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To detect temporal and spatial variability of drought is one of the most challenging issues of drought monitoring in the specific country or region due to the fact that there is no standard definition of severity and duration of different types of drought. Crop water deficit (CWD) simulated by crop water balance model IRRFIB supplemented with some in-situ soil water measurements by Time-Domain Reflectometry (TDR) measurement technique are proposed as tools for local agricultural drought monitoring in this study. Moving to regional drought monitoring the main constraint represents data availability of different sources. Available global data sets are of assistance for preparing regional drought monitoring products. In the study two specific products designed for regional scale are described: preliminary maps of the SPI (Standardized Precipitation Index) and products generated by implementation of numerical weather prediction model. It seems to be a lot of potential in both products for a first overview of key meteorological parameters in the region. The development of drought in the year 2009 was under examination and also yearly results for different periods after 1971. Dry periods in the year 2009 heavily impacted cereals in Slovenia. Maize yield showed best agreement with crop water deficit (r = 0.65) and SPI on the time scale of six months for September (r = 0.61). SPI was not suitable for describing agricultural drought in the periods with higher evapotranspiration rate. For more agricultural oriented drought monitoring more indices should be included into the consideration. Key words: drought monitoring, agriculture, IRRFIB, SPI, numerical modelling, crop water balance IZVLECEK VZPOSTAVLJANJE MONITORINGA KMETIJSKE SUSE V JUGOVZHODNI EVROPI NA RAZLICNIH PROSTORSKIH SKALAH Eden izmed vecjih izzivov na podrocju monitoringa suse v doloceni drzavi ali regiji je dolocanje casovne in prostorske variabilnosti suse, saj ne obstaja splosna definicija, ki bi dolocala intenzivnost in trajanje razlicnih tipov suse. V studiji predlagamo primanjkljaj vode pri rastlinah (CWD), simuliran z vodnobilancnim modelom IRRFIB in podprt z in-situ meritvami vode v tleh s TDR tehnologijo, kot primerno orodje za lokalno dolocanje kmetijske suse. Na sirsem, regionalnem nivoju pa se pojavi ovira pri dostopnosti podatkov, zato si pri pripravi regionalnih produktov lahko pomagamo z globalnimi nizi. Opisujemo dve moznosti, primerni za regionalno skalo: preliminarne karte standardiziranega padavinskega indeksa (SPI) in produkte, ki jih generiramo z implementacijo numericnega modela za napovedovanje vremena. Pri obeh se kaze velik potencial za prvi, splosni pregled nad stanjem glavnih meteoroloskih parametrov v regiji. Za primer smo vzeli razvoj suse leta 2009 ter letne rezultate za razlicna obdobja po letu 1971. Leta 2009 so susna obdobja hudo prizadela poljscine v Sloveniji. Pridelek koruze kaze najboljso povezanost s primanjkljajem vode CWD (r = 0,65) in z indeksom SPI na sestmesecni casovni skali za september (r = 0,61). Indeks SPI se ni izkazal za primernega pri obravnavi obdobij z visjo stopnjo potencialne evapotranspiracije. Opozarjamo se na dejstvo, da bi bilo potrebno za bolj kmetijsko usmerjen monitoring suse vkljuciti vec razlicnih indeksov. mag., univ. dipl. agr., Agencija Republike Slovenije za okolje, Vojkova 1b, 1000 Ljubljana, firstname.lastname@example.org univ. dipl. meteorol., Agencija Republike Slovenije za okolje, Vojkova 1b, 1000 Ljubljana dr., univ. dipl. meteorol., Agencija Republike Slovenije za okolje, Vojkova 1b, 1000 Ljubljana univ. dipl. mat., Agencija Republike Slovenije za okolje, Vojkova 1b, 1000 Ljubljana univ. dipl. meteorol., Biotehniska fakulteta, Univerza v Ljubljani, Oddelek za agronomijo, Jamnikarjeva 101, 1111 Ljubljana, email@example.com str. 231 - 243 Kljucne besede: monitoring suse, kmetijstvo, IRRFIB, SPI, numericno modeliranje, vodna bilanca rastlin 1 INTRODUCTION All types of drought originate from a deficiency of precipitation (Wilhite and Glantz, 1985). Meteorological drought is defined as extended period of time with significant precipitation deficit. Agricultural drought is defined more commonly by the availability of soil water to support crop and forage growth than by departure of normal precipitation over some specific period of time (Wilhite, 2007). It can also be determined by a period of reduced plant growth with a prolonged and abnormal soil water deficiency. Many attempts were made to create agricultural drought index, some of the `rainfall indices' can be related, for example, to soil and crop type, crop status and climatological parameters such as air temperatures, air humidity and wind (Maracchi, 2002). A soil water deficit within the rooting zone can result in crop water stress, depending on the crop status and climatological factors affecting evapotranspiration. Drought indicators and triggers are important for several reasons: to detect and monitor drought conditions; to determine the timing and level of drought responses; and to characterize and compare drought events. However, agricultural drought depends on soil moisture and evapotranspiration deficits. For this reason, water balance model IRRFIB was developed, which computes the main components of water balance aiming to quantify drought stress of crop canopy. On a daily basis it evaluates soil moisture content. It also computes seasonal and annual integrated drought stress by the ratio of actual to potential transpiration. A simulation study of the soil moisture content under a maize field was carried out. The approach of analysing the effect of drought on crop using dynamic crop models has the advantage to include all relevant drought impact factors of the soil-crop-atmosphere system over short time periods. This is of special interest when answering the questions whether agrometeorological model IRRFIB is sufficient to simulate the water balance and the occurrence of drought on local scale. Beside assessment of drought conditions on local scale (which can be extremely variable due to local conditions) there is a need to estimate situation on larger scale. One of possible (and frequently applied) procedures is application of point measurements and geostatistical techniques for spatial interpolation (such as kriging; see for example Pardo-Iguzquiza, 1998). There are other possibilities; numerical weather prediction (NWP) models (that are routinely used for weather forecast) are potentially useful tool for drought monitoring. Under term NWP model we usually understand mathematical set of equations describing motion of air and other events that take place in the atmosphere. Modern NWP models are constructed around the full set of primitive equations which govern atmospheric motions and are formulated in discrete numerical form; some processes are not fully resolved and are rather presented by parameterization, such as turbulent diffusion, radiation, moist processes, heat exchange, soil, vegetation, surface water, convection etc. NWP models simulate values in regular grid mesh; error is expected to be spread over whole computing domain and doesn't strongly depend on distance from nearest observation as in the case of statistical interpolation. For larger areas with various density of observation NWP models seem to be useful tool for drought detection despite their known deficiencies (see for example Ebert and McBride, 2000). 2 MATERIALS AND METHODS 2.1 Site descriptions The experimental fields were located in the southeastern Slovenia. Murska Sobota is located on flat area, at 46° 39' latitude, 16° 11' longitude, and at an elevation 188 m a.s.l. Platform for meteorological measurements is located within agricultural research area. Meteorological observations were recorded in the frame of national meteorological network. Climate is characterized by cool, wet winter and warm, dry summers, with an average (1971-2000) annual precipitation of 805 mm (Figure 1). 232 Figure 1: Climate diagram for Murska Sobota (source: EARS, 2009) Slika 1: Klimagram za Mursko Soboto (vir: ARSO, 2009) Around 60 % of the precipitation occurs between April and September. Mean annual air temperature is 10.2 °C, with the mean monthly maximum of 37.8 °C occurring in August. Precipitation during the recent vegetation periods was less than 60 % of the long-term average, primarily due to seasonal shift to more dry vegetation periods. Average annual potential evapotranspiration is around 700 mm, around 80 % occurring during the vegetation period. 