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Changing temperature and precipitation are the prominent candle bearers of climate change. Global Climate Models (GCM) simulate and predict historical, present, and future climate scenarios. This study focused on selecting the best performing GCM under High-resolution Model Intercomparison Project (HighResMIP) suitable for the Indian subcontinent for predicting daily maximum (Tasmax) and minimum (Tasmin) temperature. Entropy-TOPSIS Multicriteria Decision-Making (MCDM) approach was employed for the relative ranking of GCM models. This study compared 64 years (1951–2014) historical data of 11 high-resolution GCMs under three different nominal resolutions viz., 25 km, 50 km, and 100 km. India Meteorological Department (IMD) gridded data was used as observed data for the performance measure. The data were evaluated using three factors, i.e. Intra- and inter-annual variation of temperature, tempo-spatial extreme events study, and using selected performance indicators. The predictive ability of the GCM models spatially in different Koppen climate zones of India is also reported in this study. This study highlighted Hadgem3-Gc31 and ECMWF-IFS models for Tasmax; NICAM16-8S and MPI-ESM1-2 models for Tasmin are suitable for simulating Indian temperature. The outcome of this study will be helpful to the climate researchers and decision-makers to choose the best suitable CMIP6 GCM model at a regional level. [ABSTRACT FROM AUTHOR]

Hydrologic modeling is a complex phenomenon dependent on numerous parameters. Since the estimation of parameters is subjected to high uncertainty due to high spatial variation. Therefore, the accuracy of each parameter becomes prime necessary for hydrologic modeling. Gene Expression Programming (GEP) is employed for the first time for Land Use Land Cover (LULC) classification. In the present study, five AI techniques, namely Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), the M5 Model tree, the multivariate adaptive regression splines (MARS), and Gene Expression Programming (GEP), were studied comparatively for their image classification capability. Comparison criteria adopted for considered AI techniques were errors estimators' (omission and commission errors) and accuracy estimators' (overall accuracy and Kappa coefficient). Based on the obtained results, the performance of the GEP technique is found very much comparable with SVM and ANFIS based on overall & Kappa coefficient (>0.85). GEP has a significant advantage over other techniques in producing mathematical functions for the given set of input and output parameters. The present study recommends the use of the GEP technique for LULC image classification. [ABSTRACT FROM AUTHOR]

Prediction of the crop yield is need of time according to the change in climate conditions. In the present study, the Agro-Climatic Grape Yield (ACGY) model has been developed with monthly climatic parameters using multi-regression analysis approach. The developed model was statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the model was obtained as 1.03, 0.19, 0.03% and 0.19, respectively. Sensitivity analysis was carried out for the developed ACGY model using the parametric sensitivity method. In order to know the future grape yield using ACGY model, climate scenarios were generated under Canadian Earth System Model (CanESM2) for three emissions representative concentration pathways as RCP2.6, RCP4.5, and RCP8.5. According to the analysis using ACGY model, increasing yield was observed in grape up to year 2050 as compared to current yield. [ABSTRACT FROM AUTHOR]

Hydroinformatics is the field of study that concerns the flow of information and its knowledgeable application to the water resources and environmental engineering areas. It concerned with developing and applying mathematical models and advanced Information and Communication Technology (ICT) to the problems of water engineering as a whole. Integrated watershed management provides a framework to integrate natural resource management with community livelihoods in a sustainable way. OpenMI stands for Open Modeling Interface aims to deliver a standardized way of linking diversified models. It enables the process for better modeling by interfacing the physical and socioeconomic models. Also allows to connect at various data sources and different tools at runtime. Entire system is open to the world community for sharing of experiences which provides the social frame work and effective modeling as a decision-making system. [ABSTRACT FROM AUTHOR]