Comparative assessment of evapotranspiration in Bhima sub-basin using spatial analysis for normal and ENSO years

Evapotranspiration (ET) estimation is important for hydrological modelling and water management for irrigation. The present study estimates the reference evapotranspiration using FAO Penman-Monteith (FAO P-M) method and SWAT hydrological model, and its spatial variation during ENSO events during 1996 to 2013. The spatial variation of crop coefficient and actual evapotranspiration (ETa) is also analyzed. The results from these methods are compared for various El Niño-Southern Oscillation (ENSO) events and normal years. MODIS NDVI data was used to generate crop coefficients which were further used for generation of ETa.The results show that the ET0 estimated using FAO P-M is less during the pre-monsoon period than ET0 estimated using SWAT model. ET0values from FAO P-M show decreasing trends while those by SWAT show increasing trends. Also, ET0 shows higher values during post monsoon period of El Niño years as compared to La Niña and normal years

Performance Assessment of Bias Correction Methods for Precipitation and Temperature from CMIP5 Model Simulation

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov–Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data

Assessment of vegetation variation and its response to ENSO and IOD in the semi-arid ecosystem of Western India

The variation in the vegetation pattern reflects the change in the regional environment. Normalized Difference Vegetation Index (NDVI) data from 2000 to 2022 for the Upper Bhima sub-basin in Western India has been used to identify the response of vegetation to the El Niño-Southern Oscillations (ENSO) and Indian Ocean Dipole (IOD) events. As a novelty, the present study identifies the ENSO-sensitive and IOD-sensitive vegetation areas within the watershed using vegetation mean to difference anomalies. Monthly NDVI anomalies are used to determine sensitive pixels of vegetation using mean monthly NDVI. Local spatial autocorrelation (LISA) is performed to analyze the pattern of the NDVI anomalies and cluster maps are generated. The results of spatial variation show that NDVI is adversely affected in El Niño years. During La Niña years, the percentage area covered by dense vegetation is more than 80%, which is significantly higher than that of El Niño years in the monsoon and post-monsoon periods. Positive IOD years show significantly more sparse vegetation cover than negative IOD years. The results of LISA analysis show that the rainfall shadow zone in the study area has a cluster of negative sensitive pixels even in the monsoon and post-monsoon period except in negative IOD year. HIGHLIGHTS Vegetation is adversely affected by El Niño and not affected during La Niña.; The novelty is to identify the ENSO-sensitive and IOD-sensitive vegetation areas.; A positive IOD has an adverse effect on vegetation compared to a negative IOD event.; Rainfall shadow zone has negative sensitive pixels cluster even in monsoon and post-monsoon period.