Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling (original) (raw)
Related papers
NATIONAL CONFERENCE ON PHYSICS AND CHEMISTRY OF MATERIALS: NCPCM2020, 2021
GSMaP and TRMM data validation was carried out with rainfall data in West Borneo. This study aims to evaluate the performance of GSMAP and TRMM in estimating daily rainfall in seven watersheds, namely Ambawang (AB), Kakap (KP), Pontianak (PT), Rasar (RS), Mandor (MD), Sebadu (SB), and Segedong. (SG). The analysis was performed using statistical analysis, error calculation, and rain event presentation. The results of the GSMaP satellite data validation against the observation data showed very high correlation values in all observation locations (R> 0.75). The similar result is also found in the TRMM data. In general, all of locations show a very small RMSE value. This indicates that the GSMaP and TRMM data tend to have a good performance in estimating rainfall in West Borneo. The relative bias values show pretty good results (<15%) except for MD both GSMaP and TRMM data. The results of the estimation of rainfall data show a tendency that GSMaP is overestimated compared to TRMM data in AB, KP, PT, RS, SB, and SG while TRMM is more representative of the actual conditions. Both GSMaP and TRMM data tend not to be able to detect rains smaller than 1 mm. Overall, the three rainfall data indicate that the percentage of the frequency of rainfall events based on six rainfall classifications in all observation locations often occurs at rainfall values of 1-10 mm.
Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2011
Near real time rainfall information is necessary for early warning of rainfall triggered hazard such as floods and landslides. Remote sensing based rainfall estimation has been considered to be used to fulfill that purpose. This research is addressed to use geostationary based rainfall estimation by using Multi Transport Satellite (MTSAT) data which is blended with Tropical Rainfall Measuring Mission (TRMM) 2A12 datasets in order to provide near real time rainfall information, especially for hazard study purposes over Java Island, Indonesia. Comparison to TRMM Multi Precipitation Analysis (TMPA) datasets is performed. Spatial and temporal validation of those rainfall estimations is conducted by validating them with available rain gauge data during a rainy season in December 2007. Temporal validation result shows that TMPA demonstrated better statistical performance than MTSAT blended. However for the spatial correlation, MTSAT blended shows relatively better performance than TMPA.
Evaluation of Hydrologic Modelling Using Satellite Product, and MMR Rainfall in East Java, Indonesia
Journal of Ecological Engineering
In Indonesia, ground-based rainfall monitoring is uneven and sometimes lacks continuity especially in small watersheds, which makes hydrological modeling difficult. This paper aims to the performance evaluation of the HBV Light model from the manual measurement of rainfall (MMR), Global Precipitation Measurement (GPM-3IMERGDF), and Tropical Rainfall Measuring Mission (TRMM-3B42) as input for the hydrological model. The Hydrologiska Byrans Vattenbalansavdelning (HBV) Light hydrological model is applied to three small watersheds, namely Sampean Baru, Bedadung, and Mayang. The model's performance evaluation is assessed based on the correlation between the average rainfall data for the satellite product area and the MMR product, the stationarity of the rainfall and discharge data, and the model accuracy. The model simulation results show that the MMR rainfall in all watersheds provides a better discharge response than the other two products. Meanwhile, the simulation model of the GPM-3IMERGDF satellite product is slightly better than TRMM-3B42. The stationarity test of rainfall and discharge data needs to be enforced before modeling.
Alexandria Engineering Journal, 2020
In several research domains, the precipitation products produced based on the Satellite images are used because of their advantage of high spatiotemporal resolution and near-global coverage. And still the products applications are limited according to the uncertainty. To facilitate using these products, it's vital to identify and quantify their error characteristics. The objective of this study is to evaluate the two satellite-based precipitation products; RainFall Estimate (RFE) and Tropical Rainfall Measuring Mission (TRMM) using ground gauge-based rainfall measurements throughout the Blue Nile River sub-basin in the Sudan for the period (2001-2016). The importance of this study comes from the discontinuity of the ground data due to some difficulties such as the accessibility of the region's topography, in addition to the poorly gauged areas. Therefore, estimating the amount of rain from verified satellites is useful to obtain the pattern of rainfall that could be used in hydrological models to provide river discharges forecasts and to specify areas of flood hazard. For a quantified evaluation of satellite-based precipitation products, continuous Mean Error (ME), and Correlation Coefficient (CC) were used. The results show that RFE and TRMM products performing well in estimating and observing rainfall over the study area.
