EVALUATION OF UTILIZATION OF SATELLITE RAIN DATA, FOR CALCULATION OF FLOOD DISCHARGE, CASE STUDY: KATULAMPA SUB-WATERSHED
In calculating the flood discharge, adequate rainfall data is required which has good quality and quantity of data. But sometimes the observed rainfall data does not have good enough quality and quantity data, or even observational rainfall data is not available in a certain area. To address these c...
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| Format: | Theses |
| Language: | Indonesia |
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| Online Access: | https://digilib.itb.ac.id/gdl/view/71226 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In calculating the flood discharge, adequate rainfall data is required which has good quality and quantity of data. But sometimes the observed rainfall data does not have good enough quality and quantity data, or even observational rainfall data is not available in a certain area. To address these constraints alternative selection of rainfall data which is often used as an alternative data is by utilizing satellite rainfall data. Satellite rain data used in this study are GPM (Global Precipitation Measurement Mission) satellite rainfall data with a spatial resolution of 0.1o x 0.1o, and PERSIANN with a spatial resolution of 0.04o x 0.04o. Where GPM and PERSIANN satellite data still have higher data accuracy than other satellites (Zhang., et al., 2018). The analysis carried out in this study was by checking the correlation value or R2 in each satellite rainfall data. The data that is declared fit for use has a value of R2 > 0.6. The process of correcting satellite rainfall data is carried out using the Quantile Mapping method, which is validated by cumulative data as well as the annual maximum rainfall. This was done by looking for the smallest RMSE (Root Mean Squared Error) value indicator found on the PERSIAAN satellite rainfall data (Grid 10) which has been corrected with an RMSE value of 0.005. The next process, the comparison process is carried out by checking the hourly discharge and peak discharge of the model against the debit data that has been validated in advance against the discharge of the rain data input model that occurred on that day. Then the comparison process is validated again by checking the peak discharge of the model against the discharge data (frequency analysis results). The process of evaluating the hourly discharge is carried out by looking for the NSE indicator (Nash-Sutcliffe Error) ? 1. The NSE value ? 1 is obtained, which is found in the PERSIANN satellite rain data input model discharge corrected on the HSS ITB 2 b method with a 5 year period of 0.13. Meanwhile, for the peak discharge evaluation process, an error value indicator is used where the smallest error is found in the discharge evaluation results of the PERSIANN corrected rain data input model with HSS ITB 1 b method on 5 year period , which is 1.4%.
Of all the evaluation processes for satellite rainfall data, it is stated that the calculation of the flood discharge with the input of the correct PERSIAAN satellite rainfall data has an NSE ? 1 and has the smallest error value. From the evaluation of satellite rain data and based on research (Ginting, 2019) which states that the peak flood discharge resulting from the PERSIANN satellite rain has the highest accuracy to be used as an alternative to rainfall data for flood discharge calculations. So it can be concluded that PERSIANN satellite rainfall data can be used as an alternative rainfall data for flood discharge calculations. |
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