Cloud seeding is generally used to secure additional water resources, which is not an easy goal to achieve, as the spatial variability of rainfall is high. Instead, the increased rain may moisten the neighboring forest. This study focuses on this situation and estimates the possible increase in the net primary production (NPP) due to cloud seeding. This study considers the Boryeong Dam basin in Korea as a study area and uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate the NPP at 8-day intervals. As a result, first, the increase of the current 8-day NPP is greater when the rainfall amount during the last 16-day period is 50 mm or more. The mean increase of the 8-day NPP is estimated at about 1.873 g/m2 of carbon. Second, the increase of the NPP with the target 16-day rainfall of 50 mm is estimated at about 3%, which is about 4% with the target 16-day rainfall of 100 mm. Simply extrapolating the derived result to the entire forest in Korea, the increased carbon accumulation can be extended to about 0.6 and 0.8% of the total carbon emission in 2018, respectively. These amounts correspond to about 1.2 and 1.5% of the target amount of carbon reduction by 2030 in Korea.
{"title":"Unexpected contribution of cloud seeding to NPP increase during drought","authors":"Munseok Lee, Chulsang Yoo, Ki-Ho Chang","doi":"10.2166/nh.2023.075","DOIUrl":"https://doi.org/10.2166/nh.2023.075","url":null,"abstract":"Cloud seeding is generally used to secure additional water resources, which is not an easy goal to achieve, as the spatial variability of rainfall is high. Instead, the increased rain may moisten the neighboring forest. This study focuses on this situation and estimates the possible increase in the net primary production (NPP) due to cloud seeding. This study considers the Boryeong Dam basin in Korea as a study area and uses the Carnegie–Ames–Stanford Approach (CASA) model to estimate the NPP at 8-day intervals. As a result, first, the increase of the current 8-day NPP is greater when the rainfall amount during the last 16-day period is 50 mm or more. The mean increase of the 8-day NPP is estimated at about 1.873 g/m2 of carbon. Second, the increase of the NPP with the target 16-day rainfall of 50 mm is estimated at about 3%, which is about 4% with the target 16-day rainfall of 100 mm. Simply extrapolating the derived result to the entire forest in Korea, the increased carbon accumulation can be extended to about 0.6 and 0.8% of the total carbon emission in 2018, respectively. These amounts correspond to about 1.2 and 1.5% of the target amount of carbon reduction by 2030 in Korea.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"63 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139172530","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}
Caleb Renner, Nathan Conroy, Evan Thaler, Adam Collins, Lauren Thomas, Shannon Dillard, Joel Rowland, Katrina Bennett
Rainfall frequency and intensity are expected to increase in the Arctic, with potential detrimental impacts on permafrost, leading to enhanced thawing and carbon release to the atmosphere. However, there have been very few studies on the effect of discrete rain events on permafrost in the Arctic and sub-Arctic. Conducting controlled rainfall experiments within permafrost landscapes can provide an improved understanding of the effect of changing intensity, duration, and timing of rain events on permafrost tundra ecosystems. Here, we describe the design and implementation of the Next-Generation Ecosystem Experiment Arctic Rainfall Simulator (NARS), a variable intensity (4–82 mm/h) rainfall simulator that can be used to study the effects of rainfall on permafrost stability. The NARS design includes a 3D-printed 4 cm H-flume and uses an eTape resistivity sensor that was calibrated (R2 = 0.9–0.96) to measure discharge from the system. NARS is designed to be lightweight, simple to construct, and can be easily deployed in remote locations. As a field validation of updated rainfall simulator design and modernized controls, NARS was tested on the Seward Peninsula, AK. Because of its portability, versatility in deployment, dimensions, and rainfall intensity, NARS represents a methodological innovation for researching the impacts of rainfall on permafrost environments.
