Russian Journal of Agricultural and Socio-Economic Sciences, 2013, №12 (24) Декабр

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

3

AGRICULTURE DROUGHT RISK MANAGEMENT USING STANDARDIZED 

PRECIPITATION INDEX AND AEZ MODEL

Nazarifar M.H., Momeni R., Kanani M.H., Research Assistants

College of Abouraihan, University of Tehran, Tehran, Iran

E-mail: nazarifar@ut.ac.ir, rmomeni@ut.ac.ir, mhkanani@ut.ac.ir

ABSTRACT
The objective of this study was to assess the drought risk management in the region under 
study. The SPI method was adopted for drought monitoring in Hamadan Province, Iran. The 
temporal and spatial extent of the area vulnerable to drought was delineated using AEZ 
model, GIS and other softwares. Five zones were recognized based on the drought severity 
index. Selection of compatible crops with respect to climate and land production capability of 
a region specially in drought condition is one of the effective elements to increase the water 
productivity in agriculture, based on Agro-ecological Zoning (AEZ) model, developed by 
FAO, suitable spatial extension of wheat cultivation, which is the main crop in Hamadan 
Province, were delineated. According to this study the most suitable lands potentially 
available for wheat production are located in the north-east region and a part of the central 
region, where as, least suitable ones can be observed in the north-east and the south – east 
regions. The results of the risk analysis study show that south-east, north and central regions 
are susceptible to longest duration intense droughts where as long duration droughts are 
intensive in north, west and south-east regions. The overlaid and integrated maps of risks 
with the maps obtained after applying the AEZ model resulted into the map of spatial 
suitability of potential crop production for each class of risk (longest duration and most 
intensive durations). This enables the decision makers to define spatial priority of crop 
cultivation and manage various potential regions susceptible to drought risks.

KEY WORDS
Hamadan; Drought; GIS; Agro-ecological zoning.

Rainfall scarcity, high potential evapotranspiration and water resources constraints are 

problems in agriculture in Iran. During drought periods, when temperature rises and rainfall 
reduces, crisis arises at a faster rate. Subsequent loses due to droughts in agriculture sector, 
as well as direct and indirect vulnerability of agriculture, necessitate the accurate planning on 
the basis of potential and limited resources for sustainable agriculture. Till 1995 in all 
countries, there were not major plans for drought mitigation. Therefore, the traditional 
practice was to organize a task committee casually after the drought occurrence, to reduce 
the drought damages. These decisions were made, in those offices fast and random 
immediately after the drought reached to maximum. Therefore, little attention was paid to 
drought mitigation. The occurrence of several intense and vast droughts in the United States
like those of 1996 drew the attention of scientists, planners and the U.S. government in 
droughts management towards changing the disaster management approach to drought risk 
management. In drought risk management, the decisions are clear, applicable and dynamic. 
Moreover, in this approach the emphasis is on alertness and readiness for drought risk 
mitigations. 

As frequency and intensity of drought increases, attention shifts towards reducing 

drought risk management in most countries. Today, drought risk management in many 
countries like, the U.S., Canada, Mexico, Australia and European countries is put into effect 
practiced instead of disaster management (Cline, 2007). It can be concluded from various 
researches that, obviously, the economical losses and social damages can be reduced 
through using drought risk management. Thus, researches are conducted to predict the 
context and update the results. In planning for drought mitigation there has been a shift from 
disaster management to drought risk management, which is quite difficult when the behavior 
and characteristic of droughts and expected losses are not predictable. To solve this 
problem, it is necessary to locate and monitor the areas of high drought risk and potentially 

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

4

susceptible to drought through adopting take up the necessary precautions and plans in 
order to reduce drought risk. The indices used for drought monitoring are: precipitation 
percentile, percent of normal precipitation, Palmer Drought Severity Index (PDSI) and 
Standardized Precipitation Index (SPI), etc. There is an extensive literature for qualitative 
assessment of droughts including indices, models, and water balance simulation (Alley,1984; 
Byun and Wilhite,1999; Flug and Campbell,2005; Karl and Knight, 1985; Karamouz, Torabi 
and Araghinejad,2004; McKee, Doesken and Kleist, 1993; Palmer,1965; Sen,1989; Shin and 
Salas,2000). In 1965, Palmer developed an index to measure the departure of the moisture 
supply (Palmer, 1965). Palmer based his index on the supply-and-demand concept of the 
water balance equation, taking into account more than just the precipitation deficit at specific 
locations. The objective of the Palmer Drought Severity Index (PDSI), as this index is now 
called, was to provide measurements of moisture conditions that were standardized so that 
comparisons using the index could be made between locations and between months

