Regional Water Balance for Management of Deficit Irrigation Systems

REGIONAL WATER BALANCE FOR MANAGEMENT OF DEFICIT IRRIGATION SYSTEMS Giuseppe MENDICINO, Giancarlo PRINCIPATO Department of Soil Defence, University of Calabria Ponte Pietro Bucci, Cubo 41 b - 87036 Arcavacata di Rende (CS), Italy e-mail: [email protected]

ABSTRACT

Irrigation and agriculture projects interest large geographic areas, involving a large number of users and requiring a great amount of investments. Improvements in irrigation management and agricultural practices are essential for increasing crop production and reducing the unbalance due to excess irrigation in some places and water deficiency in other places. These problems seem to be worsened by the progressive increasing of drought observed in the last years in southern Europe.

In this context, numerous initiatives have been promoted by the European Community to face the effects induced by drought on the member States. Among these, the main initiative is represented by the INTERREG IIC Programme, which has permitted the actuation of several trans-national planning programmes, and includes among its objectives a strategy of prevention and co-operation in facing the reduction of water resources due to drought.

Specifically, in southern Italy, which is a region characterised by a meta-stable climatic regime with strong intermittences of the water balance, an integrated monitoring system for the analysis and forecast of the effects produced by prolonged water deficit periods has been realised. The system is based on an embedded coupling of GIS and models connected with a Data Acquisition System aimed at storing real time data recorded by the National tele-metering hydro-meteorological network managed by the Servizio Idrografico Mareografico Nazionale (SIMN). In this system tele-metering data is constantly updated and returned according to the input required by the simulation models. These models, for the whole southern Italy, allow the estimate of spatially distributed hydrological quantities, such as solar radiation, potential evapotranspiration and water deficit.

In this study, the system functionalities have been used to analyse water stress conditions and actual water requirements of the main agricultural southern Italy areas. A monthly water balance model has been applied to each agricultural district with the aim of spatially determining the irrigation deficit during the drought period 1995 – 2002. For the same period, the deficit was compared with the water resources available on the region, verifying both the adopted irrigation interventions and the management strategies carried out during the extremely intense drought phenomenon.

1 INTRODUCTION Alternation of intense precipitation and drought periods observed in the last years in southern Europe, and more markedly in the southern regions of Italy, underlines the necessity of a rational use of water resources, mainly in the agricultural sector which specifically absorbs the higher amount of the water resources. The meteorological conditions in southern Italy show a decrease in precipitation during the autumn-winter period followed by an unusual increase in rainfall during the late spring that often does not allow an efficient filling of the reservoirs. As a consequence, a sensible water supply limitation is involved, both in agriculture and in other productive sectors. In the case of agriculture, water resources are also worsened by the reduced efficiency of the irrigation distribution systems.

In southern Italy, specifically in Calabria, irrigation has a relevant importance due to ancient traditions; it has been characterised by a slow initial development and by a very limited local diffusion. Only in the last decades, by means of extraordinary Government actions, large areas have been made irrigable through the realisation of important irrigation systems. In Calabria public irrigation is carried out by 15 agencies (over a total number of 17), aimed at managing 83 irrigation systems on the whole region (Fig. 1). In the year 2000, 125’346 hectares were dominated by public irrigation systems; among these 92’129 hectares resulted irrigable and, only 46’998 hectares were effectively irrigated. Such values cannot be compared with those forecasted by national projects (Progetto Speciale 26 - CASMEZ, 1984), that specifically estimated for the year 2016 about 260’000 irrigable hectares (Principato, 2000, 2001).

Figure 1. Calabrian irrigation systems.

In this paper the problem concerning the management of the Calabrian irrigation areas has been faced through two different phases: the former based on a hydrological approach; the latter comparing the hydrological deficit with the water supplied in each of the considered districts.

Initially, the analysis regarded the definition of the soil water balance during the drought period 1995 - 2001. By means of a spatially distributed approach, only the meteorological effects and their interaction with the soil has been considered, without taking into account the water exchanges due to irrigation, reservoirs, wells or rivers (Mendicino e Versace, 2000, 2001). Following, water supply of each district has been compared with the corresponding irrigation deficit, with the aim of determining the critical conditions during the analysed period. From these results, in the final part of the paper, suitable irrigation interventions together with water resources management strategies are proposed.