{"title":"The Next-Generation Ecosystem Experiment Arctic Rainfall Simulator: a tool to understand the effects of changing rainfall patterns in the Arctic","authors":"Caleb Renner, Nathan Conroy, Evan Thaler, Adam Collins, Lauren Thomas, Shannon Dillard, Joel Rowland, Katrina Bennett","doi":"10.2166/nh.2023.146","DOIUrl":"https://doi.org/10.2166/nh.2023.146","url":null,"abstract":"\u0000 \u0000 Rainfall frequency and intensity are expected to increase in the Arctic, with potential detrimental impacts on permafrost, leading to enhanced thawing and carbon release to the atmosphere. However, there have been very few studies on the effect of discrete rain events on permafrost in the Arctic and sub-Arctic. Conducting controlled rainfall experiments within permafrost landscapes can provide an improved understanding of the effect of changing intensity, duration, and timing of rain events on permafrost tundra ecosystems. Here, we describe the design and implementation of the Next-Generation Ecosystem Experiment Arctic Rainfall Simulator (NARS), a variable intensity (4–82 mm/h) rainfall simulator that can be used to study the effects of rainfall on permafrost stability. The NARS design includes a 3D-printed 4 cm H-flume and uses an eTape resistivity sensor that was calibrated (R2 = 0.9–0.96) to measure discharge from the system. NARS is designed to be lightweight, simple to construct, and can be easily deployed in remote locations. As a field validation of updated rainfall simulator design and modernized controls, NARS was tested on the Seward Peninsula, AK. Because of its portability, versatility in deployment, dimensions, and rainfall intensity, NARS represents a methodological innovation for researching the impacts of rainfall on permafrost environments.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"24 25","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138589505","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}
Xiaolong Hu, Zheng Zhou, Haibin Xiong, Quan Gao, Xiayu Cao, Xuhai Yang
While many studies have compared global precipitation datasets at national, continental, and global scales, few have evaluated these data at the river basin scale. This study explored differences in the precipitation estimates and trends of 12 widely applied precipitation datasets, including gauge-, satellite-, and reanalysis-based products, for the world's 6,292 river basins. Results showed that disparities between the 12 precipitation datasets were considerable. A total of 3,125 river basins, with a land area of 5,989.1 × 104 km2, had differences in estimated annual average precipitation exceeding 500 mm year–1, and these basins were mainly distributed in Greenland, Africa, Oceania, and West Asia. Disparities between the precipitation datasets were particularly large during the dry season when the percentage difference between the highest and lowest precipitation estimates exceeded 500% in 1,390 river basins (4839.7 × 104 km2). Differences in rainfall trends also varied markedly between data sources. The data products do not agree on precipitation trends for all the river basins. These findings illustrate the importance of accurate precipitation data to ensure effective policy and planning in terms of hydropower generation, domestic water supply, flood protection, and drought relief at the river basin scale and highlight the uncertainty that exists in current global precipitation data.
{"title":"Inter-comparison of global precipitation data products at the river basin scale","authors":"Xiaolong Hu, Zheng Zhou, Haibin Xiong, Quan Gao, Xiayu Cao, Xuhai Yang","doi":"10.2166/nh.2023.062","DOIUrl":"https://doi.org/10.2166/nh.2023.062","url":null,"abstract":"\u0000 \u0000 While many studies have compared global precipitation datasets at national, continental, and global scales, few have evaluated these data at the river basin scale. This study explored differences in the precipitation estimates and trends of 12 widely applied precipitation datasets, including gauge-, satellite-, and reanalysis-based products, for the world's 6,292 river basins. Results showed that disparities between the 12 precipitation datasets were considerable. A total of 3,125 river basins, with a land area of 5,989.1 × 104 km2, had differences in estimated annual average precipitation exceeding 500 mm year–1, and these basins were mainly distributed in Greenland, Africa, Oceania, and West Asia. Disparities between the precipitation datasets were particularly large during the dry season when the percentage difference between the highest and lowest precipitation estimates exceeded 500% in 1,390 river basins (4839.7 × 104 km2). Differences in rainfall trends also varied markedly between data sources. The data products do not agree on precipitation trends for all the river basins. These findings illustrate the importance of accurate precipitation data to ensure effective policy and planning in terms of hydropower generation, domestic water supply, flood protection, and drought relief at the river basin scale and highlight the uncertainty that exists in current global precipitation data.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"48 19","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594013","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}
