AT VILLAGE LEVEL E. A. R. MELLAART
1999
ARC-Institute for Soil, Climate and Water Private Bag Xl 1208
1200 Nelspruit South Africa eduard-m@itsc.agric.za
138
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SMALL-SCALE SPATIAL RAIN DISTRIBUTION
THE EFFECT OF TEMPORAL AND SPATIAL RAIN DISTRIBUTION ON DROUGHT AND CROP YIELD AT VILLAGE LEVEL
OBJECTIVES
The objectives are the estimation of small-scale spatial rain distribution and its effect on yield, and to make recommendations to reduce risk.
INTRODUCTION
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Drought is a result of insufficient rain or unfavourable rainfall distribution. Drought should not be confused with aridity. Aridity means that the rainfall is in general low or very low. Until 1975 there was no universally accepted objective definition of drought according to Hounam (1975). de Jager and Howard (1997) distinguished between disaster drought and severe drought and defined them as follows:
- “Disaster drought is a set of climate-crop-soil conditions for which poor rainfall resulted in crop yields which will not be the exceeded 7 % of the time (I:14 years)“.
- “Severe drought is a set of climate-soil-crop conditions for which poor rainfall resulted in crop yields expected to occur between 7 % and 25 % of the time”.
Drought is defined in this paper as a set of climate-crop-soil conditions which causes an unusually low yield. Drought can occur in humid as well as in arid climates. Many rainfall distribution studies have been done in the past but only few were aimed on its effect on yield (Ethan and Bababe,1998).
The latter is required to estimate the yield risks due to drought.
Drought occurs on a small scale (such as farmers’ fields or villages) as well as across large-scale areas (such as subcontinents). Government decision makers need to know on which scale the drought occurs and what the consequences for household food security will be. (Household food security is defined in this context as access to the minimum food requirements for all members of the family).
Drought will normally cause a reduction in food production, but socio-economic conditions will determine whether families will suffer from this drought. The population of the Sahel started asking for help only five years after the onset of severe drought, as their stocks were sufficient until that time.
The relationship between the socio-economic conditions on the one hand and the risks of famine due to drought on the other will not be treated extensively in this paper. It must be stressed, however, that planning of food relief actions have to take both reduced production and the socio-economic conditions into account. It is evident that families with enough income can buy food from elsewhere, if transport facilities exist. It is also clear that families, in areas with poor transport facilities, without sufficient income or sufficient food stocks, will be the most strongly affected.
Disaster drought will require food assistance for a large percentage of the population. Less severe droughts may affect only some areas, but the severity in these areas may be high. To define which areas are most needy it is necessary to estimate the agricultural production, which can be done by
using rainfall data or satellite images. The local variability of the total rainfall and its distribution on a district and village scale must be determined.
The rainfall pattern in any area depends on the synoptic characteristics. The frontal rains which are common in Europe bring, even over large distances, relatively uniform rainfall, Stol (1972) reports for the Netherlands, for the month of December, a regression coefficient above 0.9 for distances over 60 km. But the rainfall pattern resulting from convective cloud formation, mostly ending up in thunderstorms is much more variable. Stol (1972) gives for the same area regression values of less than 0.5 at 20 km in July. The variability in dry areas, such as Arizona, is even greater, with Sharon (1972) indicating for the largest summer storms over there regression coefficients of less than 0.4 at only 5 km.
Convective storms result from systems with cloud bases from 5 to 20 km in diameter. The heaviest rainfall occurs usually in the centre. These storms move along more or less in straight lines.
The thunderstorm clouds develop, rain out and induce new clouds, thus the next event may be at 30 to 60 km from the first shower (Jacksoql989). These relatively small paths on one hand and the cycle of recession and regeneration on the other hand results in the high spatial variability under convective conditions. Figure 1 gives an example of the wave aspect and the narrow storm path of a heavy thunderstorm. This figure is a radar image of a heavy storm which passed over Nelspruit, South Africa (25”27’ S, 30”58’ E) on the 8’” of December 1999.
Figure 1. Thunderstorm over Nelspruit, 8* of December 1998. The radar in Ermelo was used to obtain this image. The rainfall occurred in less than one hour, the largest rainfall measured by a professional observer was 90 mm; ad hoc observers registered up to 130 mm. Note the two separate centers, about 30 km from each other, with a small center in between. (Courtesy SA Weather Bureau, Bethlehem Precipitation Research Project.)
The soil also plays an important role in the incidence of drought. Plants do not need rainfall as such but rather a regular water supply. The soil retains the water and thus regulates the water supply to the plants. Soils that can retain a larger part of the irregular rainfall can supply the water over a longer period and will thus mitigate the effects of drought. All soils retain some water whilst the surplus will be lost to deep drainage. But others, such as those with impermeable layers, will retain nearly all water. These soils may even retain too much water, which has a disadvantage in humid periods but an advantage in dry seasons. Van Noordwijk et al (1994) has shown that cultivation on both types of soils will reduce risks. One soil will have the better production in wetter years and the other in dry years.
This paper deals with the estimation of the spatial occurrence of low yields due to drought, on a small-scale. Small-scale rainfall distribution and methods to estimate yield will be addressed.
Households, families, villages and districts will all be affected by small-scale differences in drought, However, the standard raingauge networks, due to their limited distribution, cannot detect these. Numerous studies to investigate the local pattern of rainfall have been carried out, but mostly simulation models. The distribution of production losses can be estimated using such models combined with rainfall data from a dense rainfall network. Most rainfall networks are too wide to distinguish the spatial pattern. Satellite pictures, or even better radar observations, can provide data at a large enough scale.
