Civil Insight: A Technical Magazine Volume 2 | Page 35

TEACHERS’ SECTION CIVIL INSIGHT 2018 35 Watershed and Hydrologic Modeling Er. Suman Shrestha Lecturer Department of Civil and Geomatics Engineering, Kathmandu University [email protected] A watershed is a hydrologic unit which produces water as an end product by the interaction of precipitation and land surface. The quantity and quality of water produced by the watershed are the indices of amount and intensity of precipitation and the nature of watershed management.Watershed is considered to be an ideal unit for management of the natural resources (Jain, S.K et al; 2010). Modeling a watershed is a very complex task involving collection of necessary data, selection of methods to analyze, availability of affordable software and the knowledge of the watershed concerned. Modeling a natural event like run-off mitigates the consequences of fl oods in advance. Tools like Geographic Information systems (GIS) and watershed models contribute a lot for the modeling of natural events like fl oods. Advances in technology of computers, availability of data and necessary software also make modeling an easy task. It is important to analyze watershed and the data available in detail before applying a model. Watershed hydrologic modeling and the associated model calibration and verifi cation require a large amount of spatial and temporal data (For example: topography, land use, land covers, type of soil, rainfall and fl ow monitoring data). In practice, the availability and quality of these data are often an issue that one needs to cope with. Sometimes, one has to compromise the overall modeling quality because of insuffi cient high resolution data for developing, calibrating and validating a model (Chu, 2009). Therefore, it is a crucial task to model a river basin under limited data availability. Hydrologic models and its increasing use, support decisions at various levels and guide water resources policy formulation, management and regulations. All hydrological models are simplifi ed representation of the real world. Models can either be physical (such as laboratory scale model), electrical analogue or mathematical. The physical and analogue models have been very important in the past. However, the mathematical group of models is by far the most easily and universally applicable, the most widespread and the one with the most rapid development with regard to scientifi c basis and application (Retsgaard, 1996). The two classical types of hydrological models are: the deterministic and the stochastic . The deterministic models use data available as it is, while the stochastic models use the statistical nature of available data to predict rainfall runoff. The deterministic models can be classifi ed according to whether the model gives a lumped or distributed description of the considered area, and whether the description of the hydrological processes is empirical, conceptual or more physically based. Therefore, modern-day hydrological models can be classifi ed into empirical models, lumped conceptual models and distributed physically-based models. Out of these models, empirical models need accurate rainfall and runoff data for calibration, while distributed physically based models need a large number of spatial characteristics of the area and meteorological data to calculate the runoff for a given rainfall. Lumped conceptual models need moderately accurate rainfall and runoff data and average physical characteristics of the area concerned. Parameters of these models can be calibrated and verifi ed with historical data available. Most of the hydrological models available today come under this category.