The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.

Our current understanding of semiarid ecosystems is that they tend to display higher vegetation greenness on polar‐facing slopes (PFS) than on equatorial‐facing slopes (EFS). However, recent studies have argued that higher vegetation greenness can occur on EFS during part of the year. To assess whether this seasonal reversal of aspect‐driven vegetation is a common occurrence, we conducted a global‐scale analysis of vegetation greenness on a monthly time scale over an 18‐year period (2000–2017). We examined the influence of climate seasonality on the normalized difference vegetation index (NDVI) values of PFS and EFS at 60 different catchments with aspect‐controlled vegetation located across all continents except Antarctica. Our results show that an overwhelming majority of sites (70%) display seasonal reversal, associated with transitions from water‐limited to energy‐limited conditions during wet winters. These findings highlight the need to consider seasonal variations of aspect‐driven vegetation patterns in ecohydrology, geomorphology, and Earth system models.

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Previous studies on semi‐arid ecosystems have shown high values of soil moisture variability (SMV) primarily induced by the combined effects of non‐uniform precipitation, incoming solar radiation, and soil and vegetation properties. However, the relative impact of these various factors on SMV has been difficult to evaluate due to limited availability of field data. In addition, only a limited number of studies have analysed the role of landscape morphology on SMV. Here we use numerical simulations of a simple hydrological model, the Bucket Grassland Model, to systematically analyse the effect of each contributing factor on SMV on two different landscape morphologies. The two different landform morphologies represent landscapes dominated respectively by either diffusive erosion or fluvial erosion processes. We conducted various simulations driven by a stochastically generated 100‐year climate time series, which is long enough to capture climatic fluctuations, in order to understand the effect of various soil moisture controlling factors on the spatiotemporal SMV. Our modelling results show that the fluvial dominated landscapes promote higher spatial SMV than the diffusive dominated ones. Further, the role of landform morphology on SMV is more pronounced in regions where the spatial variability of incoming solar radiation and precipitation is high.

Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin.