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Are Meteorological Parameters Based Drought Indices Enough for Agricultural Drought Monitoring:

A Comparative Study of Drought Monitoring with SPI and Crop Simulation Model

Ajeet S. Nain, Kurt-Christian Kersebaum and Wilfried Mirschel

Department of Agrometeorology, Indira Gandhi Agricultural University Raipur-492006, India Institute for Landscape System Analysis, Leibniz Centre for Agricultural Landscape and Land Use Research

Müncheberg, Germany

ABSTRACT

Condensed abstract: In present study, we compared popular Standardized Precipitation Index (SPI) and a newly generated Crop Simulation model based drought Indices (CSDI) for agricultural drought monitoring and characterization. The CERES model was calibrated and validated for wheat at observation taken at agriculture farm of Centre of Agriculture Landscape and Land Use Research (ZALF), Müncheberg and was latter applied on average district conditions of Märkisch-Oderland. The crop simulation model was run on 53 years of observed weather data and 51 years of predicted weather data. The observed averaged district yields of 13 years (recent) were compared with simulated crop yield. A strong relationship was found between observed yield and simu-lated crop yield with correlation coefficient (r) of 0.78, which is significant at 99 % probability level. Model based drought monitoring could capture three drought years out of 4 crop yield deficit years in 13 years, while SPI based index could capture only one drought year. Results showed that model could be used effectively for monitoring and defining the agricultural drought. The CSDI have edge on other conventional method of agricul-ture drought monitoring that it is able to account the water deficit occurring due to runoff, deep percolation, evapotranspiration and precipitation deficit. Key words: SPI, Agricultural Drought, Crop Simulation based Drought Indices (CSDI), CERES-Wheat 1 INTRODUCTION Rainfed agriculture system can be characterized by the dependency of agriculture production of precipitation. The spatial and temporal distribution of rainfall is so erratic that rainfed agriculture witness drought conditions very frequently. Drought is the most complex and least understood of all natural hazards, affecting more people than any other hazard (WILHITE 2000). However drought has to be perceived as natural part of climate under all climatic regimes. It occurs in high as well as low rainfall areas. The frequency of droughts is commonly associ-ated with specific climatic regions, (e. g., it is common in African Sahel or Australia and on the contrary rarely visualized in western or central Europe). However, in Central Europe drought is well noted in the paleoclima-tological record as early Neolithic settlements (KALIS et al. 2003) were significantly influenced by drought. In the later case so-called “green droughts” i. e. droughts associated with apparent ample yearly rainfall but reduced agricultural productivity because of the poor timing of rains (rainfall distribution in relation to crop demand) or ineffective precipitation often take place.

It should be remembered that drought has also had significant impacts on the economy and life in Eastern and Central Europe in recent decades (SMITH et al. 1996). Other countries, such as those of the Mediterranean region, are also strongly affected (WATSON et al. 1997). Although the recent occurrence of droughts in these regions cannot be linked directly with human-induced climate change, estimated impacts of drought events illustrate the vulnerability of those areas to the drought episodes. Considering the projected increases in temperature over Central Europe with only a slight growth in precipitation amounts in some seasons (winter) and declines in other seasons (summer), it is very likely that the frequency of drought occurrence and its severity will increase in Cen-tral Europe in the future and the impacts associated with these events will be exacerbated. In addition, increasing demand for water and increasing pressure on other natural resources resulting from population growth, increased urbanization, and greater emphasis on environmental protection are changing the vulnerability of society to drought (MCCARTHY et al. 2001).

WILHITE & GLANTZ (1985), classified the drought events into four categories which are: 1) Meteorological drought: Usually expressions of precipitation’s departure from normal over some period

of time. Reflects one of the primary causes of a drought. 2) Hydrological drought: Usually expressions of deficiencies in surface and subsurface water supplies. Re-

flects effects and impacts of droughts. 3) Agricultural drought: Usually expressed in terms of needed soil moisture of a particular crop at a par-

ticular time. 4) Socio-economic drought: Definitions associating droughts with supply of and demand for an economic

good.

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The first three groups could be defined as environmental indicators, the last group as a water resource indicator. While meteorological, hydrological and socio-economic droughts occur when there is widespread and significant deficiency of rainfall, the agriculture drought can occur even by short dry spell during the sensitive growth stage.

