Flood forecasting models 20

Flood forecasting models are useful tools for flood warning (Cannon, 1993; Loumagne et al., 2001; Chowdary et al., 2012). The evolution of public weather forecasting during the twentieth century led, inevitably, to the provision of specialist services – including flood forecasting (Maidment, 1996; Loumagne et al., 2001; Funk et al., 2003). Flood forecasting and warning models are highly data specific (meteorological and hydrological) and demanding in terms of both the quality and quantity of information (Maidment, 1996). The models are dependent on adequate monitoring of the key meteorological and hydrological variables. Thus, one of the main constraints in flood forecasting is the decline of data collection networks and hydraulic infra-structures in developing countries (Funk et al., 2004; Asante et al., 2007c; WMO and USAID, 2012). Moreover, many measurement stations in developing countries are manually operated, with little real-time data being collected, affecting the ability of agencies to give proper real-time warnings to the communities. Flood forecasting and early warning systems currently consist of networks of rain and river gauges and hydrologic modelling for the development of general qualitative and quantitative forecasts of flood levels. These early warning systems can often reach high levels of technical accuracy (Keys, 1999; Shrestha et

al., 2008). With the passage of time many manual gauges have been linked to telemetry

systems to allow remote access to data by telephone and, by the late 1980s, radio-telemetered ALERT (Automated Local Evaluation in Real Time) and similar data collection systems have been established on several of the faster-responding rivers (Keys, 1999).

AWRA (1996) noted that a number of Hydrological models may be used for real-time operation and have been operated in worldwide. In USA for example, the National Weather Service (NWS), through its River and Flood Program (RFP), have developed a watch on the nation’s river systems which has been applied for flood forecasts services for approximately 3 000 communities (Fread et al., 1995). The NWS relies on a wide variety of sources and techniques to collect data. Many of the ground sensors are owned and operated by major NWS co-operators, including the U.S. Army Corps of Engineers (USACE), the U.S. Geological Survey (USGS), U.S. Bureau of Reclamation (USBR), and the Tennessee River Authority (TVA). In addition to real-time hydrometeorological data, historical data are used in conjunction with hydrologic and hydraulic models for flood forecasting in USA (Fread et al., 1995). A study by Hénonin et al. (2010) in France demonstrated that the Evaluation et Suivi des Pluies en

Agglomeration pour Devancer l’Alerte (ESPADA), developed by the local Government in 2010,

has been used for real-time flood forecasting and warning for approximately 150 000 people in Southern France. The ESPADA system is based on the local rainfall forecasting, using both radar of 1 km2 _{resolution and a measurement network of 10 rain gauges and 11 water levels.}

The data is transmitted every 15 minutes to the central hub where it is used as input for rainfall-runoff models to forecast the flow, with updates every 30 minutes. In Thailand, MIKE Flood Watch has been installed for real-time flood monitoring and forecasting on the Chaophraya River (Madsen et al., 2003).

Mike Flood Watch integrates data management, forecast models and dissemination methodologies in a system within a GIS platform in real-time. Operation of the system is forced by measured rainfall and stream data. These data are measured automatically by telemetric devises installed in the field; the data are then automatically recorded and stored in a data logs and transmitted via an HMF system to the computer servers. The flow is forecast with updates every 30 minutes. In southern Africa, the U.S. Geological Survey (USGS) Earth Resources Observation Systems (EROS) Data Center, as one aspect of its support of the Famine Early Warning System Network (FEWS Net), has developed a flood-monitoring system thereby improving the modelling capabilities of the Southern Regional Water Authority of Mozambique (ARA-Sul). The model has also been applied to the Limpopo River Basin. The model simulates the dynamics of the runoff processes, using precipitation in near-real-time, estimated from remotely-sensed data – the primary dynamic forcing variable (Artan et al., 2002; Asante et al., 2007c). The model is currently operational for the Limpopo Basin, and it can be used with catchment modelling units ranging from 102 _{to 10}3 _km2 _{in area, with a mean area of 3 500 km}2

(Artan et al., 2002). Artan et al. (2001; 2002) suggested that the model should be replicated for other River Basins to enable the model to help reduce human and economic losses by

providing improved monitoring and forecast information, and to guide relief activities in many African regions.

The model’s predictive skills were verified with observed streamflow data from locations within the Limpopo Basin. The model performed well while simulating the timing and magnitude of the streamflow during an episode of flooding in Mozambique in 2000 (Artan et al., 2002; Asante

et al., 2007c) and for the 2013 floods in the Limpopo Basin (DNA, 2014). This model was used

for warning people living in the floodplain areas of the towns of Chokwe, Chibuto and Xai-Xai with 48 hours of advance warning. Elsewhere, Asante et al. (2008) demonstrated the ability of the Geospatial Streamflow Modelling system to simulate flow variations, between 1998 and 2005, in the Congo, Niger, Nile, Zambezi, Orange and Lake Chad basins. The resulting simulated flows were compared with the mean monthly values from the open-access Global River Discharge Database. The study demonstrated that most of the severe flood events were independently verified by the Dartmouth Flood Observatory (DFO) and the Emergency Disaster Database (EM-DAT).

