For a flooding event what data would be required, and what tools would you use?

Rainfall is the most important factor in creating a flood. When rain falls on a catchment, the amount of rainwater that reaches the waterways depends on the characteristics of the catchment, particularly its size, shape and land use. Some rainfall is captured by soil and vegetation, and the remainder enters waterways as flow. Analysis of the synoptic conditions surrounding the event suggests that the major drivers of the extreme rainfall event were the above average perceptible water in the atmosphere.

To make progress on understanding and modeling the real world flood process, one needs to better understand how the complex interactions among weather, climate, hydrology, basin attributes, and antecedent conditions evolve over space and time.

Flood hazard estimation is traditionally performed based on extreme value statistics. Statistical models, i.e., distribution functions, are adjusted to ordered sequences of observed flood peaks and are used to extrapolate the flood magnitudes associated to very low exceedance probabilities (e.g., Gumbel, 1941). Many assumptions should apply for the method to give reliable results. For example, it is typically assumed that the flood data used are statistically independent and identically distributed, i.e., that each flood event is independent of those that came before it and that all floods have the same characteristics.

We need two items, namely data-based and modeling approaches. With atmospheric variables, land use, soil characteristics, attributes and hydrologic characteristics of basins (shape, size, depth, etc.). For the data-based approaches, we apply statistical methods for checking observational flood data. For example, the study of historical floods provides very valuable information for the period prior to systematic runoff observations, and flood characteristics, such as distribution of historical flood, floodplain sediments. In the second one including the modeling approach, it uses for assessing the sensitivity of floods to their drivers, involve hydrological model simulations using climatic variables as inputs and model parameters representing the land-use characteristics (Bronstert et al., 2002). The simulations are then repeated with changed climate or land-use characteristics and the differences between the simulations are an indication of the sensitivity of floods to their drivers.

  1. In sum, beside the above mentioned items (all relevant meteorological, topographical, hydrologic, hydraulic, and performance conditions), I want to summarize the essential data as the following list:
  2. the observing data from: synoptic stations, hydrometric stations, gridded datasets, reanalysis dataset,
  3. Knowledge about the type of storm producing the moisture, such as duration, intensity and areal extent, which can be valuable for determining possible severity of the flooding.
  4. Knowledge about the characteristics of a river's drainage basin, such as soil-moisture conditions, ground temperature, snowpack, topography, vegetation cover, and impermeable land area, which can help to predict how extensive and damaging a flood might become.
  5. Using different maps in different pressure levels, such as wind direction and wind speed, dew-point, moisture or humidity, and etc. By tracing and tracking the mentioned maps in different levels few days before the occurred flood event, we can identify the kind of flood and other important tips.
  6. Using different hydro-graph for recognizing flood intensity, duration and other characteristics, such as IDF (Intensity-Duration-Frequency) curve, SCS Dimensionless Unit Hydrograph, and etc.
  7. By remote sensing, and particularly satellite platforms. Satellites are currently the only means of providing reliable data that can be used for mapping and modeling floods. For example, airborne photogrammetry and interferometric SAR technology from different viewing geometries allows the generation of fine spatial resolution, high accuracy (typically less than 50 cm vertical error) DEMs; with satellite and sensor's data, we can achieve valuable tips about the happened flood and the related causal mechanism.

We need also to critical tools such as:

  1. Elevation data in the form of a DEM are probably the most common remote sensing-derived product and are required for all types of environmental modeling.
  2. Use ArcGIS and ENVI for drew maps and create equations for interpolation modeling of flood events.
  3. Use SWAT for modeling and prediction flood event and also simulate sediment and runoff.
  4. Apply the output of GCMs (General Circulation Models) under different scenarios.
  5. HEC-HMS computer mode
  6. And other software for simulation and modeling flood events.
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