(Palmer, 1965). The understanding that a deficit of precipitation has different impacts 

on groundwater, reservoir storage, soil moisture, snowpack, and stream flow led McKee, 
Doesken, and Kleist to develop the Standardized Precipitation Index (SPI) in 1993 (McKee, 
Doesken and Kleist, 1993). The SPI was designed to quantify the precipitation deficit for 
multiple time scales. These time scales reflect the impact of drought on the availability of the 
different water resources. Soil moisture conditions respond to precipitation anomalies on a 
relatively short scale. Groundwater, streamflow, and reservoir storage reflect the longer-term 
precipitation anomalies. For these reasons, McKee et al. (1993) originally calculated the SPI 
for 3–, 6–,12–, 24–, and 48–month time scalesMcKee, Doesken and Kleist, 1993). Agroecological Zones (AEZ) method was developed by the Food and Agriculture Organization of 
the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) 
(FAO,1996). As a mechanism method, it was frequently adopted to calculate potential 
productivity of crops at regional level (Deng, Huang and Rozelle,2006; Fischer and 
Sun,2001).The second approach, AEZ analysis, combines crop simulation models with land 
management decision analysis, and captures the changes in agro-climatic resources 
(Darwin, Tsigas, Lewabdrowski and Raneses,1995; Fischer, Shah, Tubiello and 
Velthuizen,2005). AEZ analysis categorizes existing lands by agro-ecological zones, which 
differ in the length of growing period and climatic zone. The length of growing period is 
defined based on temperature, precipitation, soil characteristics, and topography. The 
changes of the distribution of the crop zones along with climate change are tracked in AEZ 
models. Crop modeling and environmental matching procedures are used to identify cropspecific environmental limitations under various levels of inputs and management conditions, 
and provide estimates of the maximum agronomically attainable crops yields for a given land 
resources unit. However, as the predicted potential attainable yields from AEZ models are 
often much larger than current actual yields, the models may overestimate the effects of 
autonomous adaptation. Cline observed that AEZ studies tend to attribute excessive benefits 
to the warming of cold high-latitude regions, thereby overstating global gains from climate 
changes. This model has been used in several fields related to sustainable agriculture 
(Deng, Huang and Rozelle,2006; Lugue, 2009; Pratap, Pradhan, Lotta, and Nakarmi, 1992; 
Ravelo and Abril, 2009. Segal, Mandal and Vadivelu,1992;
Subramanian, 1983;

Venkateshwaralu, Ramakrishna and Rao,1996). 

This model offers much scope for developing strategies for efficient natural resource 

management and in this context, recent advances in remote sensing and GIS have made the 
task of integration and mapping of a wide range of databases much easier.In this study 
standard precipitation index(SPI) was adopted for monitoring droughts in Hamadan Province. 
The intensity and duration of droughts in this region were studied and the areas potentially 
subject to drought risk were identified. On the basis of probability of occurrence these 
regions were qualitatively classified and the risk layers were identified. These layers were 
overlayed on the layers obtained by applying AEZ model and finally the areal extension of 
wheat cultivation in drought conditions were worked out.

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

5

MATERIAL AND METHODS

The Study Area. Hamedan province lies between longitudes 48° 28′ 30″ and 49° 1′ E 

and latitudes 34° 36′ and 35° 9′ N and is shown in Figure 1. The area occupies about 944 
km2, with a mean altitude of 1950 m.a.s.l. The climate of the study area is considered to be 
semi-arid, the annual average precipitation being approximately 300 mm, of which about 
37% occurs during winter. Another feature characterizing the precipitation in the study site 
is its irregular yearly distribution. The mean air monthly temperature is highest during 
August (23.45 °C) and lowest during January (−1.91 °C) with an annual average of 
10.88°C. 

Figure 1  Location of study area

Figure 2  Monthly rainfall and evaporation in the study area

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

6

The annual potential evaporation far exceeds the annual rainfall (Figure 2) with a mean 

annual amount of approximately 1505 mm (1975–2010). The area has complicated land use 
characteristics, mainly consisting of agricultural and urban/residential areas. Groundwater 
has been used for various purposes, such as drinking, agricultural, domestic and industrial 
needs. The most important economic activity of the area is agriculture, the chief crops are 
garlic (Allium sativum), potato (Solanum tuberosum L.) and wheat (Triticum aestivum L.), 
with actual irrigation being lower than total theoretical demand, as there is a considerable 
deficit in relation to the amount of irrigated land.