2.2 Climate data The following meteorological parameters were used in the study: minimum air temperature, maximum air temperature, relative air humidity, cloudiness or duration of irradiation, wind speed and precipitation. Daily meteorological data for the period 1971-2009 were obtained from the database of the Slovenian Meteorological Office (EARS, 2009). 2.3 Crop data Phenological data for grass (Dactylis glomerata) and maize (Zea mays) have been obtained from the database of the Slovenian Meteorological Office (EARS, 2009). The phenological stages used in the study included sowing, emergence, 3rd leaf, beginning of male flowering, beginning of female flowering, milky ripe, vax ripe, full ripe and harvest of maize (middle ripening class) for the period 1971-2009 with some missing years in the dataset. For model IRRFIB verification with regard to soil moisture measurements phenological data of grass heading and flowering for the period 2006-2008 were used. For the period 1993-2008 maize yield data were obtained from Agriculture Institute of Slovenia. Over time some cultivars were changed but still remain in the same FAO ripening class. 2.4 Drought impact reports The drought impacts on crops were obtained from Agrometeorological reports of Meteorological Office of the Republic of Slovenia as timely information on the severity and spatial extent of drought and its associated impacts (EARS, 2009a). Improved information on drought impacts helped to identify the type of impacts and where they were occurring. 2.5 Soil data Soil water characteristics and hydraulic conductivity functions have been described through field capacity (Fc) and wilting point (Wp) through experimental data. Soil water holding capacity (SWC) is around 100 mm. For soil water measurement Time-Domain Reflectometry (TDR) technology was used; probes are mounted in 10, 20 and 30 cm depths at both meteorological stations. TDR device (Trime) for continuous and non-destructive determination of volumetric soil moisture consists of electronic sensor which measures dielectric constant of the material and recalculates it to the soil moisture content. Data are available in 10 minute intervals. For the model verification measurements for the period 20062008 were obtained. The experimental plots with yield data are near the meteorological station in Murska Sobota, where the soils have the same characteristics. 2.6 Model IRRFIB description IRRFIB simulates the water balance in the crop-soil-system on the daily basis. The model calculates evapotranspiration (ET) using the Penman-Monteith equation (FAO, 1998) for different crop covers considering the relevant processes of heat, water and vapour transport in the soil-crop-atmosphere interface (Table 1). Crop coefficients and rooting depths are linearly interpolated during each phenological phase and are used for calculating actual evapotranspiration and soil water reservoir, respectively. Table 1: Input and output parameters of IRRFIB model (Susnik, 2006). Tabela 1: Vhodni in izhodni parametri modela IRRFIB (Susnik, 2006). INPUT Minimal air temperature Maximal air temperature Relative air humidity Cloudiness / Duration of irradiation Wind speed Precipitation Phenological phase Crop coefficient (for phase) Roots depth Field capacity Wilting point Value Daily values Daily values Daily values Daily values Daily values Daily values Dates for maize, grass 0-1 (grass); 0-1.1(maize) 20 cm (grass); 0-50 cm (maize) Different values for locations Different values for locations OUTPUT Potential evapotranspiration Actual evapotranspiration Weather Crop Crop coefficient (daily) Crop water demand Irrigation demand Precipitation infiltration Soil water content Soil water deficit Soil Crop water simulation model IRRFIB was tested for a variety of crops and applications (Susnik et al, 2006). Model results were validated against water content measurements using a TDR sensor in 2004 at the measurement site of meteorological station in Ljubljana. Strong correlations were obtained during the testing period (r2 = 0.94) (Susnik, 2006). Model performance was also tested for a test site in Braunschweig Germany (Susnik, 2005). Recent study with model SIMPLE showed good degree of concomitance with IRRFIB model (r2 =0.69). Model runs were performed for the period 1971-2008 due to availability of phenological data. Water balance ( B ) and crop water deficit ( CWD ) Water balance of the first day assumes that water reservoir is full as follows: Daily water deficit on day i can be expressed as: CWDi is