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In the present study, long-term rainfall trend was analyzed using the traditional Mann–Kendall (MK) test and innovative trend analysis (ITA) method for annual and seasonal rainfall series for 20 districts of Punjab state for the period of 1951–2021. The autocorrelation test was performed to detect the presence or absence of serial correlation in the rainfall series. Based on the autocorrelation test, MK or modified Mann–Kendall (MMK) test and Sen's slope test were applied to determine the direction and magnitude of the rainfall trend, respectively. The ITA which revealed the presence of a trend graphically was compared with the traditional MK/MMK test. The autocorrelation test showed that all the annual rainfall series are serially independent, except for the Hoshiarpur. The MK/MMK test revealed the presence of a decreasing trend in annual rainfall series of all districts, except for Fatehgarh Sahib, Kapurthala, Patiala, and Tarn Taran of the central zone and Muktsar of the south west zone. The computed probable change point year was 1998. The innovative trend slope revealed the presence of a significant decreasing trend for the districts of Punjab missed by the traditional MK/MMK test. The highest decrease (− 4.5 mm/year) in annual rainfall was observed at Gurdaspur of the north zone and Faridkot of the south west zone. The ITA showed the statistically significant decreasing trend in annual rainfall for all the districts of the south west zone at 1% significance level. The analysis using ITA determined the presence of hidden trends missed by the traditional MK/MMK test. The decreasing pattern in rainfall over most of the districts and high irrigation requirement for largely growing paddy crop of Punjab indicated the urgent need of efficient planning of water resources. The study may guide planners and policy makers for effective implementation of the rainwater harvesting and groundwater recharge strategies to improve the status of water resources in the entire Punjab. [ABSTRACT FROM AUTHOR]

This study explores the spatio-temporal distribution and trends on monthly, seasonal, and annual scales of rainfall in the central Punjab districts of Punjab province in Pakistan by using observation and satellite data products. The daily observed data was acquired from the Pakistan Metrological Department (PMD) between 1983 and 2020, along with one reanalysis, namely the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and one satellite-based daily Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks climate data record (PERSIANN-CDR) using the Google Earth Engine (GEE) web-based API platform to investigate the spatio-temporal fluctuations and inter-annual variability of rainfall in the study domain. Several statistical indices were employed to check the data similarity between observed and remotely sensed data products and applied to each district. Moreover, non-parametric techniques, i.e., Mann–Kendall (MK) and Sen's slope estimator were applied to measure the long-term spatio-temporal trends. Remotely sensed data products reveal 422.50 mm (CHIRPS) and 571.08 mm (PERSIANN-CDR) mean annual rainfall in central Punjab. Maximum mean rainfall was witnessed during the monsoon season (70.5%), followed by pre-monsoon (15.2%) and winter (10.2%). Monthly exploration divulges that maximum mean rainfall was noticed in July (26.5%), and the minimum was in November (0.84%). The district-wise rainfall estimation shows maximum rainfall in Sialkot (931.4 mm) and minimum in Pakpattan (289.2 mm). Phase-wise analysis of annual, seasonal, and monthly trends demonstrated a sharp decreasing trend in Phase-1, averaging 3.4 mm/decade and an increasing tendency in Phase-2, averaging 9.1 mm/decade. Maximum seasonal rainfall decreased in phase-1 and increased Phase-2 during monsoon season, averaging 2.1 and 4.7 mm/decade, whereas monthly investigation showed similar phase-wise tendencies in July (1.1 mm/decade) and August (2.3 mm/decade). In addition, as district-wise analyses of annual, seasonal, and monthly trends in the last four decades reveal, the maximum declined trend was in Sialkot (18.5 mm/decade), whereas other districts witnessed an overall increasing trend throughout the years. Out of them, Gujrat district experienced the maximum increasing trend in annual terns (50.81 mm/decade), and Faisalabad (25.45 mm/decade) witnessed this during the monsoon season. The uneven variability and trends have had a crucial imprint on the local environment, mainly in the primary activities. [ABSTRACT FROM AUTHOR]

