Journal of Hydrology-Regional Studies最新文献_第10页

Deep characteristic learning model for real-time flow monitoring based on H-ADCP

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102115

Yu Li, Xin Zhao, Yibo Wang, Ling Zeng

Study region

The Luohu hydrological station, located in southeastern China, which has unstable water level- discharge relationship caused by tides.

Study focus

Real-time flow monitoring based on horizontal-acoustic Doppler current profiler (H-ADCP), which remains insufficient to deal with low monitoring accuracy, complex flow characteristics, and large data volumes caused by the construction and operation of hydraulic engineering, backwater, tides, siltation changes, and high-frequency monitoring. This study proposed a deep characteristic learning (DCL) model to identify and extract the nonlinear characteristics between flow velocity of H-ADCP cell and river cross section by incorporating multiple intelligent algorithms.

New hydrological insights for the region

The DCL model performs efficiently with a determination coefficient (R²) of 0.93 between the simulated and observed discharge, which is obviously better than the single intelligent algorithm-based models. The DCL model allows for adaptive algorithm selection and parameter adjustment according to the characteristics of river cross section and H-ADCP data. It shows strong self-learning capability and good simulation accuracy even with few training samples. Additionally, the DCL model is demonstrated to be stable and applicable in terms of model structure and practical performance. This study can serve as a reference for real-time flow monitoring under complex hydrological conditions.

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引用次数: 0

Ambient intraborehole flow in a highly productive aquifer in Ljubljana, Slovenia

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102139

Janja Svetina , Joerg Prestor , Simon Mozetič , Mihael Brenčič

Study region

Ljubljansko polje aquifer in the central part of Slovenia.

Study focus

Impeller flowmeter was used together with the water quality probe to determine the flow characteristics of water under ambient conditions (without pumping) in 13 boreholes in a highly productive alluvial aquifer. The study focused on analysing the characteristics and variability of intraborehole flows in relation to the technical features of the boreholes, as well as the geometry and geological composition of the aquifer.

New hydrological insights for the region

The study has shown that vertical hydraulic gradients are not limited to complex multiple aquifer systems but can also occur in uniform alluvial aquifers without typical aquitard layers if hydraulic head differences exist between the upper and lower parts of the aquifer. The intraborehole flows are mainly caused by pressure-driven convection in deeper boreholes with long screens. The direction and velocity of these flows are strongly influenced by the morphology of the bedrock. Downward water flows are closely related to the local deepening of the bedrock and upward flows to the local rise of the bedrock. In some boreholes, intraborehole flow rates exceed the typical pumping rates for groundwater sampling, raising concerns about the representativeness of samples collected using conventional sampling methods. Hydrochemical logs proved to be a valuable complementary method to determine the locations of the main inflows.

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引用次数: 0

Non-stationarity analysis of flood extremes under climate change and human activities in the Yuan River Basin, China

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102131

Ting Wang , Dehua Mao , Meirong Deng , Chang Feng , Guangwei Hu , Jingya Zhang , Yang Zou

Study region

Yuan River Basin in China.

Study focus

This study aims to identify temporal variations and non-stationarity in the annual maximum Peak Flow (PF) and Peak Water Level (PWL) series, along with their occurrence dates and intervals series at three stations (Anjiang, Pushi, and Taoyuan). Temporal variations were detected and non-stationary models were developed by incorporating physically-based covariates under climate change and human activities using the Mann-Kendall test, Pettitt test, and the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework.

New hydrological insights for the region

(1) The intervals between Pushi and Taoyuan stations for both PF and PWL series have significantly shortened over the past 40 years, while the Taoyuan station showed no significant upward trend over the past 70 years. (2) Covariates associated with precipitation, Normalized Difference Vegetation Index (NDVI), Reservoir Index, and Impervious Area (IA) showed a significantly increased trend within the basin, particularly the annual maximum daily precipitation (P₁) at the Pushi station. (3) The non-stationary models performed best when incorporating either the 7-day or 15-day accumulated antecedent precipitation before the flood occurrence date for the flood extremes series, while the inclusion of the IA and mean NDVI three months before the flood occurrence month provided superior fitting for the occurrence dates series. (4) The variability of P₁, 3-day accumulated precipitation and their overlap with 7-day and 15-day accumulated precipitation were most likely key factors in triggering typical flood extremes.

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引用次数: 0

Diminished water yield coefficient of glacial catchments in Northwest China

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102092

Jing Liu , Aihua Long , Mingjiang Deng , Qiang An , Xiaoya Deng , Zhenliang Yin , Pei Zhang , Xiaoying Lai , Xinchen Gu

Study region

19 glacial catchments located in Northwest China.

