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7 - The modular modelling system (MMS): a toolbox for water and environmental resources management

Published online by Cambridge University Press:  15 December 2009

By
G. H. Leavesley ,
S. L. Markstrom ,
R. J. Viger  and
L. E. Hay
Edited by
Howard Wheater ,
Soroosh Sorooshian  and
K. D. Sharma
G. H. Leavesley
Affiliation:
USGS, WRD, Denver, Colorado, USA
S. L. Markstrom
Affiliation:
USGS, WRD, Denver, Colorado, USA
R. J. Viger
Affiliation:
USGS, WRD, Denver, Colorado, USA
L. E. Hay
Affiliation:
USGS, WRD, Denver, Colorado, USA
Howard Wheater
Affiliation:
Imperial College of Science, Technology and Medicine, London
Soroosh Sorooshian
Affiliation:
University of California, Irvine
K. D. Sharma
Affiliation:
National Institute of Hydrology, India
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Summary

INTRODUCTION

Increasing demands for limited fresh-water supplies, and increasing complexity of environmental resource-management issues, present resource managers with the difficult task of achieving an equitable balance of resource allocation among a diverse group of users. Achieving such a balance is most difficult in arid and semi-arid regions. Hydrological and ecosystem models are often the tools being employed to address these resource-allocation issues.

The inter-disciplinary nature of water- and environmental-resource problems requires the use of modelling approaches that can incorporate knowledge from a broad range of scientific disciplines. Selection and application of appropriate models and tools is a function of a number of evaluation criteria, including problem objectives, data constraints, and spatial and temporal scales of application. The US Geological Survey (USGS) Modular Modelling System (MMS) (Leavesley et al., 1996b) is an integrated system of computer software that provides a research and operational framework to support the development and integration of a wide variety of hydrologic and ecosystem models, and their application to water- and environmental-resources management.

MMS supports the integration of models and tools at a variety of levels of modular design. These include individual process models, tightly coupled models, loosely coupled models, and fully integrated decision-support systems. A geographic information system (GIS) interface, the GIS Weasel, has been integrated with MMS to enable spatial delineation and characterization of basin and ecosystem features, and to provide objective parameter-estimation methods for selected models using available digital data coverages.

Type
Chapter
Information
Hydrological Modelling in Arid and Semi-Arid Areas , pp. 87 - 98
Publisher: Cambridge University Press
Print publication year: 2007