Farmers' perception about changing climate leads to farm-adaptive responses. Coherence between perceptions and meteorological trends leads to improved farm decisions. The objective of this study was to examine the degree of agreement between farmers' perception and actual climate trends in three irrigated districts of Punjab province in Pakistan. The study also inquired the role of extension services and other factors affecting farmers' perception. Using multistage sampling technique, a sample of 300 farmers was taken who were interviewed using pre-structured and field-tested questionnaire about their perceptions of climate change at farm. Climate data for 33 years regarding temperature and for 34 years for precipitation were obtained from meteorological stations in selected districts. It employed Mann–Kendall and Sen's slope tests for trend analysis in climate time series and tested nonparametric association in cross-sectional data. The results revealed that farmers' perception about precipitation trends do not match with actual trends mainly because of the way climate information is processed among farmers and scientific community. However, farmers' perceptions about increase in temperature were in line with meteorological evidence across selected irrigated cropping zones. Extension services along with income and size of land holding were key factors affecting farmers' perception about climate change. The study recommends bridging information gaps between scientific community and farmers about climate change. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess trends in maximum and minimum temperatures and total monthly rainfall in the Punjab province of Pakistan. Non parametric Mann-Kendall and Sen's slope techniques were employed to find trend strength and magnitude. Serial correlation in data was removed by using TFPW (Trend Free Pre-Whitening) approach. Monthly data of maximum and minimum temperatures and total rainfall was obtained from Regional Meteorological Office (Lahore) for nine districts of Punjab for the period 1961- 2014. The maximum temperature showed a significant increasing trend at Rawalpindi and Faisalabad with a Sen Slope of 0.010°C/year, while significant increasing trends at eight stations were observed in minimum temperature on annual basis. All stations have experienced increasing trends in rainfall out of which only two stations have shown significant trend annually. Overall in Punjab, maximum temperature has shown a significant upward trend in spring (0.028°C/year) and downward trend in summer season (-0.013°C/year). While in minimum temperature, significant increasing trends were observed in all seasons excluding summer. A significant increasing trend in rainfall was observed in summer (1.79 mm/year), autumn and on annual basis (3.23mm/year). The results indicate that spring season is warming and the decreasing trend in summer temperature is caused by increasing trend of rainfall in the province. [ABSTRACT FROM AUTHOR]

The article highlights the computation of reference evapotranspiration, its variability and trends in different agroclimatic regions of Punjab. Topics include that Reference evapotranspiration (ETo) is an important climatic variable, which drives other regional climatic parameters; and that annual evapotranspiration was lowest in the regions with high precipitation and highest in the regions with dry climate.

Recent improvements in time series studies of hydro-climatological variables have led to the belief that the effects of nonstationarity are substantial enough to call the idea of traditional stationary approaches into doubt. The mean and variability of annual and seasonal rainfall in Pakistan are changing due to anthropogenic climate change. With the use of stationary and nonstationary frequency analysis techniques, this study set out to assess the impacts of nonstationarity in Southern Punjab, Pakistan, over the historical period of 1970–2015 and the future periods of 2020–2060 and 2060–2100. Four frequency distributions, namely Generalized Extreme Value (GEV), Gumbel, normal, and lognormal, were used. The findings of the nonstationarity impact across Southern Punjab showed different kinds of impacts, such as an increase or reduction in the return level of extreme precipitation. In comparison to other distributions, GEV provided the finest fit. In Bahawalnagar, Bahawalpur, Multan, Rahim Yar Khan and DG. Khan, the annual nonstationarity impacts for the 100-year return level were increased up to 15.2%, 8.7%, 58.3%, 18.7%, and 20%, respectively. Moreover, extreme precipitation was found to be increasing during the historical and projected periods, which may increase floods, while less water availability appeared at a seasonal scale (summer) during 2061–2100. The increased nonstationarity effects emphasized adapting these nonstationarities induced by climate change into the design of water resource structures. [ABSTRACT FROM AUTHOR]

This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen's slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p < 0.05 to be statistically significant. We observed significant (p < 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p < 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan. [ABSTRACT FROM AUTHOR]

