Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

Abstract

The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

“…Therefore, the objective of this study is directed toward more understanding the characteristics of interannual variability of rainfall in the country and investigating clearly the influence of global SSTs on Ethiopian rainfall peaks at various regions and seasons (summer and spring), to reinforce the skill of rainfall predictions that would be valuable for operations of reservoirs [22], assessment and allocation of water resources [23,24], and for mitigation of disasters related to rainfall such as flooding and drought [25]. Also, better understanding of the rainfall variability plays a key role in several applications including hydrological analysis [26][27][28][29][30][31] and soil erosion risk assessment [32][33][34][35][36].…”

Assessment of the CHIRPS-Based Satellite Precipitation Estimates

“…It is important to note, however, that this characteristic is not unique to CHIRPS. Most of the satellite-based rainfall products tend to improve their general performance as the aggregation period increases owing to the effect of cancelation of errors [34,35].…”

“…CHIRPS appears to be sensitive to the precipitation from the warm-top stratiform cloud systems (e.g., near to the coast), the warm-rain processes induced by orographic lifting (e.g., the mountain areas of NEB), and the subcloud evaporation processes (e.g., the Sertão region). The first and second are mainly attributed to a fixed IRP CCD threshold (i.e., 235 K) used by CHIRPS (see Section 2.2), which may be too cold for regions where the warm-rain processes are dominant [34], while the third is a usual phenomenon in semiarid regions [19].…”

“…Based on the abovementioned conclusions, CHIRPS can serve as an alternative source of data for operational applications that require rainfall data, especially over the inland regions of NEB (see the C1 stations in Figure 6), during the wettest months of the year (see Figures 3 and 4), and at monthly or annual time scales taking advantage of the cancelation of errors of CHIRPS rainfall estimates as the duration of the integration increases [34]. However, future investigations are needed to adequately choose the operational applications of CHIRPS for each subregion of the NEB.…”

Response of Seasonal Vegetation Dynamics to Climatic Constraints in Northeastern Burundi

“…Comparing these satellite products, CHIRPS performs better than ERA5. The CHIRPS dataset has been proven valid in East Africa (Dinku et al, 2018), and has been used in diverse works (Nkunzimana et al, 2019;Muthoni et al, 2019;Cattani et al, 2018;Belay et al, 2019). Figure 2 shows the correlation between the datasets used through two different stations which are Kirundo and Muyinga.…”