Assessing Drought Vulnerability of Bulgarian Agriculture through Model Simulations
Z. Popova1*, М. Ivanova1, L. S. Pereira2, K. Doneva1, V. Alexandrov3, P. Alexandrova1, M. Kercheva1
1 N. Poushkarov Institute of Soil Science-ISSNP, Sofia, Bulgaria. 2 CEER - Biosystems Engineering, Institute of Agronomy, Technical University of Lisboa, Portugal. 3 National Institute of Meteorology and Hydrology, Sofia, Bulgaria
Abstract Simulations are performed for Plovdiv, Stara Zagora, Sandanski and Sofia (South Bulgaria) and Pleven, Lom, Silistra and Varna (North Bulgaria). Results relative to Plovdiv, show that in soils of large TAW (180 mm m-1) net irrigation requirements (NIRs) range 0-40 mm in wet years and 350-380 mm in dry years. In soils of small TAW (116 mm m-1), NIRs reach 440 mm in the very dry year. NIRs in Sofia and Silistra are about 100 mm smaller than in Plovdiv while in Sandanski and Northern Greece they are 30-110 mm larger.
Rainfed maize is associated with great yield variability in this country (29%
Keywords : Bulgaria, South East Europe (SEE), Drought vulnerability, maize, ISAREG simulation model, SPI-index, Drought vulnerability mapping.
Introduction
There are a lot of facts proving that Global Climate Change affects the frequency and severity of extreme events as meteorological and consequent agricultural drought. The necessity to develop methodologies and simulation tools for better understanding, forecasting and managing the risk of such events is evident for the society. This study assesses the vulnerability of agriculture to drought in Bulgaria, which to same extent is representative for South East Europe (SEE), using the WINISAREG model (1, 2) and seasonal standard precipitation index SPI2 for the period 1951-2004. The model was previously validated for maize hybrids of different sensitivity to water stress on soils of small, medium and large total available water (TAW) in various locations of Bulgaria (3-10).
BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 1 The objective of this study is to assess the vulnerability of rainfed/irrigated maize to drought at South (Sandanski, Plovdiv, Stara Zagora and Sofia) and North (Pleven, Lom, Silistra and Varna) Bulgaria using the validated WinISAREG model and seasonal standard precipitation index SPI2 for the period 1951-2004 (Map 1).
Map 1 Experimental fields of ISSNP and meteorological stations of NIMH in North and South Bulgaria.
Experimental
Climate The studied regions are representative for a moderate continental (Sofia, Pleven, Lom, Silistra), a transitional continental (Stara Zagora and Plovdiv), a transitional Mediterranean (Sandanski) and a northern Black Sea (Varna) climate. A version of seasonal standard precipitation index SPI (11), that is an average of the index during periods of crop sensitivity to water stress, is used as crop specific drought indicator. Average SPI2 for several periods referring to maize sensitivity to drought, such as the vegetation season “May-Aug”, the Peak Season “June-August”, and the High Peak Season “July-August” were used to define categories of agricultural drought relative to summer crops in the studied regions. The seasonal SPI2 relative to “July-Aug” that is in fact the usual irrigation period in Sofia field and Sandanski indicate that irrigation season in 1993 and 2000 was the driest there over the last 54 years (Fig. 1a, 1d). 3
2
1
0
-1
-2
-3 a) 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 2 3
2
1
0
-1
-2
-3 b) 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
3
2
1
0
-1
-2
-3 c) 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
3
2
1
0
-1
-2
-3 d) 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
3
2
1
0
-1
-2
-3 e) 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 3 3
2
1
0
-1
-2
-3 f) 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
3
2
1
0
-1
-2
-3 g) 19 5 1 1952 1953 19 5 4 1955 1956 1957 19 5 8 1959 19 6 0 1961 1962 1963 1964 1965 1966 19 6 7 1968 19 6 9 1970 1971 1972 19 7 3 1974 1975 19 7 6 1977 1978 1979 1980 1981 19 8 2 1983 1984 19 8 5 1986 1987 1988 1989 1990 19 9 1 1992 1993 1994 1995 1996 1997 19 9 8 1999 20 0 0 2001 2002
3
2
1
0
-1
-2
-3 h) 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Figure1. Evolution of High Peak Season (July-Aug) SPI2 at: a) Sofia, b) Plovdiv, c) Stara Zagora, d) Sandanski, e) Pleven, f) Lom, g) Silistra and h) Varna, 1951-2004.
The high peak seasonal SPI2 “July-Aug” in Fig.1b also show that in the region of Plovdiv and Stara Zagora, Thracian Lowland, summer is become dryer over the last 20 years when compared with the previous 34 years. However that is not the case with the regions of Pleven and Lom, North Bulgaria (Figs. 1e and 1f).