Estimation of reference evapotranspiration (ETo) is a key element in water resources management and crop water requirement which, in turn, affects irrigation scheduling. ETo is subject to the influence of various climatic parameters including minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), windspeed (WS), and sunshine hours (SH). Usually, the influence of the climatic parameters and a dominating climatic factor influencing ETo is estimated on yearly basis. However, in diverse climatic regions, ETo varies with the varying climate. Therefore, this study aims to estimate the spatiotemporal variation in the influence of the climatic parameters on ETo in Punjab, Pakistan, for the period 1950–2021, specifically focusing on decennial, annual, and monthly patterns. The study area was divided into five agroclimatic zones. The Penman–Monteith model was used to estimate ETo. The influence was assessed using geographic weighted regression (GWR) and multiscale geographic weighted regression (MGWR) as the primary methods. As per results from MGWR, ETo in Punjab was highly influenced by the Tmin, Tmax, and WS. Additionally, annual ETo exhibited a higher value in southern Punjab in comparison to northern Punjab, with a range of 2975 mm/year in the cotton–wheat zone to 1596 mm/year in the rain-fed zone. Over the course of the past seventy years, Punjab experienced an average increasing slope of 5.18 mm/year in ETo. Tmin was the highest monthly dominant factor throughout the year, whereas WS and SH were recorded to be the dominant factor in the winters, specifically. All in all, accurate estimation of ETo, which serves as an essential component for crop water requirement, could potentially help improve the irrigation scheduling of crops in the agroclimatic zones. [ABSTRACT FROM AUTHOR]

An increase in the human population, urbanization, industrialization, infrastructural development, and current agricultural practices acts as major factors leading to the decline of the groundwater table in the region. The present study analyzes the noticeable effect of anthropogenic pressure on the groundwater table in the Bist-Doab region in Punjab, India, from 1996 to 2016. Statistical techniques, viz., Mann-Kendall Z statistics and Sen's slope, were used to estimate the water table decline in the area. The results indicate that there was a slight increase in the groundwater table in the Kandi belt of Siwalik foothills and south-western parts of the region. In the rest of the areas, a significant declining trend was observed in the groundwater table. The decline in the water table ranged from 56 to 149 cm per year in the pre- and post-monsoon seasons due to increasing in rice cultivated area, which is an alarming situation in the aquifer system of the region. Hence, to reduce the further decline of the groundwater table, water management practices need to be encouraged in the region. There is requiring immediate attention to change the present land-use/cover practices and to grow less water-consuming crops instead of high water-consuming crops to reduce the pressure on groundwater. [ABSTRACT FROM AUTHOR]

Wheat is an important component of global food security, and Punjab and Haryana are the major wheat-growing areas in Northwest India, but climate change has increased the frequency of droughts in this region. Timely, precise, and effective drought monitoring is critical to ensure food security. In the present study, meteorological and agricultural droughts were assessed using standard precipitation index (SPI) and remote sensing-based indices, namely vegetation health index (VHI) and normalized vegetation supply water index (NVSWI) during the Rabi season (wheat growing season) in three different agroclimatic zones (ACZs i.e., ACZ I: Hot arid agro-climatic zone, ACZ II: Semi-arid agro-climatic zone, and ACZ III: Hot sub-humid agro-climatic zone) of Punjab and Haryana. Furthermore, correlations between NVSWI, VHI, SPI, and gross primary productivity (GPP) were investigated, as well as trend analysis of NVSWI and groundwater level to better understand their interrelationships and the impact of agricultural droughts on groundwater level. The results of SPI indicated that ACZ III witnessed more drought episodes than ACZ I and II. The driest years in ACZ I, ACZ II, and ACZ III were 2002–03, 2014–15, and 2016–17, respectively, while the year 2018–19 was observed as normal in all the ACZs. A significant high correlation was observed between the NVSWI and VHI and the NVSWI and GPP in all three ACZs, while SPI did not exhibit a significant correlation with the NVSWI in ACZ II and III. The Mann-Kendall Test and Sen's slope indicated a declining groundwater trend in all the ACZs and an increasing trend in NVSWI values. The results underline the robustness of SPI and NVSWI in drought monitoring in semi-arid and arid zones of northwest India. Rainfall variability and increased groundwater use for irrigation practices in semi-arid and hot sub-humid areas are leading to groundwater decline. [Display omitted] • Meteorological droughts, their severity and intensity were assessed using SPI. • Normalized Vegetation Water Supply Index (NVSWI) was used for agricultural droughts. • Relationships between meteorological and agricultural droughts were studied. • NVSWI can efficiently monitor the agricultural drought events in Punjab and Haryana. • Declining trend in groundwater level was observed due to irrigational practices. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization, Urban Heat Island (UHI) is considered as a major anthropogenic alteration in Earth environments, and its temporal trends and future forecasts for large areas did not receive much attention. Using land surface temperature (LST) data from MODIS (Moderate Resolution Imaging Spectro-radiometer) for years 2006 to 2020, we quantified the temporal trends of daytime and nighttime surface UHI intensity (SUHII, difference of urban temperature to rural temperature) using the Mann-Kendall (MK) trend test in six major cities of the Punjab province of Pakistan and estimated the future SUHII for the year 2030 using the ARIMA model. Results from the study revealed that the average mean SUHII for daytime was noted as 2.221 °C and the average mean nighttime SUHII was noted as 2.82 °C for the years 2006 to 2020. The average mean SUHII for daytime and nighttime exhibited increasing trends for all seasons and annually, and for the daytime spring season it showed a maximum upward trend of 0.486 °C/year (p < 0.05) and for the nighttime annual SUHII with an increasing rate of 0.485 °C/year (p < 0.05) which exhibited a maximum upward trend. The ARIMA model forecast suggested an increase of 0.04 °C in the average daytime SUHII and an increase of 0.1 °C in the average nighttime SUHII until 2030. The results from this study highlight the increasing trends of daytime and nighttime SUHII, ARIMA also forecasted an increase in daytime and nighttime SUHII, suggesting various strategies are needed for an effective mitigation of the UHI effect. [ABSTRACT FROM AUTHOR]

