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.
{"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":"47 1","pages":""},"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}
<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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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
{"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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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&Signature=5K4YepXiHJhfvsyshQSCFDkC5GkVBPz67qacJ01qBzlujq8HQnMTfQ0Q6mpQZc~wqjWmnycbQ6~4IDiPOJMDNuPWpVabtfC3nENocBjfVRgA2gRZkFDbS71DXRrNGZ3~xJuBDAhSELuG1ZGKvyl1kcKNbJJbzrkDGa~KdQmfXOOrrVZtqHFS87WW2Gj5J8rbFeCrkCDmoP2hPTwIXySbTFCDrxY7~PzsHsolXlCXIv3HgUcT4bU~rOZAGnsExN0t29B5kmM2xXSxv6nrxXZMhaSudN2lNL7nau9ajkOE02WSZPRDlAdHwLmgplv3bbq-2Wj3EJJTgXUcppW9mZItxw__&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":"33 1","pages":""},"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}
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.
{"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":"84 1","pages":""},"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}
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00130gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&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/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00130gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>To achieve sustainable development goals in Huangshui River Basin (HRB), strengthening adaptive water resources management under the dual impact of climate change (CC) and human interventions (HI) is of great significance. Multiple mathematical and statistical methods were employed to determine the runoff trend and breakpoint in HRB. The elasticity of CC and HI on the runoff decline and their contributions were quantitatively discerned based on the Budyko hypothesis, complemen
View largeDownload slideView largeDownload slide Close modal为了实现湟水流域的可持续发展目标，在气候变化（CC）和人为干预（HI）的双重影响下加强水资源的适应性管理具有重要意义。本文采用多种数理统计方法确定了湟水流域的径流趋势和断点。基于布迪科假说、互补法和 SWAT 水文模型，定量判别了 CC 和 HI 对径流下降的弹性及其贡献。结果表明：(1) 径流呈下降趋势，径流断点出现在 1990 年；(2) 弹性系数表明 P、ET0 和 n 增加 1%，径流分别增加 2.19%、减少 1.19%和减少 1.52%；(3) 布迪科框架确定 CC 和 HI 对 HRB 径流下降的贡献率分别为 37.(3) 布德科框架确定了 CC 和 HI 对 HRB 径流量下降的贡献率分别为 37.98%-41.86% 和 58.14%-62.02%，而 SWAT 水文模型估算的贡献率分别为 38.72% 和 61.28%；(4) HI 是 HRB 径流量下降的主要因素，而取水和水利工程建设等直接人为干扰是主要驱动因素。这些研究结果对人力资源局的水资源规划和管理具有重要的科学意义。
{"title":"Attribution discernment of climate change and human interventions to runoff decline in Huangshui River Basin, China","authors":"Pengquan Wang, Runjie Li, Shengkui Cao","doi":"10.2166/nh.2024.130","DOIUrl":"https://doi.org/10.2166/nh.2024.130","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00130gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&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/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00130gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.130/1/m_hydrology-d-23-00130gf01.png?Expires=1714741965&Signature=2~jEh2ompjDKicSXxMpZoxcT5~rO~4MXvu922ogjevsibIjx-B8GfOrChC68JfBfrb6BD6ON3n69mAcR~9Ym6in0MMreeVve6RAEe1bYyQ57bicNCdGf0mH~S3im8VpwUj1cUfsMBPazCmVk4V4pxfRumEN6wEoK1yxWxs7chTa2dm3wvOCBnrfRSGIFMUO6cF9hsqkdIoe5oS5HvARQs0y4PXDJakWYOjcx-HBBisTQ06ZTaXqiFg8I2BLJWFqgIiqkGXUXdCTTC~Nymn5z4GDGN979oPKwDkTf1sK2YrllJlzGlpqdsEvrQY7W7Yp5gJx0cmeFslRZOUH5eJqGMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>To achieve sustainable development goals in Huangshui River Basin (HRB), strengthening adaptive water resources management under the dual impact of climate change (CC) and human interventions (HI) is of great significance. Multiple mathematical and statistical methods were employed to determine the runoff trend and breakpoint in HRB. The elasticity of CC and HI on the runoff decline and their contributions were quantitatively discerned based on the Budyko hypothesis, complemen","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"284 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140581697","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}
