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応用水文 = Applied hydrology
01 Jan 1991-
About: The article was published on 1991-01-01 and is currently open access. It has received 1417 citations till now.
Citations
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TL;DR: In this paper, the authors used the SWAT (Soil and Water Assessment Tool) to simulate all related processes affecting water quantity, sediment, and nutrient loads in the Thur River basin, which is a direct tributary to the Rhine.
1,571 citations
Cites methods from "応用水文 = Applied hydrology"
...The settling velocity is determined using Stoke’s law (Chow et al., 1988) and is calculated as a function of particle diameter squared....
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...Peak runoff predictions are based on a modification of the Rational Formula (Chow et al., 1988)....
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TL;DR: The question of how to communicate the performance of a model to potential end-users is currently receiving increasing interest, and it is observed that researchers take much less care when communicating model performance amongst ourselves.
Abstract: How Do We Communicate Model Performance? The process of model performance evaluation is of primary importance, not only in the model development and calibration process, but also when communicating the results to other researchers and to stakeholders. The basic ‘rule’ is that every modelling result should be put into context, for example, by indicating the model performance using appropriate indicators, and by highlighting potential sources of uncertainty, and this practice has found its entry into the large majority of papers and conference presentations. While the question of how to communicate the performance of a model to potential end-users is currently receiving increasing interest (e.g. Pappenberger and Beven, 2006), we–as well as many other colleagues–observe regularly that researchers take much less care when communicating model performance amongst ourselves. We seem to assume that we are speaking about familiar performance concepts and that they have comparable significance for various types of model applications and case studies. In doing so, we do not pay sufficient attention to making clear what the values represented by our performance measures really mean. Even concepts as simple as the bias between an observed and a simulated time series need to be put into proper context: whereas a 10% bias in simulation of simulated discharge may be unacceptable in a climate change impact assessment, it may be of less concern in the context of real-time flood forecasting. While some performance measures can have an absolute meaning, such as the common measure of linear correlation, the vast majority of performance measures, and in particular quadratic-error-based measures, can only be properly interpreted when viewed in the context of a reference value (..)
557 citations
TL;DR: A framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS) is developed that is designed for the San Antonio River Basin and may be used as a prototype for model applications in other areas of the country.
445 citations
Cites background from "応用水文 = Applied hydrology"
...For the San Antonio area, most soils are classified into Hydrologic Soil Group C, which corresponds to soils having a low infiltration rate when thoroughly wetted, often with impeding layers in the soil, and CN of approximately 75–90 (Chow et al., 1988)....
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...Section 5 discusses potential results and utility of model development, and Section 6 draws some concluding remarks....
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TL;DR: The role of vegetation in protecting streams from nonpoint source pollutants and in improving the quality of degraded stream water has been extensively studied as mentioned in this paper, with a focus on the role of riparian vegetation.
Abstract: We review the research literature and summarize the major processes by which riparian vegetation influences chemical water quality in streams, as well as how these processes vary among vegetation types, and discuss how these processes respond to removal and restoration of riparian vegetation and thereby determine the timing and level of response in stream water quality. Our emphasis is on the role that riparian vegetation plays in protecting streams from nonpoint source pollutants and in improving the quality of degraded stream water. Riparian vegetation influences stream water chemistry through diverse processes including direct chemi- cal uptake and indirect influences such as by supply of organic matter to soils and channels, modification of water movement, and stabilization of soil. Some processes are more strongly expressed under certain site condi- tions, such as denitrification where groundwater is shallow, and by certain kinds of vegetation, such as channel stabilization by large wood and nutrient uptake by faster-growing species. Whether stream chemistry can be managed effectively through deliberate selection and management of vegetation type, however, remains uncer- tain because few studies have been conducted on broad suites of processes that may include compensating or reinforcing interactions. Scant research has focused directly on the response of stream water chemistry to the loss of riparian vegetation or its restoration. Our analysis suggests that the level and time frame of a response to restoration depends strongly on the degree and time frame of vegetation loss. Legacy effects of past vegetation can continue to influence water quality for many years or decades and control the potential level and timing of water quality improvement after vegetation is restored. Through the collective action of many processes, vegeta- tion exerts substantial influence over the well-documented effect that riparian zones have on stream water qual- ity. However, the degree to which stream water quality can be managed through the management of riparian vegetation remains to be clarified. An understanding of the underlying processes is important for effectively using vegetation condition as an indicator of water quality protection and for accurately gauging prospects for water quality improvement through restoration of permanent vegetation. (KEY TERMS: assessment; biogeochemistry; buffers; legacy effects; nonpoint source pollution; resilience; resto- ration; rivers ⁄streams; soils; watershed management.)
445 citations
Cites background from "応用水文 = Applied hydrology"
...Trees and taller woody shrubs on floodplains create greater roughness and flow resistance against deeper floods than JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 269 JAWRA herbaceous vegetation (Chow, 1959; Chow et al., 1988; Dudley et al., 1998)....
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TL;DR: The spatially distributed LISFLOOD model is described, which is a hydrological model specifically developed for the simulation ofhydrological processes in large European river basins, and how the model is parameterized is discussed.
