Devastating floods adversely affect human life and infrastructure. Various regions of the Hindukush-Karakoram-Himalayas receive intense monsoon rainfall, which, together with snow and glacier melt, produce intense floods. The Kabul river basin originates from the Hindukush Mountains and is frequently hit by such floods. We analyses flood frequency and intensity in Kabul basin for a contemporary period and two future periods (i.e., 2031-2050 and 2081-2100) using the RCP4.5 and RCP8.5 scenarios based on four bias-corrected downscaled climate models (INM-CM4, IPSL-CM5A, EC-EARTH, and MIROC5). Future floods are modelled with the SWAT hydrological model. The model results suggest an increasing trend due to an increasing precipitation and higher temperatures (based on all climate models except INM-CM4), which accelerates snow and glacier-melt. All of the scenario results show that the current flow with a 1 in 50 year return period is likely to occur more frequently (i.e., 1 in every 9-10 years and 2-3 years, respectively) during the near and far future periods. Such increases in intensity and frequency are likely to adversely affect downstream population and infrastructures. This, therefore, urges for appropriate early precautionary mitigation measures. This study can assist water managers and policy makers in their preparation to adequately plan for and manage flood protection. Its findings are also relevant for other basins in the Hindukush-Karakoram-Himalayas region.

Cotton has prime importance in the global economy and governs socio-economic affairs of the world. Water scarcity and high temperature are major constraints that badly affect cotton production, which shows the need for the development of drought-tolerant varieties. Ten cotton genotypes, including three drought-tolerant and seven susceptible, were identified from a panel of diverse cotton genotypes at the seedling stage under two contrasting water regimes. Three lines were crossed with seven testers under line × tester mating design. The 21 F1 cross combinations along with 10 parents were evaluated under 100% non-stress (NS) and 50% drought stress (DS) filed capacity to assess the effects of drought stress and its inheritance in the next generation. All the genotypes were evaluated till the maturity stage for combining ability, heritability, and other genetic factors to understand the drought tolerance mechanisms. The proportional contribution of lines in the total variance evidenced that lines had a significant higher contribution in total variance for days to boll opening (DBO) of 10% and proline contents (PC) of 13% under DS conditions. It indicates that lines contributed more positive alleles for such traits. Under DS condition, DTV-9 × BT-252 and DTV-9 × DTV-10 had maximum negative specific combining ability (SCA) effects for DBO. Simultaneously, DBO also had higher heritability (h2) which indicates its dominant gene action and meanwhile, the importance of these combinations for the early mature and short duration variety development. The results revealed that most of the studied traits, including days taken to maturity, yield traits, and physiological traits, are under significant genetic control, with a strong genetic basis and have a huge potential for improving drought tolerance in cotton. Drought tolerance was found to have a strong association with early maturity and agro-climatic conditions of the cultivated areas. Identified superior parents in this study are suggested to use in the future breeding program to advance the cotton growth and drought tolerance.

The development of high-yielding heat-tolerant cotton cultivars harboring plastic phenotypes across warming climatic regions is prime objectives of today’s cotton breeding programs. We evaluated eight parents and 15 F1 hybrids under normal and heat stress conditions. Agronomic and biochemical characters were analyzed using standard least square, correlation, principal component analysis (PCA), and hierarchical clustering. The results explained a significant reduction in all traits except hydrogen peroxide contents, catalase, and peroxidase activities with a prominent increase under heat stress. A significant positive correlation was observed among all agronomic and biochemical traits. POD was found to have a maximum positive correlation with CAT (0.947) and minimum with boll weight (0.050). PCA showed first two components accounting for 78.64% of the total variation, with 55.83% and 22.80% of the total variation, respectively. Based on multivariate analyses methods 23 genotypes have been placed in 3 groups: tolerant (cluster-3), moderately tolerant (cluster-2), and susceptible (cluster-1). In a general perspective hybrids have better performance across normal and heat stress supports the idea of hybrid adaptability across stress environments. In specific FH-458 × FH-313 cross performed best across both conditions for yield and physiological traits. Hence, the generated information from the present study would support breeders in developing heat-resilient cultivars to endure the prevailing extreme environmental conditions.

The high-altitude Indus basin is one of the most complex and inadequately explored mountain terrains in the World, where reliable observations of precipitation are highly lacking. Therefore, spatially distributed precipitation products developed at global/regional scale are often used in several scientific disciplines. However, large uncertainties in precipitation estimates of such precipitation data sets often lead to suboptimal outcomes. In this study, performance of 27 widely used gridded precipitation products belonging to three different categories of gauge-based, reanalysis and merged products is evaluated with respect to high-quality reference climatologies of mean monthly precipitation. Widely used statistical measures and quantitative analysis techniques are used to analyse the spatial patterns and quantitative distribution of mean monthly, seasonal and annual precipitation at sub-regional scale.Mean annual precipitation estimates of the gridded data sets are cross validated with the corresponding adjusted streamflows using Turc-Budyko nondimensional analysis. Results reveal poor to moderately good performance of the gridded data sets. Marked differences in spatiotemporal and quantitative distribution of precipitation are found among the data sets. All data sets are consistent in their patterns showing negative or dry bias in wet areas and