Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka

Abstract

The availability of accurate spatiotemporal rainfall data is of utmost importance for reliable predictions from hydroclimatological studies. Challenges and limitations faced due to the absence of dense rain gauge (RG) networks are seen especially in the developing countries. Therefore, alternative rainfall measurements such as satellite rainfall products (SRPs) are used when RG networks are scarce or completely do not exist. Noteworthy, rainfall data retrieved from satellites also possess several uncertainties. Hence, these SRPs should essentially be validated beforehand. The Mahaweli River Basin (MRB), the largest river basin in Sri Lanka, is the heart of the country’s water resources contributing to a significant share of the hydropower production and agricultural sector. Given the importance of the MRB, this study explored the suitability of SRPs as an alternative for RG data for the basin. Daily rainfall data of six types of SRPs were extracted at 14 locations within the MRB. Thereafter, statistical analysis was carried out using continuous and categorical evaluation indices to evaluate the accuracy of SRPs. Nonparametric tests, including the Mann-Kendall and Sen’s slope estimator tests, were used to detect the possibility of trends and the magnitude, respectively. Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products showed dire performances. However, IMERG also demonstrated underestimations when compared to RG data. Trend analysis results showcased that the IMERG product agreed more with RG data on monthly and annual time scales while Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis–3B42 (TRMM-3B42) agreed more on the seasonal scale. Overall, IMERG turned out to be the best alternative among the SRPs analyzed for MRB. However, it was clear that these products possess significant errors which cannot be ignored when using them in hydrological applications. The results of the study will be valuable for many parties including river basin authorities, agriculturists, meteorologists, hydrologists, and many other stakeholders.

Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka

“…including root mean square error (RMSE), percentage bias (PBias), coefficient correlation (CC), and Nash-Sutcliffe efficiency (NSE), utilized to assess the effectiveness of SbPPs [21,[25][26][27][28]. e RMSE (refer to Equation (1)), PBias (refer to Equation ( 2)), CC (refer to Equation (3)), NSE (refer to Equation ( 4)) are given below:…”

“…e SbPPs' prediction and detection abilities were assessed using four categorical indices. Probability of detection (POD), false alarm ratio (FAR), proportion correct (PC), and critical success index (CSI) are the four indices [7,[21][22][23][24][25][26][27][28][29][30]. Equations ( 5) and ( 6) of the indices are as given below: POD � (hits) (hits + misses)…”

“…(i) No/tiny rainfall -P < 1 mm (ii) Light rainfall -1 mm ≤ P < 2 mm (iii) Low moderate rainfall -2 mm ≤ P < 5 mm (iv) High moderate rainfall -5 mm ≤ P < 10 mm (v) Heavy rainfall -P ≥ 10 mm For the analysis, 1 mm/day threshold was used which falls under the "light rainfall" intensity class. e contingency table used for the analysis is also given in Table 3 [7,21].…”

“…Sri Lanka is a country in which the economy mainly depends on agricultural activities. erefore, the presence of accurate rainfall data for future drought and flood forecasting is essential [18][19][20][21]. Even though Sri Lanka has dense rainfall measuring stations (more than 500 for the total area of 65610 km 2 ), some of the stations are scattered in urbanized areas.…”

Analysis of Meandering River Morphodynamics Using Satellite Remote Sensing Data—An Application in the Lower Deduru Oya (River), Sri Lanka

“…Satellite-derived meteorological data (rainfall data, temperature data, evapotranspiration estimates, etc.) are used in data-scarce regions in particular, and these satellite technologies are becoming a good source of data because of their free availability and easy access [19,20]. The recent development of these technologies has significantly increased the availability of satellite images and the tools and software used for analysis.…”