Soil The usual soils in South Bulgaria are the chromic luvisol/cambisol of predominantly medium water holding capacity TAW (136 mm m -1) and the vertisols of large TAW (170≤TAW≤180 mm m -1) (Map2 (12).
BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 4
Map 2. Soil map of Bulgaria (12)
The typical soils in the plains of North Bulgaria are the chernozems of medium to large water holding capacity (157≤TAW≤180 mm m -1) and the vertisols (TAW≥170 mm m -1). Alluvial/deluvial meadow and some luvisol soils of small TAW≤116 mm m -1 are well identified over the terraces along the rivers.
Crop data Maize was selected as a typical summer crop. Detailed good quality crop data from long term field experiments carried out in Thracian Lowland, Sofia field and Danube Plain are available (see 13-23). Crop coefficients Kc and the yield response factor Ky (24) were calibrated and validated using independent datasets relative to experiments with a late maize varieties carried out under different irrigation schedules in Tsalapitsa, Plovdiv, Pustren and Zora, Stara Zagora, and Bojurishte, Sofia region (4-8; 10). Additional data
BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 5 on rainfed and maximum yields were used to adjust the yield response factor Ky to semi early maize hybrids for Sofia field.
Simulation model The WinISAREG model (2) is an irrigation scheduling simulation tool for computing the soil water balance and evaluating the respective impacts on crop yields. The model adopts the water balance approach of Doorenbos & Pruitt (25) and the updated methodology to compute crop evapotranspiration and irrigation requirements proposed by Allen et al. (24). Yield impacts of water stress are assessed with the Stewart one- phase model when the yield response factor Ky is known (26).Procedures for ET0-PM computation when some climate data are missing were validated using data relative to seven meteorological stations in the Thrace Lowland (27) and Sofia field (28). These procedures proved to be accurate providing small standard errors of estimates (SEE), including when only maximum and minimum temperature data are used, which yields lower standard error SEE<0.52 mm day-1 than when using Hargreaves equation, which tends to overestimate ET0 for the observed conditions.
Results and discussion
Drought vulnerability estimates
Irrigation Requirements, NIRs Probability curves of maize net irrigation requirement (NIRs, mm) were built using ISAREG model simulations over the period 1951-2004. Results relative to Plovdiv show that in soils of large TAW (180 mm m-1) net irrigation requirements (NIRs) range from 0-40 mm in wet years having probability of exceedance PNIRs>95% to 140-220 mm in average demand seasons (40% 500 450 400 350 300 NIRs (mm) NIRs 250 200 150 100 50 0 Year PNIRs (%) 2000 1994 1993 2003 1965 1952 1988 1996 1998 1986 1987 1962 1954 1970 1964 1990 1992 1981 2001 1953 1999 1958 1985 1991 1989 1968 1956 1974 1995 1978 1980 1972 1963 1984 1969 1975 1951 1973 1982 2004 1967 1979 1957 1960 1997 1966 1977 1976 1961 1955 1971 2002 1959 1983 1,4 3 5 7 9 11121416182022242527293133353638404244464749515355575960626466687071737577798182848688909294959799 a) TAW=136 mm m-1 TAW=116 mm m-1 TAW=180 mm m-1 BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 6 500 450 400 350 300 NIRs (mm) NIRs 250 200 150 100 50 0 Year 1958 2000 1993 1963 1988 2003 1965 1952 1961 1985 1987 1962 1994 1990 1996 1967 2001 1974 1956 1998 1992 1986 1989 1980 1964 1951 1953 1982 1954 1959 1981 1999 2004 1997 1972 1968 1978 1960 1971 1983 1976 1966 1984 1969 1973 1995 1955 1991 1970 1979 1977 1957 1975 2002 P (%) NIRs 1,43 5 7 911121416182022242527293133353638404244464749515355575960626466687071737577798182848688909294959799 b) TAW=136-157 mm m-1 TAW=116 mm m-1 TAW=180mm m-1 Figure 2. Net irrigation requirements (NIRs) probability of exceedance curves relative to soil of small, average and large water holding capacity (TAW) at: a) Plovdiv, South Bulgaria and b) Pleven, North Bulgaria, 1951-2004. 