Abstract
Spatial–temporal rainfall assessments are integral to climate/hydrological modeling, agricultural studies, and water resource planning and management. Herein, we evaluate spatial–temporal rainfall trends and patterns in Pakistan for 1961–2020 using nationwide data from 82 rainfall stations. To assess optimal spatial distribution and rainfall characterization, twenty-seven interpolation techniques from geo-statistical and deterministic categories were systematically compared, revealing that the empirical Bayesian kriging regression prediction (EBKRP) technique was best. Hence, EBKRP was used to produce and utilize the first normal annual rainfall map of Pakistan for evaluating spatial rainfall patterns. While the largest under-prediction was estimated for Hunza (− 51%), the highest and lowest rainfalls were estimated for Malam Jaba in Khyber Pakhtunkhwa province (~ 1700 mm), and Nok-kundi in Balochistan province (~ 50 mm), respectively. A gradual south-to-north increase in rainfall was spatially evident with an areal average of 455 mm, while long-term temporal rainfall evaluation showed a statistically significant (p = 0.05) downward trend for Sindh province. Additionally, downward inter-decadal regime shifts were detected for the Punjab and Sindh provinces (90% confidence). These results are expected to help inform environmental planning in Pakistan; moreover, the rainfall data produced using the optimal method has further implications in several aforementioned fields.
Highlights
Rainfall is a key element of the Earth’s climate as well as being an uncontrollable natural phenomenon, and food security of developing agrarian nations highly depends on it[1,2,3]
The appropriateness index (AI) was computed based only on three cross-validation parameters (i.e., MR, Root mean square error (RMSE), and Pearson R2, represented by AI-3 in Table 2), as these parameters were available for all possible interpolation techniques including the inverse distance weighting (IDW), global polynomial interpolation (GPI), and radial basis functions (RBF)
The order of suitability of the interpolation techniques according to AI-3 was empirical Bayesian kriging regression prediction (EBKRP) > universal kriging (UK)-k > empirical Bayesian kriging (EBK) > IDW > ordinary kriging (OK)-k > OK-e > RBF > OK-g > OK-s > OK-c > OK-j > universal kriging with hole effect model (UK-h) > UK-e > simple kriging (SK)-k > UK-j > SK-e > OK-h > SK-s > UK-c > UK-s > UK-g > local polynomial interpolation (LPI) > SK-c > S K-g > SK-h > SK-j > GPI
Summary
Rainfall is a key element of the Earth’s climate as well as being an uncontrollable natural phenomenon, and food security of developing agrarian nations highly depends on it[1,2,3]. Climate data in Pakistan are only collected at specific meteorological stations, which hinders the availability of comprehensive data to government officials, institutes, and environmental and resource managers for different research purposes and decision making To overcome this constraint, spatial interpolation techniques can be useful[18,19,20]. We produced a normal rainfall map for Pakistan presenting the spatial distribution, as such information has been previously unavailable For this purpose, the observed rainfall data collected at 82, out of total 97, countrywide stations (Supplementary Fig. S1) were retrieved, and several interpolation approaches (i.e., deterministic and geo-statistical) were comparatively analyzed to identify the best choice for the interpolation of rainfall in Pakistan. The identified best method was employed to produce a national-scale normal annual rainfall map of Pakistan using a spatial resolution of 11 km
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