Sergio Arturo Renteria-Guevara, Jesus Gabriel Rangel Peraza, Abel Rivera-Buelna, Sergio Alberto Monjardin-Armenta, Antonio Jesus Sanhouse-Garcia, Fernando Garcia-Paez
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00106gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&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/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00106gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Urban storm drainage is essential for the large part of the population living in cities with torrential rains to protect public urban infrastructure, private property, and human lives from flooding. The most important design parameter for urban storm drainage is the flow discharge, which is normally calculated with the area, runoff coefficient, and rainfall intensity depending on basin delineation. This requires highly accurate topographic information on the urbanized terr
View largeDownload slideView largeDownload slide Close modal城市暴雨排水对于生活在暴雨城市中的大部分人来说至关重要，它可以保护城市公共基础设施、私人财产和人类生命免受洪水侵袭。城市暴雨排水系统最重要的设计参数是流量，通常根据流域的划分，用面积、径流系数和降雨强度来计算流量。这需要城市化地形的高精度地形信息，而数字高程模型由于分辨率不足，有时无法满足这一要求。本研究提出了考虑城市化的城市流域划分标准，而不需要通常的地形测量。拟议的流域划分是基于无人机（UAV）获得的高分辨率数字高程模型和对水流方向的实地验证。结果，获得了两个城市盆地的划分：一个是一个区域内的城市盆地，其排水完全流向一个天坑；另一个盆地则另外通过分水岭的其他点排出。后一个盆地不符合传统的水文盆地概念，因为它有不止一个出口。此外，该流域的面积占排入天坑的总面积的 38%。
{"title":"Selecting criteria for urban basin delineation based on UAV photogrammetry: a case study in Culiacan, Mexico","authors":"Sergio Arturo Renteria-Guevara, Jesus Gabriel Rangel Peraza, Abel Rivera-Buelna, Sergio Alberto Monjardin-Armenta, Antonio Jesus Sanhouse-Garcia, Fernando Garcia-Paez","doi":"10.2166/nh.2023.206","DOIUrl":"https://doi.org/10.2166/nh.2023.206","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00106gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&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/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00106gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.206/1/m_hydrology-d-23-00106gf01.png?Expires=1706783115&Signature=vA3BhYuYQzXH1ensmHqd5k4RsANlQIkolard92ZDBDK0md6q0973mnoUyoOIKGZH2GkeK8tG0mv7nJUBxmy1YLERIjDE6j3OHM0kTYtHI0zTbt5vjCJgjwiZU1Lv5s-anTxFhFCgKejAUkbYV3-fKq4xv9r33xMB-fV~pxTnZovJztAoDulVAeh-gPrz0Wh4HmyJOKMgwVSpaBSU6P1uAa2nfOr85oR4NdUe7HvfDmPzvmlrkxVeRb2gxIfMlN3CB-m5uI6rK281R6Ud2LZ~LqRyJfaE55JmvRCWUlF0XMowWmbACOzVh0g0p-bfmY3bhPcOwTMFkOT~FxSCrXe~vQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Urban storm drainage is essential for the large part of the population living in cities with torrential rains to protect public urban infrastructure, private property, and human lives from flooding. The most important design parameter for urban storm drainage is the flow discharge, which is normally calculated with the area, runoff coefficient, and rainfall intensity depending on basin delineation. This requires highly accurate topographic information on the urbanized terr","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"30 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139055408","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/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00109gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&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/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00109gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>A better understanding of the distribution of nonaqueous phase liquid (NAPL) plumes is of great importance to groundwater pollution remediation and control. However, the efficiency of surrogate models in simulating the transport is still not well addressed. Selecting a leakage problem as an example, 50 sets of random permeability distributions are generated using the Monte Carlo method, and a numerical model is used to obtain benchmark data of NAPL transport. Four machine
