Target Season
Soil characteristics:

Probability of Exceedance

Probability Distribution

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Soil Water Balance Seasonal Forecast -- Dry Season

This is an experimental seasonal forecast of soil moisture driven by probabilistic climate seasonal forecasts.

Such a forecast provides added value to the agricultural community, in comparison to the climate seasonal forecast. The soil water balance model includes non-linear agronomical processes that the driving climate information doesn't contain. It allows to provide agriculture-relevant information that speak directly to the community. The probabilistic nature of the forecast with full distribution of the quantities gives both the flexibility to deliver interactive maps and point-wise distributions that become relevant to user-determined needs, along with the characterization of the uncertainty of the forecast. The comparison with historical conditions put the current forecast in context of normal conditions.

The default map shows in Bicol soil type wise seasonally (4-months) averaged daily soil moisture probability (between 0 and 1) of exceeding the 50th percentile of the distribution from historical climatology. The forecast shown by default derives from the latest climate forecast (e.g. Climate Forecast issued in December 2016 and Soil Moisture forecast for January to April 2017). Note that the soil-plant-water balance simulation is spinned up by 2 months of observed meteorological stations, therefore in the previous example: SWB is driven by observations in November and December 2016, a climate forecast was made in December 2016 to forecast January to April 2017, so the soil-plant-water balance simulation is driven by the ensemble of resampled daily climate data starting January through April. The soil-plant-water balance simulation is then re-averaged over the full target season. One can also take advantage of having the full distribution by specifying the historical percentile or a quantitative value for probability of exceedance or non-exceedance.

Clicking on a point on the map will show the local culmulative distribution and probability distribution fucntions of the forecast (green) together with the climatological distribution (black).

The KSPM was produced by the International Research Institute for Climate and Society (IRI) for review by the United States Agency for International Development and partners on the BAWP and do not necessarily reflect the views of USAID.

Dataset Documentation

Soil Characteristics: including texture and total available water derived from textures using Mualem-Van Genuchten (MVG) model from Bicol Department of Agriculture.

Precipitation Seasonal Forecast: from IRI Net Assessment.

More Information


The forecast is expressed through the full distribution of an ensemble of seasonal soil moisture. The ensemble of seasonal soil moisture is derived from an ensemble of daily outputs of a soil plant water balance model. Such a model depicts agriculture-relevant conditions of the soil or the crop. The soil water balance model is parameterized by rice crop cultivar for the drier season, and total available water in the soils. It is driven by an ensemble of daily climate data (precipitation, temperature mean and amplitude) from meteorological stations data or global gridded products. The ensemble is formed by randomly picking historical observed years with the constrain that the seasonal total precipitation distributions in the ensemble correspond to the seasonal probabilistic precipitation forecast distributions. The forecast distribution is compared to the historical distribution that is derived from running the soil water balance model for all historical data available.

Soil Water Balance Model

The water balance model is described in the IRI DL Function Documentation. The water balance is run from two months prior to the climate forecast target season and through the season. For rice of the drier season, planting date is set up at January 7th and crop cultivar Kc parameters are also set up (see yearly cycle in image below -- Kc out of the growing season is set at 1 to reflect unknown use of the soil). Those 2 key parameters (planting date and Kc) are constant through all the years of the historical simulation. In this case, the model is driven by meteorological stations data and applied to soil shapes over which depends the total available water parameter. The simulation is run for each shape with all available stations, then a shape is assigned the results of the closest station.

Agronomical Quantities and their distribution

Soil Moisture: average soil moisture (in mm/day) over the season. It indicates how satisfied were the crop needs in water for normal growth. It is considered a normal distribution.



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