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References

Arnold, J. G., Srinivasin, R., Muttiah, R. S., and Williams, J. R. (1998). Large area hydrologic modeling and assessment: Part I. Model development. JAWRA, 34 (1), 73–89.
Beven, K. J. and Binley, A. (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrolog. Proc., 6, 279–98.CrossRefGoogle Scholar
Beven, K. J., Lamb, R., Quinn, P. F., Romanowicz, R., and Freer, J. (1995). TOPMODEL. In Computer Models of Watershed Hydrology, ed. Singh, V. P.Highlands Ranch, CO: Water Resources Publications, 627–68.
Day, G. N. (1985). Extended streamflow forecasting using NWSRFS. J. Water Resour. Plan. Manag., ASCE, 111, 157–70.CrossRefGoogle Scholar
Jong, C., Machauer, R., Reichert, B., et al. (2004). An integrated geomorphological and hydrogeological MMS modelling framework for a semi-arid mountain basin in the High Atlas, southern Morocco. In Complexity and Integrated Resources Management, Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society, ed. Pahl-Wostl, C., Schmidt, S., Rizzoli, A. E. and Jakeman, A. J.. Manno, Switzerland: iEMSs, ISBN 88-900787-1-5, Vol. 2736–41.
Jong, C., Machauer, R., Leavesely, G., et al. (2005). Integrated hydrological modelling concepts for a peripheral mountainous semi-arid basin in southern Morocco. EU Proceedings Geomatics for Land and Water management: Achievements and Challenges in the Euromed Context, ed. R. Escadafal and M. L. Paracchini, Italy: Joint Research Centre, 219–27.Google Scholar
Duan, Q., Gupta, V. K., Sorooshian, S. (1993). A shuffled complex evolution approach for effective and efficient global optimization. J. Optimiz. Theory Appl., 76 (3), 501–21.CrossRefGoogle Scholar
ESRI (Environmental Systems Research Institute) (1992). ARC/INFO 6Ð1 User's Guide. Redlands, CA: ESRI.
Fulp, T. J., Vickers, W. B., Williams, B., King, D. L. (1995). Decision support for water resources management in the Colorado River regions. In Workshop on Computer Applications in Water Management, ed. Ahuja, L., Leppert, J., Rojas, K., and Seely, E.. Information Series No. 79, Fort Collins, CO: Colorado Water Resources Research Institute, 24–27.Google Scholar
Harbaugh, A. W. (2005). MODFLOW-2005, The US Geological Survey Modular Groundwater Model – The Groundwater Flow Process. US Geological Survey Techniques and Methods 6-A16.Google Scholar
Hay, L. E., Clark, M. P., Wilby, W. J., et al. (2002). Use of regional climate model output for hydrologic simulations, J. Hydrometeor., 3, 571–90.2.0.CO;2>CrossRefGoogle Scholar
Hay, L. E., Wilby, R. L., and Leavesley, G. H. (2000). A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Am. Water Resour., 36 (2), 387–97.CrossRefGoogle Scholar
Leavesley, G. H. and Stannard, L. G. (1995). The precipitation-runoff modelling system – PRMS. In Computer Models of Watershed Hydrology, ed. Singh, V. P.. Highlands Ranch, CO: Water Resources Publications, 281–310.Google Scholar
Leavesley, G. H., Lichty, R. W., Troutman, B. M., and Saindon, L. G. (1983). Precipitation-Runoff Modelling System – User's Manual. US Geological Survey Water Resources Investigation Report 83–4238.Google Scholar
Leavesley, G. H., Markstrom, S. L., Brewer, M. S., and Viger, R. J. (1996a). The modular modelling system (MMS) – the physical process modeling component of a database-centered decision support system for water and power management. Water Air Soil Poll. 90, 303–11.CrossRefGoogle Scholar
Leavesley, G. H., Restrepo, P. J., Markstrom, S. L., Dixon, M., and Stannard, L. G. (1996b). The modular modeling system – MMS: user's manual. US Geological Survey Open File Report 96–151.Google Scholar
Niswonger, R. G. and Prudic, D. E. (2005). Documentation of the Streamflow-Routing (SFR2) Package to Include Unsaturated Flow Beneath Streams – A Modification to SFR1. US Geological Survey Techniques and Methods, 6-A13.Google Scholar
Niswonger, R. G., Prudic, D. E., and Regan, R. S. (2006). Documentation of the Unsaturated Flow (UZF1) Package for Modeling Unsaturated Flow Between the Land Surface and the Water Table with MODFLOW-2005. US Geological Survey Techniques and Methods 6-A19.Google Scholar
Ravenga, C. S. M., Abramovitz, J., and Hammond, A. (1998). Watersheds of the World. Washington DC: Water Resources Institute and Worldwatch Institute.Google Scholar
Restrepo, P. J. and Bras, R. L. (1982). Automatic Parameter Estimation of a Large Conceptual Rainfall-Runoff Model: A Maximum-Likelihood Approach. Ralph M. Parsons Laboratory Report No. 267. Cambridge, MA: Massachusetts Institute of Technology, Department of Civil Engineering.Google Scholar
Rosenbrock, H. H. (1960). An automatic method of finding the greatest or least value of a function. Comp. J., 3, 175–84.CrossRefGoogle Scholar
US Department of Agriculture (1992). Forest Land Distribution Data for the United States Forest Service, http://www.epa.gov/docs/grd/forest_inventory.
US Department of Agriculture (1994). State Soil Geographic (STATSGO) Database – Data Use Information. Natural Resources Conservation Service, Miscellaneous Publication No. 1492.
Wilby, R. L., Hay, L. E., and Leavesley, G. H. (1999). A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado. J. Hydrol., 225, 67–91.CrossRefGoogle Scholar
Yapo, P. O., Gupta, H. V., and Sorooshian, S. (1998). Multi-objective global optimization for hydrologic models. J. Hydrol., 204, 83–97.CrossRefGoogle Scholar

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