Multimodel quantification of green and blue water components and its error propagations through parameter transferability approach across input choices

Abstract

A robust quantitative assessment of water security through efficient water accounting concepts like blue and green water footprints has gained a lot of attention for water resources management at river basin scale. However, such water accounting tools and models have dependencies to the choice of precipitation data used to drive the model, as the input bias propagate into simulated streamflow through interaction with different hydrological processes. This study performs a holistic investigation of sensitivity of water accounting fractions like blue water flow (BWF), green water flow (GWF), green water storage (GWS), and their dependencies on secondary precipitation datasets (SPDs) choices using a lumped conceptual rainfall-runoff model [Hydrological Simulation model (HYSIM)] and a physically based semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] in the Damodar River basin, India. These models were driven by seven gridded precipitation datasets, including India Meteorological Department (IMD) data, Indian Monsoon Data Assimilation and Analysis (IMDAA) data, Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, Watch forcing ERA-Interim data, Climate Hazard Group Infrared Precipitation with Station data (CHIRPS), PRINCETON precipitation data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Records (PERSIANN-CDR) data. The reference IMD based multimodel results revealed that the annual average blue and green water components of the Damoder River were in the range of 558–656 mm/year, 574–728 mm/year and 34–112 mm/year for BWF, GWF, and GWS respectively during 1994–2010 period. The results obtained after applying parameter transferability approach across the input space have revealed exact quantification of choice dependent annual variations in spatial and temporal characteristics of BWF, GWF, and GWS. A further evaluation based on error propagation ratios (γCC, γrRMSE, γB) has revealed a quantification of input choice induced dampening/magnification effects on flow estimations of both the HYSIM and SWAT and these quantifications were free from the compensation effects of recalibration. The findings in this study demonstrate that the change in annual average BWF varies between −46.97% to +41.38% in comparison to benchmark IMD simulations, and the corresponding changes for GWF is between −19.63% and +17.06% and for GWS is between −29.97% to +35.5% with different choices of SPD scenarios. The systematic (γB) and random (γrRMSE) error propagation factors during monsoon season are larger for SWAT model ([0.21–0.78], [0.69–0.89] respectively) than that of HYSIM model ([0.18–0.74], [0.66–0.81], respectively) and it is varying with choice of different SPDs.