2 DATA ACQUISITION AND SPATIAL DISTRIBUTION Hydrological analyses have been carried out starting from the following data:

− period 1999 – 2001: daily information recorded by hydro-meteorological stations managed by the Servizio Idrografico Mareografico Nazionale (SIMN) relative to 103 thermometers, 103 rain gauges, 17 hygrometers, 12 radiometers, 11 barometers, 9 anemometers, 4 soil moisture sensors and 4 evaporimeters; − period 1995 – 1998: daily information concerning measurements of 103 thermometers and 103 rain gauges; − period 1995 – 2001: data recorded by 7 stations managed by the Aeronautica Militare Italiana and by the Rete Agrometeorologica Nazionale; − land use derived by satellite images (1:100’000 scale) relative to CORINE (CoORdination of INformation on the Environment) Land Cover Project;

− geo-lithological information derived by the Geological Map of Italy (1:250’000 scale);

− monthly NDVI (Normalised Difference Vegetation Index) images, derived from NOAA-AVHRR satellite, for the space-time estimate of the vegetation stress.

Hydro-meteorological spatial distributions have been achieved, in some cases, directly from the observed data using spatial interpolation procedures (precipitation, temperature); in other cases, because of a smaller spatial density of the gauges, theoretical models locally calibrated with direct observations have been used (solar radiation, evapotranspiration).

3 WATER DEMAND

On the examined region, irrigation water deficit is monthly evaluated using a spatially distributed water balance model. This model follows the original approach suggested by Thornthwaite and Mather (1955) and simulates soil moisture variations, evapotranspiration and runoff on single grid cells using data sets that include climatic drivers, vegetation and soil properties. This model does not consider horizontal motion of water on the land surface, or in the soil.

In southern Italy a uniformly distributed high-resolution precipitation station network exists. Therefore, spatially distributed rainfall estimates are obtained just using interpolation techniques based on bi-dimensional splines.

Analysis carried out on rainfall data has allowed the estimate of monthly spatial distributions during the years 1995–2001. The reduced number of evaporation stations involved crop evapotranspiration estimates based on simulation models. Specifically, two different spatially distributed models were considered: during the years 1999–2001 the approach suggested by Penman (1948) and modified by Monteith (1965) was used, while for the period 1995–1998 the phenomenon was described through the relation proposed by Mendicino et al. (2002).

3.1 Crop evapotranspiration The evapotranspiration rate from a cropped surface can be directly achieved, through mass transfer measurements, through energy balance methods, or through experimental plots on which detailed soil water balance studies are carried out.

The potential evapotranspiration from a cropped surface can be also estimated starting from meteo-climatic data and information depending on the analysed cultivation (Allen et al., 1998). The most reliable evapotranspiration method was proposed by Penman (1948) and modified by Monteith (1965). This method is mainly based on the hypothesis that the phenomenon is limited by the availability of solar energy and by the possibility of air to circulate. Penman (1948) formulated a “combined” equation in which the evaporation rate was estimated as a weighted average of the energetic and aerodynamic components. Subsequently Monteith (1965) proposed a modified equation in which the concept of resistance (aerodynamic and superficial) was introduced, to make explicit the dependence of evapotranspiration from wind and vegetation cover. In its original form, the formula is given by:

1  ∆()Rn − G + 4.86 ρ cp ()ea − ed / ra  ETPM =   (1) λ  +∆ γ ()1+ rc ra 

-1 where ETPM is the potential evapotranspiration rate (mm d ), 86.4 is a conversion factor, ρ is the -3 -1 -1 atmospheric density (kg m ), cp is the specific heat of moist air (kJ kg °C ), (ea-ed) is the deficit of vapour pressure (kPa), ∆ is the vapour pressure gradient with temperature (kPa °C-1), γ -1 -1 is the psychrometric constant (kPa °C ), λ is the latent heat of vaporisation (MJ kg ), Rn is the -2 -1 -2 -1 -1 net radiation (MJ m d ), G is the soil heat flux (MJ m d ), rc is the crop canopy resistance (s m ), -1 ra is the aerodynamic resistance (s m ).

The resistance terms of equation (1) are quite difficult to estimate, because they are linked not only to climatic changes, but also to crop growth degree and to soil moisture. Even surface albedo values, from which net radiative flux Rn is derived, are influenced by canopy and soil moisture.

These difficulties in the estimate of equation (1) parameters involved the introduction of the crop evapotranspiration, which can be determined by means of the following equation:

ETc = Kc ET0 (2) where Kc represents the crop coefficient in standard conditions (i.e. with optimal water supply), while ET0 indicates the reference crop evapotranspiration, which is referred to a hypothetical reference crop with an assumed crop height of 0.12 m, a fixed surface resistance of 70 s m-1 and a surface albedo of 0.23, such as suggested by the FAO Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements (AA.VV., 1990).

