE: 0.762), the best-performing hybrid model (median NSE: 0.731, median KGE: 0.761) approaches the LSTM model and has better physical interpretability. Embedding neural networks does not inherently guarantee improved performance and may, in some cases, even result in reduced performance. The degree of performance enhancement is not significantly correlated with the number of embedded neural networks. Compared to replacing the runoff generation process, substituting the routing process with neural networks yields more substantial performance improvements and enables the learning of different routing patterns based on the catchment’s static attributes. This study underscores the importance of reasonably balancing the location, complexity, and quantity of embedded neural networks to achieve a trade-off between model performance and interpretability in hybrid modeling. These insights contribute to advancing regionalized hybrid modeling development.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"28 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790093","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":"A differentiable, physics-based hydrological model and its evaluation for data-limited basins","authors":"Wenyu Ouyang, Lei Ye, Yikai Chai, Haoran Ma, Jinggang Chu, Yong Peng, Chi Zhang","doi":"10.1016/j.jhydrol.2024.132471","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132471","url":null,"abstract":"Recent advancements in deep learning (DL) have significantly improved hydrological modeling by extracting generalities from large-sample datasets and enhancing predictive accuracy. However, DL models often rely heavily on large volumes of data, which are often unavailable or insufficient in many real-world hydrological applications. This challenge has prompted interest in integrating DL with physically based hydrological models (PBHMs). This study explores such integration using differentiable programming with the Xin’anjiang model. We introduce two advanced model variants: the differentiable Xin’anjiang model (dXAJ), which retains the Xin’anjiang model’s structure while incorporating Long Short-Term Memory (LSTM) networks for parameter learning, and the dXAJ<ce:inf loc=\"post\">nn</ce:inf> model, which replaces the traditional evapotranspiration module of dXAJ model with a neural network. Both models were evaluated against the evolutionary algorithm-calibrated XAJ model (eXAJ) across five basins in the Three Gorge region of China and eight basins from the CAMELS dataset under varying data-limited conditions. Our results showed that both dXAJ and dXAJ<ce:inf loc=\"post\">nn</ce:inf> models outperformed the eXAJ model in streamflow prediction accuracy as they have different optimization mechanism, demonstrating that the local optimization mechanism in differentiable models (DMs) tends to generalize better during validation than global optimization approaches in data-limited contexts. The DMs also provided reliable evapotranspiration estimates, even without using evapotranspiration data for calibration. Although the dXAJ<ce:inf loc=\"post\">nn</ce:inf> model offered greater flexibility, it did not consistently yield better results and exhibited a tendency toward overfitting in certain basins. The study also found that both models require a minimum of three years of training data (including a one-year warm-up period) to achieve acceptable predictive performance, with longer data records further preventing overfitting. These findings underscore the ability of DMs to effectively balance data-driven techniques and physical mechanisms, highlighting the importance of sufficient training data.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"24 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790108","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":"Flood mitigation performance of low impact development practice in a coastal city from the perspective of catchment scale","authors":"Wenchao Qi, Chao Ma, Hongshi Xu, Kui Xu, Jijian Lian","doi":"10.1016/j.jhydrol.2024.132466","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132466","url":null,"abstract":"Climate change and urbanization have imposed significant stress on urban drainage systems, resulting in hydraulic overloading and urban flooding. The implementation of Low Impact Development (LID) practices exhibits promising potential in mitigating these impacts. In this study, a modeling task was proposed for the drainage district of Haikou City, Hainan Province, China. The tracer-aided urban flood model was employed to conduct 64 simulation scenarios, aiming to evaluate the hydrological response of LID strategies under varying rainfall intensity, spatial distribution, and initial saturation level. The results have demonstrated the significant utility of the tracer-aided urban flood model in assessing the hydrological impact of LID practices, particularly in accurately identifying both the source area and hazard area affected by flooding. Notably, rainfall intensity plays a crucial role in influencing the hydrological response of LID practices. With the increase in rainfall intensity, the efficacy of LID strategies in mitigating value of peak flood volume gradually intensifies, but the reduction rate of peak flood volume diminishes progressively. In terms of LID strategies with varying spatial distributions, the upstream strategy outperforms both midstream and downstream strategy. Nevertheless, the correlation between reduction value of flood volume across different catchments suggests a complex synergistic effect among them. The reduction value of LID practices on flood volume will gradually decrease as the initial saturation increases, indicating that careful consideration should be given to the impact of rainfall patterns on their initial saturation when incorporating LID practices into an urban flood mitigation strategy. This study provides valuable insights into sustainable stormwater management by examining the effectiveness and influencing factors of LID practices.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"20 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790111","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":"A simplified method for estimating the alpha coefficient in surface velocity based river discharge measurements","authors":"Dario Pumo, Francesco Alongi, Carmelo Nasello, Leonardo V. Noto","doi":"10.1016/j.jhydrol.2024.132468","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132468","url":null,"abstract":"Remote sensing techniques for river monitoring facilitate faster measurement campaigns compared to traditional methods, reduce risks to personnel and instruments, and allow measurements under critical flow conditions. An alpha coefficient (α) is commonly employed to convert surface velocities, obtained by contactless techniques, into depth-averaged velocities, which are used for the application of the velocity-area method for assessing discharge. Some optical-based software programs use a constant α value, based on a theoretical “standard”. However, analyses of empirical vertical velocity profiles in real cases reveal that α can significantly deviate from this standard due to various factors (roughness, turbulence, etc.).","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"219 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790113","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":"Corrigendum to: ‘Discrepancy precipitation index for monitoring meteorological drought’ by Gokmen Tayfur, Journal of Hydrology 597 (2021) 126174, Doi 10.1016/j.jhydrol.2021.126174","authors":"Gokmen Tayfur","doi":"10.1016/j.jhydrol.2024.132347","DOIUrl":"10.1016/j.jhydrol.2024.132347","url":null,"abstract":"","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"645 ","pages":"Article 132347"},"PeriodicalIF":5.9,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696461","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}