Precipitation & Meteorology

These estimates are used to assess concerns such as recharge, evapotranspiration, and flood analysis. In addition, the MDA CropCast weather system can be applied in several ways as a mechanism for efficient use of available water and pricing of water rights.

Our evaluation tools include:

• MDA's proprietary database of GOES satellite data of cloud cover and brightness from 1970 to present; and
• MDA's CROPCAST algorithm which uses the interpreted and digitized GOES satellite data for the generation of rainfall/cloud type and cloud brightness coefficients.

Average Annual Precipitation

Average Annual Precipitation can be derived by analyzing precipitation data from multitemporal meteorological satellite data in conjunction with data from regional gauging stations (where available).

For example, when evaluating precipitation in the state of Falcon, Venezuela, GOES satellite data of cloud cover and brightness for the dates of interest were retrieved from MDA's proprietary database (which covers the period since 1970). MDA's CROPCAST algorithm used the interpreted and digitized GOES satellite data for the generation of rainfall/cloud type and cloud brightness coefficients. This algorithm utilizes station regional data, satellite data, or both depending on data availability. Approximations of annual precipitation were then made by summing empirical estimates of precipitation derived from cloud brightness tied to actual climatological regimes as discussed below.

For the Falcon project, two raster files were created for each data set. Estimates of inverse-distance-weighted precipitation rates for areas between weather stations were interpolated by using the three-closest-stations method. Final rainfall estimates were derived by combining surface rainfall reports and rainfall satellite estimates using MDA's CROPCAST rainfall/cloud type and cloud brightness coefficients.

Rainfall Depth Duration/Return Period Analysis

Satellite data correlated with gauge-station measurements can be used to estimate rainfall amounts that fall every six hours. These amounts are summed to estimate the amount of precipitation that fell in time periods that are multiples of six hours. For specified rainfall amounts, histograms of the number of events per time period (e.g., 12 hours) can be prepared to estimate storm return periods, as part of a rainfall return period analysis. The trends shown by these histograms can be used to extrapolate rainfall frequency for three-hour and one-hour periods.

The results of a return period analysis and standard recurrence interval formulas are applied to obtain storm probabilities for climatic region of the study area. These probabilities are used to generate intensity duration curves (precipitation versus recurrence interval) that can then be used to estimate the precipitation from storm events in each climatic region.

The CROPCAST Weather System

The MDA CROPCAST weather system can be applied in several ways as a mechanism for efficient use of available water and pricing of water rights.

• The soil moisture budget function models the crop growth based on daily weather inputs. The model then tracks water demand for specific crops and aggregates the results to the appropriate reporting level. This methodology provides consumptive use models for specific crops/natural vegetation given current weather conditions or hypothetical future weather scenarios.
• The model accepts updating of the crop mix and/or planting date and growth information. The user may then determine the water use impact of fallowed land or changes in cropping patterns. The policy maker can also use this approach for developing alternative water rights policies.
• The system employs the latest in basin precipitation estimation techniques through its Doppler radar rainfall and snowmelt algorithms.
• The model accepts hypothetical weather forecasts for predicting future water needs. This enables suppliers to anticipate demand under varying climatological conditions and accomodate extreme events that may cause major economic disruptions.
• The model can be linked with snow budget and flow models or ancillary stream gauge data to provide more accurate flow information that will lead to more efficient pricing decisions and ultimately to cost savings for a water management district.