500 450 400 350 300 250 200 NIRs (mm) 150 100 50 0 0 10 20 30 40 50 60 70 80 90 100 PNIRs (%) Pleven Silistra Sofia Plovdiv Sandanski Varna Figure 3. Comparison of Net irrigation requirements (NIRs) probability of exceedance curves relative to soils of small water holding capacity (TAW= 116 mm m-1), Bulgaria, 1951-2004. NIRs in Sofia and Silistra are about 100 mm smaller than in Plovdiv. Contrarily in Sandanski, NIRs are up to 110 mm larger when compared with Plovdiv (Figs.3 and 4). BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 7 500 450 400 350 300 250 200 NIRs (mm) 150 100 50 0 0 10 20 30 40 50 60 70 80 90 100 PNIRs (%) Pleven Silistra Sofia Plovdiv Sandanski Varna Figure 4. Comparison of Net irrigation requirements (NIRs) probability of exceedance curves relative to soils of average water holding capacity (TAW= 136-157 mm m-1), Bulgaria, 1951-2004. Considering the trend of NIRs for the period under study, an average increase by 80 mm over the whole period is found for Plovdiv; contrarily to irrigation requirements grain production of non-irrigated maize (late hybrid, H708) decreases by 19% for the period 1951-2004 on the average (Fig.5). 100 90 80 70 60 50 RYD (%) 40 30 y = 0.35x - 634 20 10 0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 a) Year Figure 5. Relative yield decrease RYD for a rainfed maize, late hybrid (H708), Ky=1.6, soil of TAW=116 mm m-1, Plovdiv, South Bulgaria 1951-2004. Rainfed maize yield and risky years Validation of Ky factor for rainfed maize Simulated relative yield decrease (RYD, %) with the yield response factor Ky = 1.6 and the option ‘maize without irrigation’ relative to a soil of small water holding capacity (TAW=116 mm m-1) at Sofia and Plovdiv or large TAW (173 mm m-1) at Stara Zagora region are sorted in a descending order. Additional RYD data from long-term experiments with semi-early maize hybrids conducted at Chelopechene field, Sofia are plotted in Fig.6a. BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 8 100 90 80 70 60 50 RYD (%) RYD 40 30 20 10 49% risky years (26/53) for semi early hybrid with Ky=1.6 0 Year 1993 2000 1952 1962 1965 1988 2001 1987 1992 1958 1994 1961 1974 1985 1956 2003 1990 1982 1954 1999 1963 2004 1969 1970 1997 1973 1953 1964 1966 1996 1978 1981 1979 1975 1998 1959 1984 1986 1983 1989 1957 1967 1955 1980 1960 1971 1972 1977 1968 1991 2002 1995 1976 PRYD (% )1,4 3 5 7 9 111315161820222426283031333537394143454648505254565860626365676971737577788082848688909293959799 RYD Threshold Observed RYD for semi-early hybrids, (Jivkov and Varlev) Simulated RYD with Ky=1.6 a) 100 90 80 70 60 RYD, % 50 40 30 71% risky years (38/54) for the late hybrids (Ky=1.6) 20 10 0 Year 2000 1993 1965 1952 1994 1987 1996 2003 1992 1990 1988 1962 1954 1985 1964 1981 1982 1998 1973 1958 1953 1995 1968 1999 1956 1986 1980 2001 1970 1969 1963 1997 1978 1984 1974 1972 1989 1967 1951 2004 1966 1960 1979 1991 1957 1975 1983 2002 1977 1976 1961 1955 1971 1959 PYD, % 1.4 3 5 7 9 11121416182022242527293133353638404244464749515355575960626466687071737577798182848688909294959799 RYD Threshold for late hybrids RYD H708 (Rafailov) RYD H708 (Vurlev, Kolev, Kirkova) b) Figure 6. Probability of exceedance curve of relative yield decrease RYD for rainfed maize, Ky=1.6, for soils of small (TAW=116 mm m-1) water holding capacity at: a) Chelopechene, Sofia field, and b) Tsalapitsa field, Plovdiv region,1951-2004. BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 9 Similar analyses used to be performed for Tsalapitsa field, Plovdiv region, and a soil of small TAW (Fig.6b) and for Pustren field, Stara Zagora, and a soil of large TAW, but using crop data relative to the late maize hybrid H708 (8, 10).Results show that a factor Ky=1.6 could reflect well the yield of rainfed maize there too (Fig.7). 100 100 y = 0.99x 80 80 R2 = 0.82 60 60 40 40 y = 0.99x 2 Simulated RYD (%) R = 0.61 20 Simulated RYD (%) 20 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Obs e rve d RYD (%) Obs e rve d RYD (%) a) RY D H708, (Raf ailov ,V urlev , Kolev , Kirkov a) b) RYD semi early hybrids, (Jivkov and Vurlev) Linear (RYD semi early hybrids, (Jivkov and Vurlev)) 100 80 60 40 y = 1.00x R2 = 0.66 Simulatedd RYD (%) RYD Simulatedd 20 0 0 20 40 60 80 100 Observed RYD (%) c) RYD H708 (Eneva) Figure 7. One to one regression between Linearobserved (RYD and H708 simulated relative yield decrease RYD (%) with Ky=1.6 relative to: (a) Tsalapitsa, Plovdiv,(Eneva)) (b) Chelopechene, Sofia and (c) Pustren, Stara Zagora. Derived “one to one” regressions between observed and simulated relative yield