Evaluating Satellite Gridded Precipitation Errors in the Sungai Sarawak Basin: A Triple Collocation Approach Mohd Nadzri Md Reba1,2*, Azalea Kamellia Abdullah2, Mazlan Hashim1,2, Mohd Rizaludin Mahmud1,2, Wan Anwar Nadir Wan Ahmad2
1Geoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia, Johor, Malaysia
2Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Johor, Malaysia
*nadzri[at]utm.my
Abstract
Satellite Precipitation Estimates (SPE) offer a valuable alternative to traditional ground measurements by providing synoptic coverage and enhancing rainfall estimation accuracy in Malaysia. To ensure confidence in the selection of the most suitable precipitation repository, validation of these rain estimates is crucial. However, conventional validation methods using rain gauges become ineffective when the quality of the reference data deteriorates, particularly in areas with sparse stations and poor coverage. In critical situations where the reference data is unreliable or possesses low accuracy, equivalent measurement parameters can serve as validation agents due to their similarity to other precipitation estimates. Past studies have shown an overestimation of rainfall by gridded Satellite Precipitation Estimates (SPEs) like CHIRPS and PERSIANN-CDR. However, the influence of spatial and temporal variability on these errors has been less thoroughly examined. This study evaluates the error estimates of CHIRPS, PERSIANN-CDR, and ERA5 precipitation datasets against rain-gauge station measurements over a 30-year period in the Sungai Sarawak basin. To achieve this, a Triple Collocation (TC) analysis was employed. To ensure spatial consistency for intercomparison, data from different spatial resolutions were interpolated using the Inverse Distance Weighting (IDW) method, with a 5-km grid, corresponding to the native CHIRPS resolution, being adopted. Temporal differences between the datasets were minimized by calculating monthly summed precipitation estimates within these 5-km grids. The error variance of simultaneously observed monthly precipitation estimates from the four datasets was formulated, and the signal-to-noise ratio (SNR) and relative rank performance were subsequently analyzed. This study highlights the effectiveness of TC in evaluating errors across various SPE origins. Rain gauges consistently ranked highest among SPEs. CHIRPS demonstrated superior performance, attributed to its development incorporating adjustments from multiple data sources. PERSIANN exhibited higher error covariance, while ERA5 showed underestimation in seasonal analysis. TC proves capable of assessing errors in both ungauged and sparsely gauged areas. Accurate error estimation is vital for drought and flood analysis, and for water resource management to address climate change impacts in tropical regions.
