Loading…

A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to captur...

Full description

Saved in:
Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-09, Vol.12 (18), p.3088
Main Authors: Dhanesh, Yeganantham, Bindhu, V. M., Senent-Aparicio, Javier, Brighenti, Tássia Mattos, Ayana, Essayas, Smitha, P. S., Fei, Chengcheng, Srinivasan, Raghavan
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83
cites cdi_FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83
container_end_page
container_issue 18
container_start_page 3088
container_title Remote sensing (Basel, Switzerland)
container_volume 12
creator Dhanesh, Yeganantham
Bindhu, V. M.
Senent-Aparicio, Javier
Brighenti, Tássia Mattos
Ayana, Essayas
Smitha, P. S.
Fei, Chengcheng
Srinivasan, Raghavan
description The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.
doi_str_mv 10.3390/rs12183088
format article
fullrecord <record><control><sourceid>doaj_cross</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_bd0e7df8e5004638a2f34054685a8786</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_bd0e7df8e5004638a2f34054685a8786</doaj_id><sourcerecordid>oai_doaj_org_article_bd0e7df8e5004638a2f34054685a8786</sourcerecordid><originalsourceid>FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83</originalsourceid><addsrcrecordid>eNpNkEtLw0AUhYMoWGo3_oJZC9F5JDOTZUlbW6hY-kBwE24yd2pKmimTWPDfm7ai3s09HA4fhxME94w-CpHQJ98wzrSgWl8FPU4VDyOe8Ot_-jYYNM2OdicES2jUC9ohSd3-AB7a8ohkfITqs5OuJs6S9gPJAr11fg91gScrnc6WixWB2pB0slqSEbRAugAZldaix7olaVXuoUXy7mpsyKYp6-2ZtHobrsmLM1jdBTcWqgYHP78fbCbjdToN56_Ps3Q4DwshRBuCUgJzxXhitSnyrm9ME5B5DgqlRFVITqkSVCpqtGVgJJeQS2BJwWJttegHswvXONhlB9_18l-ZgzI7G85vM_BtWVSY5YaiMlZjTGkkhQZuRUTjSOoYtNKyYz1cWIV3TePR_vIYzU7zZ3_zi2-KK3Ux</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><creator>Dhanesh, Yeganantham ; Bindhu, V. M. ; Senent-Aparicio, Javier ; Brighenti, Tássia Mattos ; Ayana, Essayas ; Smitha, P. S. ; Fei, Chengcheng ; Srinivasan, Raghavan</creator><creatorcontrib>Dhanesh, Yeganantham ; Bindhu, V. M. ; Senent-Aparicio, Javier ; Brighenti, Tássia Mattos ; Ayana, Essayas ; Smitha, P. S. ; Fei, Chengcheng ; Srinivasan, Raghavan</creatorcontrib><description>The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs12183088</identifier><language>eng</language><publisher>MDPI AG</publisher><subject>CFSR ; CHIRPS ; HAWQS ; rainfall comparison ; SWAT</subject><ispartof>Remote sensing (Basel, Switzerland), 2020-09, Vol.12 (18), p.3088</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83</citedby><cites>FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83</cites><orcidid>0000-0002-1818-5811 ; 0000-0001-7103-8653 ; 0000-0001-8375-6038</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Dhanesh, Yeganantham</creatorcontrib><creatorcontrib>Bindhu, V. M.</creatorcontrib><creatorcontrib>Senent-Aparicio, Javier</creatorcontrib><creatorcontrib>Brighenti, Tássia Mattos</creatorcontrib><creatorcontrib>Ayana, Essayas</creatorcontrib><creatorcontrib>Smitha, P. S.</creatorcontrib><creatorcontrib>Fei, Chengcheng</creatorcontrib><creatorcontrib>Srinivasan, Raghavan</creatorcontrib><title>A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model</title><title>Remote sensing (Basel, Switzerland)</title><description>The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.