Modern Tools in Flood Forecasting
Flood forecasting is the prediction of flow rates and water levels in river channels for periods ranging from a few hours to a few days ahead. The flow rates and water levels are predicted on the basis of rainfall-runoff and stream-flow routing models for which are based on precipitation. Flood forecasting can also make use of forecasts of precipitation in an attempt to extend the lead-time available. Flood forecasting is an essential component of flood warning, where the distinction between the two is that the outcome of flood forecasting is a set of forecast time-profiles of channel flows or river levels at various locations, while "flood warning" is the task of making use of these forecasts to take decisions for the management and timely action. Advanced flood forecasting systems can reduce flood risk and allow emergency response personnel to be better prepared for and mitigate damages. The accuracy of flood forecasts is highly determined by the skill of quantitative precipitation forecasts and its spatial distribution. Most modern hydrological models can use precipitation input from various sources like rain gauges, radar, remote sensing, or simulated precipitation from numerical weather models. High-resolution satellite data are used for Weather Research and Forecasting (WRF) model to provide high-resolution precipitation forecasts. These are capable of forecasting daily precipitation amounts and facilitate early, accurate forecasting.
Hydrological gauges are used to measure and transmit comprehensive sets of data about the factual status of rivers, lakes, and reservoirs. The modern hydrologic networks are automatized to collect and transmit measurements several times a day. Satellite data of snow cover extent are often used in a number of river basins in order to adjust long-range spring flood forecasts. Measurements of snow and soil parameters (moisture content and freeze depth) are of great importance for long-range spring flood forecasts, as they determine the initial conditions of spring flood phenomena. Network of snow gauges provide direct measurements of snow height, weight, and density (to obtain snow water equivalent (SWE) values) periodically during winter in field and forest areas separately.
Besides these modern tools, flood forecasting systems incorporate automation of gauging stations and weather radar network installation, development of forecasting techniques (from short- to long-range), incorporating them into a developed automatic forecast system structure, and developing web-based interfaces for the main users of forecasts.
Weather forecasts: Reliable forecasts of weather, in particular rainfall, can allow advance warning and forecasting of floods. Weather forecasts for the next one to seven days rely on increasingly accurate computer models of the atmosphere and ocean/atmosphere interactions. Dramatic improvements in the data available to such models (from satellite observations) and in computing power have contributed to this increased accuracy. In some parts of the world, three-day-ahead forecasts of heavy rain are now as accurate as one-day-ahead forecasts were in the mid-1990s.
The accuracy of climate and weather forecasts varies with lead time, spatial scale (or size) of the region of interest, the weather or climate variable being forecast (for example, rain, thunderstorm), as well as with latitude. Generally, temperature forecasts are more accurate than rainfall forecasts. The mid-latitudes are easier to forecast than the tropics.
Optical satellite data: Depending on its contents, water reflects electromagnetic waves differently; pure clear water reflects differently from muddy water or water containing vegetation (floating or submerged). The amount of energy measured from the satellite sensor also depends on the bands used; the blue band penetrates water up to 10 m, the red band is partially absorbed, and the near-infrared band is totally absorbed. These sensor properties consequently affect the image, so that an image acquired using the blue band will measure reflectance from any submerged vegetation within its reach, while red/nearinfrared images will show water as dark grey/black, respectively.
With the availability of more optical satellites with relatively low temporal resolutions globally, many scenes of archived data can be accessed and used for change detection studies and flood extent mapping in areas with little cloud cover.
Synthetic Aperture Radar: Synthetic Aperture Radar (SAR) can penetrate the cloud cover and provide images of flood extent and soil moisture conditions in a basin. Algorithms have been developed for this purpose. Soil moisture information proves to be valuable for describing the initial situation in a hydrological model. Flood maps derived from satellite imagery data can play an important role for improved flood modelling and forecasting. For example, when no gauging stations are available, gauging stations fail or unforeseen events (such as dike breaches) happen. Geo-referenced and classified satellite data can provide information on flood extent (area) as well as water levels for large flooded areas.
Satellite-derived DEM data: Satellite data provide topographic information in the form of digital elevation models (DEMs) generated from radar echoes of spot heights, e.g. ASTER DEM, SRTM, and SPOT DEM. The most common and freely available DEM is the Shuttle Radar Topographic Mission (SRTM) DEM flown in February 2000 which covered 85 % of the Earth’s surface. SRTM which was obtained through SAR interferometry of is available in 30 and 90 m spatial resolutions and an approximate vertical accuracy of 3.7 m. The vertical accuracy of SRTM is higher in areas with gentle slopes than on steep slopes; on low-lying floodplains SRTM has shown less than 2 m accuracy.
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