Study focus

The water yield coefficient (denoted as WY_c) of glacier catchments in Northwest China has changed over the past six decades, closely linked to shifts in land cover dynamics. However, quantifying the impacts of climate and land cover changes on glacier catchments remains challenging due to the complexity of glacier hydrological processes and the scarcity of long-term glacier runoff data. In this study, the Budyko equation was coupled with a glacier runoff model to enable quantitative analysis of the factors driving changes in water yield (WY). Path analysis was then employed to assess the direct and indirect effects of climate and landscape changes on the WY_c.

New hydrological insight for the region

The results indicated that 14 out of 19 catchments experienced a decline in WY_c, particularly in those with greater glacier coverage. Glacier retreat and increased vegetation greenness adversely affected WY_c by increasing the catchment parameter, with effects of −26.42 % and −25.21 %, respectively. Although overall WY in the catchments has increased—driven primarily by increased precipitation and glacier storage loss, with contribution rates of 100.66 % and 25.05 %, respectively—the diminished WYc is expected to decelerate the rate of WY increase. This trend poses significant challenges for water resource management in arid and water-scarce regions.

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引用次数: 0

Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102118

Wentong Hu, Donghao Miao, Chi Zhang, Zixian He, Wenquan Gu, Dongguo Shao

Study region

The Honghu Lake (HHL) and Changhu Lake (CHL) in middle China.

Study focus

Large-scale and high-precision estimation of water quality parameters (WQPs) is critical in explaining the spatiotemporal dynamics and clarifying their response to short-term hydrometeorological factors. Six machine learning models optimized by intelligent optimization algorithms (IOA-ML) were developed to retrieve WQPs using paired in situ measurements and near-synchronous Sentinel-2 reflectance (R_rs). Furthermore, the response of pixel-based WQPs to short-term hydrometeorological factors were explored by generalized additive model (GAM).

New hydrological insights for the region

The results showed that R_rs curves were significantly correlated with WQPs concentration, which provided a solid foundation for WQPs retrieval. The best IOA-ML model for total phosphorus (TP), total nitrogen (TN), and permanganate index (COD_Mn) was extreme gradient boosting optimized by genetic algorithm (GA-XGB), while that for dissolved oxygen (DO) and turbidity was categorical boosting regression optimized by GA (GA-CBR). Coefficient of determination (R²) of the best retrieval models for the test sets of TP, TN, turbidity, COD_Mn and DO were 0.545, 0.418, 0.794, 0.798, and 0.653, respectively. The best retrieval models were applied to two big inland lakes and revealed that TP, TN, COD_Mn, and turbidity in HHL increased rapidly from 2016 to 2022, especially during 2021–2022. 81.4 %-91.5 % of the WQPs variations in HHL and 63.4 %-92 % in CHL can be explained by hydrometeorological factors.

{"title":"Retrieval of water quality parameters based on IOA-ML models and their response to short-term hydrometeorological factors","authors":"Wentong Hu, Donghao Miao, Chi Zhang, Zixian He, Wenquan Gu, Dongguo Shao","doi":"10.1016/j.ejrh.2024.102118","DOIUrl":"10.1016/j.ejrh.2024.102118","url":null,"abstract":"<div><h3>Study region</h3><div>The Honghu Lake (HHL) and Changhu Lake (CHL) in middle China.</div></div><div><h3>Study focus</h3><div>Large-scale and high-precision estimation of water quality parameters (WQPs) is critical in explaining the spatiotemporal dynamics and clarifying their response to short-term hydrometeorological factors. Six machine learning models optimized by intelligent optimization algorithms (IOA-ML) were developed to retrieve WQPs using paired <em>in situ</em> measurements and near-synchronous Sentinel-2 reflectance (R<sub>rs</sub>). Furthermore, the response of pixel-based WQPs to short-term hydrometeorological factors were explored by generalized additive model (GAM).</div></div><div><h3>New hydrological insights for the region</h3><div>The results showed that R<sub>rs</sub> curves were significantly correlated with WQPs concentration, which provided a solid foundation for WQPs retrieval. The best IOA-ML model for total phosphorus (TP), total nitrogen (TN), and permanganate index (COD<sub>Mn</sub>) was extreme gradient boosting optimized by genetic algorithm (GA-XGB), while that for dissolved oxygen (DO) and turbidity was categorical boosting regression optimized by GA (GA-CBR). Coefficient of determination (R<sup>2</sup>) of the best retrieval models for the test sets of TP, TN, turbidity, COD<sub>Mn</sub> and DO were 0.545, 0.418, 0.794, 0.798, and 0.653, respectively. The best retrieval models were applied to two big inland lakes and revealed that TP, TN, COD<sub>Mn</sub>, and turbidity in HHL increased rapidly from 2016 to 2022, especially during 2021–2022. 81.4 %-91.5 % of the WQPs variations in HHL and 63.4 %-92 % in CHL can be explained by hydrometeorological factors.</div></div>","PeriodicalId":48620,"journal":{"name":"Journal of Hydrology-Regional Studies","volume":"57 ","pages":"Article 102118"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102146