Temesgen Tsehayeneh Mihret, Fasikaw A. Zemale, Abeyou W. Worqlul, Ayenew D. Ayalew, Nicola Fohrer
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00098gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&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/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00098gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Identification of hydrologically homogenous watersheds in the Upper Blue Nile Basin of Ethiopia is challenging due to the large number of watersheds and the lack of consistent and reliable data. Traditional methods, such as expert-based classification, are time-consuming, subjective, and often not reproducible. Therefore, this study aims to identify homogenous gauged watersheds using hydrometeorological and remote sensing data. In this study 76 watersheds were delineated from
{"title":"Identification of hydrologically homogenous watersheds and climate-vegetation dynamics in the Blue Nile Basin of Ethiopia","authors":"Temesgen Tsehayeneh Mihret, Fasikaw A. Zemale, Abeyou W. Worqlul, Ayenew D. Ayalew, Nicola Fohrer","doi":"10.2166/nh.2024.098","DOIUrl":"https://doi.org/10.2166/nh.2024.098","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00098gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&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/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00098gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/3/10.2166_nh.2024.098/1/m_hydrology-d-23-00098gf01.png?Expires=1714755649&Signature=SS3PNqIlhHL6n70YLvsQcCpnfo9fH-~Rq2O8G0C5YBxWsPNlPH6L5dkj~vaCioKKKx3gpS0pfZRMugegwLqNmJtic~nMKo8MwjGXXhflpp-09aOy4p6VF4OwfAUUnG55pUJr7Ccu5ZcwvOkFH7qcoQrcus0HHmycVbt0yyJ-IIDsXHucRm-NkK1pB7XPLeESAFkFWJcoVk2XSMIowmDQRGSFFCfxWi2~4h4uKVs28y-NcnwpBNDfECNhQhsJeLBTEoaog4Plypn0lsFIBq52lMpHVEm2oZLxoc7aSeaoJvgmuqMONeQfsdZfanOXhP1IcchNScJjwWTU163Q1BB2cA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Identification of hydrologically homogenous watersheds in the Upper Blue Nile Basin of Ethiopia is challenging due to the large number of watersheds and the lack of consistent and reliable data. Traditional methods, such as expert-based classification, are time-consuming, subjective, and often not reproducible. Therefore, this study aims to identify homogenous gauged watersheds using hydrometeorological and remote sensing data. In this study 76 watersheds were delineated from ","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"102 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140581540","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}
Charles Mazivanhanga, Robert C. Grabowski, Eunice Pérez-Sánchez, Victor R. Carballo-Cruz
Relationships between peak discharges and catchment size (e.g., flood scaling) in a catchment have the potential to support new river flood forecasting approaches but have not been tested in tropical regions. This study determined flood scaling relationships between peak discharge and nested drainage areas in the La Sierra catchment (Mexico). A statistical power law equation was applied to selected rainfall–runoff events that occurred between 2012 and 2015. Variations in flood scaling parameters were determined in relation to catchment descriptors and processes for peak downstream discharge estimation. Similar to studies in humid temperate regions, the results reveal the existence of log-linear relationships between the intercept (α) and exponent (θ) parameter values and the log–log power–law relationships between (α) and the peak discharge observed from the smallest headwater catchments. The flood parameter values obtained were then factored into the scaling equation (QP = αAθ) and successfully predicted downstream flood peaks, especially highly recurrent flood events. The findings contribute to a better understanding of the nature of flood wave generation and support the development of new flood forecasting approaches in unregulated catchments suitable for non-stationarity in hydrological processes with climate change.