decrease RYD (%) with Ky=1.6 for the experimental sites (Fig.7a, 7b and 7c) leading to regression coefficients (b=0.99, b=0.95 and b=1.00) and coefficients of determination (R2=0.61, R2=0.82 and R2=0.66) indicate that yield response factor Ky=1.6 is statistically reliable to be used in the study. The RYD probability of exceedance curves were built for each considered climate region in Bulgaria. The found probability curves are compared for 6 of them when total available water is medium (TAW=136-157 mm m -1) in Fig.8. BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 10 100 90 80 70 60 50 RYD (%) 40 18 % risky years (9/51) for Pleven 30 35 % risky years (18/51) for Silistra 39 % risky years(20/51) for Sofia 20 59 % risky years (30/51) for Plovdiv 82 % risky years(42/51) for Sandanski 10 49 % risky years(25/51) for Varna 0 0 10 20 30 40 50 60 70 80 90 100 PRYD (%) Pleven RYD Threshold Plovdiv/Silistra RYD Threshold Sofia RYD Threshold Pleven TAW=136-157mm m-1 Silistra TAW=136-157mm m-1 Sofia TAW=136mm m-1 Plovdiv TAW=136mm m-1 Sandanski TAW=136mm m-1 Varna 136-157mm m-1 Figure 8. Comparison of relative yield decrease (RYD, %) probability of exceedance curves, Ky = 1.6, relative to soils of average water holding capacity (TAW= 136-157 mm m-1), Bulgaria. Relative yield decrease RYD is the largest in the region of Sandanski ranging from 65 to 85% over the average years (40 BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 11 100 90 80 70 60 50 RYD (%) 40 30 12 % risky years (6/51) for Pleven 10 % risky years (5/51) for Silistra 20 20 % risky years(10/51) for Sofia 29 % risky years (15/51) for Plovdiv 10 63 % risky years(32/51) for Sandanski 14 % risky years(7/51) for Varna 0 0 10 20 30 40 50 60 70 80 90 100 PRYD (%) Pleven RYD Threshold Plovdiv/Silistra RYD Threshold Sofia RYD Threshold Pleven Silistra Sofia Plovdiv Sandanski Varna Figure 9. Comparison of relative yield decrease (RYD, %) probability of exceedance curves, Ky = 1.6, relative to soils of large water holding capacity (TAW= 180 mm m-1), Bulgaria. In North Bulgaria the economical RYD threshold is 67, 55 and 60% for Pleven, Lom and Silistra. When TAW=180 mm m -1 only about 10% of the years are risky in Pleven and Silistra that is half than in Lom. When TAW is medium (157 mm m -1) the risky years rise to 18, 35 and 45% in the three sites respectively and reach 50% in Varna (Fig.8). Results relative to the considered soil groups in Plovdiv region show that when soil water holding capacity ranges (116 BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 12 100 90 80 70 60 50 RYD (%) 40 30 64.8% risky years (35/54) for TAW=116 20 57.4% risky years(31/54) for TAW=136 31.5% risky years(17/54) for TAW=180 10 0 Year 2000 1994 1993 1965 1988 1996 1962 1981 1992 1958 1995 1980 1978 1972 1984 1963 1969 1982 1967 1951 2004 1979 1960 1997 1977 1976 1961 1955 1971 2002 1959 1983 PRY D (%) 1,43 5 7 911121416182022242527293133353638404244464749515355575960626466687071737577798182848688909294959799 a) RYD Threshold late hybrids TAW=180 mm m-1 TAW=136 mm m-1 TAW=116 mm m-1 100 90 80 70 60 50 RYD (%) 40 30 31.5% risky years (17/54) for TAW=116 20 18.5% risky years(10/54) for TAW=136-157 13% risky years(7/54) for TAW=180 10 0 Year 1958 2000 1993 1963 1965 2003 1988 1952 1961 1990 1994 1996 1962 1974 1980 1956 1989 1986 1964 1951 1997 1972 1981 1968 1960 1978 1971 1983 1995 1955 1991 1979 1970 1977 1957 1975 2002 PRY D (%) 1,43 5 7 9 11121416182022242527293133353638404244464749515355575960626466687071737577798182848688909294959799 b) RYD Threshold late hybrids TAW=180 mm m-1 TAW=136-157 mm m-1 TAW=116 mm m-1 Figure 10. Probability exceedance curves of relative yield decrease under rainfed maize RYD on the soil of small, medium and large water holding capacity TAW (116, 136, 157 and 180 mm m-1), Ky=1.6, at: a) Plovdiv, South Bulgaria and b) Pleven, North Bulgaria, for late maize hybrids (H708), 1951-2004. These differences are smaller over the very dry and very wet years. The study indicates that severe drought affects the productivity of rainfed maize during the high sensitive periods in 1993 and 2000 in Sofia field and in 2000, 1993, 1994 and 1965 in Plovdiv region when the grain yield was almost totally lost for the soils of small TAW (Fig.10a). The study indicates that in the region of