</description><subject>CFSR</subject><subject>CHIRPS</subject><subject>HAWQS</subject><subject>rainfall comparison</subject><subject>SWAT</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpNkEtLw0AUhYMoWGo3_oJZC9F5JDOTZUlbW6hY-kBwE24yd2pKmimTWPDfm7ai3s09HA4fhxME94w-CpHQJ98wzrSgWl8FPU4VDyOe8Ot_-jYYNM2OdicES2jUC9ohSd3-AB7a8ohkfITqs5OuJs6S9gPJAr11fg91gScrnc6WixWB2pB0slqSEbRAugAZldaix7olaVXuoUXy7mpsyKYp6-2ZtHobrsmLM1jdBTcWqgYHP78fbCbjdToN56_Ps3Q4DwshRBuCUgJzxXhitSnyrm9ME5B5DgqlRFVITqkSVCpqtGVgJJeQS2BJwWJttegHswvXONhlB9_18l-ZgzI7G85vM_BtWVSY5YaiMlZjTGkkhQZuRUTjSOoYtNKyYz1cWIV3TePR_vIYzU7zZ3_zi2-KK3Ux</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Dhanesh, Yeganantham</creator><creator>Bindhu, V. M.</creator><creator>Senent-Aparicio, Javier</creator><creator>Brighenti, Tássia Mattos</creator><creator>Ayana, Essayas</creator><creator>Smitha, P. S.</creator><creator>Fei, Chengcheng</creator><creator>Srinivasan, Raghavan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1818-5811</orcidid><orcidid>https://orcid.org/0000-0001-7103-8653</orcidid><orcidid>https://orcid.org/0000-0001-8375-6038</orcidid></search><sort><creationdate>20200901</creationdate><title>A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model</title><author>Dhanesh, Yeganantham ; Bindhu, V. M. ; Senent-Aparicio, Javier ; Brighenti, Tássia Mattos ; Ayana, Essayas ; Smitha, P. S. ; Fei, Chengcheng ; Srinivasan, Raghavan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>CFSR</topic><topic>CHIRPS</topic><topic>HAWQS</topic><topic>rainfall comparison</topic><topic>SWAT</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dhanesh, Yeganantham</creatorcontrib><creatorcontrib>Bindhu, V. M.</creatorcontrib><creatorcontrib>Senent-Aparicio, Javier</creatorcontrib><creatorcontrib>Brighenti, Tássia Mattos</creatorcontrib><creatorcontrib>Ayana, Essayas</creatorcontrib><creatorcontrib>Smitha, P. S.</creatorcontrib><creatorcontrib>Fei, Chengcheng</creatorcontrib><creatorcontrib>Srinivasan, Raghavan</creatorcontrib><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dhanesh, Yeganantham</au><au>Bindhu, V. M.</au><au>Senent-Aparicio, Javier</au><au>Brighenti, Tássia Mattos</au><au>Ayana, Essayas</au><au>Smitha, P. S.</au><au>Fei, Chengcheng</au><au>Srinivasan, Raghavan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2020-09-01</date><risdate>2020</risdate><volume>12</volume><issue>18</issue><spage>3088</spage><pages>3088-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.</abstract><pub>MDPI AG</pub><doi>10.3390/rs12183088</doi><orcidid>https://orcid.org/0000-0002-1818-5811</orcidid><orcidid>https://orcid.org/0000-0001-7103-8653</orcidid><orcidid>https://orcid.org/0000-0001-8375-6038</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2072-4292
ispartof Remote sensing (Basel, Switzerland), 2020-09, Vol.12 (18), p.3088
issn 2072-4292
2072-4292
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_bd0e7df8e5004638a2f34054685a8786
source Publicly Available Content Database (Proquest) (PQ_SDU_P3)
subjects CFSR
CHIRPS
HAWQS
rainfall comparison
SWAT
title A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A52%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-doaj_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Comparative%20Evaluation%20of%20the%20Performance%20of%20CHIRPS%20and%20CFSR%20Data%20for%20Different%20Climate%20Zones%20Using%20the%20SWAT%20Model&rft.jtitle=Remote%20sensing%20(Basel,%20Switzerland)&rft.au=Dhanesh,%20Yeganantham&rft.date=2020-09-01&rft.volume=12&rft.issue=18&rft.spage=3088&rft.pages=3088-&rft.issn=2072-4292&rft.eissn=2072-4292&rft_id=info:doi/10.3390/rs12183088&rft_dat=%3Cdoaj_cross%3Eoai_doaj_org_article_bd0e7df8e5004638a2f34054685a8786%3C/doaj_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c333t-a773eb7129f8dcb904509a6bba7e66e7c6200730670d8f1ad626ab6a19c158f83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true