The TROPOMI (TROPOspheric monitoring instrument) onboard Sentinel-5 Precursor (S5P) satellite provides high spatial resolution data of carbon monoxide (CO) while the MAIAC (Multiangle Implementation of Atmospheric Correction) is a newly developed algorithm applied to MODIS collection 6 observations to retrieve AOD (Aerosol Optical Depth) at a high spatial resolution of 1 km. The present study utilized the MAIAC AOD from MODIS Terra and Aqua polar-orbiting satellites between March 2000 to December 2021 and CO from Sentinel-5P during the available period July 2018-December 2021 over Pakistan. Moreover, we used three trend techniques (Linear regression, Mann–Kendall (MK), and Theil-Sen's Slope) to examine the trends of AOD and CO over Pakistan. The results show that both AOD and CO have high values over central Punjab, western Balochistan, central Sindh, and Khyber Pakhtunkhwa. The mean annual high AOD of > 1.2 is observed in eastern Punjab because of an increase in urbanization, industrialization, and economical activities whereas the AOD of ~ 1.0 is observed over Balochistan, Sindh, and a few parts of Khyber Pakhtunkhwa. The highest mean annual CO of ˃0.03 mol/m^2 is seen over central Punjab, Sindh, and Khyber Pakhtunkhwa. The results show that seasonal mean MAIAC AOD ranging from 0.7 to > 0.9 was seen over Punjab and Sindh province during the monsoon season whereas the lowest AOD is detected in the winter season over few parts of Balochistan. In contrast, the highest mean seasonal CO ranging from 0.040 to > 0.055 mol/m^2 was seen in the winter season over Punjab. The lowest CO concentration is observed in the winter season over the northern region of Pakistan. Non-parametric analyses (MK and Theil-Sen's slope) also show an increasing trend of CO over Pakistan from 2018 to 2021. Furthermore, we have also investigated the trends of AOD and CO over selected cities of Pakistan using linear regression, MK test, and Theil-Sen's slope to reveal long-term air pollution trends. [ABSTRACT FROM AUTHOR]

Background: Climate change is a nonpareil threat to the food security of hundred millions of people who depends on agriculture for their livelihood. A change in climate affects agricultural production as climate and agriculture are intensely interrelated global processes. Global warming is one of such changes which is projected to have significant impacts on environment affecting agriculture. Agriculture is the mainstay economy in trans-gangetic plains of India and maize is the third most important crop after wheat and rice. Heat stress in maize cause several changes viz. morphological, anatomical and physiological and biochemical changes. Methods: In this study during 2014-2018, impact of climate change on maize yield in future scenarios was simulated using the InfoCrop model. Average maize yield from 2001-15 was collected for Punjab, Haryana and Delhi to calibrate and validate the model. Future climatic data set from 2020 to 2050 was used in the study to analyse the trends in climatic parameters. Result: Analysis of future data revealed increasing trends in maximum temperature and minimum temperature. Rainfall would likely follow the erratic behaviour in Punjab, Haryana and Delhi. Increase in temperature was predicted to have negative impact on maize yield under future climatic scenario. [ABSTRACT FROM AUTHOR]