In order to improve the agricultural production by alleviating the drought conditions, it is a pre-requisite to identify the causes of drought. The main cause of drought is lack of rainfall at one stage of crop growth or the other. For understanding this, a climatological analysis of drought, especially its frequency, duration, intensity and time of occurrence are very important. This provides a deep insight into the causes and effects of drought on different farmers in different eco-systems.

Some studies have been carried out in Germany to characterize the meteorological drought conditions, but no study has been carried out to characterize the agricultural drought. In present paper we compare the two different approaches for characterizing the agricultural drought conditions in order to develop an agricultural drought assessment tools in the present climatic conditions. Such a study will be the basis for a more comprehensive study concentrating on an estimation of changes in drought risk and patterns of drought occurrence in the ex-pected climatic conditions through this century, as well as real time monitoring of drought risk over the area. We have also analyzed the future climatic scenarios of 51 years for drought preparedness in the study region. 2 MATERIALS AND METHODS The characterization of agricultural drought is entirely different from other droughts. The deficiency of water in sensitive crop stages even for short period can reduce the agriculture production severely. The other than agricul-tural drought, can be restored to normalcy once there is the occurrence of sufficient precipitation, but agricultural drought can not return to normalcy for that particular crop season because the damage caused on crop during any crop stage can not be repaired at other stages. The drought events should not be seen as alone event. Sometime effect of one-drought events continues affecting the area for several years so there is the need to analyze the drought events on the basis of continuous weather data. The analysis of agriculture drought involve great com-plexity as the beginning and end of any drought is hard to determine since the effects of drought often accumu-late slowly over a considerable period of time and may linger for years after the termination of the event. Sec-ond, the absence of a precise and universally accepted definition of drought, adds to the confusion about whether or not a drought exists and, if it does, its degree of severity. Therefore any realistic definition of drought must be region and application specific. Four interrelated categories of drought are usually distinguished: hydrological drought, socio-economic drought, meteorological drought and agricultural drought. The meteorological drought is expressed solely on the basis of the degree of dryness (usually related to the departure of rainfall from aver-age) and duration of the dry period. Agriculture drought links various characteristics of meteorological drought to agricultural impacts, focusing on precipitation shortages, differences between actual and potential evapotran-spiration, soil deficits etc. Any realistic definition of agricultural drought should account for the variable suscep-tibility of crops at different stages of crop development. Meteorological drought is frequently described in terms of drought indices, which are convenient and relatively simple to use. In the present study we compare, the Stan-dardized Precipitation Index (SPI) and an Agriculture drought index based on crop simulation model (CSDI). The crop simulation based analysis of drought is necessary for identification of agricultural drought because it account for water losses through percolation (soil effect), run off (slope effect), evapotranspiration (temperature effect) and continuing water deficit (from past drought effect) on the crop. 2.1 Standardized Precipitation Index The SPI was formulated by Tom Mckee, Nolan Doesken and John Kleist of the Colorado Climate Center in 1993. The purpose is to assign a single numeric value to the precipitation, which can be compared across regions with markedly different climates. Technically, the SPI is the number of standard deviations that the observed value would deviate from the long-term mean, for a normally distributed random variable. Mathematically, the SPI is based on the cumulative probability of a given rainfall event occurring at a station (MCKEE et al., 1993). The historic rainfall data of the station is fitted to a gamma distribution, as the gamma distribution has been found to fit the precipitation distribution quite well. This is done through a process of maximum likelihood esti-mation of the gamma distribution parameters, α and β. In simple terms, the process described above allows the rainfall distribution at the station to be effectively represented by a mathematical cumulative probability func-tion. Therefore, based on the historic rainfall data, an analyst can then tell what is the probability of the rainfall being less than or equal to a certain amount. Thus, the probability of rainfall being less than or equal to the aver-age rainfall for that area will be about 0.5, while the probability of rainfall being less than or equal to an amount much smaller than the average will be also be lower (0.2, 0.1, 0.01 etc, depending on the amount). Therefore if a particular rainfall event gives a low probability on the cumulative probability function, then this is indicative of a likely drought event depending on the time scale. Alternatively, a high rainfall event with a high probability on the cumulative probability function is an anomalously wet event. Therefore, the SPI can effectively represent the