Data Processing. For this research 29 years (1973-2002) precipitation data of 13 

meteorological stations were studied. The data of adjacent stations were also taken into 
consideration to reduce errors in data interpolations. The run test was used to confirm the 
homogeneity of the data. Next, the missing data were added or supplemented and 
erroneous data were corrected by using 29 years common data-period. 

SPSS Software was applied to carry out the analysis. Regression analysis was done 

between the stations and the missing data were estimated using data of different stations 
with higher correlation coefficient and SPI values. Later, the computed SPI values were 
utilized for further studies to analyze the beginning and terminating intensity, covering area, 
frequency or return period, duration and the probability of occurrence of drought. 

The following equation was used to calculate the probability of occurrences and the 

corresponding risk values:

P(N,m,n) = (n-m+1)/(N+n-2m+2)………(1)

where N is the length of data, m is the duration of drought and m is the return period. After 
quantifying the related risk of return periods of various drought events and with respect to the 
severity and the longest duration, the corresponding probability of occurence to the amount 
of risk was calculated. The available software in GIS environment such as ArcInfo and 
ArcView were used to prepare the drought maps of the region under study. The ArcView
software was applied to draw the maps and iso-intensity curves of droughts. Furthermore, 
ArcInfo Software was used to convert the geographical coordinates to UTM coordinate 
system.

In this study, the 3IDW interpolation method was applied for delineating spatial 

extension of droughts. To estimate the values of parameters of IDW interpolation technique, 
trial and error procedure was adopted and the drought maps for various durations (1, 3, 6, 12 
and 24 months), as well as, the maps for the longest durations, the maps of amount of risk 
involved in the occurrence, and the longest and most sever droughts were prepared.

In addition, to prepare AEZ maps, the physiological characteristics, also the 

phonological parameters of wheat crop were studied. Furthermore, the optimum and the 
extreme conditions of this crop were carefully investigated. Later, SPSS was used to perform 
statistical analysis to prepare all the digital layers for climatologically factors. In order to apply 
AEZ model, the required maps were scanned. These maps were digitized using R2V 
software to develop a data base containing land information of the region. Later, these maps 
were edited, and necessary corrections were made through ArcInfo software to convert the 
coordinate system into UTM. The descriptive information of maps were added using ArcView
software. Finally, the maps were classified based on the required conditions for the crop the 
topographical maps contour lines distanced with 100 meters were drawn to prepare the 
elevation map. By mapping Triangulate Irregular Network (TIN) on topographical network, 
the digital elevation model (DEM) was developed. By converting data for each 500 x 500 
pixel in ArcView software and other extensions (viz. 3D analysis), the slope values were 
computed.

The land type and the soil depth information layers were digitized using maps of land 

quantities of the scale 1:250,000 which were procured from "soil Research Office". Moreover, 
descriptive information was assigned to the maps. 

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

7

The capabilities of ArcView software such as rational function AND and the spatial 

query and map calculator were utilized to compose and overlay the maps as well as to 
delineate and introduce the suitable places for wheat crop.

In order to compare the maps more accurately, they were normalized between 0 and 1.

These were classified into five categories (i.e. highly suitable, suitable, average, weak and 
unsuitable) from crop-yield aspect. Finally, using AEZ the developed maps were overlaid.
The risk maps were also overlaid for further analysis, obtaining the results and drawing 
conclusions.

RESULTS AND DISCUSSIONS

A sample of result of the risk analysis for the longest duration, most intense or sever 

droughts for 20 years return period, and the corresponding probability of occurrence for 
various risk values were calculated and presented in Table 1. Risk maps of the longest 
durations and most sever droughts for 20 years return periods are given in Figures (3) and 
(4). As it is observed, the amount of risk for the longest drought duration is more in north, 
west and south-east regions.