Abstract: In this paper we describe the spatially distributed LISFLOOD model, which is a hydrological model specifically developed for the simulation of hydrological processes in large European river basins. The model was designed to make the best possible use of existing data sets on soils, land cover, topography and meteorology. We give a detailed description of the simulation of hydrological processes in LISFLOOD, and discuss how the model is parameterized. We also describe how the model was implemented technically using a combination of the PCRaster GIS system and the Python programming language, and discuss the management of in-and output data. Finally, we review some recent applications of LISFLOOD, and we present a case study for the Elbe river.
437 citations
References
More filters
TL;DR: In this paper, the authors used the SWAT (Soil and Water Assessment Tool) to simulate all related processes affecting water quantity, sediment, and nutrient loads in the Thur River basin, which is a direct tributary to the Rhine.
1,571 citations
TL;DR: The question of how to communicate the performance of a model to potential end-users is currently receiving increasing interest, and it is observed that researchers take much less care when communicating model performance amongst ourselves.
Abstract: How Do We Communicate Model Performance? The process of model performance evaluation is of primary importance, not only in the model development and calibration process, but also when communicating the results to other researchers and to stakeholders. The basic ‘rule’ is that every modelling result should be put into context, for example, by indicating the model performance using appropriate indicators, and by highlighting potential sources of uncertainty, and this practice has found its entry into the large majority of papers and conference presentations. While the question of how to communicate the performance of a model to potential end-users is currently receiving increasing interest (e.g. Pappenberger and Beven, 2006), we–as well as many other colleagues–observe regularly that researchers take much less care when communicating model performance amongst ourselves. We seem to assume that we are speaking about familiar performance concepts and that they have comparable significance for various types of model applications and case studies. In doing so, we do not pay sufficient attention to making clear what the values represented by our performance measures really mean. Even concepts as simple as the bias between an observed and a simulated time series need to be put into proper context: whereas a 10% bias in simulation of simulated discharge may be unacceptable in a climate change impact assessment, it may be of less concern in the context of real-time flood forecasting. While some performance measures can have an absolute meaning, such as the common measure of linear correlation, the vast majority of performance measures, and in particular quadratic-error-based measures, can only be properly interpreted when viewed in the context of a reference value (..)
557 citations
TL;DR: A framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS) is developed that is designed for the San Antonio River Basin and may be used as a prototype for model applications in other areas of the country.
445 citations
TL;DR: The role of vegetation in protecting streams from nonpoint source pollutants and in improving the quality of degraded stream water has been extensively studied as mentioned in this paper, with a focus on the role of riparian vegetation.
Abstract: We review the research literature and summarize the major processes by which riparian vegetation influences chemical water quality in streams, as well as how these processes vary among vegetation types, and discuss how these processes respond to removal and restoration of riparian vegetation and thereby determine the timing and level of response in stream water quality. Our emphasis is on the role that riparian vegetation plays in protecting streams from nonpoint source pollutants and in improving the quality of degraded stream water. Riparian vegetation influences stream water chemistry through diverse processes including direct chemi- cal uptake and indirect influences such as by supply of organic matter to soils and channels, modification of water movement, and stabilization of soil. Some processes are more strongly expressed under certain site condi- tions, such as denitrification where groundwater is shallow, and by certain kinds of vegetation, such as channel stabilization by large wood and nutrient uptake by faster-growing species. Whether stream chemistry can be managed effectively through deliberate selection and management of vegetation type, however, remains uncer- tain because few studies have been conducted on broad suites of processes that may include compensating or reinforcing interactions. Scant research has focused directly on the response of stream water chemistry to the loss of riparian vegetation or its restoration. Our analysis suggests that the level and time frame of a response to restoration depends strongly on the degree and time frame of vegetation loss. Legacy effects of past vegetation can continue to influence water quality for many years or decades and control the potential level and timing of water quality improvement after vegetation is restored. Through the collective action of many processes, vegeta- tion exerts substantial influence over the well-documented effect that riparian zones have on stream water qual- ity. However, the degree to which stream water quality can be managed through the management of riparian vegetation remains to be clarified. An understanding of the underlying processes is important for effectively using vegetation condition as an indicator of water quality protection and for accurately gauging prospects for water quality improvement through restoration of permanent vegetation. (KEY TERMS: assessment; biogeochemistry; buffers; legacy effects; nonpoint source pollution; resilience; resto- ration; rivers ⁄streams; soils; watershed management.)
445 citations
TL;DR: The spatially distributed LISFLOOD model is described, which is a hydrological model specifically developed for the simulation ofhydrological processes in large European river basins, and how the model is parameterized is discussed.
Abstract: In this paper we describe the spatially distributed LISFLOOD model, which is a hydrological model specifically developed for the simulation of hydrological processes in large European river basins. The model was designed to make the best possible use of existing data sets on soils, land cover, topography and meteorology. We give a detailed description of the simulation of hydrological processes in LISFLOOD, and discuss how the model is parameterized. We also describe how the model was implemented technically using a combination of the PCRaster GIS system and the Python programming language, and discuss the management of in-and output data. Finally, we review some recent applications of LISFLOOD, and we present a case study for the Elbe river.
437 citations