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amount of rainfall over a given time scale, with the advantage that it provides not only information on the amount of rainfall, but that it also gives an indication of what this amount is in relation to the normal. Such in-formation helps define whether a station is experiencing drought or not. Its output is in units of standard devia-tion from the median based on the record length. The longer the period used to calculate the distribution parame-ters, the more likely you are to get better results (e. g., 50 years better than 20 years). 2.2 Crop Simulation based Indices The crop simulation model (CERES-Wheat) was run on the average input data set for 53 years historical weather data set and 51 years future weather data set. The yield output of crop simulation model was used to characterize the agricultural drought. The gamma distribution has been fit in wheat yield using process of maximum likeli-hood estimation of the gamma distribution to characterize the normal, good and drought year for the wheat pro-duction in the region. 2.3 Model description Many crop simulation models are now available to users; these range from multispecies models to suites of mod-els with shared characteristics (JONES et al. 1995). A set of crop models that share a common input output data format has been developed and embedded in a software package called the Decision Support System for Agro-technology Transfer (DSSAT). DSSAT itself (IBSNAT 1989, JONES 1993, TSUJI et al. 1994) is a shell that al-lows the user to organize and manipulate crop, soils and weather data and to run crop models in various ways and analyse their outputs. The models running under DSSAT include the CERES (Crop Environment REsource Synthesis) model for rice, wheat, maize, sorghum, pearl millet and barley (RITCHIE 1985, RITCHIE & OTTER 1985, RITCHIE 1986); the CROPGRO model for peanut, soybean, and phaseolus bean; and a model for cassava and potato (TSUJI et al. 1994). In this study, we used CERES-Wheat (GODWIN et al. 1989) to characterize the drought in Märkisch-Oderland district of Brandenburg state of Germany.

The crop simulation models simulate crop growth with a daily time step from sowing to maturity, based on physiological processes that describe the crop’s response to soil and aerial environment conditions. The phase development is quantified according to the plant’s physiological age. Phenological development and growth in the CERES models are specified by cultivar-specific genotype coefficients depending on the photoperiod, ther-mal time, temperature response and dry matter partitioning. CERES model calculate dry matter accumulation as a linear function based on intercepted photosynthetically active radiation.

The soil water balance, nitrogen balance, and phosphorus balance submodels operate on the basis of soil lay-ers. The soil water balance component simulates surface runoff, evaporation, drainage, irrigation, and water extraction by the plant. The model utilizes the lower limit (LL-corresponding to the wilting point) and drained upper limit (DUL-corresponding to the field capacity) of plant-extractable water. The soil water content of verti-cal soil layers is calculated by distributing total infiltrated water among various soil layers by a simple cascading principle. Runoff is calculated by modification of the USDA Soil Conservation Service curve number method (WILLIAMS 1991), which is suitable when only daily sums of rainfall are available.

The nitrogen dynamics routines of the CERES models were designed to simulate each of the major N loss processes and the contributions to the N balance made by mineralization. The routines also describe the uptake of N by the crop and the effects of N deficiency on crop growth processes. The transformations simulated are mineralization and/or immobilization, nitrification, denitrification, and urea hydrolysis. Nitrate movement asso-ciated with water movement in both an upward and downward direction is also simulated. Since the rates of transformation of nitrogen are very much influenced by soil water status, the simulation of nitrogen dynamics requires that water balance also be simulated. Soil temperature greatly influences many of the transformation rates. Therefore, a procedure to calculate soil temperature at various depths, based on the soil temperature rou-tine (WILLIAMS et al. 1984), is also invoked in the nitrogen component of the model. 2.4 Inputs The data encompass data on the site where the model is to be operated, on the daily weather during the growing cycle, on the characteristics of the soil at the start of the growing cycle or crop sequence, and on the management of the crop. Required weather data encompass daily records of total solar radiation incident on the top of the crop canopy, maximum and mini-mum air temperature above the crop, and rainfall. CERES model requires informa-tion on the water holding characteristics of different soil layers. It needs a root-weighting factor that accommo-dates the impact of several adverse soil factors on root growth in different soil layers, such as soil pH, soil im-pedance, and salinity. Additional soil parameters are needed for computing surface runoff, evaporation from the soil surface, and drainage (RITCHIE 1972). Initial values of soil water, nitrate and ammonium are needed as well as an estimate of the above and below ground residues from the previous crop. All aspects of crop management