Standard crop coefficient is given by the ratio between single crop potential evapotranspiration and reference crop potential evapotranspiration. It depends on the crop, on its growth degree and, on the climatic conditions. Different field experiments have been carried out, also into Calabrian region, about the correct definition of the Kc coefficient monthly values (CASMEZ, 1984). Soil water balance quantities are spatially distributed on a 250 m regular grid. Starting from satellite information relative to CORINE (CoORdination of INformation on the Environment) Land Cover Project, land use data has been obtained with different levels of detail. Specifically, a third level of detail for forest and semi-natural areas was considered while, in the case of agricultural areas the detail was increased to the fourth level.

The classification proposed by the CORINE Project in some cases did not allow to univocally define monthly crop coefficients values. In these cases, crop coefficients involving higher crop evapotranspiration have been chosen, following the suggestions given by FAO (Allen et al., 1998) and the experimental data obtained in the Calabrian region (CASMEZ, 1984).

Air humidity and net solar radiation data are needful in Equation (2). In the case of air humidity, monthly linear regressions with air temperature data were used. Net radiation flux Rn received by an inclined surface was determined instead using a spatially distributed model suggested by Mendicino (2001) and Mendicino and Versace (2002).

According to the model, net radiation flux Rn,i received by an inclined surface (represented by a cell i of the Digital Terrain Model of the analysed area) is given by the equation:

R ,in = 1( − alb )(R ,idir + R ,idif + R ,iref ) + ε L ,iins − L ,iout where alb is the surface albedo, εs is the surface emissivity, Rdir, Rdif and Rref represent the direct, diffuse and reflected short-wave radiation, respectively. Long-wave radiation components Lin and Lout, representing the incoming (or atmospheric) long-wave radiation and the outgoing (or surface) long-wave radiation respectively, are approximated through the following equations:

4 L ,iin =ε a σ T ,ia ν + 1( −ν ) L ,iout (4) 4 L ,iout =ε s σ T ,is (5) where εa is the atmospheric emissivity (a function of air temperature, vapour pressure and cloudiness), σ is the Stefan-Boltzman constant, Ts,i and Ta,i are mean temperatures of surface and air respectively of cell i (°K), and ν is the skyview factor, which is the fraction of the sky that can be seen by the sloping surface. All these quantities are spatially estimated analysing the topographically heterogeneous landscape information represented by the DTM. The variables of the model, such as albedo, emissivity, sunshine fraction, mean air and surface temperatures, and clear sky transmittance, can be varied on a monthly time-scale. The solar radiation model has been developed also including algorithms aimed at determining the short-wave radiation components taking into account the effects of shading from direct sunlight by surrounding terrain at enclaved sites. Radiative fluxes simulation model produced, during the validation period 1999 – 2001, estimates with maximum errors lower than 5%. Figure 2 shows the comparison between observed and calculated monthly values considering 19 radiometric stations spatially distributed on the whole Calabrian region.

R ,in = 1( − alb )(R ,idir + R ,idif + R ,iref ) + ε L ,iins − L ,iout (3) where alb is the surface albedo, εs is the surface emissivity, Rdir, Rdif and Rref represent the direct, diffuse and reflected short-wave radiation, respectively. Long-wave radiation components Lin and Lout, representing the incoming (or atmospheric) long-wave radiation and the outgoing (or surface) long-wave radiation respectively, are approximated through the following equations:

Figure 2. Comparison between observed and calculated monthly values considering 19 radiometric stations spatially distributed on the whole Calabrian region.

Finally, wind speed estimates have been carried out using information recorded by anemometers available in the analysed region.

Climatic data are continuously available for an adequate number of stations since 1999. Crop evapotranspiration estimates during the period 1995–1998 have been achieved starting from the model proposed by Mendicino et al. (2002), based on the following equation:

ETMOD = a ETRAD +bT +cLAI +d (6) where T represents the air temperature (°C), LAI is the Leaf Area Index (-), while a(-), b (mm d-1 °C-1), c (mm d-1) and d (mm d-1) are seasonal coefficients shown in table 1.

Equation (6) is based on a functional scheme similar to the one proposed by Priestley & Taylor

(1972), in which through a parameter α’, non constant and equal to [a+(bT+cLAI+d)/ETRAD], both the advective phenomena (through the air humidity expressed as a function of temperature), and the vegetation (described by the LAI index) are considered, according to Davies & Allen (1973), Brutsaert (1982), Meyer et al. (1989), Stannard (1993), Shuttleworth (1993), Hatfield & Allen (1996), Mendicino (2001) and Mendicino & Versace (2002).

