Advances in hydrological modelling 35

Many innovations in the application of information technologies in hydrological modelling began in the late 1950s, 1960s and early 1970s (Maidment, 1996), where methods of sophisticated mathematical and statistical modelling were developed and the first remote sensing data became available. Researchers began to envision the development of Geographic Information Systems (GIS) and Hydrologic Model Interface as a result of the new technologies (Maidment, 1996; Chowdary et al., 2012). Developments in remote sensing technology and geographical information systems made it possible to capture and manage a vast amount of spatially distributed hydrological parameter and variable data (Maidment, 1996). Chow et al. (1988) considered that linking GIS and hydrological modelling was essential to achieve the desired objectives.

Integrating hydrologic modelling with GIS: GIS is a decision support system involving the integration of spatially referenced data in a problem-solving environment (Chow et al., 1988). The integration of GIS and hydrological models consists of a functional model (that describes the geometrical relationships) and a stochastic model (that describes the probabilistic characteristics of spatial data). Maidment (2002) noted that GIS provides numerous tools, which can enhance the performance of hydrologic modelling. Djokic (2004) classified these integrated technologies as: data management (manipulation, preparation, extraction, etc.); visualisation; and interface development tools. Advances in distributed parameter hydrologic modelling and integration with Geographical Information Systems (GIS) have led to the development of powerful tools for predicting runoff and simulating the physical, chemical and biochemical constituents of basins (Chowdary et al., 2012).

Many researchers (Stuebe and Johnston, 1990; White, 1988; USGS, 2000b; Chowdary et al., 2004; Pandey et al., 2008) have used land use/land cover information derived from satellite data collected by Landsat, SPOT, and the Indian Remote Sensing Satellite (IRS) and integrated these data with GIS to estimate SCS CNs (Soil Conservation Service Curve Numbers) and runoff. Several hydrologic models (including ANSWERS (Areal Nonpoint Source Watershed Environment Response Simulation – Beasely et al., 1980); AGNPS (Agricultural Nonpoint Source Pollution – Young et al., 2000); WEPP (Water Erosion Prediction Project – Foster and Lane, 1987); GeoSFM (Geospatial Streamflow Modelling – Asante et al., 2007a); SWAT (Soil Water Assessment Tool – Arnold et al., 1993); and MIKE 11 Flood Watch (Madsen et al., 2003)) have GIS linkages. These models are being extensively used for flood forecasting and sediment simulation in countries such as Bangladesh (Islam and Sado, 2002), Kenya, Mozambique (Artan et al., 2002; DHI, 2006) and Nepal (Shrestha et al., 2008).

Flood forecasting models: The a priori choice of a flood forecasting modelling for use in the

most projects is difficult since these models were not all developed for the same purpose (Loumagne et al., 2001). Cheng and Chau (2004) urged that an integrated flood forecast system should include: the choice of hydrologic models; initial condition set and modification of antecedent soil moisture; real-time forecasts; simulation forecasts; and revised forecasts. The basic steps to be followed in selecting the flood forecasting model to integrate with reservoir simulation (Jensen et al., 1999; Loumagne et al., 2001; Cheng and Chau, 2004) are as follows:

 use a model (as simple as possible) that can maintain continuity with the existing modelling tools of the project customers;

 ensure the model is not too demanding in terms of input; is easy for the customers to use, understand and implement;

 ensure the model is capable of using the information brought by soil moisture data derived from Earth Observation.

Among the existing flood forecasting models, ranging from purely mathematical (black box) to complex physical methods, a selection should be made considering the four criteria above. Most of the models appear to be able to provide consistent and reliable streamflow results (Franchini and Pacciani, 1991). Considering the results of these numerical tests (Perrin et al., 2003), several model structures are recommended in the context of the flood forecasting and reservoir operation because of their consistent performance and reliability. A list of several of the models is provided here:

IHACRES (Littlewood et al., 1997; Kasetsart and Taesombat, 2011) can be applied over a

range of spatial and temporal scales – from small experimental catchments to basins; using minute, daily or monthly time steps. The model can be used to fill gaps in data, extend streamflow records, as well as explore the impact of climate change and identify the effects of land use changes (Littlewood et al., 1997). IHACRES has been successfully applied worldwide for catchments of different sizes and under different climate conditions from 1 ha experimental catchments to 100 000 km2 _{catchments, in various regions across the world –}

from the Thames River in the UK to the Upper Ping River Basin in northern Thailand (Littlewood et al., 1997).

TOPMODEL (Beven, 1997), was originally developed to simulate catchment under humid

conditions in the U.K (Beven, 1986; 1987). It is a conceptual model in which the dynamics of surface and subsurface saturated areas are estimated on the basis of storage discharge relationships established from a simplified steady state theory for downslope saturated zone flows. The model has provided good simulation of discharge rates and dynamic saturated areas (as demonstrated by many authors) (Beven, 1986; 1987; Sivapalan et al., 1990, Blazkova and Beven, 1997).

GeosFM (Artan et al., 2001; Asante et al., 2008) is designed to run operationally using widely

available remotely sensed data sets and ground observations. The hydrologic analysis module consists of linear soil moisture accounting routine, a more complex nonlinear soil moisture accounting routine. This model has been tested by many authors in many regions (e.g., Limpopo Basin in Mozambique and Bagmati Basin in Nepal) (Artan et al., 2001; 2002; Entenman, 2005; Shrestha et al., 2008) and found to generate reliable results. The integration of hydrologic models for streamflow forecasting into reservoir models can help reduce the human and economic losses in many flood prone areas located downstream of major dams (Loumagne et al., 2001). Several reservoir model structures are recommended for integration with flood forecasting models (US Army Corps of Engineers, 2003; DHI, 2010).