{"title":"Performance evaluation of surrogate models for simulating multiphase NAPL transport in heterogeneous aquifers","authors":"Litang Hu, Menglin Zhang, Lei Tian, Shiqi Huang","doi":"10.2166/nh.2023.209","DOIUrl":"https://doi.org/10.2166/nh.2023.209","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00109gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&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/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00109gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.209/1/m_hydrology-d-23-00109gf01.png?Expires=1706783784&Signature=xYuqz8BB5GGlvRIdVHPF8v18al8eqWfAaFL8gYEduSUfYv0~RrA-qbz9nHuoDLdsIswnjWr1anW2Hr~oj75wDXMLfa537uV0jGK-E~e6DzK2cjd6u6FlPbf~RjD0-8Hgx2hK1r9PWvsKFMTMvqueDtydBbof0BGcCgmxTWO3s3dr~l9eHPJRVTJLQ9LfGsb7YC9qp7OLd1-DygtjiCqBAgH0Z55TslyHa9iZq-yoPWpQIoa5e4uYi~ov1TDIJ8VRR925lZGGhFOmj-rUh6jDirrBbjyl7pPP3I4FaFP0WKkzC6KkE0uLPFI9nBWlZYFtHG4F4mPZpLJAih3DvDhx0A__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>A better understanding of the distribution of nonaqueous phase liquid (NAPL) plumes is of great importance to groundwater pollution remediation and control. However, the efficiency of surrogate models in simulating the transport is still not well addressed. Selecting a leakage problem as an example, 50 sets of random permeability distributions are generated using the Monte Carlo method, and a numerical model is used to obtain benchmark data of NAPL transport. Four machine ","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"3 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139055481","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/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00069gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&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/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00069gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Hydrological runoff prediction is vital for water resource management. The non-linear and non-stationary runoff series and the complex hydrological features for large-scale basins make it difficult to predict. Long short-term memory (LSTM) is effective for runoff prediction but unstable for large-scale basins. This study develops three hybrid models combined with two-stage decomposition and LSTM, including wavelet transformation (WT) combined with complete ensemble empiric
{"title":"Runoff time series prediction based on hybrid models of two-stage signal decomposition methods and LSTM for the Pearl River in China","authors":"Zhao Guo, Qian-Qian Zhang, Nan Li, Yun-Qiu Zhai, Wen-Tao Teng, Shuang-Shuang Liu, Guang-Guo Ying","doi":"10.2166/nh.2023.069","DOIUrl":"https://doi.org/10.2166/nh.2023.069","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00069gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&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/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00069gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.069/1/m_hydrology-d-23-00069gf01.png?Expires=1706779823&Signature=T4J9Cch3pWtu9XXuIwMPe3pczrMwIHaXaPseuA~j4geczuo35YFLolh8rLecrb93b8T7uoa7uDPpeiPrITKYHH6xwUmsLAzpxDQnROYdhnZOXeAsg0u8sCWXT2vsU8O~Rq1uOdytZm9ZRGPZvdUc2ROMLbJaZCoHnhFXtkUnGFOWzL4aEV3dD9GjTR19BelXmRt6N6LY4PjTWLCNymSj7bAYqNRNDyuZ-0OK3CHblCob7gYqlvEveJaXM8uPIJpxz11X7e2qK6c1fAXoMM3W1lQ2VJNaM42F14CnYhO1mzMneDdkAaNq6S6YpoKH6byVwnUmdiGBCcmZi9Z6mIyZ9w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Hydrological runoff prediction is vital for water resource management. The non-linear and non-stationary runoff series and the complex hydrological features for large-scale basins make it difficult to predict. Long short-term memory (LSTM) is effective for runoff prediction but unstable for large-scale basins. This study develops three hybrid models combined with two-stage decomposition and LSTM, including wavelet transformation (WT) combined with complete ensemble empiric","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"30 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139055460","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}
Haeun Jung, Jeongeun Won, Shinuk Kang, Sangdan Kim
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00137gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&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/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00137gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>To assess vegetation drought, it is important to understand the relationship between climate and vegetation and to accurately measure the response of vegetation activity to meteorological drought. In this study, we used the vegetation health index (VHI) to investigate the propagation time and time-lag of vegetation response to different meteorological drought indices, including the standardized precipitation index (SPI), evaporative demand drought index (EDDI), standardize