a difference between daily precipitation and crop evapotranspiration ( ETri ), which ETri = K ci EToi CWDi = Pi - ETri , if Bi -1 > Bthreshold CWDi = Pi - ETri 2 , if Bi -1 Bthreshold ... (3) ... (4) ... (5) In our study only daily water deficits with values less than Bthreshold were used. The water balance on day i, is defined as B1 = PK Z PK vol. % of water at PK Z rooting depth [mm] For the crop coefficient Bi = Bi -1 + CWDi if Bi < TVi Z i is equal to TVi Z i ... (1) if Bi > PK i Z i is equal to PK i Z i ... (6) ... (7) Number of days with CWD was summarized over vegetation period. and rooting depth Kc linear approximations in the vegetation period were performed. The volume of plant available water (PAW) is defined as ( 1 - pp ) . In this study pp equals 0.5. The lower threshold of available water is defined as 2.7 Drought stress days (DS) Drought stress occurs in evapotranspiration ( ETr ) is situations less where than crop potential Bthreshold Bthreshold : = (TV + ( PK - TV ) pp ) Z evapotranspiration ( ETo ). Inside IRRFIB model drought stress is simulated as impact of soil water availability on ... (2) Following data are needed for the water balance calculation for i-th day: reference evapotranspiration Pi , crop coefficient water balance on previous day Bi -1 . precipitation EToi , daily K c i , rooting depth Z i and ETr , assuming that soil moisture limited ETr beyond a threshold suction value (50 % of Fc) and than decreased linearly to zero at permanent wilting point. The limit of crop available soil water is a threshold, under which a half of actual evapotranspiration is subtracted from precipitation amount, resulting in daily water deficit. During vegetation period drought stress intervals were analysed. Days with the soil water content under this limit are considered as days with drought stress. 234 Establishment of agricultural drought monitoring at different spatial scales in southeastern Europe 2.8 Standardized precipitation index (SPI) Standardized Precipitation Index represents the transformation of the precipitation time series into standardised normal distribution. Detailed description of transformation procedure can be found in Guttman (1999). Due to simplicity (it requires only precipitation data) it became one of most frequently applied tools for drought monitoring. Variable time scale of SPI calculation enables description of drought conditions in meteorological, agricultural and hydrological applications. Drought dynamics is another important feature that is addressed with variable time scale; it is capable of determining the onset, duration and intensity of drought. We have implemented a modified version of SPI software from Colorado Climate Center, which is capable of calculating SPI on specified time scale and also accounts for zero precipitation. Time scale was specified as the number of days or months provided daily or monthly precipitation sums at meteorological stations as input data. 2.9 Statistical methods Statistical measures like average error (AE), root mean square error (RMSE), modelling efficiency (EF), coefficient of residual mass (CRM) and Pearson's correlation coefficient (r) are used in the study. For the classification of vegetation periods regarding drought severity, Conrad-Chapman percentile classification (Susnik, 2005) is used. 3 RESULTS 3.1 Model IRRFIB verification Comparison of modelled grass water balance with measurements was made for three years (2006-2008) in Murska Sobota. Values of AE, RMSE and CRM are indicating little deviation between measured and simulated values when they are near zero. On the other hand, the optimum value for EF is 1, representing a good modelling efficiency (Elmaloglou and Malamos, Table 2: 2000). CRM is negative, where IRRFIB obviously overestimates soil water content. Correlation r with measurements is significant (Table 2). Relatively small overall differences imply that IRRFIB can be a useful tool in simulating soil water balance (Figure 2). Five statistical criteria for comparison of measured (Trime) and simulated (IRRFIB) values of soil water content in Murska Sobota Tabela 2: Pet statisticnih kriterijev za primerjavo izmerjenih (Trime) in simuliranih (IRRFIB) vrednosti kolicine vode v tleh v Murski Soboti location AE [vol.