A system for drought monitoring and severity assessment

[摘要] English: The objectives of this study were:(i) to develop a near real-time crop-specific droughtmonitoring system that delimits drought stricken areas andassesses the severity of droughts in these areas,(ii) to produce products from the system which can be used fordecision support by decision makers, and,(ii) to test the system for maize production using historicalproduction seasons.Objectives (i) and (ii)An agricultural drought monitoring system was designed, whichcombined crop growth modelling and a Geographic InformationSystem (GIS) . The use of crop models made it possible to assessthe drought damage suffered by crops, in relation to their growthstage. As drought is a spatially related phenomenon, a GIS wasused to present the geographic distribution of a droughtsituation.A grid based, spatially distributed, system was designed. Themap units of the South African 1.:250 000 map series were used asthe base units on which to present information. Each base unitwas divided into cells covering an area of 2' by 2' minutes oflatitude and longitude. There were thus 1.800 grid cells in onesuch unit. The models were run for each of these cells.The data inputs required by the crop models therefore had to bespatially distributed. Methods of creating spatially distributedweather data bases, were implemented or developed. Existinginterpolation techniques were used to create the rainfall andtemperature data bases. A technique developed for determiningdaily irradiance, from the Japanese Geostationary MeteorologicalSatellite, was adapted for use on METEOSAT data obtained overSouth Africa. A spatially distributed soil data base was alsocreated. Maize was chosen as the crop to monitor in the initial evaluationof the system. Drought monitoring was undertaken at fortnightlyintervals from the beginning of the crop production season. Ateach interval, observed weather data was used up to the presentdate, and the season completed with surrogate data. Threesurrogate scenarios were used: a below normal rainfall year, anormal rainfall year, and, an above normal rainfall year.Surrogate data were created for each homogeneous climate zone(HCZ) within the study area. The HCZ within which the cell laywas determined and its data used to complete the season. Arainfall data generator, the accuracy of which had been proved,was used in establishing the surrogate data.The cumulative probability distribution function (CDF) ofseasonal yield, was used as the norm against which to measurecurrent season performance at the conclusion of each monitoringsession. CDF's were established for all combinations of soil,climate, and planting dates used within the bounds of aparticular 1:250 000 map unit.The yield simulated for each cell was compared with theappropriate CDF, and the probability range within which it lay,determined. A drought index value was assigned based on thiscomparison. The indices were:1- Extreme Drought (CDF probability range 0- 10%),2- Severe Drought (>10- 20%),3- Moderate Drought (>20- 30%),4- Mild Drought (>30- 40%), and,5 - No Drought (>40 - 100%).Maps showing the distribution, and tables providing the extentof area classified, were produced.Objective (iii)The drought monitoring system was tested for three maizeproduction seasons. The accuracy of the system was determined by comparing the average maize yield per magisterial districtwith measured yield data. Individual farm records were alsoevaluated. The system accurately portrayed the general maizeproduction trends during a severe drought (91/92}, while an r 2 of0.59 was obtained for the individual yields.The crop modelling approach to drought assessment takes theinteraction of the soil, plant and atmosphere into account andis crop specific. The important influence of both the amount andtiming of rainfall in relation to crop growth stages is thereforereflected in the drought index.

[发布机构] University of the Free State