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.

The present study deals with the detection of underground concealed voids/cavities/galleries in Chinchuria railway station, Raniganjh coalfield in India for ground stabilization using electrical resistivity tomography (ERT) technique. Initially, numerical analysis of ERT data was carried out using Wenner–Schlumberger and dipole–dipole array configurations corrupted by 5–10% Gaussian random noise for better understanding of real field conditions. The two types of voids, i.e., water and air, filled in old mine workings were simulated accordingly in the forward model. The dipole–dipole array provides superior results compared to Wenner–Schlumberger array. Considerable accuracy of voids/cavities/ galleries dimension could be acquired from dipole–dipole array. In Beld, two number of ERT profiles (Profile-AA' and Profile-BB') were conducted over the study area using said configurations. The data was acquired using Syscal-Pro resistivity imaging system with 96 electrodes and the data processing was carried out using the tomographic inversion software RES2DINV to analyse true resistivity based on the robust or blocky regularization inversion technique (L$_1$-norm). High resistivity contrast with backgrounds in the occurrence of possible old mine workings consisting of hard rock and alternative coal pillars interconnected cavities and mining galleries was identified. Considering the indirect approach of ERT method, a borehole was being drilled along the profile-AA' of dipole–dipole array, drilling and resistivity results indicated the presence of air-filled cavity associated with old mine workings. Hence, it is concluded that the technique used in this study is useful in increasing the technical merit of electrical resistivity interpretation for old mine workings areas.