Pleven severe droughts for maize occurred in 1958, 2000, 1993, 1963 and 2003 (Figs.2b and 10b). Rainfed maize is associated with great yield variability in this country (29 BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 13 Table 1. Variability of rainfed maize grain yield characterized by the average value, kg ha-1, and the coefficient of variation Cv, %, for the considered climate regions and soil groups in Bulgaria, 1951-2004. South Bulgaria North Bulgaria Danube Plain Sofia field Thracian Lowland Sandanski Stara Sofia Plovdiv Zagora Sandanski Pleven Lom Silistra Varna North Transitional Black Climate region Continental Transitional continental Mediterranean Continental Sea Maize hybrid semi early late late late late late late Total Avr. Avr. Avr. Avr. available Yield, Yield, Yield, Yield, water kg/ha- Cv, kg/ha- Cv, kg/ha- Cv, kg/ha- Cv, Cv, TAW mm m-1 1 % 1 % 1 % 1 % Cv, % % Cv, % Cv, % small 116 4421 42 3894 69 3723 59 2292 72 50 55 46 50 medium 136 4920 37 4550 59 4299 52 2906 59 44 47 40 42 180 5896 29 5915 43 4250 41 34 35 30 30 large 173 5483 41 In Sofia field Cv is within the range 29-42% for semi early maize hybrids. The smaller Cv=29% refers to the soils of largest TAW while Cv=42% is typical for the soil group of small TAW. Late hybrids (Н708, 2Л602 and ВС622) grown without irrigation on soils of small TAW<116 mm m-1 in South Bulgaria produced the most variable yields in Sandanski (Cv=72%) and Plovdiv (Cv=69%). A value of Cv=59% is found for Stara Zagora. The variability of rainfed maize in Pleven, Varna and Silistra is much lower than in the Thracian Lowland. Results indicate that, regardless of the fact that drought impacts are mitigated in North Bulgaria, in some areas (Lom) it is a key factor of yield variability under rainfed conditions (35 Deriving of drought vulnerability categories Seasonal SPI2 computed for crop specific periods important for yield formation as the whole season “May- Aug”, the Peak Season “June-Aug” and the High Peak Season “July-Aug” was related to simulated yield decrease of rainfed maize RYD and irrigation requirements NIRs for the considered climate regions and soil groups in Bulgaria. In Plovdiv region reliable relationships (R2 >91%) were found for seasonal agricultural drought relating the SPI2 for “July-Aug” with the simulated RYD of rainfed maize (Fig.11a) while in Sandanski, Sofia, Pleven and Varna the relationships were less accurate (73 BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 14 RYD threshold RYD threshold 67 60 y = -21.57x + 35.72 RYD raimfed maize (%)maize raimfed RYD y = -23.61x + 47.22 (%)maize raimfed RYD R2 = 0.79 R2 = 0.91 0 0 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00 2,50 -2,50 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00 2,50 a) SPI (2) for "July - Aug" b) SPI (2) for "July - Aug" Figure 11. Relationships between seasonal SPI2 “July-Aug” (X-axis) and relative yield decrease RYD with Ky=1.6 (Y-axis) for soils of large TAW=180 mm m-1: a) Plovdiv and b) Pleven; late maize hybrids. The study found statistically significant correlations between SPI2 “July-Aug” and simulated RYD for Stara Zagora, Lom and Silistra as well (83 NIRs threshold NIRs threshold 251 235 NIRs maize (mm)maize NIRs NIRs maize (mm)maize NIRs y = -93.81x + 189.90 y = -84.34x + 148.40 2 R2 = 0.89 R = 0.75 0 0 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00 2,50 -2,50 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00 2,50 a) b) SPI (2) for "July - Aug" SPI (2) for "July - Aug" Figure 12. Relationships between seasonal SPI2 “July-Aug” (X-axis) and net irrigation requirements NIRs (Y-axis) for soils of large TAW=180 mm m-1 at: a) Plovdiv and b) Pleven The specific NIR thresholds of 240, 235 and 190 mm were identified for the risky years in Sandanski, Plovdiv and Sofia regions. In North Bulgaria the NIR thresholds are 250, 225 and 215 mm for the regions of Pleven, Silistra and Lom respectively. The parameters of the specific relationships between simulation results for RYD and NIRs and seasonal SPI2 “July-Aug” are summarized across the considered soil groups and climate regions in Table 2. Results in the table indicate that slope coefficient b does not depend on TAW of the soil but it is influenced by the climate region and decreases when precipitation