Extending a Large-Scale Model to Better Represent Water Resources without Increasing the Model’s Complexity

Robyn Horan,Harry Dixon,Virginie D J Keller,Prabhas K Mishra,Gwyn Rees,Nathan J Rickards,Alexandra Kaelin,Thomas Thomas,Kaushal K Garg,Sharad K Jain,Helen A Houghton-Carr,Rishi Pathak,Manish K Nema,Helen E Baron,Sekhar Muddu

doi:10.3390/w13213067

Abstract

The increasing impact of anthropogenic interference on river basins has facilitated the development of the representation of human influences in large-scale models. The representation of groundwater and large reservoirs have realised significant developments recently. Groundwater and reservoir representation in the Global Water Availability Assessment (GWAVA) model have been improved, critically, with a minimal increase in model complexity and data input requirements, in keeping with the model’s applicability to regions with low-data availability. The increased functionality was assessed in two highly anthropogenically influenced basins. A revised groundwater routine was incorporated into GWAVA, which is fundamentally driven by three input parameters, and improved the simulation of streamflow and baseflow in the headwater catchments such that low-flow model skill increased 33–67% in the Cauvery and 66–100% in the Narmada. The existing reservoir routine was extended and improved the simulation of streamflow in catchments downstream of major reservoirs, using two calibratable parameters. The model performance was improved between 15% and 30% in the Cauvery and 7–30% in the Narmada, with the daily reservoir releases in the Cauvery improving significantly between 26% and 164%. The improvement of the groundwater and reservoir routines in GWAVA proved successful in improving the model performance, and the inclusions allowed for improved traceability of simulated water balance components. This study illustrates that improvement in the representation of human–water interactions in large-scale models is possible, without excessively increasing the model complexity and input data requirements.

Highlights

The monthly bias, Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Log-Nash Efficiency (LNE) values for each sub-catchment for each model version can be found in Table A3 in Appendix B
In sub-catchments without major reservoirs, Global Water Availability Assessment (GWAVA) produced the same results as GWAVA-Res, and GWAVA-GW produced the same results as GWAVA 5.1
The regulated reservoir routine allows for a release from the major reservoirs throughout year and the output of reservoir storage capacity throughout the simulation period

Summary

Introduction

Humans are increasingly altering the hydrological cycle through the construction of reservoirs, changes in land-use, water abstractions, and urbanisation [1]. Accurate quantification of freshwater flows and storage is important to support water management and governance in the near and far future [2]. Large-scale hydrological modelling estimates water fluxes, such as evapotranspiration, river discharge, and groundwater recharge, and water storage, including soil water, groundwater, and reservoirs [1,3,4] at a basin or continental scale. More than half of the irrigation water globally is abstracted from groundwater sources [5]. It is important to select a model that can accurately simulate the generation of groundwater, in basins where the main source of baseflow depends upon groundwater storage [6]

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Conclusion