Hydrology Research最新文献

{"title":"Research on the coupling effect of water security and socio-economy in five economic zones of Sichuan Province, China","authors":"Zuli He, Bin Liu, Jian Liu, Xinyu Xia, Suyue Han, Ke Pan, Jiajia Li, Long Tang","doi":"10.2166/nh.2024.044","DOIUrl":"https://doi.org/10.2166/nh.2024.044","url":null,"abstract":"\u0000 \u0000 The uneven distribution, scarcity, and pollution of water resources can significantly hinder socioeconomic development. A conceptual framework of Water Resources Endowment-Efficiency-Pressure-Response-Structure-Cycle (2EPRSC) was proposed, and 16 indicators were selected to establish the evaluation index system. Taking the five economic zones in the Sichuan Province of China as the research area, the genetic algorithm optimized entropy weighting method-cloud model was applied to determine the water security grades. Subsequently, the coupling coordination degree (CCD) model was established based on water security system (WSS)-SES to analyze the CCD. The results showed that (1) precipitation and temperature were the two indicators with more significant impacts on water security in Sichuan Province. (2) From 2012 to 2022, water security in Sichuan Province as a whole presented a decreasing and then increasing trend. (3) From 2012 to 2022, CCDs of the WSS-SES in Sichuan Province's economic zones were mostly at moderate imbalance, with the Chengdu Plain economic zone showing the highest CCD. Overall, the CCD scores across the economic zones were on an ascending trajectory. The study, grounded in the state of water security and CCD in Sichuan, can forge a scientific foundation for the sustainable development of WSS-SES.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How does the daily regulation hydropower station reduce the hydrological regime impact? A case study in upper Yellow River","authors":"Xue Yang, Fengnian Li, Shi Li, Xiaohua Fu, Jungang Luo, Ganggang Zuo, Chong-Yu Xu","doi":"10.2166/nh.2024.031","DOIUrl":"https://doi.org/10.2166/nh.2024.031","url":null,"abstract":"\u0000 The regulation of hydropower stations has profoundly altered river flow patterns. While studies have extensively assessed the impact of large or multiyear regulated hydropower stations on hydrological regimes using indicators of hydrological alteration (IHA) and range of variability approach (RVA), the impact of daily regulation hydropower stations has received comparatively less attention. This study aims to evaluate the influence of daily regulation hydropower stations on hydrological regime changes. The analysis focuses on the upper Yellow River region in China, which houses cascade hydropower stations, utilizing daily runoff data from the Guide hydrological station spanning from 1954 to 2020. The Mann–Kendall test revealed 27 of 32 IHAs with a significant trend when considering the operation of the multiyear regulated hydropower station (Longyangxia). However, this number decreased to 18 IHAs when daily regulation hydropower stations (Laxiwa and Nina) were included. Evaluation using the RVA method indicated that only 46.87% of IHAs showed high alterations from the natural regime when considering the operation of daily regulation hydropower stations, a decrease from 75.00% when solely considering Longyangxia. Findings suggest that daily regulation hydropower stations can effectively mitigate the adverse effects of multiyear regulated hydropower stations, bringing the hydrological regime closer to a natural state.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The phenomenon of drought in Ethiopia: Historical evolution and climatic forcing","authors":"G. Mulualem, U. J. P. Raju, M. Stojanovic, Rogert Sorí","doi":"10.2166/nh.2024.192","DOIUrl":"https://doi.org/10.2166/nh.2024.192","url":null,"abstract":"\u0000 \u0000 This study examines drought patterns in Ethiopia's 12 major river basins from 1981 to 2018 using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Both indices reveal historical drought episodes with slight variations, with significant differences in 1984, 2009, and 2015. Except for the Wabi-Shebelle catchment in southern Ethiopia, all river basins show an increasing trend in SPI12 and SPEI12 indices. The eastern and central regions experience more drought according to SPEI3. Seasonal correlations show that during the March–May rainy season, precipitation is negatively correlated with the Indian Ocean Dipole (IOD) index, while in the June–September season, it negatively correlates with Nino 3.4 and positively with IOD. The study also found that El Niño leads to less rainfall in the Ethiopian highlands, while La Niña results in more rainfall in the central and northern highlands but less in the south.