relative to “July-Aug” augment. The intercept a however depends on both TAW and High Peak Season precipitation. BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 15 Table 2. Parameters of specific relationships y=a+bx between relative yield decrease RYD (%) / net irrigation requirements NIRs (mm) and High Peak Season SPI2 “July-Aug” across considered soil groups and climate regions, Bulgaria, 1951-2004 Soil groups according to TAW Small Medium Large Climate Region 116 mm m-1 136-157 mm m-1 173-180 mm m-1 RYD % NIRs RYD NIRs mm RYD NIRs mm mm % % Sandanski Intercept a 79.6 312,3 74.1 294.2 62.1 256.6 Slope coefficient -15.0 -66,4 -15.7 -66.2 -16.1 -65.8 b R2 (%) 75 70 77 70 78 70 Stara Zagora Intercept a 67.0 259.2 61.81 243.3 51.3 211.4 Slope coefficient -19.9 -83.2 -20.5 -83.1 -20.5 -83.4 b R2 (%) 80 77 82 78 83 79 Plovdiv Intercept a 65.2 244.4 59.4 226.7 47.2 189.9 Slope coefficient -24.8 -97.3 -24.7 -96.4 -23.6 -93.8 b R2 (%) 92 89 92 89 91 89 Lom Intercept a 57.7 202.5 51.3 184.7 38.5 148.7 Slope coefficient -23.8 -81.7 -23.6 -81.5 -22.1 -78.9 b R2 (%) 86 80 86 80 86 81 Sofia Intercept a 48.4 178.8 42.6 162.5 31.2 129.2 Slope coefficient -21.0 -78.2 -20.5 -77.1 -18.8 -73.3 b R2 (%) 76 76 75 75 73 73 Silistra Intercept a 56.1 190.3 49.1 171.7 35.9 135.4 Slope coefficient -20.5 -68.5 -20.3 -68.5 -19.3 -67.4 b R2 (%) 86 84 86 85 86 85 Pleven Intercept a 53.5 202.4 47.6 184.8 35.7 148.4 Slope coefficient -23.3 -88.1 -23 -87.1 -21.6 -84.3 b R2 (%) 82 77 81 76 79 75 Varna Intercept a 63.6 212.3 56.9 195 43 158.1 Slope coefficient -18.1 -59.6 -17.6 -58.7 -16.5 -56.72 b R2 (%) 82 73 81 74 80 73 The economical thresholds of High Peak Season SPI2 “July-Aug” relative to climate regions and soil groups in Bulgaria are summarized in Table 3. BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 16 Table 3 Economical threshold of High Peak Season SPI2 “July-Aug” (the average SPI2 for July and August) relative to rainfed maize, climate regions and soil groups in Bulgaria Soil groups according to TAW Climate Region Small Medium Large 116 mm m-1 136-157 mm m-1 173-180 mm m-1 Sandanski +1.40 +1.00 +0.20 Stara Zagora +0.50 +0.10 -0.50 Plovdiv +0.15 0.00 -0.50 Lom +0.15 -0.1 -0.75 Sofia 0.00 -0.25 -0.90 Silistra -0.15 -0.5 -1.25 Pleven -0.50 -0.75 -1.50 Varna +0.21 -0.21 -1.05 The results indicate that in North Bulgaria rainfed maize is significantly less vulnerable to drought than in South Bulgaria. If TAW≥180 mm m-1 rainfed agriculture is associated with important economical losses only during the very dry years i.e. when seasonal SPI2 “July-Aug” <-1.5 in Pleven (Fig.11b), SPI2 “July-Aug” <- 1.25 in Silistra, SPI2 “July-Aug” <-1 in Varna and SPI2 “July-Aug” <-0.75 in Lom. When SPI2 “July-Aug” is below the defined economical threshold for the considered climate regions and soil groups, rainfed maize yield losses are economically significant.Results in Table 3 prove that in North Bulgaria rainfed agriculture is related to the highest risk and economical losses in the northern Black Sea climate zone. In that case the economical risk of drought occurs even during the normal years when the specific thresholds of seasonal SPI2 “July-Aug” <+0.2. In final analyses positive economical threshold of seasonal SPI signifies a region highly vulnerable to drought even during the wet years, while negative SPI threshold identifies territories of low degree of vulnerability to drought. Drought vulnerability mapping The derived reliable relationships and specific thresholds are curently used for drought vulnerability mapping at national and regional (SEE) scale. Maps of High Peak Seasonal SPI2 “July-Aug” relative to the very dry (2000) (Map 3), the average irrigation demand year (1970) (Map 4) and a dry (1981) (Map 5) in Bulgaria are presented below. Map 3 Seasonal SPI2 “July-Aug” relative to the very dry 2000, Bulgaria BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 17 Map 4 Seasonal SPI2 “July-Aug” relative to the year average irrigation demand 1970, Bulgaria Map 5 Seasonal SPI2 “July-Aug” relative to a dry year 1981, Bulgaria BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 18 Map 6. Soil-Geographical regions in Bulgaria (12) BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 19 Map 7. Water holding capacity (TAW, mm) of the soils in Bulgaria