{"title":"Improvement of socio-hydrological model to capture the dynamics of combined river and urban floods: a case study in Lower Kelani River Basin, Sri Lanka","authors":"C. Perera, Shinichiro Nakamura","doi":"10.3178/hrl.16.40","DOIUrl":"https://doi.org/10.3178/hrl.16.40","url":null,"abstract":": Identifying the complex patterns of human-flood interac‐ tions over longer periods of time is very important in flood‐ plain management activities. The recently introduced socio-hydrology (SH) model contributes to capture these long-term behaviors of human-flood systems. This model can be utilized to explain the long-term dynamics of human-water interaction in floodplains. The current SH model exclu‐ sively illustrates the impact of river floods on floodplain communities. However, in some river basins, urban floods (due to high intensity rainfall) are dominant, whereas in other river basins, both river floods and urban floods influ‐ ence the dynamics of the system. It is often difficult to dis‐ tinguish the type of flood from actual local disaster data sets. In this study, we proposed an improvement to the existing SH model to capture the dynamics of both river floods and urban floods based on a case study from the Lower Kelani Basin, Sri Lanka, using simulated historical flood damages. The improved model was applied to capture flood damages in the target watershed, and the results fur‐ ther emphasize the importance of flood risk perception in flood damage reduction.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spatiotemporal variability of hydrogen stable isotopes at a local scale in shallow groundwater during the warm season in Tottori Prefecture, Japan","authors":"Y. Yoshioka, H. Yoshioka","doi":"10.3178/hrl.16.25","DOIUrl":"https://doi.org/10.3178/hrl.16.25","url":null,"abstract":"","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kazuya Takami, R. Kamamoto, Kenji Suzuki, K. Yamaguchi, E. Nakakita
{"title":"Relationship between newly fallen snow density and degree of riming estimated by particles’ fall speed in Niigata Prefecture, Japan","authors":"Kazuya Takami, R. Kamamoto, Kenji Suzuki, K. Yamaguchi, E. Nakakita","doi":"10.3178/hrl.16.87","DOIUrl":"https://doi.org/10.3178/hrl.16.87","url":null,"abstract":"","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69395051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Shiraki, S. Kawana, Haruna Tsujinaka, Sakura Ariyoshi, Y. Uchiyama
{"title":"A preliminary observation for quantifying detached stemflow","authors":"K. Shiraki, S. Kawana, Haruna Tsujinaka, Sakura Ariyoshi, Y. Uchiyama","doi":"10.3178/hrl.16.1","DOIUrl":"https://doi.org/10.3178/hrl.16.1","url":null,"abstract":": Detached stemflow has been defined as rainwater that breaks away from the stemflow and falls around the trees as throughfall. Quantitative measurements of detached stem‐ flow were taken for two sample broadleaf trees on the university campus. Zelkova, with smooth bark, has a tree structure that concentrates rainwater, producing a large amount of stemflow. A rainwater collection system installed around the trunk can capture large amounts of throughfall as detached stemflow. The detached stemflow amount had almost doubled in water height equivalent to throughfall at the tree stand. Therefore, some trees generate much throughfall in the forest near the trunk. In the case of the Katsura tree, however, the stemflow was low. The throughfall attributable to the detached stemflow was less than the average throughfall. This low stemflow generation was assumed to be due to the roughness of the Katsura bark. The rainwater which attaches to the trunk and branches breaks away easily. Presumably, the leaves near the trunk intercept raindrops and disperse the rainwater to the surroundings. The detached stemflow can constitute a large quantity. It can be expected to be related closely to the stemflow generation mechanism.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yi Yang, M. Maki, Rongling Ye, Daiki Saito, Thanyaluck Nontasri, M. Srisutham, Supranee Sritumboon, S. Sukchan, Koshi Yoshida, K. Oki, K. Homma
{"title":"Yearly change in severely salt-damaged areas in paddy fields in Ban Phai in Northeast Thailand","authors":"Yi Yang, M. Maki, Rongling Ye, Daiki Saito, Thanyaluck Nontasri, M. Srisutham, Supranee Sritumboon, S. Sukchan, Koshi Yoshida, K. Oki, K. Homma","doi":"10.3178/hrl.16.7","DOIUrl":"https://doi.org/10.3178/hrl.16.7","url":null,"abstract":": Future expansion of salt-damaged areas is anticipated in Northeast Thailand. We conducted a field investigation of paddy fields from 2016 to 2019 in Ban Phai district, Khon Kaen province in Northeast Thailand to evaluate yearly changes in the effect of salinity damage on rice production. The investigation area was classified into severely salt-affected areas (2 nd of 5 classes) based on the definition used in Thailand. Since salinity severely damages rice produc‐ tion, rice cultivation was abandoned in some fields, although some were still planted. The soil electrical con‐ ductivity (EC) in the rice-planted paddy fields changed yearly in association with the amount of precipitation. The effect of the difference in EC on rice yield was moderate, suggesting that rice yield was mediated by surface water. Some areas in the abandoned fields did not have any vege‐ tation, and quite high soil EC values were observed. The non-vegetated areas evaluated based on yearly unmanned aerial vehicle (UAV) images changed partly due to the amount of precipitation. However, some non-vegetated areas decreased in contrast to the decrease in precipitation, probably because of the effect of groundwater. Although the continuous expansion of severely salt-damaged areas was not observed, the monitoring of salinity levels is rec‐ ommended for the future.