Modelling the effects of land‐use change on water and salt delivery from a catchment affected by dryland salinity in south‐east Australia

Abstract

Abstract:A comprehensive framework for the assessment of water and salt balance for large catchments affected by dryland salinity is applied to the Boorowa River catchment 1550 km 2 , located in south-eastern Australia. The framework comprised two models, each focusing on a different aspect and operating on a different scale. A quasi-physical semi-distributed model CATSALT was used to estimate runoff and salt fluxes from different source areas within the catchment. The effects of land use, climate, topography, soils and geology are included. A groundwater model FLOWTUBE was used to estimate the long-term effects of land-use change on groundwater discharge. Unlike conventional salinity studies that focus on groundwater alone, this study makes use of a new approach to explore surface and groundwater interactions with salt stores and the stream.Land-use change scenarios based on increased perennial pasture and tree-cover content of the vegetation, aimed at high leakage and saline discharge areas, are investigated. Likely downstream impacts of the reduction in flow and salt export are estimated. The water balance model was able to simulate both the daily observed stream flow and salt load at the catchment outlet for high and low flow conditions satisfactorily. Mean leakage rate of about 23Ð2 mm year 1 under current land use for the Boorowa catchment was estimated. The corresponding mean runoff and salt export from the catchment were 89 382 ML year 1 and 38 938 t year 1 , respectively. Investigation of various land-use change scenarios indicates that changing annual pastures and cropping areas to perennial pastures is not likely to result in substantial improvement of water quality in the Boorowa River. A land-use change of about 20% tree-cover, specifically targeting high recharge and the saline discharge areas, would be needed to decrease stream salinity by 150 µS cm 1 from its current level. Stream salinity reductions of about 20 µS cm 1 in the main Lachlan River downstream of the confluence of the Boorowa River is predicted.The FLOWTUBE modelling within the Boorowa River catchment indicated that discharge areas under increased recharge conditions could re-equilibrate in around 20 years for the catchment, and around 15 years for individual hillslopes.

Impact of bushfire and climate variability on streamflow from forested catchments in southeast Australia

“…All these four catchments have long-term reliable streamflow records spanning from the pre-bushfire period to the post-bushfire period. Therefore, they can be used for 25 19661967-19821983229109 Starvation Creek 84.12 31.5 26.47 1973-20041974-19821983-2004 Yarra River at Little Yarra 45.58 149.4 68. 1 1971-2000 1972-1982 1983-2000 405205 Murrindindi River at Murrindindi above Colwells 1975-2009 1975-1982 1983-2009 405227 Big River at Jamieson -626.…”

“…Vertessy et al 1995, 1996, Haydon et al 1997, Watson et al 1999b, Vaze et al 2004. Fire is the ecological trigger for E. regnans and other ash-type eucalypt forests (mainly E. delegatensis).…”

Predicting streamflow for land cover changes in the Upper Gilgel Abay River Basin, Ethiopia: A TOPMODEL based approach

“…Conversion often leads to natural ecosystem deterioration and degradation of catchments (Fohrer et al, 2001;Vaze et al, 2004;Rasmussen, 2005;Mango et al, 2011). Land cover changes affect various hydrological processes such as interception, infiltration and evaporation thereby influencing runoff generation.…”

Partitioning the effects of pine plantations and climate variability on runoff from a large catchment in southeastern Australia

“…At annual timescale, the R 2 varies between 44% and 91%. In Australian conditions, R 2 values for the lumped rainfall‐runoff models, typically varies in the range 40 to 75% [ Tuteja et al , 2003; Vaze et al , 2004b]. In the Delegate subcatchment, proportion of the streamflow values in the range <1 mm/d, 1–5 mm/d, and >5 mm/d were 93.4%, 6.2%, and 0.4%, respectively.…”