Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

The source region of the Indus River (SRIR), which is located in the Hindukush, Karakoram and Himalayan (HKH) mountainous range and on the Third Pole (TP), is very sensitive to climate change, especially precipitation changes, because of its multifarious orography and fragile ecosystem. Climate changes in the SRIR also have important impacts on social and economic development, as well as on the ecosystems of the downstream irrigation areas in Pakistan. This paper investigates the changes in precipitation characteristics by dividing the daily precipitation rate into different classes, such as light (0–10 mm), moderate (10.1–25 mm) and heavy precipitation (>25 mm). Daily precipitation data from gauging and non-gauging stations from 1961–2015 are used. The results of the analysis of the annual precipitation and rainy day trends show significant (p < 0.05) increases and decreases, respectively, while light and heavy precipitation show significant decreasing and increasing trends, respectively. The analysis of the precipitation characteristics shows that light precipitation has the highest number of rainy days compared to moderate or heavy precipitation. The analysis of the seasonal precipitation trends shows that only 18 stations have significant increasing trends in winter precipitation, while 27 stations have significant increasing trends in summer precipitation. Both short and long droughts exhibit increasing trends, which indicates that the Indus Basin will suffer from water shortages for agriculture. The results of this study could help policymakers cope with floods and droughts and sustain eco-environmental resources in the study area.

The identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight into how to improve the GCMs projections and can be helpful for further climate change impact studies.

Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.

Climate change has significant impacts on hydrology in high altitude snow and glacier covered mountainous regions. These regions are highly sensitive to changes in climate variables, such as temperature and precipitation and producing high runoffs. Runoff produced from different altitude ranges and their sensitivity to current and changing climate is also unknown. This study was carried out in high altitude mountainous cryosphere Hunza River Catchment (HRC) which is located in Hindukush and Karakoram ranges and is the major tributary of the Indus River Basin. Snowmelt-Runoff Model (SRM) was used to analyse the current and projected hydrological regimes and the sensitivity of Snow Cover Area (SCA) at different altitude levels under current and changing climate. Under the current condition (i.e., 2001–2010 except 2006), the results showed that about half of the mean annual streamflows at the outlet of the HRC is contributed by the altitude ranges of 4500–5500 m a.s.l. Climatic projections under the RCP8.5 and RCP4.5 scenarios were used for the climate change impact assessment. Compared to the baseline climate, the mean annual temperature would increase by 0.7 (0.6), 2.4 (1.3) and 4.6 (1.9) ℃, respectively during 2030s, 2060s and 2090s; and the mean annual precipitation would increase by 63.3 (33.6) mm during 2090s under the RCP8.5 (RCP4.5) projections. Moreover, two SCA scenarios were developed, i.e., the baseline unchanged SCA and the hypothetical change in SCA scenarios. In the first SCA scenario, the results showed that additional streamflows of 43 (34), 153 (83.4) and 304 (115.7) m³ s⁻¹ under RCP8.5 (RCP4.5) will be added into baseline annual streamflows of 269 m³ s⁻¹ during 2030s, 2060s and 2090s, respectively. In the second scenario, we found that 10 % and 15 % decrease in SCA would result in increases (or decrease) in streamflows approximately by 18 (2) % and 42 (7) % under the RCP8.5 (RCP4.5) scenario during 2060s and 2090s, respectively. Whereas altitude range 4500–5500 m a.s.l showed increasing trend during pre-monsoon (April–June) and monsoon (July–August) season under changed SCA scenario for both RCPs scenarios. Current and near future climate pattern is favourable for Indus River regarding high water flows. However, future water flow pattern is declining because of disappearance or decrease in snow and glaciers melt area which correspondingly means that mid/downstream water allocation will be effected or reduced at some extent. Proper adaptations or managements strategies should be executed for upcoming harsh conditions.