HYDROLOGIC DATA COLLECTION AND MODELING: Perspectives for Water Resource Planning and Management
A TERM PAPER
AGRICULTURAL AND BIO-RESOURCES ENGINEERING DEPARTMENT
FEDERAL UNIVERSITY OF TECHNOLOGY
The world is facing severe and growing challenges in maintaining water quality and meeting the rapidly growing demand for water resources. In addition, water used for irrigation, the largest use of water in most developing countries, will likely have to be diverted increasingly to meet the needs of urban areas and industry whilst remaining a prime engine of agricultural growth.
This paper reviews the state of the art of modeling approaches towards integrated water resources planning and management, with particular focus on the potential of coupled economic hydrologic models, and concludes with directions for future modeling exercises.
Key Words: Hydrological Data, Hydrological Models, Water Resource Planning,
A key component of the effective operation of any water control structure is the measurement, and processing of hydrologic data. Hydrological models are usually predictive (to obtain a specific answer to a specific problem) or investigative (to further our understanding of hydrological processes) (Andersen, J et al., 2001)
The foundation of all hydrologic, hydraulic and operational modeling and decision support software packages is quality data. The quality of data collected can be improved by the use of better acquisition methods and equipment, well designed networks, redundancy and maintenance schedules, and modern quality control applications (Chow et al., 1988)
Hydrologic data collection involves sensing (or making measurements), recording, transmitting, and post-processing/analyzing the data. Each of these steps is quite involved and often there are many variations on a theme of how each step can be achieved. The exact variation is usually dependent on the circumstances present at the field site, both hydrologic and otherwise. (Robert R. 2007)
Hydrological modeling is the discipline that tries to quantitatively describe the land phase processes of the hydrological cycle (Singh and Woolhiser, 2002). This is done by developing and setting up mathematical models, i.e. sets of linked mathematical equations, which describe in a simplified form the behaviour of the various components of the hydrological phenomena taking place in real hydrological systems. A hydrological system is defined as a structure or volume in space, surrounded by a boundary, that accepts water and other inputs, operates on them internally, and produces them as outputs (Chow et al., 1988).
In a general conceptualized form, a hydrological model can be represented as illustrated in Figure 1: it is an entity (a system of equations) that receives certain inputs (meteorological variables and model parameters) and transforms them into the desired output, the so-called model response. The model inputs comprise meteorological time series, also defined as forcing data, such as rainfall, snow, temperature and sunshine hours, as well as a set of model parameters, which describe the physical features of the hydrological system considered. The model parameters are divided into physical parameters, representing physically measurable properties of the system (for example the catchment area, the surface slopes and similar), and process parameters, describing characteristics that are not directly measurable (such as the average depth of the root zone, the time constants of various model storage blocks and similar). The outputs are defined depending on the system and the scope of the modeling. They can be, for example, river runoff, in the case of rainfall-runoff (RR) models, or groundwater flow and...
References: Andersen, J., Refsgaard, J. C. and Jensen, K. H. (2001). Distributed hydrological modeling of the Senegal River Basin – model construction and validation Journal of Hydrology, 247, 200-214.
Beven, K. J., Lamb, R., Quinn, P. F., Romanowicz, R. and Freer, J. (1995). Chapter 18: TOPMODEL. In: Singh, V. P. (ed.), Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, Colorado, 627-668.
Boyle, D. P. (2000). Multicriteria calibration of hydrological models. Ph.D. dissertation, Department of Hydrology and Water Resources, University of Arizona, Tucson.
Boyle, D. P., Gupta, H. V. and Sorooshian, S. (2000). Toward improved calibration of hydrologic models: Combining the strengths of manual and automatic methods. Water Resources Research, 36(12), 3663-3674
Burnash, R. J. C. (1995). The NWS river forecast system-catchment modeling. In: Singh, V. P., (ed.), Computer Models of Watershed Hydrology, Water Resources Publication, Highlands Ranch, Colorado, 311-366.
Chow, V. T., Maidment, D. R. and Mays, L. W. (1988). Applied Hydrology. McGraw-Hill, New York, NY.
Graham, D.N. and Butts, M.B. (2006). Flexible, integrated watershed modeling with MIKE In V.P. Singh and D.K. Frevert (Eds.), Watershed Models, CRC Press, Boca Raton, pp. 245–272. R.-S. Blasone et al.474
Harbaugh, A. W. (2005). MODFLOW-2005, the U.S. Geological Survey modular ground-water model - the Ground-Water Flow Process: U.S. Geological Survey Techniques and Methods 6-A16.
Havnø, K., Madsen, M. N. and Dørge, J. (1995). MIKE 11 – a generalized river modelling package. In: Singh, V. P. (ed.), Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, Colorado, 733-782.
Hipel, K. H. and McLeod, A. I. (1994). Time Series Modelling of Water Resources and Environmental Systems. Elsevier, Amsterdam, The Netherlands.
Krueger, T., J. Freer, J. N. Quinton, C. J. A. Macleod, G. S. Bilotta, R. E. Brazier, P. Butler, and P. M. Haygarth (2010), Ensemble evaluation of hydrological model hypotheses, Water Resources Research, 46(W07516), 1-17, 10.1029/2009WR007845.
Liu, Y. L., J. Freer, K. Beven, and P. Matgen (2009), Towards a limits of acceptability approach to the calibration of hydrological models: Extending observation error, Journal of Hydrology, 367(1-2), 93-103, 10.1016/j.jhydrol.2009.01.016.
Madsen, H. (2003). Parameter estimation in distributed hydrological catchment modeling using automatic calibration with multiple objectives Advances in Water Resources, 26, 205-216.
Robert R.H (2007). Field Methods for Hydrologic and Environmental Studies. pp. 5-77
Singh, V. P. (1995). Watershed Modeling. In Singh, V. P. (ed.), Computer Models of Watershed Hydrology, Water Resources Publications, Highlands Ranch, Colorado, 1-22.
Singh VP, Frevert DK (eds). 2002. Mathematical Models of Large Watershed Hydrology. Water Resources Publications: Highlands Ranch, CO.
Singh, V. P. and Woolhiser, D. A. (2002). Mathematical Modeling of Watershed Hydrology. Journal of Hydrologic Engineering, 7(4), 270-292.
World Meteorological Organization: Hydrological Data Management: Present State and Trends. pp. 6-65. In: An Operational Hydrology Report, No. 48. A. Terakawa, WMO Geneva, Switzerland (2003)
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