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins","authors":"Y. Sawada, S. Okugawa, Takayuki Kimizuka","doi":"10.3178/hrl.16.73","DOIUrl":"https://doi.org/10.3178/hrl.16.73","url":null,"abstract":": Verification processes of rainfall-runoff modeling are important to improve the skill of hydrological models to reproduce water cycles in river basins. It is ideal that newly developed models are compared with many benchmarking conventional models in many river basins as part of the ver‐ ification process. However, this robust verification is time-consuming if model developers prepare data and models from scratch. Here we present a useful dataset which can accelerate the robust verification of hydrological models. Our newly developed dataset, Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp), provides runoff simulation by 44 calibrated conceptual hydrological models in 135 Japanese river basins as well as meteorological forcing which is necessary to drive conceptual hydrological models. By comparing simulated runoff with river discharge observations which are not used for the calibration of hydrological models, we find that the best models in the 44 models can reproduce observed river runoff with KGE larger than 0.6 in most of the 135 river basins, so that the runoff simulation of MERV-Jp is reasonably accurate. MERV-Jp is publicly available to support all hydrological model developers to robustly verify their model improvement.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69395001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Estimating freshwater lens volume in highly permeable aquifers","authors":"Satoshi Tajima, Jiaqing Liu, T. Tokunaga","doi":"10.3178/hrl.16.12","DOIUrl":"https://doi.org/10.3178/hrl.16.12","url":null,"abstract":": A unique freshwater lens shape observed in Tarama Island, Japan, in which hydraulic conductivity is on the order of 10 −2 m s −1 , has posed a question as to how well we can estimate the fresh groundwater volumes in extremely permeable aquifers. We applied both an analytical model and numerical simulations with various hydraulic conduc‐ tivities, including extremely permeable conditions, and compared their results. The simulation showed that, when the hydraulic conductivity was extremely high, saline groundwater existed near the coast. The analytical model overestimated the freshwater volume compared with those estimated from the numerical simulations, and the discrep‐ ancy became more significant with increasing hydraulic conductivity. These findings imply that, when hydraulic conductivity is extremely high, numerical simulations con‐ sidering density-dependent flow and dispersive mass trans‐ port processes should be applied to better assess the shapes and volumes of freshwater lenses.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Naoya Shibata, F. Nakai, Kensuke Otsuyama, Shinichiro Nakamura
{"title":"Socio-hydrological modeling and its issues in Japan: a case study in Naganuma District, Nagano City","authors":"Naoya Shibata, F. Nakai, Kensuke Otsuyama, Shinichiro Nakamura","doi":"10.3178/hrl.16.32","DOIUrl":"https://doi.org/10.3178/hrl.16.32","url":null,"abstract":"","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jun Inaoka, K. Kosugi, N. Masaoka, T. Itokazu, K. Nakamura, M. Fujimoto
{"title":"Effects of geological differences on rainfall–runoff characteristics based on field measurements","authors":"Jun Inaoka, K. Kosugi, N. Masaoka, T. Itokazu, K. Nakamura, M. Fujimoto","doi":"10.3178/hrl.16.80","DOIUrl":"https://doi.org/10.3178/hrl.16.80","url":null,"abstract":"","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69395041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Use of deep learning to identify optimal meteorological inputs to forecast seasonal precipitation","authors":"Shingo Zenkoji, T. Tebakari, K. Sakakibara","doi":"10.3178/hrl.16.67","DOIUrl":"https://doi.org/10.3178/hrl.16.67","url":null,"abstract":": Using deep learning to identify meteorological factors has enabled optimal predictions of Thailand’s seasonal pre‐ cipitation two months in advance. A combination of surface temperature and pressure, specific humidity, and wind speed (zonal and meridional components) was tested. Examining each combination of meteorological factor has created optimal input data for seasonal precipitation fore‐ casts. In addition, the hyperparameters of each model were calculated by Bayesian optimization. Predictive model per‐ formance tended to be better when the weight for pressure was higher, while a higher weight for specific humidity reduced predictive performance. Finally, visualization of the positive neuron values in all the coupled layers of the first layer showed that the regions with the highest fre‐ quency of occurrence were the El Niño monitoring areas such as the “Indian Ocean Basin Wide” (IOBW) and “NINO WEST”.","PeriodicalId":13111,"journal":{"name":"Hydrological Research Letters","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69394986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}