In Ethiopia, research in flood forecasting modelling conducted by FEWS-Net and the Ethiopian Government (Moges, 2007), convinced policy makers and planners that there was a clear need to establish a Flood Forecasting and Early Warning System (FFEWS) to mitigate the impact of flood. The study recommended and suggested a possible institutional framework and real-time communication strategy with the stakeholder institutions. It also highlighted the need for research and development support in the process of developing the FFEWS. The need for training and capacity building was also considered a critical element of any successful FFEWS programme (Moges, 2007). Depending on the availability of: hydrological and hydro-meteorological data; basin characteristic information; computational facilities available at the forecasting stations; the warning time required; the purpose of the forecast; various different flood forecasting techniques are being used in Asia and southern Africa (LFEWS, 2012; WMO and USAID, 2012). These techniques include: a simple flow-stage (Q-h) relationship – developed using the measured flow; observed stages and data collected at the sub-basin area. Most of the techniques, however, are suitable for small- to moderate-sized catchments, rather than for large-sized catchments, where hydrologic models are required (Artan et al., 2002). Stochastic models have primarily been applied by researchers and academics for real-time flood forecasting. However, their application is restricted to only a few places, since stochastic models not only try to use models for predicting hydrological variables, but also try to quantify the errors in model outcomes. In practice the exact values of the errors in model predictions are not known; otherwise it would be possible to correct the modelling outcomes (Bierkens, 2002; Singh, 2012). The computing techniques (such as ANN (Artificial

Neural Networks) and Fuzzy logic) are currently in the development stage and their application is primarily by academics and researchers (Lund and Guzman 1999; Dubrovin et al., 2002). The Fuzzy logic system consists of unsupervised algorithms to solve a clustering problem by classifying a given data set through applying a certain number of cluster priorities. Meanwhile the Artificial Neural Networks model attempts to emulate the architecture and information representation scheme of the human brain (Lund and Guzman, 1999).

Both the Fuzzy logic systems and the Artificial Neural Networks have advantages when unclear or prior knowledge is required (Nasira et al., 2008). In southern Africa basins – for instance the Zambezi Basin – WMO and USAID (2012) recommended that a highly interdependent and fully co-ordinated system needs to be established and should be composed of environmental monitoring, preparation of forecasts and warning and their dissemination. Therefore the Fuzzy logic systems and the Artificial Neural Networks may be a solution for the Zambezi Basin for flood forecasting, rather than using fully co-ordinated and interdependent systems. The commonly used techniques for flood forecasting and early warning system are Climatic Outlooks (SARCOF, CPC, and ECMWF). The Southern African Regional Climate Outlook Forum (SARCOF) is the most commonly used flood forecasting and early warning system in the Zambezi Basin (INAM, 2002; Fonseca, 2012; WMO and USAID, 2012). The SARCOF products issue seasonal rainfall forecasts at the beginning of a rainy season (September) and they are updated throughout the rainfall season in terms of their probability of occurrence. Other products include seasonal outlooks produced by the National Prediction Center (CPC) and from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). Flood forecasting is associated with many uncertainties and its success depends on following the guidelines proposed by Moges (2007). In the case of the Zambezi Basin, a study conducted by WMO and USAID (2012), recommended the implementation of an Integrated Flood Early Warning System (IFEWS) based on the following guidelines:

 Establishment of a Regional Forecasting and Warning Centre (RFWC) to be responsible for collection, evaluation and issuing of warning messages; monitoring the development of a flood threat and offering advice and assistance to local emergency organisations; and also responsible for the appropriate training of staff;  Establishment of a Local Emergency Centre (LEC) to be responsible for particular

activities in their local areas (such as door-to-door warning, search and rescue, evacuation of residents, moving valuables etc.);

 Establishment of a platform for an active participation of other organisations (including the Red Cross, churches, schools, universities, charity organisations, non-governmental organisations, mass media and the general public).

According to Moges (2007), WMO and USAID (2012), the system status needs to be reviewed on a regular basis and, if necessary, constantly updated. Activities envisaged within the framework will follow the general operation module (Figure 2.5) proposed by Moges (2007). WMO (2011) advised that to form an effective real-time flood forecasting system, the basic structures needed to be linked in an organised manner. This essentially requires:

 The provision of specific forecasts relating to rainfall, for both quantity and timing, for which numerical weather-prediction models are necessary;

 The establishment of a network of manual or automatic hydrometric stations; linked to a central control by a reliable form of telemetric communication;

 Selection of flood forecasting model software, linked to the observing network and operating in real-time.

Figure 2.5: An example of typical flood forecasting and warning activities (as proposed by Moges, 2007, which may adopted for the southern Africa River Basin)