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including modifications to the environment (e. g. photoperiod extension) as imposed in some crop physiology studies, are needed. Typical crop management factors include planting date, planting depth, row spacing, plant population, fertilization, irrigation and inoculation. Plant bed configuration and bund height is also necessary for some crops. The input data required to run the CERES model have been tabulated in Table 1. Table 1. Input data required to run the CERES model

Site Latitude and longitude, elevation; average annual temperature; average annual amplitude in temperature

Slope and aspect; major obstruction to the sun (e. g. nearby mountain); drainage (type, spacing and depth); surface stones (coverage and size)

Weather Daily global solar radiation, maximum and minimum air temperatures, precipitation

Soil Classification using the local system and (to family level) the USDA-NRCS taxonomic system

Initial conditions Basic profile characteristics by soil layer: in situ water release curve characteristics (satu-rated drained upper limit, lower limit); bulk density, organic carbon; pH; root growth factor; drainage coefficient

Management Previous crop, root, and nodule amounts; numbers and effectiveness of rhizobia (nodulating crop)

Water, ammonium and nitrate by soil layer

Cultivar name and type

Planting date, depth and method; row spacing and direction; plant population

Irrigation and water management, dates, methods and amounts or depths

Fertilizer (inorganic) and inoculants applications

Residue (organic fertilizer) applications (material, depth of incorporation, amount and nu-trient concentrations), Tillage

Environment (aerial) adjustments

Harvest schedule

2.5 Study Area and Data Märkisch-Oderland, a district of Brandenburg state of Germany was selected for the case study. The soil of Mär-kisch-Oderland district is mostly sandy with low ground water table (> 1,200 cm), which has low water retention capacity. Soil input data, which included soil water availability, bulk density, total carbon in the soil profile and soil pH were obtained from Centre for Agricultural Landscape and Land Use Research (ZALF), Müncheberg, which is located at 52° 31´ N and 14° 07´ E at the elevation of 62 m from mean sea level. The initial water status of the soil in each year was assumed at drained upper limit (0.03 Mpa) in a fallow field. Daily weather data were collected from the Agro-meteorological observatory situated in Agriculture farm of ZALF. Solar radiation was estimated from recorded sunshine hours using Angstrom (1924) relationship.

Crop management data related to agronomic practices such as sowing, fertilizer amount and application date, irrigation quantity and time and harvesting date and method were kept constant at average adopted conditions of the farmers of the region. The year-to-year variability in sowing dates was generated by using auto sowing op-tion providing the sowing limits as 1 October to 30 November, in DSSAT3.5 modelling environment. Nitrogen at 140 kg/ha N in three split doses has been prescribed for the model run. The genotype coefficients of Bussard cultivar, which is popular wheat cultivar of the region, were used for the simulation run. CERES-Wheat model has been calibrated and validated in Müncheberg, which is a part of Märkisch-Oderland and the results of the study have been already presented in International workshop on ‘Modelling water and nutrient dynamics in soil-crop systems’ (NAIN & KERSEBAUM 2004).

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3 RESULTS AND DISCUSSIONS 3.1 Observed Wheat Yields and Simulation Wheat Yields The observed wheat yields of Märkisch-Oderland district have been analysed for recent 13 years from 1985 to 2001. The wheat yields for the years 1994 to 1997 were not available due the reorganization of Märkisch-Oderland district. Prior to unification of the Germany there were three districts namely Bad Freienwalde, Seelow and Strausberg, which were grouped, and new district Märkisch-Oderland came into existence. The yield and area of the three districts have been used to calculate the wheat yield of new district Märkisch-Oderland. The wheat yield ranged from 3,830 kg/ha in the year 1988 to 6,390 kg/ha in the year 1999 with an average wheat yield of 4,985 kg/ha. The standard deviation of the observed yield was 845.40 kg/ha, which show that there are high uncertainties in year-to-year wheat yields. In general farmers of the region prescribe similar amount of nutrients to the wheat crop every year so the variability in the wheat yield is mainly due to the moisture regime during the growth cycle of wheat. CERES model was run on average district conditions of Märkisch-Oderland for 53 years weather data set (from 1952 to 2004). The simulated yields ranged from 2,511 kg/ha in the year 2,000 kg/ha to 9,174 in the year 1965. The standard deviation of the simulated yield was 1,567 kg, which again suggest that there is high year-to-year variability in wheat yield to differing moisture regimes. As all the other inputs were kept constant during simulation run the variability in wheat yield may be attributed to the varying amount of inter-annual precipitation.