{"title":"Spatiotemporal variability of vegetation response to meteorological drought on the Korean Peninsula","authors":"Haeun Jung, Jeongeun Won, Shinuk Kang, Sangdan Kim","doi":"10.2166/nh.2023.237","DOIUrl":"https://doi.org/10.2166/nh.2023.237","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00137gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&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/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00137gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.237/1/m_hydrology-d-23-00137gf01.png?Expires=1706779572&Signature=eVcslLvs0982AcjCrJebE2KfIjcCwTO83F-XhyMa1VpSqc0PSFJYYAyk1JvbPBXNZXLnyqldAZYDCzlQAcWyq~gCVK88EpJA~wm5fr-PD9QyizdLIZsd-sA2Fjn2zOqrofySNx~8OUe~dEKT4CJ7SHqs~Fx03jReiXIurWEgUvbuUn3all8NrAO1yUVDvFzuU2rm3rlQh7N7DYVzoqZgNxQSm2JInR3U0H~CKDMScEcyHoSRLOUHoJ1OyVW3Oh-A2g6QA0fac-gczATXuT9oObfsxJOGGKdTJFVbTYYllKoaanTWdstmOldwhpWRN-rlAZL0-ZglxPjn2LY4swAgWw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>To assess vegetation drought, it is important to understand the relationship between climate and vegetation and to accurately measure the response of vegetation activity to meteorological drought. In this study, we used the vegetation health index (VHI) to investigate the propagation time and time-lag of vegetation response to different meteorological drought indices, including the standardized precipitation index (SPI), evaporative demand drought index (EDDI), standardize","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"27 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139064831","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}
Guillermo Barrientos, Rafael Rubilar, Efrain Duarte, Alberto Paredes
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00116gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&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/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydrology-d-23-00116gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Persistent drought events frequently intensify the aridity of ecosystems and cause catchment runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature, and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between the years 1980 and 2020 in southern-central Chile. We identified the hydrological years where aridity index intensified, analyzed
{"title":"Runoff variation and progressive aridity during drought in catchments in southern-central Chile","authors":"Guillermo Barrientos, Rafael Rubilar, Efrain Duarte, Alberto Paredes","doi":"10.2166/nh.2023.116","DOIUrl":"https://doi.org/10.2166/nh.2023.116","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00116gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&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/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydrology-d-23-00116gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/hr/54/12/10.2166_nh.2023.116/1/m_hydrology-d-23-00116gf01.png?Expires=1706803564&Signature=OPSxnnGpTKnr0DgO74MSqejQhZm38GWXHZdPQD7yeLuyfnSKEfXOyuXmSLrHInBbcy~El0OE~9BUr-8kLFvMfeUjy2zXFyekW4ZHLrXd2-UuvJ23FqmrOsp0TNLmljmMiUR13ZtK81Tpj82RhPvRIbX3lO7YCzSdl4pZRLG7vr-fHMNJYEqfbjhJ9tN0YLdREYwD31QDHqhuRl-f5c3Yqy7ZGKufRKObPGeB7azTLTCWsbocprLlyXi056yrGr07aons3IDO0fyyFr~rwrTHxg6FGqHON7Wtx3EnLuhFkLXF5d1JpiYmT-XCttb-8UgfwPMjGTjZ~KbfoUJucD9yZw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Persistent drought events frequently intensify the aridity of ecosystems and cause catchment runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature, and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between the years 1980 and 2020 in southern-central Chile. We identified the hydrological years where aridity index intensified, analyzed ","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"127 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139055467","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}
Abstract This work compares the accuracy of streamflow estimated by a data-driven artificial neural network (ANN) and the physically based soil and water assessment tool (SWAT). The models were applied in two small watersheds, one highly urbanized and the other primarily covered with evergreen forest and shrubs, in the San Antonio Region of central south Texas, where karst geologic features are prevalent. Both models predicted daily streamflow in the urbanized watershed very well, with the ANN and SWAT having the Nash–Sutcliffe coefficient of efficiency (NSE) values of 0.76 and 0.72 in the validation period, respectively. However, both models predicted streamflow poorly in the nonurban watershed. The NSE values of the ANNs significantly improved when a time series autoregressive model structure using historical streamflow data was implemented in the nonurban watershed. The SWAT model only achieved trivial performance improvement after using the SWAT-CUP SUFI-2 calibration procedure. This result suggests that an ANN model may be more suitable for short-term streamflow forecasting in watersheds heavily affected by karst features where the complex processes of rapid groundwater recharge and discharge strongly influence surface water flow.
