Performance evaluation of CHIRPS satellite precipitation estimates over Turkey

Aksu, Hakan; Akgül, Mehmet Ali

doi:10.1007/s00704-020-03301-5

Cited by 46 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CHIRPS satellite data is a quasi-global rainfall data that is available from 50 N to 50 S and 180 E to 180 W coverages (Aksu and Akgül 2020). This datasets is available since 1981 to present (Sacré Regis M. et al 2020).…”

Section: Methodsmentioning

confidence: 99%

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari³

2021

Preprint

View full text Add to dashboard Cite

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in groundwater basin of Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail.CM (CMORPH) has larger underestimation compared to other satellite-based rainfall. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) is the lowest. CM has a good performance to detect no rain, while IMR (GPM-IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM) especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall has large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall except to merge both IMR and TR together.

show abstract

Section: Methodsmentioning

confidence: 99%

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…CHIRPS satellite data is a quasi-global rainfall data that is available from 50° N to 50° S and 180° E to 180° W coverages (Aksu and Akgül 2020). This dataset is available from 1981 to present (Sacré Regis M. et al 2020).…”

Section: Datamentioning

confidence: 99%

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari

2021

Theor Appl Climatol

View full text Add to dashboard Cite

Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in the groundwater basin of the Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail. CM (CMORPH) has larger underestimation compared to other satellite-based rainfall assessments. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) has the lowest. CM has a good performance to detect no rain, while IMR (GPM IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM), especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall assessments have large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall than to merge both IMR and TR together.

show abstract

“…Rivera et al (2018) carried out an assessment study in the central Andean region of Argentina, using the data of 57 rainfall stations covering the years 1987-2016, observed that CHIRPS performance is better in regions with the highest summer precipitation than the drier regions. Aksu and Akgül (2020) conducted a performance assessment study of CHIRPS data over Turkey covering the years from 2008 to 2018 by 77 rainfall station data in daily, compared with ten-daily and monthly values. They found that the CHIRPS estimations were highly correlated at monthly and ten-day time scales, but the correlation decreased for daily estimations.…”

Section: Introductionmentioning

confidence: 99%

Areal Precipitation Estimation Using Satellite Derived Rainfall Data over an Irrigation Area

Akgül¹,

Aksu

2021

Turkish JAF Sci.Tech.

Self Cite

View full text Add to dashboard Cite

The average precipitation on the irrigation field can be estimated from the Meteorology Observation Stations by using spatial interpolation methods such as Thiessen polygon and isohyetal curves. However, the fact that precipitation doesn't occur homogenous in spatial scales, spatial interpolation methodologies need a large number of meteorology stations for more accurate results. In recent years, remote sensing methods have diversified to estimate precipitation. In this study, performance of the satellite-based precipitation data was assessed to determine areal precipitation over an irrigation area. This study was conducted over left bank irrigation area located in the Çukurova Plain of Turkey. Relationship between CHIRPS satellite based on monthly precipitation data and 4 meteorology stations’ data were analyzed. Determination coefficients (R2) of the stations were found between 0.64 and 0.77, for point based comparison, R2 was calculated as 0.84 with Thiessen polygon method. It is concluded that the precipitation amount in the irrigated area can be estimated as accurately as classical methods such as Thiessen polygon with satellite-based precipitation data.

show abstract

Performance evaluation of CHIRPS satellite precipitation estimates over Turkey

Cited by 46 publications

References 39 publications

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Performance of daily satellite-based rainfall in groundwater basin of Merapi Aquifer System, Yogyakarta

Areal Precipitation Estimation Using Satellite Derived Rainfall Data over an Irrigation Area

Contact Info

Product

Resources

About