Yuning Luo , Ke Zhang , Wen Wang , Xinyu Chen , Jin Feng , Haijun Wang , Wei Liu , Cheng Guo , Cuiying Chen , Xiaozhong Wang

Study region: We selected the China’s Huaihe River Basin as the study region.

Study focus: Bias correction is crucial for improving the accuracy of Global Climate Model (GCM) predictions. We proposed an improved statistical bias correction method called the EquiDistant Cumulative Distribution Function (CDF) matching method with least square fitting of CDF’s deviation (LS-EDCDF) to correct biases in GCM precipitation. The LS-EDCDF method incorporates recurrence periods and least squares with the determination of transfer functions relating observation to model simulation. We applied this and three other methods—EquiDistant Cumulative Distribution Function matching method (EDCDF), combined Linear Scaling and Cumulative Distribution Function matching method (LS-CDF) and Quantile-quantile mapping method (QUANT)—to correct biases in the CMCC-CM2-SR5 model. Performance was evaluated in four aspects. Taylor diagram statistical metrics and correlation coefficients are used to measure agreements between the basin-average observations and bias-corrected model precipitation and their CDFs during the validation period (2015–2020), respectively. The Expert Team on Climate Change Detection and Indices (ETCCDI) extreme precipitation indices and spatial patterns of extreme precipitation in July 2020 in the observations and bias-corrected data were then compared to evaluate the effectiveness of these methods on capturing precipitation extremes.

New hydrological insights for the region: Results show that the LS-EDCDF method outperforms the other three methods in correcting monthly CDFs, reducing biases, and preserving extreme precipitation. All results of these methods indicate that the CMCC-CM2-SR5 model tends to overestimate precipitation but performs well during the main flood seasons, especially for extreme precipitation. Future projections suggest decreasing interannual variability of precipitation, with a lower change rate during the flood season and a higher rate during the non-flood season. The bias-corrected data from this method can serve as a valuable input for projecting future floods in this important area of China.

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引用次数: 0

Advancing hydrological modeling through multivariate calibration of multi-layer soil moisture dynamics

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102125

Yan He , Huihui Mao , Chen Wang , Jinghao Hu , Sarawut Ninsawat , Xianfeng Song , Guifei Jing , Runkui Li , Mingyu Wang , Zheng Duan

Study region

The Meichuan Basin, China

Study focus

Soil water processes are critical in hydrological modeling, yet most studies focus on surface moisture due to data limitations, which hampers accurate simulations of root zone soil moisture dynamics. To address this gap, we developed three calibration schemes: M1 and M2, two benchmarks that rely solely on traditional streamflow data and incorporate both streamflow data and top-layer soil moisture data, respectively. In contrast, M3 integrates both streamflow data and multi-layer soil moisture information from SMCI 1.0. These schemes aim to assess the added value of integrating multi-layer soil moisture data to enhance hydrological modeling performance.

New hydrological insights for the region

The M3 scheme yielded the most accurate simulation of the spatial and temporal distribution of multi-layer soil moisture compared to M1 and M2 benchmarks. In this subtropical humid basin, the M3 model effectively captured the pronounced fluctuations in soil moisture driven by frequent and intense precipitation events, as well as the seasonal variability between wet and dry periods. M3 also improved the accuracy of evapotranspiration simulations across all subbasins, while maintaining acceptable streamflow simulations at gauge stations. These findings underscore the importance of using advanced multi-layer soil moisture data in models to regulate hydrological processes and control water distribution within the hydrological cycle.

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引用次数: 0

Estimation of inelastic skeletal storativity based on SAR-derived land subsidence and groundwater variation in Beijing Plain, China

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102161

Chenxia Li , Yanbing Wang , Jie Yu , Huili Gong , Xiaojuan Li , Xiyue Yang , Haowen Cheng , Xiaohuan Li , Kemiao Shao

Study region

Beijing plain in the eastern of Beijing, China.