Several data sets, including small-scale rainfall data, were available for spatial distribution studies.
The individual sets have been used for various purposes. The sets used are:
4)
South Africa, Mpumalanga province: lo-day satellite vegetation index pictures for the years 1981-‘98, density Istationl32 km2. The data from the crop growth index image for the area variability’s do not coincide with the largest areas.
The Saria data have been used as input for the WOFOST model. Running this model for different
Table I, Yield classification for five stations, for Sorghum. Yields were classified as good, poor or failure for three the Ricatla project, The confidence intervals for the average values of each village are significantly different, except for Michafutene and Ricatla.
Village Minimum Median Maximum Average StdBev.
Michafutene 1580 1950 2750 2093 438
clay and loamy sand were equal. Thus the sandy clay with impermeable underground is always better than sandy clay. Loamy sand soil has the same maximum values as the sandy clay with impermeable layer but a lower average than the previous one. Loamy sand has the same minimum values as sand, but a bigger average as well as maximum values. And compared to sandy clay there is the same average but a higher variability for loamy sand, and thus more risk.
The Bethlehem data showed the cyclic creation, recession and regeneration of thunderstorms, Better correlation’s were found between relatively distant stations (in the line of movement of the thunderstorm) than between nearby stations. These data showed simulated yield differences of over 2 tons at sites only 6 km apart.
Figure 5. The Water Satisfaction Indices for some stations around Nelspruit, (Mpumalanga province of South Africa) compared with the Crop Yield Indicator obtained from satellite picture, 3’d decade of April, 1995. Note that half of the values above 75 are situated in the yellow to green (white to light grey) areas and most below 75 in the brown to red (darker) areas. The WSI values are about twice the Crop Yield Indicator values.
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DISCUSSION
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Small-scale rainfall variability
All data clearly show the existence of important differences in rainfall over relatively short distances, even 1 km. However, the degree of variability changes from year to year and from region to region. Research over several years is needed to obtain a representative picture for the spatial distribution for an area.
The variability for n-days totals decreases when n increases. This indicates the need to verify which periods are still representative for use in the chosen model; are IO-day totals acceptable or is it necessary to use daily data?
Mellaart (1988) has shown that the variability at the beginning and end of the season is greater than during mid-season. This variability will affect the length of the season and drought risks at maturity. This higher variability emphasizes the importance of using daily values for individual fields, as it would be more difficult to detect using 1 O-day values.
Rainfall and soil type
All models give different results for different soil types. The Burkina Faso and Saria data show clearly that the sensitivity, hence yield variability, is higher on some soils and lower on other soils.
Comparison between different years showed that most soils follow the same pattern, but some behave differently.
Rainfall and yield distribution
These examples show that there is, even with the buffering effect of the soil, significant variability in yield. The Burkina Faso example showed that the variability over a large area could be nearly the
when the variability is the same over the whole area, as in Burkina Faso, and when the agricultural general patterns in the overall variability but there remains high variability over short distances.
Size of area and variability
There can be differences in drought impact between neighboring villages.
Local drought at village level can occur even when the general production for the area is satisfactory.
A single rainfall station is not representative for an area, experiencing convective rainfall conditions. Only multiple raingauges can give a good estimation.
A group of seven to ten stations can already give an indication of the variability, which exists over
Using a combination of soils or crops with non-similar responses on drought can reduce the risks, i.e. create a lower interannual variability, with relative higher total yields in dry years.
Yield variability can be important at a small scale and that variability can remain nearly constant over a large area. The trends over such an area are in general less than the small-scale variability, but large small-scale variability can occur together with large local trends (Ricatla).
Crop growth simulating models are useful tools for estimation of temporal and spatial yield variability, which results from a particular rainfall distribution. However, simulated yields values still have to be validated with field data. graphic materials. Further, appreciation for the Universities in Ougadougou and Groningen, and the Eduardo Mondlane University, the INRA in Saria and the INIA in Maputo is expressed. Special
Burkina Faso. University of Groningen, Research report.
Ethan, S. and Bababe, B. 1998. Rainfall analysis for production of millet (Pennisetum glaucum, L.R.B.) in Maiduguri-Nigeria. Agriculutura Tropica et Subtropica Universitas
Agriculturae Praga, Vol.31, p. 37-44. juin 1986 dans la region de Ouagadougou (Burkina Faso). Veille climatique satellitaire
no. 20, novembre 1987.
Jackson, I.J. 1989 Climate, Water and Agriculture in the Tropics. Longman. New York Jager, J.M. de, and Howard, M.D., 1997. Computing Drought Severity and Forecasting its
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Keulen, H. van, and Wolff, J., 1986. Modelling of Agricultural Production. Weather, Soils and Crops. PUDOC. Simulation Monographs. Wageningen
Massingue, F.I., Mellaart, E.A.R. and Brito, R.M.C.L., 1997. Rainfall and its spatial variability in sorgho et de mil. Publications du Projet CEDRES/AGRISK. OUAGADOUGOUIGRO- NINGEN. ISBN 90 367 0158
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Noordwijk, M. van, Dijksterhuis, G.H., and van Keulen, H., 1994. Risk management in crop produc- tion and fertilizer use with uncertain rainfall; how many eggs in which baskets. Neth.
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Sharon, D., 1972. The spottiness of rainfall in a desert area. Journal of Hydrology, Vol 17(3),