Landslides, floods, and droughts are recurring natural disasters in Nepal related to too much or too little water. The summer monsoon contributes more than 80% of annual rainfall, and rainfall spatial and inter-annual variation is very high. The Gandaki River, one of the three major rivers of Nepal and one of the major tributaries of the Ganges River, covers all agro-ecological zones in the central part of Nepal. Time series tests were applied for different agro-ecological zones of the Gandaki River Basin (GRB) for rainfall trends of four seasons (pre-monsoon, monsoon, post-monsoon and winter) from 1981 to 2012. The non-parametric Mann-Kendall and Sen’s methods were used to determine the trends. Decadal anomalies relative to the long-term average were analyzed using the APHRODITE precipitation product. Trends in number of rainy days and timing of the monsoon were also analyzed. We found that the post-monsoon, pre-monsoon and winter rainfalls are decreasing significantly in most of the zones but monsoon rainfall is increasing throughout the basin. In the hill region, the annual rainfall is increasing but the rainy days do not show any trend. There is a tendency toward later departure of monsoon from Nepal, indicating an increase in its duration. These seasonally and topographically variable trends may have significant impacts for the agriculture and livestock smallholders that form the majority of the population in the GRB.

This paper presents temporal and spatial pattern of drought phenomena in central Nepal using standardized precipitation index (SPI) at multiple time scales. The study is based on 32 years of monthly precipitation data from 40 meteorological stations from 1981 to 2012. Results indicate that, while there is no distinct trend in regional precipitation, interannual variation is large. Trend analysis of drought index shows that most stations are characterized by increases in both severity and frequency of drought and trend is stronger for longer drought time scales. Over the study period, the summer season of 2004, 2005, 2006, 2009 and winters 2006, 2008 and 2009 were the worst widespread droughts. These dry periods have a serious impact on agriculture–livestock production of central Nepal. Better understanding of these SPI dynamics could help in understanding the characteristics of droughts and also to develop effective mitigation strategies.

Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM) 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP), the Climate Prediction Center Morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS)) were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications.

Nepal has an extreme altitudinal range from 60–8850 m with heterogeneous topography and distinct climatic zones. The country is considered a biodiversity hotspot, with nearly a quarter of the land area located in protected areas. Nepal and the surrounding Himalayan region are particularly vulnerable to climate change because of their abrupt ecological and climatic transitions. Tens of millions of people rely on the region’s ecosystem services, and observed and modeled warming trends predict increased climate extremes in the Himalayas. To study the ecological impacts of climate change in Nepal and inform adaptation planning, we review the literature on past, present, and predicted future climatic changes and their impacts on ecological diversity in Nepal. We found few studies focusing on organisms, while research on species and communities was more common. Most studies document or predict species range shifts and changes in community composition. Results of these few investigations highlight major lacunae in research regarding the effects of changing climate on species comprising the Himalayan biota. Further empirical work is needed at all levels of biological organization to build on information regarding direct ecological impacts of climatic changes in the region. Countries face an ever-increasing threat of climate change, and Nepal has strong physiographic, elevational, and climatic gradients that could provide a useful model for studying the effects of climate change on a mountainous, and highly biodiverse, area.

Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas.