Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes

For reliable prediction of sediment yield in a watershed, fine-scale projections for hydro-climate components were first obtained using the statistical bias correction and downscaling scheme based on the combination of an Artificial Neural Network (ANN), Nonstationary Quantile Mapping (NSQM) and Sto...

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Main Authors: Soojin Moon, Boosik Kang
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:Water
Subjects:
Online Access:http://www.mdpi.com/2073-4441/8/10/433
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spelling doaj-6505ed9965e2481c985dca4465be5fea2020-11-25T00:37:07ZengMDPI AGWater2073-44412016-10-0181043310.3390/w8100433w8100433Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic ExtremesSoojin Moon0Boosik Kang1Hydrometeorological Cooperation Center, 11, Gyoyukwon-ro, Gwacheon-si 13841, KoreaDepartment of Civil and Environmental Engineering, Dankook University, 152, Jukjeon-ro, Suji-gu, Yongin-si 16890, KoreaFor reliable prediction of sediment yield in a watershed, fine-scale projections for hydro-climate components were first obtained using the statistical bias correction and downscaling scheme based on the combination of an Artificial Neural Network (ANN), Nonstationary Quantile Mapping (NSQM) and Stochastic Typhoon Synthesis (STS) sub-modules. Successively, the hydrologic runoff and sediment yield from the land surfaces were predicted through the long-term continuous watershed model, Soil and Water Assessment Tool (SWAT), using the bias-corrected and downscaled Regional Climate Model (RCM) output under the Intergovernmental Panel on Climate Change’s (IPCC’s) A1B climate change scenario. The incremental improvement of the combined downscaling process was evaluated successfully during the baseline period, which provides projected confidence for the simulated future scenario. The realistic simulation of sediment yield is closely related to the rainfall event with high intensity and frequency. During the long-term future period, the Coefficient of River Regime (CORR) reaches 353.9 (27.2% increase with respect to baseline). The projection for annual precipitation by 2040 and 2100 is a 25.7% and a 57.2% increase with respect to the baseline period, respectively. In particular, the increasing CORR rate (33.4% and 72.5%) during the flood season is much higher than that for the annual total amount. However, the sediment yield is expected to increase by 27.4% and 121.2% during the same periods, which exhibits steeper trends than the hydrologic runoff. The June, July, August (JJA) season occupies 83.0% annual total sediment yield during the baseline period, which is similar during the projection period. The relative change of sediment yield is 1.9-times higher than that of dam inflows.http://www.mdpi.com/2073-4441/8/10/433NSQMdownscalingSWATsediment
collection DOAJ
language English
format Article
sources DOAJ
author Soojin Moon
Boosik Kang
spellingShingle Soojin Moon
Boosik Kang
Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes
Water
NSQM
downscaling
SWAT
sediment
author_facet Soojin Moon
Boosik Kang
author_sort Soojin Moon
title Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes
title_short Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes
title_full Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes
title_fullStr Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes
title_full_unstemmed Terrestrial Sediment Yield Projection under the Bias-Corrected Nonstationary Scenarios with Hydrologic Extremes
title_sort terrestrial sediment yield projection under the bias-corrected nonstationary scenarios with hydrologic extremes
publisher MDPI AG
series Water
issn 2073-4441
publishDate 2016-10-01
description For reliable prediction of sediment yield in a watershed, fine-scale projections for hydro-climate components were first obtained using the statistical bias correction and downscaling scheme based on the combination of an Artificial Neural Network (ANN), Nonstationary Quantile Mapping (NSQM) and Stochastic Typhoon Synthesis (STS) sub-modules. Successively, the hydrologic runoff and sediment yield from the land surfaces were predicted through the long-term continuous watershed model, Soil and Water Assessment Tool (SWAT), using the bias-corrected and downscaled Regional Climate Model (RCM) output under the Intergovernmental Panel on Climate Change’s (IPCC’s) A1B climate change scenario. The incremental improvement of the combined downscaling process was evaluated successfully during the baseline period, which provides projected confidence for the simulated future scenario. The realistic simulation of sediment yield is closely related to the rainfall event with high intensity and frequency. During the long-term future period, the Coefficient of River Regime (CORR) reaches 353.9 (27.2% increase with respect to baseline). The projection for annual precipitation by 2040 and 2100 is a 25.7% and a 57.2% increase with respect to the baseline period, respectively. In particular, the increasing CORR rate (33.4% and 72.5%) during the flood season is much higher than that for the annual total amount. However, the sediment yield is expected to increase by 27.4% and 121.2% during the same periods, which exhibits steeper trends than the hydrologic runoff. The June, July, August (JJA) season occupies 83.0% annual total sediment yield during the baseline period, which is similar during the projection period. The relative change of sediment yield is 1.9-times higher than that of dam inflows.
topic NSQM
downscaling
SWAT
sediment
url http://www.mdpi.com/2073-4441/8/10/433
work_keys_str_mv AT soojinmoon terrestrialsedimentyieldprojectionunderthebiascorrectednonstationaryscenarioswithhydrologicextremes
AT boosikkang terrestrialsedimentyieldprojectionunderthebiascorrectednonstationaryscenarioswithhydrologicextremes
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