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The present study aims to explore the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting meteorological time series data exhibiting seasonal patterns. We compared the performance of SARIMA models with different configurations and evaluate their forecasting accuracy using real-world meteorological datasets for three different agroclimatic zones of Punjab (sub mountainous region, central region and south west region) was analyzed to forecast mean monthly maximum air temperature, minimum air temperature and rainfall. The weather data was used from 1984-2022 for sub-mountainous zone (Ballowal Saunkhri), 1970-2022 for Central zone (Ludhiana) and 1977-2022 for south west zone (Bathinda). The results provide insights into the suitability and limitations of SARIMA models for meteorological forecasting and offer practical recommendations for practitioners and researchers in the field. The goodness of fit was tested against residuals using Ljung-Box test. The accuracy of the model was tested using Mean Absolute Error (MAE) and root square mean error (RMSE). The model achieved Mean Absolute Errors (MAE) ranging from 0.61 to 0.78 for maximum temperature, 0.74 to 0.49 for minimum temperature, and 32.12 to 45.44 for rainfall, with lower MAE values indicating higher predictive accuracy. The fitted model was able to capture dynamics of the temperature time series and produce a sensible forecast. However, the model was unable to forecast rainfall series efficiently. [ABSTRACT FROM AUTHOR]

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]

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study's spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon's summer high rain and floods in the plain of Punjab (Pakistan). [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]

Aim of the study This study aims to quantify uncertainty in assessing climate change impact on crop production by using all available climate models (GCMs) under both harsh and mild emission scenarios from 2020 to 2095, which has not yet been done in the studied region to date. Material and methods A comparative study was carried out for Ludhiana district, Punjab, India, in which Global Climate Model (GCM) outputs for daily maximum (Tmax) and minimum temperature (Tmin) and rainfall under A1B scenario concerning Mid Century (MC) (2020-2050) and End Century (EC) (2070-2095) were extracted from ECHam5-GCM and PRECIS model. DSSAT v.4.6.1 model and Papadakis method were used to study the climate change behavior under these two time-slices. In addition, climate data from RCP scenarios for the future were extracted from five randomly selected GCMs under scenarios RCP 4.5 and RCP 8.5 using the MarkSim DSSAT weather generator. These models were analyzed statistically for RMSE and NRMSE. One of the models, HAD GEM2-ES, was selected as having the least NRSME for the impact assessment studies. Results and conclusions The results showed that the annual minimum temperature would increase by 2.4°C and 2.45°C for EC using ECHAM5 and PRECIS models. In contrast, under RCPs 4.5 and 8.5 scenarios, the mean annual temperature would increase by 1.56°C in MC and 3.11°C in EC compared to that of the baseline period, and 2.75°C in MC and 5.46°C in EC compared to that of the baseline period, respectively. The corresponding likely decrease in annual RF under RCP 4.5 is 98 mm and 90 mm during MC and EC, respectively. The corresponding increase in annual RF under RCP 8.5 is 153 mm and 251 mm, respectively. Hence, our findings show that the uncertainty is prevalent even in relatively small regions, while selecting different climate change scenarios. Cel badań Niniejsze badania mają na celu ilościowe określenie niepewności w ocenie wpływu zmian klimatu na produkcję roślinną przy użyciu wszystkich dostępnych modeli klimatycznych (GCM) zarówno w przypadku ekstremalnych, jak i łagodnych scenariuszy emisji w latach 2020-2095. W omawianym regionie są to pierwsze badania nad tym zagadnieniem. Materiał i metody W tym celu przeprowadzono badanie porównawcze dla regionu Ludhiana w stanie Pendżab w Indiach. Dane wyjściowe globalnego modelu klimatu (GCM) dla dziennej maksymalnej temperatury (Tmax) i dziennej minimalnej temperatury (Tmin) oraz opadów w scenariuszu A1B dotyczącym połowy wieku (MC) (2020-2050) i końca wieku (EC) (2070-2095) zostały wyodrębnione z modelu ECHam5-GCM i PRECIS. Zostały one wykorzystane do zbadania zachowania zmian klimatu. W tych dwukrotnych przekrojach zastosowano następnie Model DSSAT v.4.6.1. oraz metodę Papadakisa. Ponadto dane klimatyczne ze scenariuszy RCP na przyszłość zostały wyodrębnione z pięciu losowo wybranych GCM w ramach scenariuszy RCP 4.5 i RCP 8.5 przy użyciu generatora pogodowego MarkSim DSSAT. Modele te poddano analizie statystycznej dla RMSE i NRMSE. Jeden z modeli, HAD GEM2-ES, został wybrany do badań oceny skutków jako posiadający najmniej NRSME. Wyniki i wnioski Z badań wynika, że na koniec wieku (EC) roczna minimalna temperatura wzrosłaby o 2,4°C i 2,45°C przy użyciu modeli ECHAM5 i PRECIS. Natomiast w scenariuszach RCP 4.5 i 8.5 średnia roczna temperatura wzrosłaby odpowiednio o 1,56°C w połowie wieku (MC) i o 3,11°C na koniec wieku (EC) w porównaniu z okresem bazowym oraz o 2,75°C w połowie wieku (MC) i 5,46°C na koniec wieku (EC) w porównaniu z okresem bazowym. Odpowiedni prawdopodobny spadek rocznego RF, jeśli przyjmiemy scenariusz RCP 4.5, wynosi odpowiednio 98 mm i 90 mm w połowie (MC) i na koniec wieku (EC). Z kolei przyjmując scenariusz RCP 8.5, otrzymamy wzrost rocznego RF odpowiednio o 153 mm i 251 mm. Nasze ustalenia pokazują zatem, że przy wyborze różnych scenariuszy zmian klimatycznych niepewność występuje nawet w stosunkowo małych regionach. [ABSTRACT FROM AUTHOR]

