Pub Date : 2024-12-11DOI: 10.1016/j.jhydrol.2024.132504
M. Girons Lopez, T. Bosshard, L. Crochemore, I.G. Pechlivanidis
Seasonal hydrological forecasts are vital for managing water resources and adapting to climate change, aiding in diverse planning and decision-making processes. Currently it is unknown how different forecasting methods, considering initial hydrological conditions and dynamic meteorological forcing, perform across the Swedish river systems, despite the significant socio-economic implications. Here we explore the drivers that mostly impact streamflow predictions and attribute the added quality of these predictions to local hydrological regimes. We compare the accuracy of seasonal streamflow forecasts driven by dynamic GCM-based meteorological forecasts with those generated by the Ensemble Streamflow Prediction (ESP) method. The analysis spans across about 39,500 sub-catchments in Sweden encompassing various climatic, geographical and human-influenced factors. Results show that the streamflow predictability varies in space due to the country’s diverse hydrological regimes. Regardless of the regime, updating the models to achieve the best possible initial conditions is crucial for enhancing forecast skill across all seasons for up to 4 months. GCM-based meteorological forcing notably improves short-term streamflow accuracy, showing significant impact particularly up to 4–8 weeks lead time depending on the local hydrological regime. In the snow-driven northern regions, ESP demonstrates superior performance over GCM-based streamflow forecasts in winter. Conversely, in the southern regions, where conditions are predominantly influenced by rainfall, GCM-based forecasts show higher performance up to 4–6 weeks ahead, regardless of the season. In river systems with high human influences, streamflow climatology outperforms ESP and GCM-based forecasts underscoring the challenges of accurately modelling artificial reservoir management and the need for better access to management data. These insights guide the development of an advanced national seasonal hydrological forecasting service, and highlight the need for region-specific forecasting strategies indicating areas where predictability is enhanced by improved monitoring, hence initial conditions, and/or meteorological forcings. Finally, we discuss the applicability of these forecasting methods to other regions worldwide, thereby placing our new insights within a global context.
{"title":"Leveraging GCM-based forecasts for enhanced seasonal streamflow prediction in diverse hydrological regimes","authors":"M. Girons Lopez, T. Bosshard, L. Crochemore, I.G. Pechlivanidis","doi":"10.1016/j.jhydrol.2024.132504","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132504","url":null,"abstract":"Seasonal hydrological forecasts are vital for managing water resources and adapting to climate change, aiding in diverse planning and decision-making processes. Currently it is unknown how different forecasting methods, considering initial hydrological conditions and dynamic meteorological forcing, perform across the Swedish river systems, despite the significant socio-economic implications. Here we explore the drivers that mostly impact streamflow predictions and attribute the added quality of these predictions to local hydrological regimes. We compare the accuracy of seasonal streamflow forecasts driven by dynamic GCM-based meteorological forecasts with those generated by the Ensemble Streamflow Prediction (ESP) method. The analysis spans across about 39,500 sub-catchments in Sweden encompassing various climatic, geographical and human-influenced factors. Results show that the streamflow predictability varies in space due to the country’s diverse hydrological regimes. Regardless of the regime, updating the models to achieve the best possible initial conditions is crucial for enhancing forecast skill across all seasons for up to 4 months. GCM-based meteorological forcing notably improves short-term streamflow accuracy, showing significant impact particularly up to 4–8 weeks lead time depending on the local hydrological regime. In the snow-driven northern regions, ESP demonstrates superior performance over GCM-based streamflow forecasts in winter. Conversely, in the southern regions, where conditions are predominantly influenced by rainfall, GCM-based forecasts show higher performance up to 4–6 weeks ahead, regardless of the season. In river systems with high human influences, streamflow climatology outperforms ESP and GCM-based forecasts underscoring the challenges of accurately modelling artificial reservoir management and the need for better access to management data. These insights guide the development of an advanced national seasonal hydrological forecasting service, and highlight the need for region-specific forecasting strategies indicating areas where predictability is enhanced by improved monitoring, hence initial conditions, and/or meteorological forcings. Finally, we discuss the applicability of these forecasting methods to other regions worldwide, thereby placing our new insights within a global context.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"63 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825415","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}
Pub Date : 2024-12-11DOI: 10.1016/j.jhydrol.2024.132508
Kewei Lyu, Yihan Dong, Wensheng Lyu, Yan Zhou, Sufen Wang, Zhaomeng Wang, Weizhe Cui, Yaobin Zhang, Qiulan Zhang, Yali Cui
Ecological water replenishment (EWR) integrates surface and groundwater regulation to promote riverine baseflows and support groundwater recovery, affecting their interactions. This study introduces SWAT-LSTM-MODFLOW, an advanced SWAT-MODFLOW model incorporating LSTM networks to improve predictive accuracy in data-scarce watersheds. Applied to the Beijing section of the Yongding River Basin, the model evaluates the impact of EWR via reservoir and reclaimed water releases on groundwater recovery and SW-GW interactions. Results show that EWR enhanced groundwater levels in the short term, particularly at the mountain-plain boundary, with increases up to 5 m during high-volume replenishments. Repeated replenishments from 2019 to 2022 shifted dynamics from river seepage to increased groundwater recharge, particularly near replenishment zones. These findings highlight EWR’s role in transforming SW-GW dynamics and enhancing hydrological connectivity. This study provides a quantitative framework for assessing artificial recharge effects on groundwater and SW-GW interactions, offering a scalable methodology for similar hydrogeological conditions.