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140972953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel deep learning rainfall–runoff model based on Transformer combined with base flow separation","authors":"Shuli Wang, Wei Wang, Guizhang Zhao","doi":"10.2166/nh.2024.035","DOIUrl":"https://doi.org/10.2166/nh.2024.035","url":null,"abstract":"\u0000 \u0000 Precise long-term runoff prediction holds crucial significance in water resource management. Although the long short-term memory (LSTM) model is widely adopted for long-term runoff prediction, they encounter challenges such as error accumulation and low computational efficiency. To address these challenges, we utilized a novel method to predict runoff based on a Transformer and the base flow separation approach (BS-Former) in the Ningxia section of the Yellow River Basin. To evaluate the effectiveness of the Transformer model and its responsiveness to the base flow separation technique, we constructed LSTM and artificial neural network (ANN) models as benchmarks for comparison. The results show that Transformer outperforms the other models in terms of predictive performance and that base flow separation significantly improves the performance of the Transformer model. Specifically, the performance of BS-Former in predicting runoff 7 days in advance is comparable to that of the BS-LSTM and BS-ANN models with lead times of 4 and 2 days, respectively. In general, the BS-Former model is a promising tool for long-term runoff prediction.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video velocity measurement: A two-stage flow velocity prediction method based on deep learning","authors":"Xiaolong Wang, Qiang Ma, Genyi Wang, Guocheng An","doi":"10.2166/nh.2024.128","DOIUrl":"https://doi.org/10.2166/nh.2024.128","url":null,"abstract":"<p>Due to the uncertainty in output caused by environmental changes, significant discrepancies are expected between the surface flow velocities predicted using deep learning methods and the instantaneous flow velocities. In this paper, a two-stage deep learning flow velocity measurement algorithm is proposed. During the external calibration process, the upper and lower frames of the recorded water flow video are cyclically traversed to acquire predicted flow velocity values using the deep learning velocity measurement algorithm. Meanwhile, the pixel displacement is obtained using the sparse optical flow tracking method and then post-processed to derive the velocity calibration value and pixel calibration value. During the detection process, the deep learning-predicted flow velocity is internally calibrated using the velocity calibration value and the pixel calibration value to adapt to changes in water flows. Compared with the pre-improved algorithm, the method achieves the minimum root mean square error in five different flow velocity videos and maintains high accuracy when the flow velocity changes rapidly. The obtained results are very promising and can help improve the reliability of video flow rate assessment algorithms.</p>","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of water shortage and instream flows of shared rivers in South Korea according to the dam operations in North Korea","authors":"Jae-Kyoung Lee, Suk Hwan Jang","doi":"10.2166/nh.2024.145","DOIUrl":"https://doi.org/10.2166/nh.2024.145","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/5/10.2166_nh.2024.145/1/m_hydrology-d-23-00145gf01.png?Expires=1720093519&amp;Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00145gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/5/10.2166_nh.2024.145/1/m_hydrology-d-23-00145gf01.png?Expires=1720093519&amp;Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div></div><div content- data-reveal=\"data-reveal\"><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/5/10.2166_nh.2024.145/1/m_hydrology-d-23-00145gf01.png?Expires=1720093519&amp;Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00145gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/5/10.2166_nh.2024.145/1/m_hydrology-d-23-00145gf01.png?Expires=1720093519&amp;Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>The Korean Peninsula's mountainous terrain poses challenges to effective water resource management. Notably, two significant river basins, North Han River and Imjin River basins, are essentially shared rivers originating in North Korea. After the construction of various dams in North Korea, billions of tons per year of water annually decreased from the upper reaches of these rivers of North Korea to South Korea. This study conducted an impact analysis on two major river basins","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An approach for flood flow prediction utilizing new hybrids of ANFIS with several optimization techniques: a case study","authors":"Negin Ahmadi, Sina Fard Moradinia","doi":"10.2166/nh.2024.191","DOIUrl":"https://doi.org/10.2166/nh.2024.191","url":null,"abstract":"<p>Using machine learning methods is efficient in predicting floods in areas where complete data is not available. Therefore, this study considers the Adaptive Neuro-Fuzzy Inference System (ANFIS) model combined with evolutionary algorithms, namely Harris Hawks Optimization (HHO) and Arithmetic Optimization Algorithm (AOA), to predict the flood of Shahrchay River in the northwest of Iran. The data used included the daily data of precipitation, evaporation, and runoff in the years 2016 and 2017, where 70% of the data were used for model training and the rest for testing the models. The results showed that although the ANFIS model provided values with high errors in several steps, especially in steps with maximum or minimum values, the use of HHO and AOA optimization algorithms resulted in a significant reduction in the error values. The ANFIS-AOA model utilizing an input scenario including the flow in the previous one to three days exerted the most promising results in the test data, with Nash Sutcliffe Efficiency (NSE) Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.93, 1.34, and 0.69, respectively. According to Taylor's diagram, the ANFIS-AOA hybrid algorithm predicts flood values with greater performance than the other models.