Evapotranspiration is an important hydrological parameter that takes place due to water bodies and many physical features (plants, soil) on earth. Punjab lies in the zone of Pakistan that is mostly affected by climate change. A decrease in rainfall and an increase in temperature and potential evapotranspiration (PET) tend toward a more disastrous situation particularly in the southern region of Punjab. MODIS satellite imagery (MOD16) and climatic data over the 16 and 23 years' time periods (1993–2016) were utilized in this study. Firstly for the estimation of the average spatial and temporal trend of rainfall patterns. Maximum of 2000 mm annual rainfall was estimated in 1997, while in 2016 about 1300 mm of annual rainfall was estimated. A declining trend of annual rainfall has been observed both in satellite imagery and observed data results from 1993 to 2016. Modified Penman–Monteith equation was used to estimate PET and moisture index. Thornthwaite Moisture Index was applied on different scales for moisture observation. It was estimated that from 1993 to 2016 the trend of PET increased while that of moisture content decreased in the study area. Results obtained from both datasets showed that southern Punjab having less amount of rainfall and a gradually increasing trend in potential evapotranspiration as compared to the northern part of the province Punjab. [ABSTRACT FROM AUTHOR]

An integrated approach is developed to assess major crops water productivity (CWP) in relation to water supply across the irrigated areas of Punjab province in central Pakistan during 1982–2015. The role of precipitation (effective rainfall) in term of add on to water supply and impacts on wheat yield is also studied. The water supply from canals displayed a significant declining trend for Rabi season (wheat mainly) during 1996−2015. Crop water productivity for harvestable area (CWPA) has decreased; however, it has increased in case of production (CWPP) and yield (CWPY). The role of tube wells is analyzed to assess the improved CWP even under decreased canal water supply. Contribution of water supply through effective rainfall showed a significant add on of 7.6 mm to 291.8 mm to water supply and 0.48 kg ha−1 mm−1 to 8.19 kg ha−1 mm−1 towards CWPY during the studied 34-year period. The precipitation (effective rainfall) contribute water supply of 10.6 mm (6.64 mm), 23.4 mm (14.6 mm), and 25.5 mm (16.0 mm) for January, February, and March, respectively. Average canal water supply for irrigation is observed to be 254.9 mm against irrigation need of 418.6 mm with a deficit of 39.1%, across irrigated areas. The multiple regression models are developed with seasonal predictive capacity for wheat harvestable area and yields with an explained variability of ~ 81% and ~ 78%, respectively. A decrease in temporal satellite-based weekly composite NDVI profile during 1986, 1992, 1994, 1997, 2002, and 2010 corresponded to El Niño episodes, since negative phases of ENSO are known to cause droughts across Punjab province. The synthesized information calls for a more comprehensive management of declining canal water supply for sustainable major crops water productivity such as wheat which is a staple food crop. [ABSTRACT FROM AUTHOR]

Early outlook on food production is important for policy making and can be formed on the basis of the recorded rainfall. Simplistically, a good monsoon produces a bumper harvest and scanty rainfall causes crop failure. Econometric modelling of past data shows that the reality is much more complex. Food production in a state is sensitive to rainfall in the state as well as other states depending on geography. Rainfall distribution in the growing season and pre-sowing months can matter significantly. Moreover, the rainfall effect can be favorable as well as adverse. In the sample period, sowing and growing season rainfall in the state had little favorable effect on area and yield in Punjab and its effect on Rice yield was even harmful in Punjab and West Bengal. Rainfall in Uttar Pradesh and Madhya Pradesh however had beneficial effects on the respective state's and on each's other production. Rainfall in the Himalayas is a powerful impact on food production resulting from river dynamics and water management but the dominance of adverse effect over beneficial ones is a sign of poorly managed upstream downstream linkages. Because production is sensitive also to economic variables, the government, which can modulate subsidies and support prices, also has control over food production. Improving efficiency of water distribution with an integrated geographical perspective can also be a potent public instrument for production planning. [ABSTRACT FROM AUTHOR]