{"title":"Analysis of scaling relationships for flood parameters and peak discharge estimation in a tropical region","authors":"Charles Mazivanhanga, Robert C. Grabowski, Eunice Pérez-Sánchez, Victor R. Carballo-Cruz","doi":"10.2166/nh.2024.111","DOIUrl":"https://doi.org/10.2166/nh.2024.111","url":null,"abstract":"<p>Relationships between peak discharges and catchment size (e.g., flood scaling) in a catchment have the potential to support new river flood forecasting approaches but have not been tested in tropical regions. This study determined flood scaling relationships between peak discharge and nested drainage areas in the La Sierra catchment (Mexico). A statistical power law equation was applied to selected rainfall–runoff events that occurred between 2012 and 2015. Variations in flood scaling parameters were determined in relation to catchment descriptors and processes for peak downstream discharge estimation. Similar to studies in humid temperate regions, the results reveal the existence of log-linear relationships between the intercept (<em>α</em>) and exponent (<em>θ</em>) parameter values and the log–log power–law relationships between (<em>α</em>) and the peak discharge observed from the smallest headwater catchments. The flood parameter values obtained were then factored into the scaling equation (<em>Q<sub>P</sub></em> = <em>αA<sup>θ</sup></em>) and successfully predicted downstream flood peaks, especially highly recurrent flood events. The findings contribute to a better understanding of the nature of flood wave generation and support the development of new flood forecasting approaches in unregulated catchments suitable for non-stationarity in hydrological processes with climate change.</p>","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"170 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002263","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}
Jeonghoon Lee, Jeonghyeon Choi, Suhyung Jang, Sangdan Kim
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00144gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&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/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00144gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Explaining the significant variability of rainfall in orographically complex mountainous regions remains a challenging task even for modern raingauge networks. To address this issue, a real-time spatial rainfall field estimation model, called WREPN (WRF Rainfall-Elevation Parameterized Nowcasting), has been developed, incorporating the influence of mountain effect based on ground raingauge networks. In this study, we examined the effect of mountainous rainfall estimates on the
{"title":"Effect of mountainous rainfall on uncertainty in flood model parameter estimation","authors":"Jeonghoon Lee, Jeonghyeon Choi, Suhyung Jang, Sangdan Kim","doi":"10.2166/nh.2024.144","DOIUrl":"https://doi.org/10.2166/nh.2024.144","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00144gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&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/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00144gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/55/2/10.2166_nh.2024.144/2/m_hydrology-d-23-00144gf01.png?Expires=1712256201&Signature=HW77hrWzuxW5py8XJYUh77cgakqPGbfCOyzpQaDtimFGLs3kjR21vi1fAul74A1bx3nexNDZcO5VN7k0KP3KtaOCWRu1u3zGzXOYsy-10raLVkV2mC6ydJujU9470lWGGB7ozLlDmSKIpW3mveZFgd4xpJrL67YP-AeZgZD3hE8yKkaeoBjSQTovUGxoENq2qh-bgTglAqap9XPTm7c0Hn9P7uihvMqUbXV2AIpcXnf~B8IDiIhlJsGHDbcWf9DWeq8FL~Jronw9KGVdCEoB2W8rcyIjnCT0NslF~Mu5-gVPh09DCzLM9Losz~o1JxGRi7oZoaha0gd1AvqeB1RNHg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Explaining the significant variability of rainfall in orographically complex mountainous regions remains a challenging task even for modern raingauge networks. To address this issue, a real-time spatial rainfall field estimation model, called WREPN (WRF Rainfall-Elevation Parameterized Nowcasting), has been developed, incorporating the influence of mountain effect based on ground raingauge networks. In this study, we examined the effect of mountainous rainfall estimates on the","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"41 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002267","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":"Corrigendum: Hydrology Research 52 (6), 1357–1371: Sampling uncertainty of UK design flood estimation, Anthony Hammond, https://dx.doi.org/10.2166/nh.2021.059","authors":"","doi":"10.2166/nh.2024.001","DOIUrl":"https://doi.org/10.2166/nh.2024.001","url":null,"abstract":"Abstract not available","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002387","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}
Successful sustainable groundwater management requires accurate information on recharge for a given aquifer system. However, recharge estimates are usually used in a relative term rather than an absolute sense. A review of available studies on groundwater recharge estimate uncertainty as well as tools for uncertainty analysis was conducted. Nonetheless, except for the handful research that have conducted proper uncertainty analysis, most studies inclined to implement multiple methods as an indication of the range of uncertainty. The global trend indicates that considering the significant number of methods for recharge estimation, very little has been done to assess the uncertainty of each method. Therefore, more focus should be given to the individual uncertainty analysis of selected methods as much as using multiple methods recommended for investigating uncertainty. Insight of the review indicates, when used carefully, that tracer-based analysis can be effective and coupling is required for uncertainty analysis. Furthermore, spatial uncertainty due to input data could be potentially minimized by using input data from multiple sources. Better conceptualization of the hydrogeological process can reduce the uncertainty of numerical modelling. This review is limited to widely used methods and excludes uncertainty due to inappropriate method implementation as well as controlled experimental uncertainties.
