AGRICULTURAL DROUGHT INDICATORS AT REGIONAL SCALE BASED ON MODELS OF WATER BALANCE AT LEFT MARGIN OF GUADIANA RIVER N. Kotsovinos, P. Angelidis Democritus University of Thrace School of Engineering Greece

DESERTIFICATION Climatic data Vegetation cover data Land use Agricultural data Socioeconomic data Soil degradation data FIRST GENERATION CLIMATIC DATA INDICATORS: SPI (standardized precipitation index) Standardized Aridity Index (STARI) Palmer drought severity index (PDSI) Deciles Index Percent of Normal

FIRST GENERATION OF WATER RESOURCES INDICATORS : STATISTICAL ANALYSIS OF CLIMATIC DATA WHY WE NEED MORE INDICATORS?

SPI quantifies the deviation of the seasonal rainfall from the corresponding mean values over many years of data. Large negative values of SPI indicate “drought”, but not necessarily aridity.

The SPI index alone does not indicate if the annual precipitation at a given location is high enough to sustain vegetation or small enough to lead to aridity. An arid region can have very small aridity index ( ZERO VEGETATION) but positive SPI (i.e. above normal –wet(!) drought index).

WE NEED SECOND GENERATION WATER INDICATORS: BASED UPON THE OUTPUT OF ADVANCED COMPUTER MODELING OF SOIL WATER BUDGET AND EROSION (e.g. the model SWAT).

MODEL OF WATER BALANCE : P = ET + RO + dSW + D; Where P=precipitation (mm) ET= actual evapotranspiration (mm) RO=surface runoff (mm) D= groundwater recharge (mm) SW= soil water (mm) dSW = change in soil water over the time step ( day or month)

WE USE DAILY PRECIPITATION DATA FROM 13 STATIONS FOR 30 YEARS MEAN YEAR PRECIPITATION STATION MEAN YEAR PRECIPITATION [mm] Amareleja530,74 Amieira579,74 Barrancos557,49 Herdade da Valada510,59 Mesquita472,79 Mirtola433,01 Minas de sto Domingos561,63 Pedrogto do Alentejo518,80 Reguengos555,61 Santa Iria460,34 Santo Aleixo da Restaurahto491,96 Serpa528,31 Sobral da Adiha526,69

THIESSEN POLYGONS

WATER BALANCE MODEL OF THORNTHWAITE : ‘Box' model representation of regional monthly water balance

The required model input : surface monthly rainfall over RGG, surface monthly temperature, water saturation soil capacity Smax ( which is related to soil properties, vegetation, land use etc and is related to the soil curve number CN ), “loss” to groundwater recharge Potential Evapotranspiration (PET), calculated according to Thornthwaite methodology.

MODEL OUTPUT,WHICH ARE USED TO INTRODUCE NEW WATER DROUGHT INDICATORS: the monthly soil water ( soil moisture) the monthly real evapotranspiration. the monthly runoff

Figure 7.4. The simulation of the annual water budget at RGG for Smax=100mm.

Simulation of mean monthly water budget for 30 hydrologic years for Smax =100 (CN=71.85). It is apparent that on the average the monthly soil water vanishes for four consecutive months (June to September).

The simulation of monthly soil water status for 30 hydrologic years for Smax =100 (CN=71.85). It is apparent the absence for many months of soil water for the extreme dry years of 1974, 1980, 1994 and 2004, in agreements with the findings from the regional standardized aridity index.

Figure 7.5 Drought index based on the number of consecutive months per year with zero monthly soil moisture. Results of simulation for Smax=100 mm. It is apparent that for 7 or more consecutive months,the monthly soil moisture is zero for the extreme dry hydrologic years of , , , , and

Introduction of a new indicator, the Agricultural Drought Indicator: This indicator is based on the ( calculated from the model ) number of days or months per hydrologic year which have very small soil moisture,e.g soil moisture close to the wilting point (“zero soil moisture”).

We propose a new Indicator, the Normalized Agricultural Drought Index (NADI j ) for the j- hydrologic year which is calculated from the following relation: Where Z is the average over n hydrologic years of months with zero monthly soil moisture M j is the number of months with zero soil moisture for the hydrologic year j.

Classification of the Normalized Agricultural Drought Index (NADI) NADI VALUENADI Classifications Less than -3 much below normal, extremely dry From -3 to -1below normal, dry From – 1 to 1near normal Values higher than one above normal, wet

The Normalized Wilting Drought Index (NADI) for 30 years for Smax=100mm.

SWAT model: Advanced water budget simulation, but it requires a lot of information for detailed input data regarding the soil,vegetation etc

SWAT MODEL The basin is divided in 13 sub basins.

CONCLUSIONS THE FIRST GENERATION OF INDICATORS PROVIDED VALUABLE DATA TO ESTABLISH THE TREND OF CHANGING CLIMATIC PARAMETERS. THE SECOND GENERATION DESERTIFICATION INDICATORS SHOULD BE BASED ON ANALYZING THE RESULTS RUNNING MODERN SOFTWARE PACKAGES OF WATER BUDGET AT LOCAL AND REGIONAL SCALES AT DAILY TIME STEP. CLIMATIC CHANGES CAN BE INCORPORATED IN THESE MODELS TO PROPOSE OPTIMUM ACTIONS FOR SUSTAINABILITY.