%] Murska Sobota 2,11 ** Correlation is significant at 0.01 level. RMSE [%] 20,40 EF 0,55 CRM -0,10 r 0,84** Figure 2: Comparison of measured (Trime) and simulated (IRRFIB) values of soil water content in Murska Sobota in the period 2006-2008 Slika 2: Primerjava izmerjenih (Trime) in simuliranih (IRRFIB) vrednosti kolicine vode v tleh v Murski Soboti v obdobju 2006-2008 3.2 Comparison of SPI with water balance The case study for year 2009 revealed the importance of relationship between drought duration and water balance during the growing season, when monitoring agricultural drought. In order to estimate the degree of agreement between index values and water balance dynamics, the graphical comparison between SPI and soil water balance was made for growing season in 2009 (Figure 3). Results revealed best agreement between cumulative water deficit and SPI on the time scale of two months (SPI2). SPI2 was calculated for every day of vegetation season and compared with soil water balance for the reference crop. Daily values of soil water balance were calculated as a difference between precipitation and evapotranspiration cumulative for a period of two months. When the soil moisture deficit increased as a consequence of higher evapotranspiration rates, SPI2 wasn't capable to identify drought situations. Figure 3: SPI values on the time period of two months (left y axis) and cumulative water deficit (right y axis) during vegetation period 2009 in Murska Sobota Slika 3: Vrednosti indeksa SPI na dvomesecni skali (leva y os) in kumulativne vodne balance (desna y os) v vegetacijskem obdobju 2009 v Murski Soboti 236 This can be seen at the end of July, with a period of high water deficit, whereas SPI2 remained positive. Same situation occurred after an extreme precipitation event at the 4th of August, which was followed by the period of very hot weather with an occurrence of a heat wave. Thus, SPI solely couldn't explain the water balance dynamics in the root zone during periods, when soil moisture couldn't meet the plant needs. Table 3: 3.3 Relationship between yield and different drought indicators Crop yield at the harvest is a good indicator of climate, soil and management practices during the vegetation season. Correlation coefficients (r) among yield and drought indicators in the period 1983-2008 in Murska Sobota Tabela 3: Koeficienti korelacije (r) med pridelkom in razlicnimi kazalniki suse za obdobje 1983-2008 v Murski Soboti Yield MS ETo CWD DS SPI 6 ,614** ,001 26 Pearson Correlation ,462* -,397 ,652** -,510* Sig. (2-tailed) ,027 ,061 ,001 ,013 N 23 23 23 23 ** Correlation is significant at the 0.01 level (2-tailed). Figure 4: Yield vs. crop water deficit (CWD) for maize on soil with medium water holding capacity in Murska Sobota in the period 1983-2008 Slika 4: Pridelek v odvisnosti od primankljaja vode (CWD) za koruzo na tleh s srednjo zadrzevalno sposobnostjo za obdobje 1983-2008 v Murski Soboti term period. Strong correlation is observed between yield and crop water deficit, whereas it was weaker with only one meteorological parameter like ETo or P (Table 3). The correlation coefficients (r) between the crop water deficit and yield are significant indicating that higher crop water deficit lead to yield decrease (Figure 4). Under standardized management practice routines and potential nutrition applications, climate determines the variability of crop yield for a certain soil type. SPI and model IRRFIB were used in order to investigate the effects of different indicators like precipitation ( P ), evapotranspiration ( ETo ), crop water deficit ( CWD ) and drought stress ( DS ) on the maize yield over long- Figure 5: Standardized yield vs. standardized precipitation index on the time scale of 6 months (SPI6) in Murska Sobota in the period 1983-2008 Slika 5: Standardiziran pridelek v odvisnosti od indeksa SPI na sestmesecni skali za obdobje 1983-2008 v Murski soboti Proxy data (drought impact reports) were also used for comparison due to the fact that long-term data on yield are unfortunately not available for Slovenia. The results showed that crop water deficit represents a good indicator when linking it with drought reports. Based on our understanding soil moisture can significantly affect the yield, but other factors like diseases and pests can trigger the decrease of yield as well (for example year 2005). A comparison was also made between maize yield and SPI on different time scales. In order to make a