摘要本文比较了基于数据驱动的人工神经网络(ANN)和基于物理的水土评估工具(SWAT)估算径流的精度。该模型应用于德克萨斯州中南部圣安东尼奥地区的两个小流域，一个高度城市化，另一个主要覆盖常绿森林和灌木，喀斯特地质特征普遍存在。两种模型均能较好地预测城市化流域的日流量，其中ANN和SWAT模型在验证期内的Nash-Sutcliffe效率系数(NSE)分别为0.76和0.72。然而，这两种模型对非城市流域的流量预测都很差。在非城市流域采用历史流量数据的时间序列自回归模型结构后，人工神经网络的NSE值显著提高。在使用SWAT- cup SUFI-2校准程序后，SWAT模型仅实现了微不足道的性能改进。这一结果表明，人工神经网络模型可能更适合于受岩溶特征影响较大的流域的短期流量预测，在这些流域，地下水快速补给和排放的复杂过程强烈影响地表水的流量。
{"title":"Comparative evaluation of daily streamflow prediction by ANN and SWAT models in two karst watersheds in central south Texas","authors":"Xiaohan Mei, Patricia K. Smith, Jing Li","doi":"10.2166/nh.2023.229","DOIUrl":"https://doi.org/10.2166/nh.2023.229","url":null,"abstract":"Abstract This work compares the accuracy of streamflow estimated by a data-driven artificial neural network (ANN) and the physically based soil and water assessment tool (SWAT). The models were applied in two small watersheds, one highly urbanized and the other primarily covered with evergreen forest and shrubs, in the San Antonio Region of central south Texas, where karst geologic features are prevalent. Both models predicted daily streamflow in the urbanized watershed very well, with the ANN and SWAT having the Nash–Sutcliffe coefficient of efficiency (NSE) values of 0.76 and 0.72 in the validation period, respectively. However, both models predicted streamflow poorly in the nonurban watershed. The NSE values of the ANNs significantly improved when a time series autoregressive model structure using historical streamflow data was implemented in the nonurban watershed. The SWAT model only achieved trivial performance improvement after using the SWAT-CUP SUFI-2 calibration procedure. This result suggests that an ANN model may be more suitable for short-term streamflow forecasting in watersheds heavily affected by karst features where the complex processes of rapid groundwater recharge and discharge strongly influence surface water flow.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"42 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282337","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 Pilling, Jon Millard, Julia Perez, Katie Egan, Russel Turner, Anthony Duke
Abstract In recognition of the increased risk to national resilience from flooding, we provide an overview of recent and future improvements to flood risk forecasting and communication at the Flood Forecasting Centre (FFC). We draw on the analysis of fluvial and surface water flooding across England and Wales in 2021 to highlight these areas of improvement. Already implemented improvements in both the underpinning science and our long lead-time product are described in the context of high-magnitude, high-impact floods. In addition, we consider more substantial developments from improved modelling of convection to translating this to surface water flood risk and to the essential communication and service provision. Finally, recognising that many of the challenges are shared internationally, we distil our key recommendations for future improvement. These improvements rely on collaboration for them to be successful.
{"title":"2021 UK floods: improvements and recommendations from the flood forecasting centre","authors":"Charles Pilling, Jon Millard, Julia Perez, Katie Egan, Russel Turner, Anthony Duke","doi":"10.2166/nh.2023.023","DOIUrl":"https://doi.org/10.2166/nh.2023.023","url":null,"abstract":"Abstract In recognition of the increased risk to national resilience from flooding, we provide an overview of recent and future improvements to flood risk forecasting and communication at the Flood Forecasting Centre (FFC). We draw on the analysis of fluvial and surface water flooding across England and Wales in 2021 to highlight these areas of improvement. Already implemented improvements in both the underpinning science and our long lead-time product are described in the context of high-magnitude, high-impact floods. In addition, we consider more substantial developments from improved modelling of convection to translating this to surface water flood risk and to the essential communication and service provision. Finally, recognising that many of the challenges are shared internationally, we distil our key recommendations for future improvement. These improvements rely on collaboration for them to be successful.","PeriodicalId":13096,"journal":{"name":"Hydrology Research","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346461","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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