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Environmental Modelling & Software
Volume 22, Issue 10, October 2007, Pages 1519-1528
Modelling, computer-assisted simulations, and mapping of dangerous phenomena for hazard assessment
 
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doi:10.1016/j.envsoft.2006.10.006    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2006 Elsevier Ltd All rights reserved.

A distributed model of water balance in the Motueka catchment, New Zealand

Robbie M. Andrewa and John R. DymondCorresponding Author Contact Information, a, E-mail The Corresponding Author

aLandcare Research, Private Bag 11052, Palmerston North, New Zealand

Received 16 January 2006; 
revised 30 August 2006; 
accepted 3 October 2006. 
Available online 18 January 2007.

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Abstract

A distributed water balance model is used to simulate the soil moisture regime of the Motueka catchment. The model is a major simplification of the Distributed Hydrology–Vegetation–Soil Model (DHVSM) with modifications suitable for the study area. The model was applied at 25-m resolution with a 1-day time-step for 10 years. The simulated hydrograph showed good correspondence with the observed hydrograph and there was good agreement of simulated and measured mean annual discharges (57.3 m3 s−1 as compared with 58.7 m3 s−1). Five different land cover scenarios were used to predict the effects of vegetation change on the hydrological regime: (1) current land cover; (2) prehistoric land cover; (3) maximum pine planting; (4) pine trees on easy slopes; and (5) pine trees on steep slopes. The pine scenarios all reduced the mean annual flow by about 2 m3 s−1, while the prehistoric scenario reduced the mean annual flow by about 6 m3 s−1. The pine scenarios (3, 4, and 5) reduced the 7-day 5-year low flow from 7.4 m3 s−1 to between 6.5 m3 s−1 and 6.8 m3 s−1, respectively; and the prehistoric scenario reduced the 7-day 5-year low flow to 5.3 m3 s−1.

Keywords: Water balance model; Soil moisture regime; Simulation; Land cover scenarios; Hydrologic regime

Article Outline

1. Introduction
2. Study area
2.1. Model development
2.2. Rainfall interpolation
2.3. Evapotranspiration
2.4. Water balance
3. Input data
3.1. Land cover
3.2. Digital elevation model
3.3. Climate data
3.4. Soil properties
3.5. Soil moisture initialisation
4. Model calibration
4.1. Hydrograph post-processing
5. Land cover scenarios
6. Results
7. Discussion
8. Conclusions
Acknowledgements
References







Environmental Modelling & Software
Volume 22, Issue 10, October 2007, Pages 1519-1528
Modelling, computer-assisted simulations, and mapping of dangerous phenomena for hazard assessment
 
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