direct comparison with SPI, yield data were standardized for a period between 1983 and 2008. Best agreement was found between maize yield and SPI on the time scale of six months for September (Figure 5), which is in the time frame of maize growing season. There were significant differences in years with high temperature variability during growing season (Figure 6). Year 2006 shows highest disagreement; in that case the vegetation period was characterized by above normal precipitation (SPI value of 1.5), but the yield was below average. Lower yield was the consequence of high temperatures and low precipitation amounts during June and July. This is the period when maize is approaching tasseling and shows high degree of vulnerability to high temperatures and water deficiencies (Cergan et al., 2008). Large evapotranspiration rates during this period have limited potential development rate, which affected grain filling later in the season and consequently lowered the final yield. 238 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 -3 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 yield SPI6 Figure 6: SPI on the time scale of 6 months and standardized maize yield in Murska Sobota in the period 1983-2008 Slika 6: Indeks SPI na sestmesecni skali in standardiziran pridelek koruze v Murski Soboti v obdobju 1983-2009 3.4 Crop water deficit as a measure of drought severity In the end of this section we focused on the crop water deficit as a measure of drought severity. In order to investigate whether CWD is reliable in time before records of yield are available, we used CWD for the Table 4: period of available phenological data for maize and meteorological data since 1971. The results show that the driest vegetation period in Murska Sobota occurred in 2003 (Figure 7). Very dry years were 1983 and 1993 and dry years 1984, 1988, 2000 and 2001 (Table 4). Percentile classes of crop water deficit (CWD) for maize on soil with medium water holding capacity in Murska Sobota in the period 1972-2008 Tabela 4: Percentilni razredi primanjkljaja vode (CWD) za koruzo na tleh s srednjo zadrzevalno sposobnostjo za obdobje 1972-2008 v Murski Soboti extremely dry < -237 2003 extremely wet > -70 1975 very dry -237 to -198 1983 1993 dry -198 to -174 1984 1988 2000 2001 normal -174 to -107 1981 1985 1986 1987 1990 1991 1994 1998 1999 2002 2004 2006 2007 2008 wet -107 to -82 1973 1982 1989 1997 2005 very wet -82 to -70 1972 1980 The data have been checked with the reported drought impacts over a specified period of time. Drought impacts on cereals were confirmed in all extremely dry, very dry and dry vegetation seasons (HMZ / EARS, 1983-2003). The seasonal water deficit up to 240 mm was recorded in dry seasons (Table 4) in Murska Sobota. -50 -100 -150 -200 -250 -300 CWD [mm] stress days Figure 7: Crop water deficit (CWD) for maize on soil with medium water holding capacity in Murska Sobota in the period 1972-2008 in descending order Slika 7: Primanjkljaj vode (CWD) za koruzo na tleh s srednjo zadrzevalno sposobnostjo za obdobje 1972-2008 v Murski Soboti v padajocem vrstnem redu accumulation of simulations, nested into ERA-Interim reanalyses (Simmons et. al., 2007) for stations in Slovenia haven't exceeded value 0.8 (in some cases it remained below 0.5), while in case of accumulation of evapotranspiration R2 exceeded value 0.9 for all stations that were taken into account (Roskar and Gregoric, 2010). Overall performance of NWP model for drought monitoring is therefore promising; the question remains whether it is appropriate for drought impact assessment in local scale. 3.5 NWP simulations as a tool for drought monitoring Potential evapotranspiration and precipitation are among NWP simulated variables that are relevant for assessing drought conditions. It is known that numerical simulation of precipitation amount is among least reliable NWP output. This was confirmed by application of NMM numerical meteorological model on domain situated over SE Europe with approximately 8 km horizontal resolution; R2 for 60-day precipitation 240 Figure 8: Comparison of maize crop water deficit in Murska Sobota with NWP simulations of surface water balance in the period 1989-2007 Slika 8: NWP simulacije povrsinske vodne balance v odvisnosti od primankljaja vode (CWD) za koruzo za obdobje 1989-2007 v Murski Soboti Percentile classes of maize crop water