{"title":"Data-driven and numerical simulation coupling to quantify the impact of ecological water replenishment on surface water-groundwater interactions","authors":"Kewei Lyu, Yihan Dong, Wensheng Lyu, Yan Zhou, Sufen Wang, Zhaomeng Wang, Weizhe Cui, Yaobin Zhang, Qiulan Zhang, Yali Cui","doi":"10.1016/j.jhydrol.2024.132508","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132508","url":null,"abstract":"Ecological water replenishment (EWR) integrates surface and groundwater regulation to promote riverine baseflows and support groundwater recovery, affecting their interactions. This study introduces SWAT-LSTM-MODFLOW, an advanced SWAT-MODFLOW model incorporating LSTM networks to improve predictive accuracy in data-scarce watersheds. Applied to the Beijing section of the Yongding River Basin, the model evaluates the impact of EWR via reservoir and reclaimed water releases on groundwater recovery and SW-GW interactions. Results show that EWR enhanced groundwater levels in the short term, particularly at the mountain-plain boundary, with increases up to 5 m during high-volume replenishments. Repeated replenishments from 2019 to 2022 shifted dynamics from river seepage to increased groundwater recharge, particularly near replenishment zones. These findings highlight EWR’s role in transforming SW-GW dynamics and enhancing hydrological connectivity. This study provides a quantitative framework for assessing artificial recharge effects on groundwater and SW-GW interactions, offering a scalable methodology for similar hydrogeological conditions.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"77 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825418","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}
Pub Date : 2024-12-09DOI: 10.1016/j.jhydrol.2024.132487
Cui Gan, Zhaobo Luo, Chengyuan Su, Caixi Hu, Lei Tong, Jianbo Shi
The hyporheic zone is a crucial ecohydrological interface that plays a substantial role in the biogeochemical activity of iron and its mediated pollutant conversion. It is significantly influenced by dissolved oxygen and temperature fluctuations, but the combined effects and mechanisms are unknown. In this study, the co-transport behavior of goethite colloid (Goe), aqueous Fe2+ and oxytetracycline (OTC) in groundwater discharge was simulated by column experiments. Our findings reveal that compared with room temperature (25 °C), the penetration rates of these compounds were generally promoted (0.1–7.0 % Goe, 14.1–43.1 % Fe2+, 0–19.6 % OTC) at low temperature (10 °C) but inhibited (0–5.0 % Goe, 0–51.0 % Fe2+, 0–3.8 % OTC) at high temperature (35 °C). At room temperature (25 °C), only 5 % of the Goe can penetrate the triadic transport system, where the Fe-OTC complex decreased the Zeta potential of Goe, hence improving its transport capacity. Compared with the penetration of individual Fe2+, the Fe2+ transport was increased by 13.2 % due to the promoting effect of OTC on Fe redox cycling, whereas the electron transfer effect between Goe and Fe2+ inhibited the transport by 46.6 %. The impact of μg/L OTC on the migration of Fe and Goe was dramatically diminished compared to the mg/L level. OTC was eliminated mainly by complex internal oxidation with Fe, weak adsorption, chemisorption, and hydroxyl degradation effects, but these were diminished at low temperatures while intensified at high temperatures. This study provides a deeper understanding of the intricate mechanisms of Fe and antibiotic transport in hyporheic zones, highlighting the significant roles of temperature and chemical interactions, particularly during seasonal changes.