Table 1  Risk values and probability of occurrence for 20 years return period drought

The results obtained from the application of AEZ model were used to draw the zoning 

maps. These maps show spatial variation of the capable regions for crop cultivation in five 
classes. Figure (5) presents the spatial variation of capability of lands for wheat cultivation in 
Hamadan Province. According to this study the most suitable lands potentially available for 
wheat production are located in the north-east region and a part of the central region, where 
as, least suitable ones can be observed in the north-east and the south – east regions.
The utilities of ArcView software makes it possible to overlay and integrate the maps of 
drought risk obtained from AEZ model. Thus, the maps of spatial variations of suitability of 
crop production for each class of risk of occurrence (longest duration and most intensive) of 
droughts were obtained. These are presented in Figures (6) and (7).Based on the results 
obtained it is clear that the priority of cultivation of a crop in a class of drought will be in 
regions having higher spatial suitability of crop production potential. In other words, in a 
region located in a drought class the spatial suitability of crop production is variable. 
Therefore, it is necessary to allocate higher priority to regions with higher crop production 
potential in agricultural drought risk management.

Station

Longest Duration (month)
Intensity according to SPI

Ddrought 

duration month

Risk

percent

Probability of 
occurrence

Duration 
(month)

Risk 

percent

Probability of 
occurrence

Aagtappa
34
76.47
probable
26
51.00
probable

Bahar
49
59.57
average
43
62.86
high

Gonbad
28
40.00
possible
28
54.00
probable

Jowkar
87
53.66
probable
87
53.66
probable

Keytoo
66
55.56
probable
66
55.56
probable

Korijan
43
62.86
high
43
62.86
high

Nashr
31
90.91
expected soon
31
90.91
Expected soon

Shoorin
47
60.47
high
31
80.91
high

Dargazin
42
63.64
high
42
63.64
high

Hamadan
36
71.43
high
25
64.00
high

Malayer
32
84.62
high
32
84.62
high

Novejeh
62
56.16
probable
62
56.16
probable

Soolan
39
66.67
high
39
66.67
high

Ghazvin
35
71.68
high
35
73.68
high

Kermanshah
53
58.18
probable
53
58.18
probable

Saghez
34
76.47
high
34
76.47
high

Sanandaj
37
69.57
probable
37
69.57
high

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

8

Figure 3  Map of probability of occurrence of most intensive durations potential

Figure 4  Map of probability of occurrence of longest durations

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

9

Figure 5  Spatial variation of Classified land capabilities for wheat production

Figure 6  Overlaid and integrated map of probability of occurrence of longest durations with map

of spatial variation of production potential

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

10

Figure 7  Overlaid and integrated maps of probability of occurrence of most intensive durations

with map of spatial variation of production potential

Figure 8  Overlaid and integrated map of probability of occurrence of longest durations with map of 

spatial variation of production potential

Figure 9  Overlaid and integrated maps of probability of occurrence of most intensive durations with 

map of spatial variation of production potential

0

2

4

6

8

10

12

14

Class 1
Class 2
Class 3
Class 4
Class 5

The classes of risk  of occurrence of longest durations

Ferquency of crop 

production potensial

0

2

4

6

8

10

12

14

16

Class 1
Class 2
Class 3
Class 4
Class 5

The classes of risk  of occurrence ofmost intensity durations

Ferquency of crop production 

potensial

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

11

Thus, the study of the developed figures through regional analysis functions of GIS can 

give more insight into the variation trend of spatial suitability of crop production potential in 
each class of drought occurrence (longest duration and/or most intensive duration). This 
analysis was performed for the region under study but a sample is presented in Figures 8
and 9.This analysis shows that regions located in class-4 risk of occurrence of the longest 
drought durations have higher frequency of crop production potential with compare to regions 
located in class-4 risk of occurrence of most intensive durations.

CONCLUSION

With experience of consecutive droughts and depletion of available water resources, 

more accurate planning based on capabilities and constraints in all regions are necessary for 
increasing productivity of water and soil resources. Drought risk management and drought 
monitoring with preliminary precautionary measures and adaptation can definitely reduce 
loses. The development and integration of the results of Agro-ecological Zoning model with 
the maps of drought risk in a region can help in suitable planning for optimal operation of 
water and soil resources as well as for looses reduction.

REFERENCES

[1]
Alley, W.M. The Palmer Drought Severity Index: Limitations and assumptions. Journal 
of Climate and Applied Meteorology. 1984, 23:1100–1109.

[2]
Byun, H. R., and Wilhite, D. A. Objective quantification of drought severity and 
duration. J. of Climate.1999,12: 2747-2756.

