Abstract
This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.
Highlights
Rainfall is a key component of the hydrological cycle and has a diverse range of impacts on human society, for example, agricultural activities [1], hydropower generation [2], vegetation phenology [3], and sustainability of biodiversity [4]
Mean annual rainfall (1983–2018) in the Bhilangana river basin is observed as 1196 mm across an average of 66 rainy days per year, with marked spatial variability and seasonality
Maximum rainfall is recorded during the monsoon season (78.6%), along with an average of 44 rainy days, followed by the winter (10.1%), pre-monsoon (8.4%), and post-monsoon (3.0%) along with around 8, 11, and 3 rainy days correspondingly
Summary
Rainfall is a key component of the hydrological cycle and has a diverse range of impacts on human society, for example, agricultural activities [1], hydropower generation [2], vegetation phenology [3], and sustainability of biodiversity [4]. It is widely accepted that recent climate change has altered annual and seasonal patterns of rainfall along with its spatial distribution. The particular significance of rainfall variability has been highlighted for the high mountain regions of the Indian Himalayan Region (IHR) [5,6]. High mountain regions are potentially ideal localities for evaluating climate change and associated impacts because of its large elevation range and vulnerable biota. As about one-fifth of the world’s mountain inhabitants is found in the wider Himalayan region [7], with an ongoing increasing population, the region is likely to play a crucial role in livelihoods of future generations [8]. Understanding rainfall variability in the Himalayan region becomes extremely critical for integrated Himalayan spatial planning
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