Frequency and intensity of extreme weather events are immensely changing throughout the world. This study aims to give insight into the changing climatic patterns leading to severe flood and drought conditions in the interior of Punjab province of Pakistan and devise adaptation strategies for risk governance. Landsat TM 30 m imagery was processed for flood analysis, while for drought analysis, Land Surface Temperature, Normalized Difference Vegetation Index (NDVI), and Temperature Vegetation Dryness Index (TVDI) were calculated from MODIS (MOD11A2 and MOD13A2) imageries. A questionnaire-based survey was conducted to prepare a management plan based on views of experts and local communities. The main regions affected by extreme events (flood and drought) in interior Punjab were Bahawalpur, Faisalabad, Mianwali, Multan, Sargodha, and Rahim Yar Khan. Supervised classification shows flooding in the Mianwali district during August 2010, and TVDI values show that drought prevailed during the year 2000 in the Bahawalpur district. Annual precipitation showed a significant trend in Sargodha and Mianwali with the magnitude of 6.95 mm/year and 2.97 mm/year, respectively. However, SPI moves to positive in the case of all studied districts except Sargodha where temperature change is 0.6 °C. Climatic analysis and survey show that the people of districts, i.e., Faisalabad, Mianwali, Multan, and Sargodha, were more affected by floods, whereas the people of districts, i.e., Bahawalpur and Rahim Yar Khan, were affected by both flood and drought. Adaptation strategies suggest that the flood water should be conserved which can help farmers in irrigation throughout the year. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Conceptualizing the implications of climate change for crop evapotranspiration (ETc) and subsequent net irrigation water requirement (NIWR) is critical to sustaining Pakistan's agriculture and food security. In this article, future ETc, NIWR, and design water requirements (DWR) were projected for the rice–wheat system of Punjab, Pakistan. Consistently increasing temperatures signify an impending hotter transition in the future thermal regime, accompanied by a substantial increase in monsoon rainfall. Future climate warming accelerated ETc and NIWR, which were compensated by 2–5 and 1–2 additional irrigations during the rice and wheat seasons, respectively. Future rice and wheat required 13–18 and 2–5 irrigations per season, respectively. Effective rainfall increments did not compensate for the warming-driven higher ETc and NIWR because of uneven and erratic rainfall distribution. Rainfall occurrence and the duration of peak irrigation demand were mismatched, resulting in surplus rainwater availability during the future rice season. The results suggest that DWR for 5- and 10-year return period droughts during the baseline period (965 and 1000 mm, respectively) should be revised to accommodate the additional 100–200 mm of irrigation water per season; otherwise, the study area will face an acute water shortage in the future. [ABSTRACT FROM AUTHOR]

The rapidly changing climatic scenario is demanding periodic evaluation of groundwater quality at the temporal and spatial scale in any region for its effectual management. The statistical, geographic information system (GIS), geostatistical, and map overlay approaches were applied for investigating the spatio-temporal variation in groundwater quality and level data of 242 monitoring wells in Punjab, Pakistan during pre-monsoon and post-monsoon seasons of the years 2015 and 2016. The analysis indicated the higher variation in data for both the seasons (pre-monsoon and post-monsoon) as coefficient of variation (CV) values were found in the range of 84–175% for groundwater quality parameters. Based on the t-test values, the marginal improvement in groundwater electrical conductivity (EC), sodium absorption ratio (SAR) and residual sodium carbonate (RSC) and decrease in groundwater level (GWL) were observed in 2016 as compared to 2015 (p = 0.05). The spatial distribution analysis of groundwater EC, SAR and RSC indicated that the groundwater quality was unfit for irrigation in the lower south-east part of the study area. The groundwater level (GWL) was also higher in that part of the study area during the pre-monsoon and post-monsoon seasons in 2015 and 2016. The overlay analysis also indicated that the groundwater EC, RSC and GWL values were higher in south-east parts of the study area during pre-monsoon and post-monsoon seasons of 2015 and 2016. Hence, there is an instant need to apply groundwater management practices in the rest of the region (especially in the lower south-east part) to overcome the future degradation of groundwater quality. [ABSTRACT FROM AUTHOR]

