TY - JOUR
T1 - Spatial–temporal characterization of rainfall in Pakistan during the past half-century (1961–2020)
AU - Ali, Ghaffar
AU - Sajjad, Muhammad
AU - Kanwal, Shamsa
AU - Xiao, Tingyin
AU - Khalid, Shoaib
AU - Shoaib, Fariha
AU - Gul, Hafiza Nayab
N1 - Funding Information:
We are thankful to the Pakistan Meteorological Department (PMD) for providing raw data on the rainfall observations and the Higher Education Commission (HEC) of Pakistan. We are also thankful to NASA’s Shuttle Radar Topography Mission (SRTM), which is responsible for Digital Elevation products. Part of this research work was completed during the stay of the Sajjad M. at Princeton University, which was supported by a partial fund from the Chow Yei Ching School of Graduate Studies, City University of Hong Kong, Hong Kong–SAR (ID: 000669).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/3/25
Y1 - 2021/3/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85103393101&partnerID=8YFLogxK
U2 - 10.1038/s41598-021-86412-x
DO - 10.1038/s41598-021-86412-x
M3 - Journal article
C2 - 33767320
SN - 2045-2322
VL - 11
JO - Scientific Reports
JF - Scientific Reports
M1 - 6935
ER -