Study focus

Over the past three decades, more than 2 billion m³ of groundwater have been pumped annually in the Beijing Plain, resulting in approximately 431 km² of land subsidence of more than 50 mm annually. While most studies have identified a correlation between land subsidence and groundwater overexploitation, quantifying their relationship has been challenging. In this paper, the land subsidence data were obtained based on the persistent scatterer interferometric synthetic aperture radar (PS-InSAR, PSI) and the least square (LS) method. The parameter of inelastic skeletal storativity (S_i) of the confined aquifer was used as a quantitative indicator to describe the relationship between land subsidence and groundwater exploitation in the Beijing Plain.

New hydrology insights

Moreover, the paper found a robust correlation between groundwater overexploitation and land subsidence in the deep confined aquifer through groundwater monitoring data. From 2005–2016, S_i showed a gradual and continuous increase in a specific range. The abnormal change in S_i value during 2014–2015 may be associated with the recharge of shallow confined and unconfined aquifers in the Beijing Plain by the South-to-North Water Diversion Project. The S_i estimated in the study area can be utilized to accurately deduce the regional water level shifts, thereby aiding in the efficient and sustainable management of groundwater resources.

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引用次数: 0

Estimation of water storage changes in a tropical lake-floodplain system through remote sensing

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102173

Thijs de Klein , Victor Bense , Syed Mustafa

Study region

Lake Urema and its floodplain in Gorongosa National Park, Mozambique

Study focus

Tropical lowland lake-floodplain systems are increasingly threatened by climate change effects and other human-induced pressures. Determining the effect of these pressures on the water balance is challenging because of a lack of hydrological monitoring data, which impedes water management decisions. A collection of optical remote sensing and Synthetic Aperture Radar (SAR) scenes is used in combination with supervised classification algorithms and topographical data to derive lake volumes for the period 1984–2023, which are analyzed for trends and correlation with satellite-derived climate data.

New hydrological insights for the region

Although lake volumes show strong interannual variability, no significant historical trend is identified. A precipitation response time of approximately two months is observed, suggesting a considerable contribution of groundwater to the lake’s water balance. Minimum lake volumes found for the period 2014–2017 coincide with a prolonged period of below-average precipitation, indicating the effect of decreased groundwater recharge. Dry season lake volumes show weak correlation with cumulative precipitation in comparison to rainy season lake volumes, further indicating the importance of groundwater inflow for the dry season water balance. Results suggest that climate change effects and anthropogenic activities may have little short-term impact on the lake’s dry season volume, while altering groundwater recharge may have more significant long-term effects.

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引用次数: 0

Geochemical-based appraisal of karst groundwater quality, west Nile Valley, central Egypt, for drinking and irrigation

IF 4.7 2区地球科学 Q1 WATER RESOURCES

Journal of Hydrology-Regional Studies

Pub Date : 2025-02-01 DOI: 10.1016/j.ejrh.2024.102152

Hassan Alzahrani , Abdullah Basaloom , Sayed Mosaad

Study region: west El-Minya region is located West Nile Valley in the central part of Egypt.

Study focus: The research aims to evaluate the geochemical processes affecting the groundwater and its suitability for drinking and agricultural purposes. This was achieved through integrated the analysis of ionic concentration data, using Piper and Gibbs diagrams, ionic ratios, multivariate analysis, and Water Quality Indices (WQI).

New hydrological insight: Funding indicated that the groundwater varies from fresh to brackish. The water types are characterized by Na-Cl-SO₄ and Ca-Mg-SO₄-Cl. Binary diagrams and factor analysis identified key mechanisms regulating major ion chemistry, including silicate weathering and ion exchange, with a minor contribution from carbonate and gypsum dissolution. The Drinking Water Quality index (DWQI) indicated that 75 % of groundwater samples are appropriate for drinking, especially in the southern and middle of the study area. Various agricultural indices were used to assess groundwater suitability for irrigation, indicating that most samples are suitable for irrigation use. The Irrigation Water Quality Index (IWQI) indicated that 42.86 % of the groundwater samples fell in a moderate restriction, while the rest of the sample was divided between the high and low restriction classes. The study shows vulnerability in water quality, highlighting the need for improved groundwater management. Adjusting watering frequency and using modern irrigation methods, like drip and sprinkler systems, can enhance water-use efficiency and reduce aquifer depletion.

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引用次数: 0