Spatial and temporal precipitation data acquisition is highly important for hydro-meteorological applications. Gridded precipitation products (GPPs) offer an opportunity to estimate precipitation at different time and resolution. Though, the products have numerous discrepancies that need to be evaluated against in-situ records. The present study is the first of its kind to highlight the performance evaluation of gauge based (GB) and satellite based (SB) GPPs at annual, winter, and summer monsoon scale by using multiple statistical approach during the period of 1979–2017 and 2003–2017, respectively. The result revealed that the temporal magnitude of all the GPPs was different and deviate up to 100–200 mm with overall spatial pattern of underestimation (GB product) and overestimation (SB product) from north to south gradient. The degree of accuracy of GB products with observed precipitation decreases with the increase in the magnitude of precipitation and vice versa for SB precipitation products. Furthermore, the observed precipitation revealed the positive trend with multiple turning points during the period 1979–2005. However, the gentle increase with no obvious break point has been detected during the period of 2005–2017. The large inter-annual variability and trends slope of the reference data series were well captured by Global Precipitation Climatology Centre (GPCC) and Tropical Rainfall Measuring Mission (TRMM) products and outperformed the relative GPPs in terms of higher R2 values of ≥ 0.90 and lower values of estimated RME ≤ 25% at annual and summer monsoon season. However, Climate Research Unit (CRU) performed better during winter estimates as compared with in-situ records. In view of significant error and discrepancies, regional correction factors for each GPPs were introduced that can be useful for future concerned projects over the study region. The study highlights the importance of evaluation by the careful selection of potential GPPs for the future hydro-climate studies over the similar regions like Punjab Province. [ABSTRACT FROM AUTHOR]

Identifying the changes in precipitation and temperature at a regional scale is of great importance for the quantification of climate change. This research investigates the changes in precipitation and surface air temperature indices in the seven irrigation zones of Punjab Province during the last 50 years; this province is a very important region in Pakistan in terms of agriculture and irrigated farming. The reliability of the data was examined using double mass curve and autocorrelation analysis. The magnitude and significance of the precipitation and temperature were visualized by various statistical methods. The stations' trends were spatially distributed to better understand climatic variability across the elevation gradient of the study region. The results showed a significant warming trend in annual Tmin (minimum temperature) and Tmean (mean temperature) in different irrigation zones. However, Tmax (maximum temperature) had insignificant variations except in the high elevation Thal zone. Moreover, the rate of Tmin increased faster than that of Tmax, resulting in a reduction in the diurnal temperature range (DTR). On a seasonal scale, warming was more pronounced during spring, followed by that in winter and autumn. However, the summer season exhibited insignificant negative trends in most of the zones and gauges, except in the higher-altitude Thal zone. Overall, Bahawalpur and Faisalabad are the zones most vulnerable to warming annually and in the spring, respectively. Furthermore, the elevation-dependent trend (EDT) indicated larger increments in Tmax for higher-elevation (above 500 m a.s.l.) stations, compared to the lower-elevation ones, on both annual and seasonal scales. In contrast, the Tmin showed opposite trends at higher- and lower-elevation stations, while a moderate increase was witnessed in Tmean trends from lower to higher altitude over the study region. An increasing trend in DTR was observed at higher elevation, while a decreasing trend was noticed at the lower-elevation stations. The analysis of precipitation data indicated wide variability over the entire region during the study period. Most previous studies reported no change or a decreasing trend in precipitation in this region. Conversely, our findings indicated the cumulative increase in annual and autumn precipitation amounts at zonal and regional level. However, EDT analysis identified the decrease in precipitation amounts at higher elevation (above 1000 m a.s.l.) and increase at the lower-elevation stations. Overall, our findings revealed unprecedented evidence of regional climate change from the perspectives of seasonal warming and variations in precipitation and temperature extremes (Tmax and Tmin) particularly at higher-elevation sites, resulting in a variability of the DTR, which could have a significant influence on water resources and on the phenology of vegetation and crops at zonal and station level in Punjab. [ABSTRACT FROM AUTHOR]