deficit (CWD - see Table 4) and NWP simulated surface water balance in 5 percentile classes for years 1989-2008 Table 5: Tabela 5: Razdelitev primankljaja vode za koruzo (CWD) in simulirane povrsinske vodne balance (NWP) v pet percentilnih razredov za obdobje 1989-2008 Year 1989 1990 1991 1993 1994 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 CWD extremely/very wet wet normal extremely/very dry normal extremely/very wet normal normal dry dry normal extremely/very dry normal wet wet normal NWP simulation extremely/very wet normal wet dry normal wet normal wet extremely/very dry dry normal extremely/very dry normal normal normal dry Since CWD appears to be parameter that adequately represents local drought conditions, it can be used as measure of success of NWP simulation. Figure 8 shows comparison of modelled surface water balance (cumulative evapotranspiration subtracted from cumulative precipitation between May and September) to measurement-based calculation of CWD in period 1989-2007. Cumulative water balance between May and September was found to be closest parameter derived from NWP simulations using normal post-processing techniques to measurement-based CWD for maize. However, adjusted value of R2 was only 0.29. Similar as in the case of SPI index, basic post-processing of NWP simulations could not explain significant part of interannual variability of drought stress estimated through CWD. This fact is presented also in Table 5 which contains measurement based CWD and NWP in percentile classes as in Table 4. Only 5 percentile classes were used in this case (two most extreme classes on both sides were joined into single extreme wet or extreme dry class). In 7 years (out of total 16) the percentile classes don't match. In 5 out of 7 cases there is "dry bias" of NWP derived water balance (in 1990, 2005 and 2006 "normal" opposed to "wet"; in 2007 "dry" opposed to "normal" and in 2000 "extremely dry" opposed to "dry"). In two cases (1991, 1999) "wet bias" was observed ("normal" opposed to "wet"). 4 DISCUSSION AND CONCLUSIONS The following conclusions can be reached on the basis of above comparison and analysis: (1) Water balance model IRRFIB simulations are of good quality. The relative difference of calibration results between IRRFIB and measurements is small (r = 0.8). In addition, model detection of crop water deficit, its drought stress and impact on yield is less consistent. In other words, CWD possess very micro-location capability. (2) The yield decreases with the increase of CWD and DS. The fitting R-squares is 0.652 and 0.510, respectively. This indicates that CWD could represent drought conditions, while the fitting Rsquares of P and ETo are only 0.462 and 0.397, respectively. From the scatter points distribution of CWD dry years are confirmed with reported drought impacts. This is a clear demonstration of drought information for local scale and specific crop. (3) Best agreement was found between maize yield and SPI on the time scale of six months for September, which is in the time frame of maize growing season. There were significant differences in years with high temperature variability during growing season. The SPI can be used to monitor conditions on a variety of time scales and is to be useful in both short-term agricultural and long-term applications. (4) Comparison of NWP derived accumulated water balance to measurement based CWD indicates correlation to be rather poor (R-squared reaches values around 0.3 for various periods of accumulation). Although it is statistically significant at 0.01 level it is not possible to use NWP output directly to estimate drought impacts on crops. However, due to capability of NWP models to simulate temperature and evapotranspiration anomalies (and less successfully precipitation), there is potential to develop drought monitoring tools for regional scale. (5) The integration of existing drought monitoring tools is essential for improving local and regional drought monitoring. A proactive approach emphasizing integration requires the collective use of multiple tools, which can be used to detect trends in crop water availability and provide early indicators at local, national, and regional scales on the likely occurrence of drought. 5
Acta Agriculturae Slovenica – de Gruyter
Published: Sep 1, 2010
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