{"title":"A review on sources of uncertainties for groundwater recharge estimates; insight to data Scares Tropical, Arid, and Semiarid regions","authors":"T. D. Beyene, F. A. Zimale, S. Gebrekristos","doi":"10.2166/nh.2023.221","DOIUrl":"https://doi.org/10.2166/nh.2023.221","url":null,"abstract":"Successful sustainable groundwater management requires accurate information on recharge for a given aquifer system. However, recharge estimates are usually used in a relative term rather than an absolute sense. A review of available studies on groundwater recharge estimate uncertainty as well as tools for uncertainty analysis was conducted. Nonetheless, except for the handful research that have conducted proper uncertainty analysis, most studies inclined to implement multiple methods as an indication of the range of uncertainty. The global trend indicates that considering the significant number of methods for recharge estimation, very little has been done to assess the uncertainty of each method. Therefore, more focus should be given to the individual uncertainty analysis of selected methods as much as using multiple methods recommended for investigating uncertainty. Insight of the review indicates, when used carefully, that tracer-based analysis can be effective and coupling is required for uncertainty analysis. Furthermore, spatial uncertainty due to input data could be potentially minimized by using input data from multiple sources. Better conceptualization of the hydrogeological process can reduce the uncertainty of numerical modelling. This review is limited to widely used methods and excludes uncertainty due to inappropriate method implementation as well as controlled experimental uncertainties.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"29 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157007","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}
A thorough understanding of the ecological impacts behind the hydrologic alteration is still insufficient and hinders the watershed management. Here, we used eco-flow indicators, multiple hydrological indicators, and fluvial biodiversity to investigate the ecological flow in different temporal scales. The case study in the Han River shows a decrease in high flows contributed to the decrease in eco-surplus and increase in eco-deficit in summer and autumn, while the decrease in eco-deficit can be attributed to the change of low flow in spring. An integrated hydrologic alteration was over 48% degree and was under moderate ecological risk degree in impact period I, while DHRAM scores showed the Huangzhuang station faced a high ecological risk degree in impact period II. The decrease (increase) in total seasonal eco-surplus (eco-deficit) was identified after alteration with the change in seasonal eco-flow indicators contributions. Shannon index showed a decreasing trend, indicating the degradation of fluvial biodiversity in the Han River basin. Eco-flow indicators such as eco-surplus and eco-deficit are in strong relationships with 32 hydrological indicators and can be accepted for ecohydrological alterations at multiple temporal scales. This study deepens the understanding of ecological responses to hydrologic alteration, which may provide references for water resources management and ecological security maintenance.
对水文变化背后的生态影响的透彻了解仍然不足，这阻碍了流域管理。在此，我们利用生态流量指标、多种水文指标和河道生物多样性来研究不同时间尺度的生态流量。汉江案例研究表明，大流量的减少导致了夏秋季生态盈余的减少和生态亏损的增加，而生态亏损的减少可归因于春季小流量的变化。综合水文变化程度超过 48%，在影响期 I 属于中度生态风险，而 DHRAM 评分显示黄庄站在影响期 II 面临高度生态风险。随着季节性生态流量指标贡献率的变化，确定了改变后季节性生态盈余（生态亏损）总量的减少（增加）。香农指数呈下降趋势，表明汉江流域河流生物多样性退化。生态盈余和生态亏损等生态流量指标与 32 个水文指标关系密切，可用于多时间尺度的生态水文变化。该研究加深了人们对水文变化生态响应的认识，可为水资源管理和生态安全维护提供参考。
{"title":"Assessment of ecological flow alterations induced by hydraulic engineering projects in the Han River, China","authors":"Lele Deng, Shenglian Guo, Jinghan Tian, Heyu Wang","doi":"10.2166/nh.2023.220","DOIUrl":"https://doi.org/10.2166/nh.2023.220","url":null,"abstract":"\u0000 \u0000 A thorough understanding of the ecological impacts behind the hydrologic alteration is still insufficient and hinders the watershed management. Here, we used eco-flow indicators, multiple hydrological indicators, and fluvial biodiversity to investigate the ecological flow in different temporal scales. The case study in the Han River shows a decrease in high flows contributed to the decrease in eco-surplus and increase in eco-deficit in summer and autumn, while the decrease in eco-deficit can be attributed to the change of low flow in spring. An integrated hydrologic alteration was over 48% degree and was under moderate ecological risk degree in impact period I, while DHRAM scores showed the Huangzhuang station faced a high ecological risk degree in impact period II. The decrease (increase) in total seasonal eco-surplus (eco-deficit) was identified after alteration with the change in seasonal eco-flow indicators contributions. Shannon index showed a decreasing trend, indicating the degradation of fluvial biodiversity in the Han River basin. Eco-flow indicators such as eco-surplus and eco-deficit are in strong relationships with 32 hydrological indicators and can be accepted for ecohydrological alterations at multiple temporal scales. This study deepens the understanding of ecological responses to hydrologic alteration, which may provide references for water resources management and ecological security maintenance.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"38 3","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138946015","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}
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