Climate change is a serious threat to agriculture and food security. Extreme weather conditions and changing patterns of precipitation lead to a decrease in the crop productivity. High temperatures and uncertain rainfall decrease the grain yield of crops by reducing the length of growing period. Future projections show that temperature would be increased by 2.5°C up to 2050. The projected rise in temperature would cause the high frequent and prolong heat waves that can decline the crop production. The rise in temperature results in huge reduction in yield of agronomic crops. Sustaining the crop production under changing climate is a key challenge. Therefore, adaptation measures are required to reduce the climate vulnerabilities. The adverse effect of climate change can be mitigated by developing heat tolerant cultivars and some modification in current production technologies. The development of adaptation strategies in context of changing climate provides the useful information for the stakeholders such as researchers, academia, and farmers in mitigating the negative effects of climate change.

Historical and future projected changes in climatic patterns over the largest irrigated basin in the world, the Indus River Basin (IRB), threaten agricultural production and food security in Pakistan, in particular for vulnerable farming communities. To build a more detailed understanding of the impacts of climate change on agriculture s in the IRB, the present study analyzes (1) observed trends in average temperature, precipitation and related extreme indicators, as well as seasonal shifts over a recent historical period (1997–2016); and (2) statistically downscaled future projections (up to 2100) from a set of climate models in conjunction with crop-specific information for the four main crops of the IRB: wheat, cotton, rice and sugarcane. Key findings show an increasing trend of about over 0.1 °C/year in observed minimum temperature across the study area over the historical period, but no significant trend in maximum temperature. Historical precipitation shows a positive annual increase driven mainly by changes in August and September. Future projections highlight continued warming resulting in critical heat thresholds for the four crops analyzed being increasingly exceeded into the future, in particular in the Kharif season. Concurrently, inter-annual rainfall variability is projected to increase up to 10–20% by the end of the 21st century, augmenting uncertainty of water availability in the basin. These findings provide insight into the nature of recent climatic shifts in the IRB and emphasize the importance of using climate impact assessments to develop targeted investments and efficient adaptation measures to ensure resilience of agriculture in Pakistan into the future.

Management of the freshwater resources in a sustained manner requires the information and understanding of the surface water hydrology and streamflow is of key importance in this nexus. This study evaluates the performance of eight different precipitation products (APHRODITE, CHRS CCS, CHRS CDR, CHIRPS, CPC Global, GPCC, GPCP, and PERSIANN) for streamflow prediction in two sub-catchments (Chirah and Dhoke Pathan) of the data-scarce Soan River Basin (SRB) in Pakistan. A modified version of the hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) known as HBV-light was used to generate streamflow. The model was separately calibrated and validated with observed and estimated precipitation data for streamflow simulation with optimized parameterization. The values of R², NSE, KGE and PBIAS obtained during the calibration (validation) period for the Chirah sub-catchment were 0.64, 0.64, 0.68 and −5.6% (0.82, 0.81, 0.88 and 7.4%). On the other hand, values of R², NSE, KGE, and PBIAS obtained during the calibration (validation) period for the Dhoke Pathan sub-catchment were 0.85, 0.85, 0.87, and −3.4% (0.82, 0.7, 0.73 and 6.9%). Different ranges of values were assigned to multiple efficiency evaluation metrics and the performance of precipitation products was assessed. Generally, we found that the performance of the precipitation products was improved (higher metrics values) with increasing temporal and spatial scale. However, our results showed that APHRODITE was the only precipitation product that outperformed other products in simulating observed streamflow at both temporal scales for both Chirah and Dhoke Pathan sub-catchments. These results suggest that with the long-term availability of continuous precipitation records with fine temporal and spatial resolutions, APHRODITE has the high potential to be used for streamflow prediction in this semi-arid river basin. Other products that performed better were GPCC, GPCP, and CHRS CCS; however, their scope was limited either to one catchment or a specific time scale. These results will also help better understand surface water hydrology and in turn, would be useful for better management of the water resources.

This work evaluates the suitability of linear scaling (LS) and empirical quantile mapping (EQM) bias correction methods to generate present and future hydrometeorological variables (precipitation, temperature, and streamflow) over the Chitral River Basin, in the Hindukush region of Pakistan. In particular, LS and EQM are applied to correct the high-resolution statistically downscaled dataset, NEX-GDDP, which comprises 21 state-of-the-art general circulation models (GCMs) from the coupled model intercomparison project phase 5 (CMIP5). Raw and bias-corrected NEX-GDDP simulations are used to force the (previously calibrated and validated) HBV-light hydrological model to generate long-term (up to 2100) streamflow projections over the catchment. Our results indicate that using the raw NEX-GDDP leads to substantial errors (as compared to observations) in the mean and extreme streamflow regimes. Nevertheless, the application of LS and EQM solves these problems, yielding much more realistic and plausible streamflow projections for the XXI century.