Incorporating land-use changes and surface-groundwater interactions in a simple catchment water yield model

M Gilfedder, David W. Rassam, M Stenson, I Jolly, Glen Walker, Mark Littleboy

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Pressure on limited water resources and the environment requires better understanding of how landscape change impacts river flow. Rainfall-runoff models have traditionally focused on estimating total river flows with less emphasis on modelling the groundwater component or the consequences of different land-use change scenarios. In this paper, we present the GWlag model, a water-generation model that predicts river flows with explicit accounting of the impacts of catchment land-use change and surface-groundwater interactions. The paper firstly describes the theory that underpins the model and its calibration then presents a case study application in the Tarcutta Creek catchment of the Murray-Darling Basin, Australia. The case study aims at: (i) demonstrating the ability of the model to predict daily river flows; (ii) modelling the impacts of hypothetical plantation forestry expansions on river flows; and (iii) showing the impacts of reduced recharge on the low-flow regime using three indices, namely, Q 90/Q 50 (where Q n refers to nth percentile flow), slope of low-flow part of flow duration curve, and % of zero-flow days. Results showed that predicted flows agreed favourably to those observed at the gauge especially during low-flow conditions. The hypothetical plantation expansion from 32% to 87% of the catchment area has resulted in reductions of 48% and 32%, in Q 50 and Q 20, respectively. The low-flow indices demonstrated the great sensitivity of low flow to reductions in recharge with the trend of the low-flow response changing to non-linear for recharge reductions beyond 10%. GWlag daily river flow predictions compared favourably to those obtained from four other rainfall-runoff models in terms of the Nash-Sutcliffe model efficiency (E). However, GWlag produced the highest E-value for log-transformed flows thus highlighting the model's superior predictive capability during low-flow conditions. © 2012.
Original languageEnglish
Pages (from-to)62-73
Number of pages12
JournalEnvironmental Modelling and Software
Publication statusPublished - 2012
Externally publishedYes


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