Validation of Satellite Daily Rainfall Estimates Over Indonesia
Forum Geografi
Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82...
Applied Water Science
Makkah region is one of the most flash flood-prone areas of Saudi Arabia due to terrain characteristics and the synoptic-scale weather conditions that intensify through interaction with the local topography causing high convective short-lived rainfall events, although these conditions are quite infrequent. Most of these events last for less than two hours. This study aims to assess the performance of five satellite precipitation products over a 1725 km2 sparsely gauged, arid basin. A fully distributed, physically based hydrologic model was forced by the five satellite precipitation products, and the evaluation included the hydrographs and runoff maps predicted by the model. Moreover, the propagation of the satellite rainfall errors into runoff predictions was quantified. Large variations and significant biases were found in satellites precipitation estimates compared to the available ground rainfall measurements. The Early IMERG product showed the best agreement with the reported to...
2020
Precipitation is a crucial driver of hydrological processes. Ironically, reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurement using rain gauges can be more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) can be good alternatives, especially for data-scarce basins like in Ethiopia. However, SREs rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SREs products (i.e., CHIRPS2, IMERG6, TAMSAT3 and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrologic modelling approaches were used for the performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SREs products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal and monthly timescales. The hydrologic simulation based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrologic response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products can be reliable rainfall data sources for hydrologic analysis of the Dhidhessa River Basin.
Hydrological applications of satellite data: 1. Rainfall estimation
Journal of Geophysical Research, 1996
In this study we investigate the ability of satellite visible and infrared data to produce reliable rainfall amount estimates that could be used by hydrological models to predict streamflow for large basins. Rainfall estimates are obtained by (1) classification of clouds to raining and nonraining clouds and (2) applying a multivariate statistical model between rainfall and indices derived from the satellite observations. Satellite data corresponding to 180 randomly selected days in the period May-September 1982-1988 are used in this study that focuses on the estimation of daily rainfall. The Des Moines River basin in the midwestern United States is the application area. The correlation coefficient between model-predicted and rain gauge-observed mean areal precipitation over areas of order 10,000 km 2 is found to be about 0.85. In an example application the satellite rainfall estimates are used to force the operational National Weather Service hydrologic forecast model for a subbasin of the Des Moines River basin. The model has been calibrated with rain gauge data. The results show that differences between rain gauge and satellite rainfall input generate differences in flow forecasts and upper soil water model estimates, which are a function of the antecedent soil water conditions. A companion paper [Guetter et al., this issue] quantifies the effects that the differences between rain gauge and satellite rainfall estimates have on flow and upper soil water model predictions for various spatial scales and for hydrologic models calibrated with and without satellite data. Wyss, J., E.R. Williams, and R.L. Bras, Hydrologic modeling of New England river basins using radar rainfall data,
Water resources and climate change studies in data-scarce regions of the world are increasingly employing satellite rainfall estimates (RFEs) and rainfall outputs from general circulations models (GCMs). The reliability of these data sources is seldom verified with observed data prior to application. This chapter outlines the application of simple evaluation techniques to assess the potential of RFE and GCMs outputs as a potential rainfall information sources in the Mara River basin (MRB), Kenya/Tanzania. Results of the assessment show that proper care is required in comparing/mixing of results from studies using different RFE in the MRB. In general, RFE and GCMs are promising sources of information, but refining the estimates with a much improved algorithms is essential.
Evaluating the performance of satellite rainfall estimates using data from NAME program
This study investigates the performance of the NESDIS Hydro Estimator (HE) rainfall algorithm against observations taken from the North American Monsoon Experiment (NAME) program. A recent rainfall observation network established in NAME program provides precipitation measurements of convective origin over large complex topographical areas while comprehensive validations of the satellite-estimated precipitation characteristics such as the frequency, intensity, diurnal evaluation and its relation to the complex regional topography have been absent to date due to the unavailability of pre-existing dense observation network. The independence of the HE on radar data, on the other hand, makes its applicability appropriate for mountainous regions. The rainfall estimates are validated against the measurements obtained through Aug 2 to Sep 14, 2002 to report whether the results from the HE is capable of capturing terrain-induced precipitation characteristics. It seems that satellite estimat...