Spatiotemporal Assessment of Temperature Data Products for the Detection of Warming Trends and Abrupt Transitions over the Largest Irrigated Area of Pakistan

Abstract

Reliable and accurate temperature data acquisition is not only important for hydroclimate research but also crucial for the management of water resources and agriculture. Gridded data products (GDPs) offer an opportunity to estimate and monitor temperature indices at a range of spatiotemporal resolutions; however, their reliability must be quantified by spatiotemporal comparison against in situ records. Here, we present spatial and temporal assessments of temperature indices (Tmax, Tmin, Tmean, and DTR) products against the reference data during the period of 1979–2015 over Punjab Province, Pakistan. This region is considered as a center for agriculture and irrigated farming. Our study is the first spatiotemporal statistical evaluation of the performance and selection of potential GDPs over the study region and is based on statistical indicators, trend detection, and abrupt change analysis. Results revealed that the CRU temperature indices (Tmax, Tmin, Tmean, and DTR) outperformed the other GDPs as indicated by their higher CC and R2 but lower bias and RMSE. Furthermore, trend and abrupt change analysis indicated the superior performances of the CRU Tmin and Tmean products. However, the Tmax and DTR products were less accurate for detecting trends and abrupt transitions in temperature. The tested GDPs as well as the reference data series indicate significant warming during the period of 1997–2001 over the study region. Differences between GDPs revealed discrepancies of 1-2°C when compared with different products within the same category and with reference data. The accuracy of all GDPs was particularly poor in the northern Punjab, where underestimates were greatest. This preliminary evaluation of the different GDPs will be useful for assessing inconsistencies and the capabilities of the products prior to their reliable utilization in hydrological and meteorological applications particularly over arid and semiarid regions.

Assessment of Long-Term Rainfall Variability and Trends Using Observed and Satellite Data in Central Punjab, Pakistan

“…Recent increased frequency of unpredictable occurrences of rainfall impacted rice, maize, wheat, sugarcane, and cotton production along with other minor crops, such as millet, vegetables, and tree fruits, including citrus and mango in the central Punjab districts, including Nankana Sahib, Sialkot, Gujranwala, Hafizabad, Sheikhupura, and Sargodha [33]. This region is fairly vulnerable to climate change because of the large variety and intense events that occur during the summer and winter monsoon precipitation seasons [34][35][36][37][38]. Spatial and temporal evaluation of the GPPs (gridded satellite precipitation products) is vital to examine in-depth hydro-meteorological scenarios across the study region, where observed data are inadequate.…”

“…Additionally, anthropogenic activities, such as industrial growth, infrastructural activities, urbanization, and global increasing air temperature are also liable [38]. In Phase-2, annually, rainfall increased by 19%, which can be ascribed to the increasing tendency towards extreme events and huge temperature gradients, resulting in rapid land-ocean interactions [11,71,72].…”

Exploring trends and variability of climate change indices in the agro‐ecological zones of Pakistan and their driving mechanisms

“…Therefore, to ensure the data quality, 40 synoptic stations for which the long‐term daily climate records are available (i.e., less than 5% missing data) have been chosen across AEZs of Pakistan (Figure 1 and Table S1). The usage of gridded data in complex landscapes creates uncertainties in results and affects long‐term climate trends (Baudouin et al, 2020; Dahri et al, 2021; Nawaz et al, 2020). Hence, this work used observational data of high quality with adequate coverage (i.e., space and time) within AEZs of Pakistan.…”

Population Exposure Changes to Mean and Extreme Climate Events Over Pakistan and Associated Mechanisms

“…The detailed descriptions of selected meteorological stations are given in (Table S1 in Supporting Information S1 ). The use of gridded climate products in complex terrain produces uncertainties in results, which may affect the long‐term trend of climate variables (Baudouin et al., 2020 ; Dahri et al., 2021 ; Nawaz et al., 2020 ). Therefore, in this study, we make use of high‐quality observational data with sufficient spatial and temporal coverage across AEZs of Pakistan.…”