Articles written in Journal of Earth System Science

    • Trend analysis of evapotranspiration over India: Observed from long-term satellite measurements

      Sheshakumar Goroshi Rohit Pradhan Raghavendra P Singh K K Singh Jai Singh Parihar

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      Owing to the lack of consistent spatial time series data on actual evapotranspiration (ET), very few studies have been conducted on the long-term trend and variability in ET at a national scale over the Indian subcontinent. The present study uses biome specific ET data derived from NOAA satellite’s advanced very high resolution radiometer to investigate the trends and variability in ET over India from 1983 to 2006. Trend analysis using the non-parametric Mann–Kendall test showed that the domain average ET decreased during the period at a rate of 0.22 mm year−1. A strong decreasing trend (m=−1.75 mm year−1, F=17.41, P 0.01) was observed in forest regions. Seasonal analyses indicated a decreasing trend during southwest summer monsoon (m=−0.320 mm season−1year−1) and post-monsoon period (m=−0.188 mm season−1year−1). In contrast, an increasing trend was observed during northeast winter monsoon (m=0.156 mm season−1year−1) and pre-monsoon (m=0.068mm season−1year−1) periods. Despite an overall net decline in the country, a considerable increase ( 4 mm year−1) was observed over arid and semi-arid regions. Grid level correlation with various climatic parameters exhibited a strong positive correlation (r> 0.5) of ET with soil moisture and precipitation over semi-arid and arid regions, whereas a negative correlation (r−0.5) occurred with temperature and insolation in dry regions of western India. The results of this analysis are useful for understanding regional ET dynamics and its relationship with various climatic parameters over India. Future studies on the effects of ET changes on the hydrological cycle, carbon cycle, and energy partitioning are needed to account for the feedbacks to the climate.

    • Evaluating the performance of RegCM4 in studies on irrigated and rainfed cotton crops


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      With the changing climate, reliable climate projections are essential for agriculture risk management. The present study aims to explore the output of a regional climate model (RCM) at different climatic regimes and its applications in crop simulation models. Here, a comparative study of the cotton crop growth and yield response for Akola in the central and Hisar northern agroclimatic zone of India represents rainfed and irrigated growing regions of cotton, respectively. The RegCM4 projections and its bias-corrected values of temperature and precipitation data for the period 1971–2005 are compared with the observations to assess its reliability with the crop simulation models as weather inputs. The results signify that the RCM model is wet, which implies that, it shows high rainfall intensity in terms of frequency as a number of rainy days and amount. The model also shows night warming as there is a significant decline in maximum temperature and minimal decline in minimum temperature, thus there is a reduced diurnal temperature difference. Overall model highly underestimates temperature and overestimates rainfall. Strikingly reduced numbers of intense warm and cold events are simulated. Model is highly biased for rainfall events${/ge}$0 mm/day and 5mm/day, and moderately biased for rainfall${/ge}$5 mm/day. Precipitation bias-correction, using quantile mapping approach, shows excellent agreement at an annual scale. But precipitation variability could not be captured that well as it is a ‘distribution-based method’. However, it worked well in the irrigated Hisar region than the rainfed Akola region. The bias-corrected RegCM4 climate inputs are utilized in Decision Support System for Agro-technology Transfer (DSSAT) simulations for cotton yields, Leaf Area Index (LAI) and ball number at maturity/m$^2$ (NM) for both regions. Bias-corrected outputs are in better agreement with corresponding observations than non-bias-corrected outputs in both regions. Future research could apply these simulated model data complemented with reliable bias correction techniques to explicitly study climate change’s impact on crop productivity.

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