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Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

A comprehensive investigation of historical hydro-climatic changes at a county level is an essential prerequisite of developing any adaptation or mitigation strategies to deal with the destructive impacts of climate change. In this study, spatial distributions and trends in thirty-seven hydro-climatic mean and extreme indices across Iran were analyzed based on the state-of-the-art reanalysis datasets (ERA5-Land and AgERA5) at the county level from 1986 to 2015 using several nonparametric approaches such as multiple modified Mann–Kendall statistical tests and Sen's Slope estimator. Their interannual oscillations were also examined using continuous wavelet transform to portray a complete picture of the temporal variability of the variables. The micro-scale of the reanalysis datasets enabled us to deal with the sparse distribution of weather stations in Iran, study various hydro-climatic variables that have received less attention, and subsequently provide a comprehensive, all-in-one reference for later policy actions and further studies. Analyzing diverse, intercorrelated variables provides a great opportunity to explore the driving forces of the temporal patterns. The results showed that trends in hydro-climatic mean and extreme indices highly depended on their geographical location and climate zones. The snow and the warm temperate climates encountered larger hydro-climatic changes compared to the arid and semi-arid climates zones. The most noticeable trends were found for temperature which significantly increased over all counties while drying trends were observed in precipitation, surface reservoir content, and runoff. Wind speed and surface albedo in most counties experienced upward and downward trends, respectively, whereas solar radiation, surface air pressure, and evaporation exhibited a high spatial variability. In terms of extreme events, Iran was faced with an increase in hot climate extremes and a decrease in cold and precipitation extremes. At last, the wavelet power spectrum analysis demonstrated the annual and seasonal dominant periods for precipitation and an annual dominant period for temperature, and there were significant periodic variations at 2–3 years for hot and cold spell durations and intensity-based precipitation index and 4–6 years for consecutive dry and wet days. The annual fluctuation of precipitation reduced over the study period, but that of temperature remained almost constant. [ABSTRACT FROM AUTHOR]

As one of the most important climatic variables, annual evapotranspiration ( E) reflects the complex interactions between atmosphere, vegetation, soil and hydrological process at global and regional scales. It is largely determined by potential evapotranspiration ( E) and precipitation ( P). Aridity index ( E/ P) which can be defined as the ratio of annual potential evapotranspiration to annual precipitation, can be used to represent annual evapotranspiration. As result it has become an important indicator to assess water-related climate change. This study utilizes the aridity index to reveal the recent climate change patterns in the Lancang River Basin, China. A new non parametric estimator of climate elasticity has also been developed. Using the daily meteorological records (1961-2005) of precipitation, air temperature, wind speed, relative humidity and sunshine hours from 35 stations, changes in spatial and temporal characteristics of P, E , E, and E/ P are analyzed. The results reveal some important new findings, which can be summarized as: (a) new findings show that the spatial distribution of, and the correlation coefficients between P, E, E, and E/ P possess strong latitude zonality; (b) high consistency in the temporal variation of P and E has been detected. The main periodicity of the E and E/ P series varies greatly for the three sub basins, and an anti-phase relationship between them has also been found; (c) our newly developed nonparametric estimator of climate elasticity can be successfully used in assessing the sensitivity of climate system. Climate elasticity of E in relation to E and P increases for most of the regions in the study area, suggesting that E becomes more sensitive to changes in the corresponding climatic variable during the study period. The study demonstrates the importance of the aridity index in the water-related climate change studies which has been neglected for a long time by most hydrologists and managers. [ABSTRACT FROM AUTHOR]