{"title":"Temperature-dependent co-transport behavior of goethite, Fe2+, and antibiotic in the hyporheic zone","authors":"Cui Gan, Zhaobo Luo, Chengyuan Su, Caixi Hu, Lei Tong, Jianbo Shi","doi":"10.1016/j.jhydrol.2024.132487","DOIUrl":"https://doi.org/10.1016/j.jhydrol.2024.132487","url":null,"abstract":"The hyporheic zone is a crucial ecohydrological interface that plays a substantial role in the biogeochemical activity of iron and its mediated pollutant conversion. It is significantly influenced by dissolved oxygen and temperature fluctuations, but the combined effects and mechanisms are unknown. In this study, the co-transport behavior of goethite colloid (Goe), aqueous Fe<ce:sup loc=\"post\">2+</ce:sup> and oxytetracycline (OTC) in groundwater discharge was simulated by column experiments. Our findings reveal that compared with room temperature (25 °C), the penetration rates of these compounds were generally promoted (0.1–7.0 % Goe, 14.1–43.1 % Fe<ce:sup loc=\"post\">2+</ce:sup>, 0–19.6 % OTC) at low temperature (10 °C) but inhibited (0–5.0 % Goe, 0–51.0 % Fe<ce:sup loc=\"post\">2+</ce:sup>, 0–3.8 % OTC) at high temperature (35 °C). At room temperature (25 °C), only 5 % of the Goe can penetrate the triadic transport system, where the Fe-OTC complex decreased the Zeta potential of Goe, hence improving its transport capacity. Compared with the penetration of individual Fe<ce:sup loc=\"post\">2+</ce:sup>, the Fe<ce:sup loc=\"post\">2+</ce:sup> transport was increased by 13.2 % due to the promoting effect of OTC on Fe redox cycling, whereas the electron transfer effect between Goe and Fe<ce:sup loc=\"post\">2+</ce:sup> inhibited the transport by 46.6 %. The impact of μg/L OTC on the migration of Fe and Goe was dramatically diminished compared to the mg/L level. OTC was eliminated mainly by complex internal oxidation with Fe, weak adsorption, chemisorption, and hydroxyl degradation effects, but these were diminished at low temperatures while intensified at high temperatures. This study provides a deeper understanding of the intricate mechanisms of Fe and antibiotic transport in hyporheic zones, highlighting the significant roles of temperature and chemical interactions, particularly during seasonal changes.","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"39 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825421","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}
Pub Date : 2024-12-09DOI: 10.1016/j.jhydrol.2024.132478
Vitor Recacho, Márcio P. Laurini
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.
{"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}
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 87Sr/86Sr 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 (87Sr/86Sr), stable isotope ratios (δ18O and δD), salinity and dissolved solutes. The mean 87Sr/86Sr 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 ‰ δ18O). The radiogenic 87Sr/86Sr (115 m bgl: 0.71681 and 333 m bgl: 0.71995) and depleted δ18O (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 × 104 and 12 × 105 mol/year for shallow aquifers, and between 1.78 × 104 and 8.26 × 104 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.
沿海地下水由于与海水和其他地表水的相互作用而容易发生物理化学变化。地表水-地下水（SW-GW）相互作用可以改变多深度含水层海水和地下水的锶浓度和放射性成因87Sr/86Sr特征。本研究利用放射性锶同位素（87Sr/86Sr）、稳定同位素比值（δ18O和δD）、盐度和溶解溶质，记录了热带海洋（孟加拉湾[BoB]）与喜马拉雅山源恒河形成的大型巨型三角洲系统在浅层（10-50 m）和深层（115和333 m）的沿海含水层之间的相互作用。浅海沿岸含水层（10-50 m bgl: 0.71094）的平均87Sr/86Sr表明海水与陆源浅层地下水混合，使其变成半咸淡水。稳定的同位素特征（14-25 m bgl:−3.63 ~−0.7‰和30-50 m bgl:−3.5 ~−1.2‰δ18O）进一步支持了这一点。放射性成因87Sr/86Sr （115 m bgl: 0.71681和333 m bgl: 0.71995）和贫化δ18O （115 m bgl: - 5.04 ~ - 1.61‰和333 m bgl: - 4.43 ~ - 2.38‰）表明，在更深的深度，海水与陆源居民地下水的混合相对较少或可以忽略。混合过程的另一个特征是沿海含水层向BoB排放了大量的Sr通量，浅层的Sr通量在7.7 × 104 ~ 12 × 105 mol/年之间，深层的Sr通量在1.78 × 104 ~ 8.26 × 104 mol/年之间。深含水层老地下水Sr的总体贡献分别为1.43% （115 m bgl）和0.66% (333 m bgl)，而浅层含水层的Sr贡献更高，占BoB Sr预算的6.18% ~ 9.57%。研究表明，浅层再循环咸淡水对海洋收支的贡献比深层高5倍以上，是全球海洋收支的重要组成部分。
{"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}
Pub Date : 2024-12-04DOI: 10.1016/j.jhydrol.2024.132405
Xuanye Liu, Minghong Chen, Yun Li, Lu Bai, Jiansong Guo
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.
{"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}
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.
{"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}
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 R2 = 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.
{"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}
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.
{"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}
Pub Date : 2024-12-02DOI: 10.1016/j.jhydrol.2024.132471
Wenyu Ouyang, Lei Ye, Yikai Chai, Haoran Ma, Jinggang Chu, Yong Peng, Chi Zhang
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 dXAJnn 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 dXAJnn 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 dXAJnn 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.
{"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}
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