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Comparison of SWAT-based Ecohydrological Modeling in the Rawa Pening Catchment Area, Indonesia

Jurnal Pendidikan IPA Indonesia · Vol. 13 No. 1 (2024) · pp. 1–11

DOI
10.15294/jpii.v13i1.45277
ISSN
2089-4392, 2339-1286
Published
2024-03-29
Publisher
Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)
Language
eng

Abstract

The Soil and Water Assessment Tool (SWAT) is an ecohydrological model widely applied to assess water quality and watershed management. This tool also has the advantage of building watershed models even with limited monitoring data availability. The essential data required by this tool includes digital elevation models (DEM), land use maps, climate data, and soil data. Nonetheless, the availability of spatial data is still often a challenge in developing hydrological models, especially in developing countries such as Indonesia. This research will compare the accuracy of freely available data in Indonesia in facilitating the development of hydrological models from SWAT in the Rawa Pening catchment area. This research is crucial since Rawa Pening Lake is a priority lake for revitalization, so the research results will help provide suggestions regarding presenting data in SWAT modeling. This research compares SWAT models built from different land use and DEM (Digital Elevation Models) data. The land use data being compared is the result of processing from the Google Earth Engine (GEE) platform using machine learning with land use data from government agencies, namely the Ministry of Environment and Forestry, while the DEM data being compared is SRTM and DEMNAS data. The validation results using R, R2, RMSE, and NSE show that, in general, the model built from land use from GEE is the best compared to the other models. In modeling SWAT in Indonesia, we recommend using good-quality land-use data. Utilizing supervised classification through Random Forest (RF) algorithms within GEE can facilitate the acquisition of this data. To reduce computation time, the DEM can be SRTM with a small sacrifice of accuracy.

Keywords

Collaboration network of the authors

40 authors · 143 co-authorship links · 32 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • State University of Semarang
  • Diponegoro University
  • Universitas Tidar
  • University of Edinburgh
  • Indonesia University of Education
  • University of Brawijaya
  • Universitas Islam Negeri Salatiga
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abdul JabbarUniversity of Brawijaya10
Habil SultanAffiliation unknown7
Dwi RahmawatiAffiliation unknown6
Andhina Putri HeriyantiDiponegoro University4
Alfia RahmalidyaAffiliation unknown3
Amnan HarisAffiliation unknown2
Cintiya Egi PurwadiAffiliation unknown2
Mahendra GunawanAffiliation unknown2
Dhiyanita ShabrinaAffiliation unknown2
Lenny Helmalia MustikawatiAffiliation unknown2
Bambang Eko SusiloState University of Semarang2
Sri SukaesihState University of Semarang2
Nuni WidiartiState University of Semarang2
Siti WahyuniAffiliation unknown2
Aditya MariantiAffiliation unknown2
Virgania SariAffiliation unknown2
Stephani Diah PamelasariState University of Semarang2
ParminState University of Semarang1
Endah Fitriani RahayuState University of Semarang1
Ika SukmawatiUniversitas Tidar1
Muhammad Radian Nur AlamsyahUniversitas Tidar1
Purwanto PurwantoDiponegoro University1
Hartuti PurnaweniDiponegoro University1
Erna Noor SavitriState University of Semarang1
Sigit Ari PrabowoUniversitas Islam Negeri Salatiga1
Oky Eviana Putri RahmadaniState University of Semarang1
Anisia KholidahState University of Semarang1
M. H. YusaState University of Semarang1
Muhamad TaufiqState University of Semarang1
Parmin ParminState University of Semarang1
A. LevianaState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Septiko AjiAffiliation unknown1
Ni Luh TirtasariAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/nju/index.php/jpii/oai, first harvested 29 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Comparison of SWAT-based Ecohydrological Modeling in the Rawa Pening Catchment Area, Indonesia</dc:title>
	<dc:creator>Amalia, A. V.</dc:creator>
	<dc:creator>Fariz, T. R.</dc:creator>
	<dc:creator>Lutfiananda, F.</dc:creator>
	<dc:creator>Ihsan, H. M.</dc:creator>
	<dc:creator>Atunnisa, R.</dc:creator>
	<dc:creator>Jabbar, A.</dc:creator>
	<dc:subject xml:lang="en-US">DEM; Google Earth Engine; land use; SWAT; streamflow</dc:subject>
	<dc:description xml:lang="en-US">The Soil and Water Assessment Tool (SWAT) is an ecohydrological model widely applied to assess water quality and watershed management. This tool also has the advantage of building watershed models even with limited monitoring data availability. The essential data required by this tool includes digital elevation models (DEM), land use maps, climate data, and soil data. Nonetheless, the availability of spatial data is still often a challenge in developing hydrological models, especially in developing countries such as Indonesia. This research will compare the accuracy of freely available data in Indonesia in facilitating the development of hydrological models from SWAT in the Rawa Pening catchment area. This research is crucial since Rawa Pening Lake is a priority lake for revitalization, so the research results will help provide suggestions regarding presenting data in SWAT modeling. This research compares SWAT models built from different land use and DEM (Digital Elevation Models) data. The land use data being compared is the result of processing from the Google Earth Engine (GEE) platform using machine learning with land use data from government agencies, namely the Ministry of Environment and Forestry, while the DEM data being compared is SRTM and DEMNAS data. The validation results using R, R2, RMSE, and NSE show that, in general, the model built from land use from GEE is the best compared to the other models. In modeling SWAT in Indonesia, we recommend using good-quality land-use data. Utilizing supervised classification through Random Forest (RF) algorithms within GEE can facilitate the acquisition of this data. To reduce computation time, the DEM can be SRTM with a small sacrifice of accuracy.</dc:description>
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	<dc:date>2024-03-29</dc:date>
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	<dc:identifier>https://journal.unnes.ac.id/nju/jpii/article/view/45277</dc:identifier>
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	<dc:source xml:lang="en-US">Jurnal Pendidikan IPA Indonesia; Vol 13, No 1 (2024): March 2024; 1-11</dc:source>
	<dc:source>2089-4392</dc:source>
	<dc:source>2339-1286</dc:source>
	<dc:source>10.15294/jpii.v13i1</dc:source>
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