Map 6 of soil geographical regions and Soil map of Bulgaria (Map 2), Map 7 of Total available water TAW in the top soil layer and data from Table 3 were used to elaborate Map 8 of economical specific threshold of SPI2 “July-Aug” in the plains of Bulgaria. Dark blue colour in Map 8 signifies the least vulnerable territories to drought, i.e.those where rainded agriculture does not cause economical losses even during the dry seasons, while the areas marked in green, yellow and orange are related to great economical risk for rainfed agriculture during respectively light, moderately the very dry July and August. BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 20 Map 8: Mapping the specific threshold of High Peak Seasonal SPI2 “July-Aug”, under which soil moisture deficit leads to severe impacts on rainfed maize productivity in Bulgaria Conclusions The study relative to drought vulnerability estimation for eight representative climate regions, three soil groups and the period 1951-2004 in Bulgaria shows that: In Plovdiv soils of large TAW (180 mm m-1) net irrigation requirements (NIRs) range 0-40 mm in wet years and 350-380 mm in dry years. In soils of small TAW (116 mm m-1), NIRs reach 440 mm in the very dry year. NIRs in Sofia and Silistra are about 100 mm smaller than in Plovdiv while in Sandanski and Northern Greece they are 30-110 mm larger. Rainfed maize is associated with great yield variability in this country (29% BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 Results indicate that when rainfed maize is grown on soils of large TAW maize development is less affected by the water stress. In that case, South Bulgaria, economical losses are produced when high peak season SPI2 <+0.2 in Sandanski, SPI2 < -0.50 in Plovdiv and Stara Zagora and SPI2 < -0.90 in Sofia. In North Bulgaria the respective threshold ranges between SPI2 “July-Aug” < -0.75 (Lom) and SPI2 < -1.5 (Pleven). The corresponding NIR thresholds were identified. The findings of the study are used for elaboration of drought vulnerability maps. Economical specific threshold SPI2 “July-Aug” is mapped for Bulgaria so far. Maps of Seasonal SPI2 “July-Aug” for the very dry, dry and the average irrigation demand years are prepared as well. These maps could serve as a basis for identification of drought prone territories at national level. The derived reliable relationships and specific thresholds of seasonal SPI2 “July-Aug”, under which soil moisture deficit leads to severe impact of drought on rainfed maize yield in the studied climate regions and soils, are to some extend representative of a wider area of South East Europe. Acknowledgements We gratefully acknowledge the financial support of Drought Management Center for South East Europe Project, South East Europe Transnational Cooperation Programme co-funded by the European Union, for implementation and dissemination of our studies’ results on crop vulnerability to droughts and irrigation management in Bulgaria. References (1) Teixeira, J.L. and L.S. Pereira. ISAREG, an irrigation scheduling simulation model. In: Pereira, L.S., Perrier, A., Ait Kadi, M. and Kabat (guest editors) Crop Water Models. Special issue of ICID Bulletin, 41(2): 29-48 (1992) (2) Pereira L. S., P. R. Teodoro, P. N. Rodrigues, and J. L. Teixeira. “Irrigation scheduling simulation: the model ISAREG”, in Tools for Drought Mitigation in Mediterranean Regions, G. Rossi, A. Cancelliere, L. S. Pereira, T. Oweis, M. Shatanawi, A. Zairi (Eds.). Kluwer, Dordrecht, pp. 161-180 (2003) (3) Popova Z., and L. S. Pereira. “Irrigation scheduling for furrow irrigated maize under climate uncertainties in the Thrace plain, Bulgaria”, Journal of Biosystem engineering, vol. 99, no. 4, pp. 587-597. ISSN: 15375110 DOI: 10.1016/j.biosystemseng.2007.12.005 SW-Soil and Water (2008) (4) Popova Z., S. Eneva, and L. S. Pereira. “Model validation, crop coefficients and yield response factors for irrigation scheduling based on long-term experiments”, Biosystems Engineering, vol. 95, no. 1, pp. 139- 149 (2006) (5) Popova Z. and L. S. Pereira. “Model validation for maize irrigation scheduling in Plovdiv region”, in 2010 BALWOIS Conference “Water observation and information system for decision making”, CD-ROM paper 648, Ohrid (2010) (6) Popova Z., and L. S. Pereira. “Modeling