The 13 years observed wheat yield of Märkisch-Oderland were compared with the same 13 years simulated yield in order to evaluate the model performance on average district conditions. The technological advancement factor in observed yield was removed by linearly regressing the yield with the year and removing the contribu-tion of trend from the observed wheat yield. The year-to-year deviation in observed wheat yield and simulated wheat yields was calculated and compared. The comparison of observed wheat yield and simulated wheat yield shows that there is very good agreement with observed and simulated wheat yield with correlation coefficient (r) of 0.78, which is significant at 99 % probability level (Figure 1).

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Figure 1. De-trended observed wheat yield and simulated wheat yield of Märkisch-Oderland for 13 years from 1985 to 2001. The data for the period 1994 to 1997 were not available

3.2 Drought analysis 3.2.1 SPI based The SPI calculation for any location is based on the long-term precipitation record for a desired period. This long-term record is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero (EDWARDS & MCKEE 1997). Positive SPI values indicate greater than median precipitation, and negative values indicate less than median precipitation. Because

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the SPI is normalized, wetter and drier climates can be represented in the same way, and wet periods can also be monitored using the SPI. The SPI values from –0.99 to 0.99 represent the near normal conditions while positive value of more than 2 represent the extremely wet conditions. The SPI value of less than or equal to –2 represent extremely dry conditions which is known as Drought. On the basis of Table 2 we can characterise given period as Normal, drought and wet. MCKEE et al. (1993), used the classification system shown in the SPI values table to define drought intensities resulting from the SPI.

Table 2. SPI values and their corresponding climatic conditions

SPI Values 2.0+ Extremely wet 1.5 to 1.99 Very wet 1.0 to 1.49 Moderately wet -.99 to .99 Near normal -1.0 to -1.49 Moderately dry -1.5 to -1.99 Severely dry -2 and less Extremely dry

Monthly, periodically, seasonally and yearly Standardized precipitation index (SPI) was calculated for the 53 (from 1952 to 2004) years precipitation data set. All the SPI values show drought conditions in different years ranging from mild to severe drought but none of them could perfectly explain the agriculture drought conditions.

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Figure 2. Inter-annual wheat yield variability and drought analysis using Crop Simulation model based Drought Indices and SPI (Jan to Dec and Dec to Jul) The yearly (Jan-Dec) SPI and periodically (Dec to July) SPI could, to the some extent explain the drought condi-tions during 13 years of observed yield. In the duration of 13 years, four years (1988, 1989, 1992 and 2000) witnessed the drought conditions, but SPI based drought measuring technique could identify only one drought year (1989) (Figure 2). In addition to that, yearly SPI analyses showed that there is severe to moderate drought in the year 1991 and 1999, while these two years witnessed good wheat yields. This may be due the fact that there is below normal rainfall during these years but wheat growth season (Dec to July) have normal rainfall as sug-gested by SPI values of Dec to July months.

The SPI analysis of precipitation for the past 53 years (1952-2004) (only for the wheat season; SPI from Dec to July) showed that there were four severe or extremely severe drought years (1952, 1963, 1964 and 1978), while there were only two weak drought years (1971 and 1989) (Figure 3). There were 35 normal years and 12 favourable or very good years respectively for wheat production. The yearly analysis of SPI shows that there were three severe or extremely severe drought years (1971, 1982 and 1991), three weak years (1963, 1976 and

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1989), while 34 years were normal and 13 years are favourable or good respectively, for wheat production. The different type of drought occurrence tendency has been observed with yearly and periodically SPI. This differ-ence may be attributed to the erratic temporal distribution of rainfall.

