Rapid Urbanization, and other anthropogenic activities, have amplified the change in land-use transition from green space to heat emission in built-up areas globally. As a result, there has been an increase in the land surface temperature (LST) causing the Urban Heat Island (UHI) effect, particularly in large cities. The UHI effect poses a serious risk to human health and well-being, magnified in large developing cities with limited resources to cope with such issues. This study focuses on understanding the UHI effect in Kathmandu Valley (KV), Delhi, and Dhaka, three growing cities in South Asia. The UHI effect was evaluated by analyzing the UHI intensity of the city with respect to the surroundings. We found that the central urban area, of all three cities, experienced more heat zones compared to the peri-urban areas. The estimated average surface temperature ranged from 21.1 ${}^{\circ }$C in March 2014 to 32.0 ${}^{\circ }$C in June 2015 in KV, while Delhi and Dhaka experienced surface temperature variation from 29.7 ${}^{\circ }$C in June 2017 to 40.2 ${}^{\circ }$C in June 2019 and 23.6 ${}^{\circ }$C in March 2017 to 33.2 ${}^{\circ }$C in March 2014, respectively. Based on magnitude and variation of LST, highly built-up central KV showed heat island characteristics. In both Delhi and Dhaka, the western regions showed the UHI effect. Overall, this study finds that the UHI zones are more concentrated near the urban business centers with high population density. The results suggest that most areas in these cities have a rising LST trend and are on the verge of being UHI regions. Therefore, it is essential that further detailed assessment is conducted to understand and abate the impact of the temperature variations.

This study introduces two novel explainable AI (XAI) frameworks, Interclass-Grad-CAM and Spectral-Grad-CAM, designed to enhance the interpretability of semantic segmentation models for Crop and Land Cover Land Use (CLCLU) mapping. Interclass-Grad-CAM provides insights into interactions between land cover classes, revealing complex spatial arrangements, while Spectral-Grad-CAM quantifies the contributions of individual spectral bands to model predictions, optimizing spectral data use. These XAI methods significantly advance understanding of model behavior, particularly in heterogeneous landscapes, and ensure enhanced transparency in CLCLU mapping. To demonstrate the effectiveness of these innovations, we developed a framework that addresses data asymmetry between the United States and Mexico in the transboundary Middle Rio Grande region. Our approach integrates pixel-level multi-sensor fusion, combining dual-month moderate-resolution optical imagery (July and December 2023), Synthetic Aperture Radar (SAR), and Digital Elevation Model (DEM) data, processed using a Multi-Attention Network (MANet) with a modified Mix Vision Transformer (MiT) encoder to process multiple spectral inputs. Results indicate a uniform improvement in class-specific Intersection over Union (IoU) by approximately 1% with multi-sensor integration compared to optical imagery alone. Optical bands proved most effective for crop classification, while SAR and DEM data enhanced predictions for non-agricultural types. This framework not only improves CLCLU mapping accuracy but also offers a robust tool for broader environmental monitoring and resource management applications.

Extreme precipitation (rain or/and snow) in upstream areas of the Himalayan region often triggers water and sediment hazards, including flash floods, landslides, debris flow, and river damming. These hazards often interact with the environmental and human systems, resulting in acute and chronic disasters that deleteriously impact the economy, infrastructure, and livelihoods. As cascading and compound hazards become more common in the Himalayan region, a more holistic approach is needed to understand the complex interaction among human, natural, and engineered systems and tackle problems connected to several system constraints. Recent catastrophes like the 2021 Melamchi disaster cannot be attributed to a single cause; rather, they result from several geomorphologic and hydroclimatic factors and physical processes. Individual and interacting dynamics and their cascading and compounding effects occurred in various settings, from high elevations to river valleys along the Melamchi River. This study diagnoses the likely causal effects of hydroclimatic processes of the 2021 Melamchi disaster by integrating space-borne remotely sensed data, hydrologic and hydrodynamic modeling, and machine learning techniques. We have evaluated several scenarios of landslide damming and breaching and analyzed flooding impacts on major settlements across the Melamchi River. Model outputs evaluated using satellite flood imagery, field-based surveys, and published reports were used to understand the characteristics of the 2021 Melamchi disaster. We find that earlier rain episodes had saturated the soil and increased the river’s water level. Therefore, after the outburst of the landslide dam coupled with heavy downpours, a sudden release of huge water had enormous energy to inundate vast areas of flood plains and damaged settlements near riverbanks far downstream up to the Melamchi Bazar. It was not just one extreme event that led to the devastation but a series of non-extreme events. This further reinforces the idea that not just extreme events, but several not-extreme series of events may lead to disaster. Thus, the chances of such a sequence of cascading events must be investigated for risk computation along with extreme events.

Streamflow and sediment flux variations in a mountain river basin directly affect the downstream biodiversity and ecological processes. Precipitation is expected to be one of the main drivers of these variations in the Himalayas. However, such relations have not been explored for the mountain river basin, Nepal. This paper explores the variation in streamflow and sediment flux from 2006 to 2019 in central Nepal’s Kali Gandaki River basin and correlates them to precipitation indices computed from 77 stations across the basin. Nine precipitation indices and four other ratio-based indices are used for comparison. Percentage contributions of maximum 1-day, consecutive 3-day, 5-day and 7-day precipitation to the annual precipitation provide information on the severity of precipitation extremeness. We found that maximum suspended sediment concentration had a significant positive correlation with the maximum consecutive 3-day precipitation. In contrast, average suspended sediment concentration had significant positive correlations with all ratio-based precipitation indices. The existing sediment erosion trend, driven by the amount, intensity, and frequency of extreme precipitation, demands urgency in sediment source management on the Nepal Himalaya’s mountain slopes. The increment in extreme sediment transports partially resulted from anthropogenic interventions, especially landslides triggered by poorly-constructed roads, and the changing nature of extreme precipitation driven by climate variability.