for maize irrigation scheduling using long term experimental data from Plovdiv region, Bulgaria”, Agricultural Water Management, vol. 98, no. 4, pp. 675-683. doi:10.1016/j.agwat.2010.11.009 (2011) (7) Popova Z.. Application of model simulation for evaluation and optimization of irrigation scheduling, yield and environmental impacts under maize and wheat. Thesis for Dr of science. “N.Poushkarov” Institute of soil science, Sofia, p.326 (in Bulgarian) (2008). (8) Popova Z., Ivanova M., Alexandrova P., Alexandrov V., Doneva K., Pereira L.S. Impact of drought on maize irrigation and productivity in Plovdiv region. Transactions of National conference with international participation “100 years soil Science in Bulgaria”, Sofia, Vol.1, pp.394-399 (2011) (9) Ivanova M., Popova Z. Model validation and crop coefficients for maize irrigation scheduling based on field experiments in Sofia. Transactions of National conference with international participation “100 years soil Science in Bulgaria”, Sofia, Vol.2, pp.542-548 (in Bulgarian) (2011). (10) Popova Z., Pereira L.S., Ivanova М., Alexandrova P., Doneva K., Alexandrov V. and Kercheva M. Assessing drought vulnerability of Bulgarian agriculture through model simulations. International Conference on Climate Change and Global Warming 28-30 November, Venice, Italy World Academy of Science, Engineering and Technology, 59, pp. 2572-2585 (2011). BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 22 (11) Pereira L.S., J.T.Mexia, C.A.L.Pires, Eds. Gestao do Risco em Secas. Metodos, tecnologias e desafios. Ed. Colibri and CEER, Lisbon, pp.344 (2010) (12) Koinov V., Kabakchiev I., Boneva K. Atlas of the soils in Bulgaria. Zemizdat, Sofia (1998). (13) Varlev I., N. Kolev, I. Kirkova. Yield-water relationships and their changes during individual climatic years. In: Proceedings of 17th Europ. Reg. Conf. of ICID, Varna, vol. 1, pp. 351 – 360 (1994). (14) Eneva S. Napoiavane I efekt ot napoiavaneto pri polskite kulturi.[Irrigation and irrigation effect on field crops. Problems of crop production science and practice in Bulgaria.] Agricultural University of Plovdiv, 287 (1997). (15) Varlev I. Z. Popova. Water-Evapotranspiration-Yield. Irrig. and Drain. Inst., Sofia, p:143 (1999). (16) Stoichev D. Ecological aspects of anthropogenic loading of soils. Synthesis of dissertation thesis for the scientific degree “Doctor of agricultural sciences”, p. 52 (1997) (17) Alexandrova P. Microclimate of maize under irrigation and rainfed conditions. Synthesis of dissertation, PhD thesis, p. 32 (1990). (18) Rafailov R. Annual reports of ISS N.Poushkarov, Sofia (1995). (1998). (19) Jivkov J., Role of the crop and irrigation on intensive use of irrigated area. Papers of Institute of Hydrotechniques and Melioration, Vol.XXIV:351-357 (1994). (20) Mladenova B., Varlev I. Sensitivity of maize under water deficit during the different fazes of vegetation in Sofia field (1997). (21) Moteva M. Parameters of irrigation scheduling of maize for grain under irrigation through a furrow on chromic luvisol soil. PhD Thesis “N.Poushkarov” Institute of soil science, Sofia, p.118 (in Bulgarian) (2006). (22) Varlev I. Potential, efficiency and risk under maize cultivation in Bulgaria. Sofia p. 128 (2008). (23) Stoyanov P. Agroecological potential of maize cultivated on typical for its production soils in the conditions in Bulgaria. Habilitation paper for the scientific degree “Professor” (2008). (24) Allen R.G., L.S. Pereira, D. Raes, M. Smith. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. Irrigation and Drainage Paper 56, FAO, Rome (1998). (25) Doorenbos J., W. O. Pruitt. Crop Water Requirements. FAO Irrigation and Drainage Paper 24. FAO, Rome, Italy, p 144 (1977). (26) Doorenbos J., A.H. Kassam. Yield Response to Water. Irrigation and Drainage Paper 33, FAO, Rome, p 193. (1979). (27) Popova Z., Kercheva M. and Pereira L.S. Validation of the FAO methodology for computing ETo with limited data. Application to South Bulgaria. Irrig. and Drain., 55, 2, pp. 201–215 (2006). (28) Ivanova M., Popova Z. Validation of FAO-56 methodology for computing ETo with missing climate data application to Sofia field. Agricultural Science vol. XLIV, №2, pp. 3-13 (in Bulgarian) (2011). BALWOIS 2012 - Ohrid, Republic of Macedonia - 28 May, 2 June 2012 23