Temporal studies of climatic and environmental parameters and their possible impact on selected reservoir fish production of Madhya Pradesh: A different methodological approach.

Abstract

Climate change has a significant influence on global biodiversity and would like to affect the structure and function of naturalecosystems. This study assessed the quantitative impact of climatic change on the selected reservoirs. The climatic data of MadhyaPradesh were collected from the Indian Meteorological Department (IMD). The temperature and rainfall data under the A1B scenariwere processed using Linux-based Ferret software, whereas chlorophyll-a and water surface temperature data were retrieved fromLandsat-8 imagery using SNAP and ARC GIS software. Annual Anomaly analysis for average temperature and rainfall found,warming in annual temperature at Indirasagar reservoir under A1B scenarios during 1991–2015, 2016–2040 and 2041–2065 while,rainfall decreased between 1991–2015 and 2016–2040 but would like to be increased during (20412065). Similar results were foundin the case of the Gandhisagar reservoir as well. Seasonal trend analysis was done using a statistical non-parametric test, Mann-Kendall (M-K) test, which showed an increasing trend for average temperature in post-monsoon and Winter, while the decreasingtrend was found in Monsoon and Pre-Monsoon in Indira sagar reservoir. This study also found a significant decreasing trend forrainfall in all three seasons except Monsoon. In the case of Gandhi sagar reservoir, an increasing trend has found on average temperature for all the seasons while rainfall showed a significantly decreasing trend in all the seasons except Post-Monsoon. Thisstudy also predicted the reservoir fish production using different prediction models and Auto-Regressive Integrated Moving Average with exogenous variables (ARIMAX) model found as the most suitable model in case of forecasting. It was also found that mean maximum temperature, mean minimum temperature and mean wind speed were the most significant variables in predicting fish production.