Drought is a severe threat, especially in the arid regions of Pakistan, such as the Balochistan Province. The aim of this study is to analyze drought trends in Balochistan using Standard Precipitation Index (SPI) at the 3-month accumulation timescale. The monthly rainfall data of 10 stations were collected from the Pakistan Meteorological Department (PMD) for 37 years (1980–2017). Drought trends were analyzed at each station using the Mann-Kendall test. The SPI identified extreme drought events in 1996, 2001, 2002, 2004, 2009, and 2014. Barkhan was the station that most frequently experienced extreme to severe drought events, as defined using SPI. A statistically significant decreasing precipitation trend was found in four stations (Dalbandin, Jiwani, Quetta, and Zhob). The analysis of drought characteristics showed Barkhan faced the most prolonged drought, of 22 months from 1999 to 2001. The findings from the present study can give guidance on how strategies of water management should be adjusted based on the changing patterns of droughts in the Balochistan Province.

Pakistan is among the top ten countries adversely affected by climate change. More specifically, there is concern that climate change may cause longer and severer spells of droughts. To quantify the change in the characteristics of droughts in Pakistan over the years, we have evaluated spatio-temporal trends of droughts in Pakistan over the period 1902–2015 using Standardized Precipitation Evapotranspiration Index (SPEI). Additionally, the Spatial “K” luster Analysis using Tree Edge Removal (SKATER) method was employed to regionalize droughts into five contiguous zones. The run theory was then applied to each zone to identify drought events and characterize them in terms of duration, severity, intensity, and peak. Moreover, the Modified Mann–Kendall trend test was applied to identify statistically significant trends in SPEI and drought characteristics in each zone. It was found that the southern areas of Pakistan, encompassing Sindh and most of Baluchistan, have experienced a decrease in SPEI, indicating a drying trend. Central Pakistan has witnessed a wetting trend as demonstrated by an increase in SPEI over time, whereas no statistically significant trend was observed for the northern areas of Pakistan. On a zonal basis, the longest duration drought to occur in Pakistan lasted 22 months in zone 5 (Sindh) from 1968 to 1970. In addition, the droughts of 1920 and 2000 can be said to be the worst drought in the history of the region as it affected all the zones and lasted for more than 10-months in three zones.

In the wake of a rapidly changing climate, droughts have intensified, in both duration and severity, across the globe. The Germanwatch long-term Climate Risk Index ranks Pakistan among the top 10 countries most affected by the adverse effects of climate change. Within Pakistan, the province of Balochistan is among the most vulnerable regions due to recurring prolonged droughts, erratic precipitation patterns, and dependence on agriculture and livestock for survival. This study aims to explore how the characteristics of droughts have evolved in the region from 1902–2015 using 3-month and 12-month timescales of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The region was divided into six zones using Spatial “K”luster Analysis using Tree Edge Removal (SKATER) method, and run theory was applied to characterize droughts in terms of duration, severity, intensity, and peak. The results of the non-parametric Mann–Kendall trend test applied to SPEI indicate prevailing significant negative trends (dryer conditions) in all the zones. Balochistan experienced its most severe droughts in the 1960s and around 2000. The effects of climate change are also evident in the fact that all the long duration droughts occurred after 1960. Moreover, the number of droughts identified by 3-month SPEI showed a significant increase after 1960 for all six zones. The same trend was found in the 12-month SPEI but for only three zones.

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in many cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.