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Assessment of agricultural drought based on CHIRPS data and SPI method over West Papua - Indonesia

Faisol Arif, Indarto Indarto, Novita Elida, Budiyono Budiyono

Journal of Water and Land Development

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2022

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no. 52

44--52

EN

This study aims to utilise Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data and Standardised Precipitation Index (SPI) method to assess agricultural drought in West Papua, Indonesia. The data used in this study is monthly CHIRPS data acquired from 1996 to 2019, daily precipitation data recorded from 1996 to 2019 from the five climatological stations in West Papua, Indonesia located at Sorong, Fakfak, Kaimana, Manokwari, and South Manokwari. 3-month SPI or quarterly SPI are used to assess agricultural drought, i.e., SPI January-March, SPI February-April, SPI March-May, SPI April-June, SPI May-July, SPI June-August, SPI July-September, SPI August-October, SPI September-November, and SPI October-December. The results showed that in 2019 agricultural drought in West Papua was moderately wet to severely dry. The most severely dry occurred in September-December periods. Generally, CHIRPS data and SPI methods have an acceptable accuracy in generating drought information in West Papua with an accuracy of 53% compared with climate data analysis. Besides, the SPI from CHIRPS data processing has a moderate correlation with climate data analysis with an average R2= 0.51.

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Validation of CHIRPS satellite based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin, Iran

Mokhtari Shirin, Sharafati Ahmad, Raziei Tayeb

Acta Geophysica

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2022

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Vol. 70, no 1

465--484

EN

The present study compares the daily and monthly precipitation estimates of the CHIRPS satellite data with the in situ measurements at four stations scattered over the Kosar Dam basin in southwestern Iran. The uncertainty of the satellite precipitation estimates was calculated through simulation with the Copula functions. For this purpose, 55% of the stations, daily and monthly rainfall data relative to the 1987–2012 period were used for training (simulation), and the other 45% were used for testing (validation) the performance of the Copula model. First, the daily, monthly, and annual satellite precipitation estimates were statistically compared with precipitation observed at the stations and the whole basin using the Pearson correlation coefficient (CC), root mean square error (RMSE), and Bias statistics. The computed CC between the areal average of observed and satellite precipitation estimation at the basin is 0.49, 0.82, and 0.33 for daily, monthly, and annual time scales, respectively. The difference (biases) between the satellite estimates and in situ measurements was then calculated for daily, monthly, and annual time scales over the training period. The obtained biases were subsequently fitted with the General Extreme Value distribution function coupled with the Gaussian Copula model to generate a series of similar random biases for all precipitation events. Then, the generated random biases were summed with the original satellite estimates to correct the associated biases. The bias-corrected precipitation for the training period was then compared to the original estimates of the satellite at the stations and the whole basin using the P-factor, R-factor, Bias, RMSE, and CC statistics. The statistics show that the random biases generated by the Copula method for the monthly CHIRPS satellite data relative to the 14-year training period have reduced the error rate of the satellite data by 74 to 95 percent when compared to observations. The satellite precipitation estimates of the 11-year test period were also corrected using the generated random biases in the training period. The results show that the bias correction considerably improved the monthly estimates and reduced the error rate of the satellite estimation by about 76 percent. In general, the simulation of the satellite precipitation with the Gaussian Copula model was performed satisfactorily at the monthly time scale, but it was less efficient at the daily time scale.

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Assessment of the GPM IMERG and CHIRPS precipitation estimations for the steppe region of the Crimea

Popovych Victor F., Dunaieva Ielizaveta A.

Meteorology Hydrology and Water Management. Research and Operational Applications

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2021

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Vol. 9, Iss. 1-2

1--13

EN

This paper compares the spatial distribution datasets on monthly precipitation totals derived from the Famine Early Warning System Network FEWS NET service (CHIRPS 2.0 product) and the International Mission of the Global Precipitation Measurement GPM (IMERG v06 product) with ground-based observations of a stationary weather stations located in the steppe region of the Crimean Peninsula in order to assess the representativeness of the precipitation spatial distribution and the applicability of the datasets for water balance calculations and agricultural crop dynamics modeling. A close convergence was observed between the estimated monthly precipitation totals and the precipitation gauge data during the study period (January 2017 - July 2020), with mean correlation coefficients of 0.75 and 0.73 for the GPM IMERG and CHIRPS, respectively. Both products generally overestimated the precipitation values compared to the measured data, with GPM IMERG (final run) exhibiting the greatest overestimations (1.3-2.1 times the weather station values). Our results demonstrate the requirement of GPM-derived precipitation estimations (particularly those from the GPM_3IMERDL v06 daily accumulated late run dataset) to be additionally verified and calibrated based on data from regional weather stations or the CHIRPS 2.0 product (if available).