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Figure 3. Drought analysis of Märkisch-Oderland for the period 1952-2004 using SPI and crop simulation based indices 3.2.2 Crop simulation based Crop simulation model has been used to simulate the wheat yield of the Märkisch-Oderland for 53 years from 1952 to 2004. The average district conditions were prescribed in the model, which were kept constant throughout the study period to observe the variation in wheat yield due to differing inter-annual moisture regime. The simu-lated wheat yields were converted into indices using the gamma distribution function. The Crop Simulation ba-sed Drought Indices (CSDI) could capture three drought years (1989, 1992 and 2000) out of four drought years (1988, 1989, 1992 and 2000) during the 13 years of observed wheat yields (Figure 3). The analysis of 53 years weather data showed that there were five severe or extremely severs drought years (1975, 1976, 1989, 1992 and 2000), while there are three weak drought years (1964, 1970, 1982), 13 favourable years and remaining 32 years were normal for wheat production. Characterization of drought with crop simulation model has advantages that it calculates the water deficiency due to all possible causes including deficient rainfall, runoff, deep percolation and evapotranspiration. PANDEY & SASTRI (2004) concluded that for the characterization of agriculture drought water balance approach on weekly or less data set is required because water deficiency for even short duration in sensitive growth stage is more harmful then long duration water deficiency during insensitive growth stages. 3.2.3 Drought probability in future climatic scenarios The drought analysis has been also done for 51 years (2005-2055) future climatic scenarios using SPI and crop simulation based approach (Figure 4). SPI analysis shows that out of 51 years four years (2005, 2019, 2029 and 2045) will be either severe or extremely severe drought years, while there will be eight weak drought years, six years would be good to extremely good and rest 33 years will witness normal conditions. The SPI analysis of wheat growing season (Dec-Jul) show that there would be four years (2005, 2019, 2026 and 2054) of severe or extremely drought conditions, seven years of weak drought conditions, while there would be seven good to ex-tremely good years and rest 33 years would be normal years.

Crop Simulation based Drought Indices (CSDI) shows that there will be six severe or extremely severe drought years (2019, 2034, 2036, 2044, 2048 and 2050) and two (2040 and 2041) will be weak drought years, while only one year (2008) would be favourable year for wheat production. Rest other years (42) years will

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probably witness the normal conditions as far as wheat production is concerned. The analysis of 51 years pre-dicted data (2004-2055) showed that at least 8 years will be drought years for wheat production and 42 years will be normal years, while only one years would be good for wheat crop. On the basis of analysis it can be con-cluded that last five decades were good for wheat production, while coming five decades will faces more chal-lenges for wheat production. Especially in the last decade of coming half century (2040-2050) would be worst for the wheat production as three out of ten years will face severe or extremely severe water deficiency.

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Figure 4. Drought analysis of Märkisch-Oderland for the future climatic scenarios (2005-2055) using SPI and crop simulation based indices 4 CONCLUSION It can be concluded from the preceding result and discussion that agriculture drought monitoring with crop simu-lation model has edge over other conventional and popular drought monitoring approach such as Standardized Precipitation Index (SPI). Though there is flexibility in SPI analysis and indices can be generated ranging from month to years. Still SPI have limitations that it cannot account for water deficit caused by evapotranspiration, deep percolation, and run off. The Crop Simulation model based Drought Indices (CSDI) has additional advan-tage that it also accounts for the type and stage of crop on evapotranspiration losses. SPI do not consider the intensity factor and temporal distribution of rainfall within the base unit, for example month. The intensity factor is necessary to calculate the water losses through runoff, while temporal distribution is necessary to calculate the water stress during the crop growth cycle. For example a good amount of precipitation in very early stage of month may cause drought like condition in later phase (especially in sandy soils), while similar amount of pre-cipitation well spread over the month will save the crop from any stress. SPI will show the similar values as it only consider the total amount or precipitation during the base unit such as month. This is reflected in the present results as Crop Simulation based Drought Indices (CSDI) could identify three crop yield deficient years out of four yield deficient years in the 13 years observed data set. 5 ACKNOWLEDGEMENTS Authors acknowledge the help received from Centre of Agriculture Landscape and Land Use Research (ZALF), Müncheberg. The present study was carried out during the stay of ASN at Müncheberg under Advanced Re-search Program of DAAD, Germany. ASN acknowledge the receipt of DAAD fellowship and the facilities and help extended by LSA and all colleagues of ZALF. ASN also acknowledge the sponsorship of Organizing com-mittee of 21st ICID conference and the part of sponsorship extended by Dr Karl-Otto Wenkel, Head of LSA, ZALF for attending the conference.

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ICID 21st European Regional Conference 2005 - 15-19 May 2005 - Frankfurt (Oder) and Slubice - Germany and Poland