scholarly journals Validasi Nilai Erosivitas Hujan Dari Data Penginderaan Jauh TRMM 3B42 Di Bali Selatan

1970 ◽  
Vol 16 (1) ◽  
Author(s):  
I Wayan Sandi Adnyana ◽  
Abd. Rahman As-syakur
Keyword(s):  
Rain Gauge ◽  
Monthly Rainfall ◽  
Bias Error ◽  
Rainfall Erosivity ◽  
Rain Gauge Data ◽  
Point Analysis ◽  
Gauge Data ◽  
High Bias ◽  
Precipitation Analysis ◽  
Very High

Rainfall erosivity is a measure for the erosive force of rainfall. Rainfall kinetic energydetermines the erosivity and is in turn greatly dependent on rainfall intensity. Research hasbeen conducted to validate monthly rainfall erosivity derived from the Tropical Rainfall MeasuringMission (TRMM) Multisatellite Precipitation Analysis(TMPA)3B43 version 7 usingraingauge data analysis from 2003 to 2012. Rain gauge located in the south Bali regions wereemployedto monitor erosivity value from two different methods that are base on Bols (1978)andAbdurachman(1989). Therelationship of erosivity and their other factor from TRMM3B43andrain gauge data statistical analysis measures consisted of the linear correlation coefficient,themean bias error (MBE), and the root mean square error (RMSE). Data validation wasconductedwith point-by-point analysis. The results of these analyses indicate that satellitedatahave lower values than the gauge estimation values. The point-by-point analysis indicatedsatellite data values of high to very high correlation, while values of MBE and RMSEtendedto indicate underestimations with high square errors. Moreover,monthly rainfall erosivityderived from TRMM give high correlation from both methods, with has high bias androot-mean-squareerror. In general, the data from TRMM3B43 version 7 are potentially usabletoreplace rain gauge data based on erosivity estimation, but after inconsistencies and errorsaretaken into account.

scholarly journals Comparing Rainfall Erosivity Estimation Methods Using Weather Radar Data for the State of Hesse (Germany)

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1424 ◽  
Author(s):  
Jennifer Kreklow ◽  
Bastian Steinhoff-Knopp ◽  
Klaus Friedrich ◽  
Björn Tetzlaff
Keyword(s):  
Climate Change ◽  
Rain Gauge ◽  
The State ◽  
Estimation Methods ◽  
Rainfall Erosivity ◽  
Spatiotemporal Resolution ◽  
Rain Gauge Data ◽  
R Factor ◽  
Gauge Data ◽  
Erosive Rainfall

Rainfall erosivity exhibits a high spatiotemporal variability. Rain gauges are not capable of detecting small-scale erosive rainfall events comprehensively. Nonetheless, many operational instruments for assessing soil erosion risk, such as the erosion atlas used in the state of Hesse in Germany, are still based on spatially interpolated rain gauge data and regression equations derived in the 1980s to estimate rainfall erosivity. Radar-based quantitative precipitation estimates with high spatiotemporal resolution are capable of mapping erosive rainfall comprehensively. In this study, radar climatology data with a spatiotemporal resolution of 1 km2 and 5 min are used alongside rain gauge data to compare erosivity estimation methods used in erosion control practice. The aim is to assess the impacts of methodology, climate change and input data resolution, quality and spatial extent on the R-factor of the Universal Soil Loss Equation (USLE). Our results clearly show that R-factors have increased significantly due to climate change and that current R-factor maps need to be updated by using more recent and spatially distributed rainfall data. Radar climatology data show a high potential to improve rainfall erosivity estimations, but uncertainties regarding data quality and a need for further research on data correction approaches are becoming evident.

scholarly journals Improving Multisensor Precipitation Estimation via Adaptive Conditional Bias–Penalized Merging of Rain Gauge Data and Remotely Sensed Quantitative Precipitation Estimates

2019 ◽  
Vol 20 (12) ◽  
pp. 2347-2365 ◽  
Author(s):  
Ali Jozaghi ◽  
Mohammad Nabatian ◽  
Seongjin Noh ◽  
Dong-Jun Seo ◽  
Lin Tang ◽  
...  
Keyword(s):  
Computational Cost ◽  
Mean Field ◽  
Remotely Sensed ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Radar Satellite ◽  
Quantitative Precipitation Estimates

Abstract We describe and evaluate adaptive conditional bias–penalized cokriging (CBPCK) for improved multisensor precipitation estimation using rain gauge data and remotely sensed quantitative precipitation estimates (QPE). The remotely sensed QPEs used are radar-only and radar–satellite-fused estimates. For comparative evaluation, true validation is carried out over the continental United States (CONUS) for 13–30 September 2015 and 7–9 October 2016. The hourly gauge data, radar-only QPE, and satellite QPE used are from the Hydrometeorological Automated Data System, Multi-Radar Multi-Sensor System, and Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR), respectively. For radar–satellite fusion, conditional bias–penalized Fisher estimation is used. The reference merging technique compared is ordinary cokriging (OCK) used in the National Weather Service Multisensor Precipitation Estimator. It is shown that, beyond the reduction due to mean field bias (MFB) correction, both OCK and adaptive CBPCK additionally reduce the unconditional root-mean-square error (RMSE) of radar-only QPE by 9%–16% over the CONUS for the two periods, and that adaptive CBPCK is superior to OCK for estimation of hourly amounts exceeding 1 mm. When fused with the MFB-corrected radar QPE, the MFB-corrected SCaMPR QPE for September 2015 reduces the unconditional RMSE of the MFB-corrected radar by 4% and 6% over the entire and western half of the CONUS, respectively, but is inferior to the MFB-corrected radar for estimation of hourly amounts exceeding 7 mm. Adaptive CBPCK should hence be favored over OCK for estimation of significant amounts of precipitation despite larger computational cost, and the SCaMPR QPE should be used selectively in multisensor QPE.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

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