A novel partitioning of gross primary production and water use efficiency for sustaining water and food security using Budyko hypothesis.

This study coupled the green water and blue water accounting with the existing standard Budyko framework and Fu's 1-parameter Budyko framework to diagnose the basin hydrological behavior. Both Budyko frameworks were employed to determine green water consumption (ETGreen) and blue water consumption (ETBlue) which, in turn, were used to map the blue water index (BWI) hotspots and green water index (GWI) bright spots. The relative contributions of green water and blue water were quantified for sustaining water and food security. A new methodology is proposed using BWI and GWI for partitioning the Gross Primary Production (GPP) and Water Use Efficiency (WUE) into GPPBlue, GPPGreen and WUEBlue and WUEGreen. The methodology was applied to five sub-basins of the Central Godavari River Basin (CGRB): Purna, Dhalegaon, GR Bridge, Yeli and Delta. Results showed that all five basins exhibited larger deviations from the theoretical Budyko curve of Fu's 1-parameter Budyko framework than did the standard Budyko framework and the Dhalegaon basin showed the largest deviations. The partitioning of GPP and WUE by the proposed methodology showed that the proportion of GPPGreen to the total GPP was much higher than that of the GPPBlue. Similarly, the proportion of WUEGreen to WUE was more than that of WUEBlue. The mapping of GPPBlue and GPPGreen, and WUEBlue and WUEGreen showed that the Delta and Yeli basins had the highest values of both GPPGreen & GPPBlue and WUEBlue and WUEGreen (bright spot basins) and the Dhalegaon and parts of GR Bridge basin had the lowest values (hot spot basins). The proposed partitioning of GPP and WUE will help identify the relative contributions of green water and blue water (for managing agricultural waters) and formulate agronomical and engineering practices for stakeholders and policy makers for increasing the overall WUE and GPP to sustain water and food security.

Inter-comparisons and applicability of CMIP5 GCMs, RCMs and statistically downscaled NEX-GDDP based precipitation in India.

The applicability of GCMs (produced at 2° to 4°) and RCMs (produced at ~0.5°) vary and they might produce lots of ambiguity in their outcomes, because of their resolutions. This is true fact and already been reported in several studies. In this study, we have explored the precipitation variabilities in India involved in different resolution climate model based data-sets such as GCMs and RCMs under two extreme Representation Concentration Pathway (RCP) experiments (e.g. RCP4.5 and RCP8.5). Precipitation inter-comparisons have been done between different resolution datasets (e.g. GCMs, RCMs, NEX-GDDP and observed precipitation) to explore precipitation ambiguity (or variabilities) and their applicability in a long-term period (1951-2100) across the India. The observed gridded precipitation (1951-2005) and NEX-GDDP datasets (2006-2100) have been used as a reference data-sets to assess the accuracy of GCMs and RCMs. Variations in precipitation trends have been explored at each grid scale utilizing non-parametric Mann-Kendall test and statistical p-value test at 95% confidence interval. Inter-comparison analysis results showed that a significant diversity existed in the precipitation amounts among all climate model datasets, which have been non-uniformly distributed across the India. Results from model inter-comparisons, percentage of change analysis and Q-Q analysis performed between GCMs versus observed precipitation, RCM versus observed precipitation, GCMs versus RCMs and RCMs versus NEX-GDDP models showed a high variability existed in precipitation amount across the India during1951-2100. In opposite, at several locations a good association in precipitation between different resolution datasets was observed. GCMs based precipitation was underestimated and RCMs showed overestimation across the India. Overall, RCMs based rainfalls have found comparatively closer to observed and NEX-GDDP based rainfalls, yet RCMs have highly overestimated in several regions of India. Seasonal trends uncertainty estimation showed a better correlation in precipitation between NEX-GDDP and RCMs, especially during monsoon and pre-monsoon season.