Environment Systems and Decisions

, Volume 33, Issue 3, pp 440–456 | Cite as

Improving watershed decisions using run-off and yield models at different simulation scales

  • Nathan Wangusi
  • Gregory Kiker
  • Rafael Muñoz-Carpena
  • Wesley Henson


Water managers face the daunting task of balancing limited water resources with over-subscribed water users among competing demands. They face the additional challenge of taking water planning decisions in an uncertain environment with limited and sometimes inaccurate observed and simulated hydrological data. Within South African watersheds, spatial parameterization data for hydrological models are now available at two different basin management resolutions (termed quaternary and quinary). Currently, water management decisions in the Crocodile River watershed are often made at a more coarse resolution, which may exclude crucial insights into the data. This research has the following aims (1) to explore whether model performance is improved by parameterization using a more detailed quinary-scale watershed data and (2) to explore whether quinary-scale models reduce uncertainty in allocation or restriction decisions to provide better informed water resources management and decision outcomes. This study used the Agricultural Catchments Research Unit (ACRU) agro-hydrological watershed model, to evaluate the effects of spatial discretization at the quaternary and quinary scales on watershed hydrological response and runoff within the Crocodile River basin. Model performance was evaluated using statistical comparisons of results using traditional goodness-of-fit measures such as the coefficient of efficiency (C eff), root mean square of the error and the coefficient of determination (R 2) to compare simulated monthly flows and observed flows in six subcatchments. Traditional interpretation of these goodness-of-fit measures may be inadequate as they can be subjectively interpreted and easily influenced by the number of data points, outliers and model bias. This research utilizes a recently released model evaluation program (FITEVAL) which presents probability distributions of R 2and C eff derived by bootstrapping, graphical representation of observed and simulated stream flows, incorporates statistical significance to detect the sufficiency of the R 2and C eff and determines the presence of outliers and bias. While analyses indicate that the ACRU model performs marginally better when parameterized and calibrated at the quinary scale, the measurements at both scales show significant variability in predictions for both high and low flows that are endemic to southern African hydrology. The improved evaluation methods also allow for the analysis of data collection errors at monitoring sites and help determine the effect of data quality on adaptive water planning management decisions. Given that many water resource challenges are complex adaptive systems, these expanded performance analysis tools help provide deeper insights into matching watershed decision metrics and model-derived predictions.


Hydrological modeling ACRU FITEVAL South Africa Adaptive management 



Funding for this research was generously provided for by the Department of Agricultural and Biological Engineering at the University of Florida and fieldwork studies by the Rotary Foundation. The authors also wish to recognize the contributions and support of Dr. Jeff Smithers, Dr. Mark Dent and Mark Horan of the University of Kwa-Zulu Natal Department of Bioresources Engineering and Environmental Hydrology, Dr. Tally Palmer of the Akili Initiative, Harry Biggs and Craig Mc Loughlin of the Kruger National Park Service Scientific Services and Brian Jackson of the Inkomati Catchment Management Agency.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Agricultural and Biological Engineering DepartmentUniversity of FloridaGainesvilleUSA
  2. 2.Agricultural and Biological Engineering DepartmentUniversity of FloridaGainesvilleUSA
  3. 3.School of Statistics, Mathematics and Computer ScienceUniversity of KwaZulu-NatalDurbanSouth Africa
  4. 4.Agricultural and Biological Engineering DepartmentUniversity of FloridaGainesvilleUSA
  5. 5.Water InstituteUniversity of FloridaGainesvilleUSA

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