</p>","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 2021 extreme rainfall in Gävle, Sweden: impacts on municipal welfare services and actions towards more resilient premises and operations","authors":"E. Glaas, M. Hjerpe, Sofie Storbjörks","doi":"10.2166/nh.2024.107","DOIUrl":"https://doi.org/10.2166/nh.2024.107","url":null,"abstract":"\u0000 Climate-related risks, vulnerabilities, and impacts are increasing in cities, illustrated by precipitation-driven pluvial floods. Post-event analyses can aid in reducing urban flood risks, but knowledge gaps exist regarding how welfare services and premises are impacted and can be adapted. This study analyses an extreme precipitation-driven event generating extensive flooding in Gävle, Sweden, in 2021. The objective is to increase knowledge about how municipal welfare services are vulnerable to pluvial floods, and of appropriate actions towards improving the response capacity and building more resilient welfare premises and operations. The study shows that the Swedish weather warning system generally worked well, but the analysed property companies lacked strategies and equipment to evade flooding in their properties. Flood damages in 60 analysed buildings were generated by different causes, demonstrating the importance of contemplating the vulnerability of welfare buildings when conducting flood risk assessments. Although the flood event did not generate deaths or serious personal injuries, the study identified impacts on welfare service operations in both the short and long terms. The event increased learning on climate adaptation but did not trigger adaptive action. Identified keys for adaptation include prioritizing premises to protect, knowledge of flood protection equipment, insurance company requirements, and updated emergency plans.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Watershed water policy based on a glacier water service perspective in the typical glacier basins, Western China","authors":"Zhenqi Sun, Shijin Wang, Lixia Wang","doi":"10.2166/nh.2024.148","DOIUrl":"https://doi.org/10.2166/nh.2024.148","url":null,"abstract":"\u0000 \u0000 Taking the Yangtze River Source Basin (YRSB) and Shule River Basin (SRB) as two typical cases, the sustainability of the water resources in these two basins was evaluated using the level of water stress (LWS) from sustainable development goal 6.4.2, and the regulating effect of the glacier runoff on the LWS was quantified. From 2000 to 2030, the level of socioeconomic development in the YRSB is low, and the total water consumption is only about 0.18 × 108 m3, whereas the SRB has a relatively high level of socioeconomic development and total water consumption is about 10 × 108 m3, i.e., 50 times higher than that in the YRSB. For the aforementioned reasons, the SRB's LWS is much higher than the YRSB's, resulting in a very low sustainability of water resources. As natural assets, glaciers flow downstream in the runoff mode, so compensation at the watershed scale should be considered. In the basin, the optimal allocation of water resources is needed. At the inter-basin scale, the compensation mechanism of glacier water resources needs to be improved.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140384758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid deep learning approach for streamflow prediction utilizing watershed memory and process-based modeling","authors":"B. Yifru, Kyoung Jae Lim, J. Bae, Woonji Park, Seoro Lee","doi":"10.2166/nh.2024.016","DOIUrl":"https://doi.org/10.2166/nh.2024.016","url":null,"abstract":"\u0000 Accurate streamflow prediction is essential for optimal water management and disaster preparedness. While data-driven methods’ performance often surpasses process-based models, concerns regarding their ‘black-box’ nature persist. Hybrid models, integrating domain knowledge and process modeling into a data-driven framework, offer enhanced streamflow prediction capabilities. This study investigated watershed memory and process modeling-based hybridizing approaches across diverse hydrological regimes – Korean and Ethiopian watersheds. Following watershed memory analysis, the Soil and Water Assessment Tool (SWAT) was calibrated using the recession constant and other relevant parameters. Three hybrid models, incorporating watershed memory and residual error, were developed and evaluated against standalone long short-term memory (LSTM) models. Hybrids outperformed the standalone LSTM across all watersheds. The memory-based approach exhibited superior and consistent performance across training, evaluation periods, and regions, achieving 17–66% Nash–Sutcliffe efficiency coefficient improvement. The residual error-based technique showed varying performance across regions. While hybrids improved extreme event predictions, particularly peak flows, all models struggled at low flow. Korean watersheds’ significant prediction improvements highlight the hybrid models’ effectiveness in regions with pronounced temporal hydrological variability. This study underscores the importance of selecting a specific hybrid approach based on the desired objectives rather than solely relying on statistical metrics that often reflect average performance.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}