Having an extreme topography and heterogeneous climate, the Upper Indus Basin (UIB) is more likely to be affected by climate change and it is a crucial area for climatological studies. Based on the monthly minimum temperature (T_min), maximum temperature (T_max) and precipitation from nine meteorological stations, the spatiotemporal variability of temperature and precipitation were analyzed on monthly, seasonal, and annual scales. Results show a widespread significant increasing trend of 0.14 °C/decade for T_max, but a significant decreasing trend of −0.08 °C/decade for T_min annually, during 1955–2016 for the UIB. Seasonally, warming in T_max is stronger in winter and spring, while the cooling in T_min is greater in summer and autumn. Results of seasonal T_max indicate increasing trends in winter, spring and autumn at rates of 0.38, 0.35 and 0.05 °C/decade, respectively, while decreasing in summer with −0.14 °C/decade. Moreover, seasonal T_min results indicate increasing trends in winter and spring at rates of 0.09 and 0.08 °C/decade, respectively, while decreasing significantly in summer and autumn at rates of −0.21 and −0.22 °C/decade respectively for the whole the UIB. Precipitation exhibits an increasing trend of 2.74 mm/decade annually, while, increasing in winter, summer and autumn at rates of 1.18, 2.06 and 0.62 mm/decade respectively. The warming in T_max and an increase in precipitation have been more distinct since the mid-1990s, while the cooling in T_min is observed in the UIB since the mid-1980s. Warming in the middle and higher altitude (1500–2800 m and >2800 m) are much stronger, and the increase is more obvious in regions with elevation >2800 m. The wavelet analysis illustrated sporadic inter-annual covariance of seasonal T_max, T_min and precipitation with ENSO, NAO, IOD and PDO in the UIB. The periodicities were usually constant over short timescales and discontinuous over longer timescales. This study offers a better understanding of the local climate characteristics and provides a scientific basis for government policymakers.

The effects of climate change are unparalleled in magnitude, ranging from changing weather patterns that endanger food production to increasing sea levels that increase the likelihood of catastrophic flooding. Therefore, determining the extent of such variations on regional and local scales is imperative. We used monthly precipitation data from 25 meteorological stations in northern Pakistan (NP) to document the observed changes in seasonal and annual precipitation. The station density in the NP is small and unevenly distributed; therefore, ERA-5 reanalysis data were used to supplement the observed dataset to assess the spatial trends in NP. The non-parametric Mann–Kendall (MK), Sen’s Slope estimator (SSE), and Sequential Mann–Kendall (SQMK) tests were performed to assess the trends. In addition, the wavelet analysis technique was used to determine the association of precipitation with various oceanic indices from 1960 to 2016. Results indicate that maximum precipitation was shown in the annual and summer seasons. In NP, annual, winter, spring, and summer precipitation declined, while an increase in autumn was observed at a rate of 0.43 mm/decade between 1989 and 2016. The spatial trends for observed and ERA-5 reanalysis datasets were almost similar in winter, spring, and autumn; however, some disagreement was observed in both datasets in the summer and annual precipitation trends in NP during 1960–2016. Between 1989 and 2016, summer and annual precipitation increased significantly in Region III. However, seasonal and annual precipitation decreased in NP between 1960 and 2016. Moreover, there were no prominent trends in annual precipitation until the mid-1980s, but an apparent increase from 1985 onwards. Annual precipitation increased in all elevations except at the 500–1000 m zone. The ENSO (El Niño–Southern Oscillation) shared notable interannual coherences among all indices above 16–64 months. Inter-decadal coherence with the ENSO, AO (Arctic Oscillation), and PDO (Pacific Decadal Oscillation) in NP for 128 months and above. Generally, AO, AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation) exhibited less coherence with precipitation in NP. The regression of seasonal and annual precipitation revealed that winter and spring precipitation levels had higher linear regression with the AO and ENSO, respectively, while both the AO and ENSO also dominated at the annual scale. Similarly, the IOD and PDO indices had a higher influence in summer precipitation. The findings may help water resource managers and climate researchers develop a contingency plan for better water resource management policies in the face of changing climate change in Pakistan, particularly in NP.

Karst rocky desertification (KRD) has become the primary ecoenvironmental problem in the karst regions of southwest China. The rapid and efficient acquisition of exposed bedrock fractions (EBF) is crucial for the monitoring and assessment of KRD degree and distribution within the highly heterogeneous landscapes. Remote-sensing indices provide a useful method for the quick mapping of the EBF at large scales. The currently available rock indices, however, are faced with insensitivity to bedrock change characteristics, which greatly limits their performances and suitability. To address this problem, we proposed a novel karst bare-rock index (KBRI) that applies shortwave-infrared (SWIR) and near-infrared (NIR) bands from Landsat-8 OLI imagery to maximally distinguish between exposed bedrock and other land cover types in southwest China. A linear regression model was thus established between KBRI and the EBF derived from in situ measurements. The model developed here was then validated with an independent experiment and applied over a large geographic area to produce regional maps of EBF in southwest China. Experimental results showed good performance on root mean square error (5.59%), mean absolute error (4.63%), root mean absolute percentage error (13.59%), and coefficient of determination (0.72), respectively. The advantages of the proposed method are reflected in its simplicity and minimal requirements for auxiliary data while still achieving comparatively better accuracy than existing related indices. Thus, the KBRI has the great potential for the application in other regions around the world with the similar geological backgrounds, thereby helping to address the similar or other related environmental issues. Results of this study provide baseline data for the KRD assessment and karst-ecosystem management in southwest China.

Karst areas in Southwest China, with Guizhou as the focal center, are confronted with ecological deterioration and large areas of rocky desertification. Human activities are defined as the driving force behind the soil erosion. Further, local farmers in the area suffer from poverty due to a lack of drinking water, food and a weak living environment. Over one-third of national poverty-stricken counties occur in this part of China. To balance ecological protection and economic development in the region and help local farmers out of poverty we propose integrated controls and discuss on ground water exploration and sustainable use, soil conservation and remediation, and vegetation restoration (especially economic plant species) in this paper.

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year⁻¹ and 0.0020 year⁻¹, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.