Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand

Land use changes such as deforestation and urban development influences the river discharge, soil erosion and sediment yield. It is important to evaluate tools which can be used to assess such impacts on water and sediment yield. Therefore, this study evaluated the Annualized Agricultural Non-Point...

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Autores principales:	A. Jirasirichote, S. Ninsawat, S. Shrestha, N.K. Tripathi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	AnnAGNPS model Surface runoff Sediment yield Watershed management Thailand Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/d03a7a84fb6348ec8789e81ac4c5c1c2
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id	oai:doaj.org-article:d03a7a84fb6348ec8789e81ac4c5c1c2
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spelling	oai:doaj.org-article:d03a7a84fb6348ec8789e81ac4c5c1c22021-12-02T05:03:01ZPerformance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand2405-844010.1016/j.heliyon.2021.e08396https://doaj.org/article/d03a7a84fb6348ec8789e81ac4c5c1c22021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021024993https://doaj.org/toc/2405-8440Land use changes such as deforestation and urban development influences the river discharge, soil erosion and sediment yield. It is important to evaluate tools which can be used to assess such impacts on water and sediment yield. Therefore, this study evaluated the Annualized Agricultural Non-Point Source Pollutant (AnnAGNPS) model's performance in simulating runoff and sediment loads in Nan Province, Thailand using seven years of continuous monitoring data. The river discharge and sediment yield data from 2011–2013 were used for calibration, and data from 2014–2017 were used for validation. Several input parameters were computed using methods suggested by other researchers and previous studies. In this study, the runoff curve number, soil erodibility factor (K), and RUSLE-C value were used to accurately simulate runoff and sediment loads. The results indicate that the model satisfactorily simulated runoff and sediment loads (R2 = 0.65 and NSE = 0.53 for runoff volume, and R2 = 0.62 and NSE = 0.60 for sediment yields). Moreover, the model estimated the total sediment yield, which contributed 12,932 hundred tons of material to the Nan River in 2017. The maximum sediment yield was obtained below the catchment (Na Noi sub-district, Na Noi district), which corresponds to areas with high crop densities. Cropland generated the highest soil erosion of all investigated land use (87.52% of total soil erosion). Thus, the AnnAGNPS model has the potential to use for investigating management practices to reduce soil erosion and controlling floods and droughts in Nan Province of Thailand.A. JirasirichoteS. NinsawatS. ShresthaN.K. TripathiElsevierarticleAnnAGNPS modelSurface runoffSediment yieldWatershed managementThailandScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08396- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AnnAGNPS model Surface runoff Sediment yield Watershed management Thailand Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	AnnAGNPS model Surface runoff Sediment yield Watershed management Thailand Science (General) Q1-390 Social sciences (General) H1-99 A. Jirasirichote S. Ninsawat S. Shrestha N.K. Tripathi Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand
description	Land use changes such as deforestation and urban development influences the river discharge, soil erosion and sediment yield. It is important to evaluate tools which can be used to assess such impacts on water and sediment yield. Therefore, this study evaluated the Annualized Agricultural Non-Point Source Pollutant (AnnAGNPS) model's performance in simulating runoff and sediment loads in Nan Province, Thailand using seven years of continuous monitoring data. The river discharge and sediment yield data from 2011–2013 were used for calibration, and data from 2014–2017 were used for validation. Several input parameters were computed using methods suggested by other researchers and previous studies. In this study, the runoff curve number, soil erodibility factor (K), and RUSLE-C value were used to accurately simulate runoff and sediment loads. The results indicate that the model satisfactorily simulated runoff and sediment loads (R2 = 0.65 and NSE = 0.53 for runoff volume, and R2 = 0.62 and NSE = 0.60 for sediment yields). Moreover, the model estimated the total sediment yield, which contributed 12,932 hundred tons of material to the Nan River in 2017. The maximum sediment yield was obtained below the catchment (Na Noi sub-district, Na Noi district), which corresponds to areas with high crop densities. Cropland generated the highest soil erosion of all investigated land use (87.52% of total soil erosion). Thus, the AnnAGNPS model has the potential to use for investigating management practices to reduce soil erosion and controlling floods and droughts in Nan Province of Thailand.
format	article
author	A. Jirasirichote S. Ninsawat S. Shrestha N.K. Tripathi
author_facet	A. Jirasirichote S. Ninsawat S. Shrestha N.K. Tripathi
author_sort	A. Jirasirichote
title	Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand
title_short	Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand
title_full	Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand
title_fullStr	Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand
title_full_unstemmed	Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand
title_sort	performance of annagnps model in predicting runoff and sediment yields in nan province, thailand
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d03a7a84fb6348ec8789e81ac4c5c1c2
work_keys_str_mv	AT ajirasirichote performanceofannagnpsmodelinpredictingrunoffandsedimentyieldsinnanprovincethailand AT sninsawat performanceofannagnpsmodelinpredictingrunoffandsedimentyieldsinnanprovincethailand AT sshrestha performanceofannagnpsmodelinpredictingrunoffandsedimentyieldsinnanprovincethailand AT nktripathi performanceofannagnpsmodelinpredictingrunoffandsedimentyieldsinnanprovincethailand
_version_	1718400738880651264

Performance of AnnAGNPS model in predicting runoff and sediment yields in Nan Province, Thailand

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