[3]
Cline, W. Global Warming and Agriculture: Impact Estimates by Country. Washington, 
DC: Center for Global Development and Peterson Institute for International
Economics. 2007.

[4]
Darwin, R., M. Tsigas, J. Lewabdrowski, and A. Raneses. World Agriculture and
Climate Change. Agricultural Economic Report.1995. no. 703 (June). Washington, 
DC: US Department of Agricultural, Economic Research Service.

[5]
Deng X Z, Huang J K, Rozelle S et al.. Cultivated land conversion and potential 
agricultural productivity in China. 2006. Land Use Policy, 26(3): 372–384.

[6]
FAO (Food and Agriculture Organization of the United Nations). Agro-ecological 
Zoning Guidelines. Rome: FAO Soils Bulletin. 1996.

[7]
Fischer G, Sun L. Model based analysis of future land-use development in China. 
Agriculture, Ecosystems and Environment. 2001.85(1–3): 163–176.

[8]
Fischer, G., M. Shah, F. N. Tubiello, and H. van Velthuizen. Socio-economic and
Climate Change Impacts on Agriculture: an Integrated Assessment, 1990–2080.
Philosophical Transactions of the Royal Society B. 2005. 360: 2067–2083.

[9]
Flug, M., and Campbell, S.G. Drought Allocations Using the Systems Impact 
Assessment Model: Klamath River. ASCE J. of WRPM. 2005. 131(2):110-115.

[10]
Karl, T.R.; and R.W. Knight. Atlas of Monthly Palmer Hydrological Drought Indices 
(1931–1983) for the Contiguous United States. Historical Climatology Series 3–7, 
National Climatic Data Center, Asheville, North Carolina. 1985.

[11]
Karamouz, M., Torabi, S., and Araghinejad, S. Analysis of Hydrologic and Agricultural 
Droughts in Central Part of Iran. ASCE, J. of Hydrologic Eng. 2004. 9(5): 402-414.

[12]
Lugue, L. Zonificación agroecológica del fresno (Fraxinus pennsylvanica Marshall) y 
roble(Quercus robur L.) en el Valle de Calamuchita, Córdoba, Argentina 
(Agroecological zoning of ash (Fraxinus pennsylvanica Marshall) and oak (Quercus 
robur L.) in the Calamuchita valley, Córdoba, Argentina). Master thesis, Graduate 
School, Fac. de C. Agrop. Universidad Nac. Cordoba. 2009.185 p.

[13]
McKee, T.B.; N.J. Doesken; and J. Kleist. Drought monitoring with multiple time 
scales. Preprints, 9th Conference on Applied Climatology. 1993.pp. 233–236. 
January 15–20, Dallas, Texas.

Russian Journal of Agricultural and Socio-Economic Sciences, 12(24)

12

[14]
Palmer, W.C. Meteorological drought. Research Paper No. 45, U.S. Department of 
Commerce Weather Bureau, Washington, D.C. 1965.

[15]
Pratap, T., Pradhan, P., Lotta, P.K., Mya, S., Karim and Nakarmi, G. Geographic 
Information Systems and Technology application in Agro-ecological zonation of 
mountain agriculture. Eds. N.S. Jodha, M. Banskota and Tej Pratap, Oxford & IBH 
Publishing Co. Pvt. Ltd., New Delhi. 1992.

[16]
Ravelo A.C., Abril E.G. 2009. Remote sensing Section IV.8, 9 pages. K. Stigter (Ed.). 
In press. 

[17]
Segal, J.L., Mandal, D.K., Mandal, C. and Vadivelu, S. Agro-ecological Region of
India. NBSS & LUP (ICAR) Publication. 1992. 24, Nagpur.

[18]
Sen, Z. Probabilistic formulation of spatio-temporal drought pattern. Theoretical 
Applied Climatology. 1998.61: 197-206. 

[19]
Shin, H.S., and Salas, J.D. Regional Drought Analysis Based on Neural Networks. 
ASCE J. of Hydrologic Eng. 2000. 5(2): 145-155.

[20]
Subramanian, A.R. Agro-ecological Zones of India. Arch. Met. Geoph. Biocl. 1983. 
Ser. B32; 329-333.

[21]
Venkateshwaralu, J., Ramakrishna, Y.S. and Rao, A.S. Agro-climatic zones of India.
Annals of Arid Zone. 1996.35 (1); 1-8.