The new equation applied in a distributed form on the whole region has been compared with the results obtained using equation (2). Figure 3 shows for some months the results obtained, pointing out the good fitting between the empirical method and the more rigorous one of Penman and Monteith. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec a(-) 0.63 b (mm d-1 °C-1) 0.08 0.08 0.10 0.13 0.16 0.18 0.19 0.17 0.14 0.10 0.08 0.08 c (mm d-1) 0.14 0.23 0.35 0.47 0.55 0.59 0.56 0.48 0.36 0.24 0.15 0.11 d (mm d-1) -0.27 -0.21 -0.61 -1.32 -2.11 -2.70 -2.88 -2.60 -1.94 -1.14 -0.48 -0.18 Table 1. Monthly values of the coefficients a, b, c and d.

Figure 3. Comparison between the evapotranspiration values obtained on the DTM cells relative to the irrigated areas using equation (2), in abscissa, and equation (6), in ordinate, during the months of: (a) may, (b) july and, (c) september 2001.

3.2 Water balance On the examined region, water deficit is monthly evaluated using a spatially distributed water balance model. This model follows the original approach suggested by Thornthwaite (1948) and Thornthwaite and Mather (1955), and simulates soil moisture variations, evapotranspiration and runoff on 250 m regular grid cells using data sets that include climatic drivers, vegetation and soil properties. This model does not consider horizontal motion of water on the land surface, or in the soil. The governing equation is based on a simplified mass balance:

Pu = S + E ∆+ W (7) where Pu is effective rainfall, E is evapotranspiration, S is water surplus, and ∆W is the change in soil moisture storage. With “effective rainfall” the amount of precipitation useful to satisfy water needs of cultures is intended. Analyses carried out on daily precipitation, neglecting values smaller than 10 mm, pointed out effective rainfall values equal to 85% of total precipitation.

All the quantities are evaluated in millimeters per month. Equation (7) does not differentiate between surface runoff and groundwater runoff, it allows to determine water surplus S as the water which does not evaporate or remain in soil storage and is available to generate surface and subsurface runoff.

The procedure schematises the soil column through a reservoir whose maximum capacity is given by the soil-water holding capacity WHC. This quantity for a given soil use and type is obtained multiplying the difference between the field capacity minus the permanent wilting point by the root zone thickness. From a theoretical point of view, water contained in the soil is available for plants until the wilting point is reached, indeed plants reduce their suction capacity rather above this limit. Then, to take into account such a reduction, a smaller soil-water holding capacity WHC is hypothesised. Specifically, according to Allen et al. (1998) for many types of cultivation is assumed: RAW = 0.5 WHC (8) where the Readily Available Water RAW (mm) represents the effectively available water content in the root zone.

Grid cells represent balance operators, each of them characterised by a set of vertically interconnected relationships. The state variable representing soil water content in the single cell at the end of the month i is defined Wi. It can increase until the limit of RAW or it can decrease, depending on the difference between effective rainfall Pu,i and crop evapotranspiration ETc,i.

Specifically, if Pu,i ≥ ETc,i then: Wi = min[Wi−1 + (P ,iu − ET ,ic ,) RAW ] (9) and soil moisture is recharged up to the maximum value RAW. When Wi is equal to RAW, further positive values (Pu,i - ETc,i) are considered as surplus. Negative values of (Pu,i - ETc,i) indicate the amount by which precipitation fails to supply ETc,i requirements. In this case water will be withdrawn from the soil moisture, resulting in an exponential soil moisture depletion, and the actual evapotranspiration Ei is less than ETc,i. The soil moisture depletion curve is given by the following equation:

( −APWLi / RAW ) Wi = RAW e (10) where APWL (Accumulated Potential Water Loss) represents a variable that describes the dryness of the soil. For months characterised by negative values of (Pu,i – ETc,i) this water loss is calculated as follows:

APWLi = APWLi−1 + (P ,iu − ET ,ic ) (11) For months characterised by water surplus, then APWL equals 0. If month i-1, with a surplus of water, is followed by month i, with a deficit, a starting APWL value has to be calculated using the following equation:

 Wi−1  APWLi−1 −= RAW ln  (12)  RAW 

Actual evapotranspiration Ei equals ETc,i when Pu,i > ETc,i, otherwise:

Ei = P ,iu + ∆Wi (13) where ∆Wi is the change in soil moisture storage during the month i. When ETc,i > Ei, then the difference (ETc,i - Ei) represents the soil water deficit Di or the amount of water that would be supplied by irrigation to the soil during the month i.

4 WATER SUPPLY Water supply for each irrigation system has been achieved starting from flow rate granted by the Agencies aimed at managing the same districts (Tab. 2). These values were decreased of a 30% with the aim of taking into account variations in flow rate during the irrigation season, hypothesised equal to 6 months (Principato, 1999). Volumes obtained represent only indicative estimates, which are referred to the whole irrigation season, because of the unavailability of monthly values of flow rate granted.

5 RESULTS Each irrigation system shown in table 2 has been analysed according to the soil water balance procedure described in the previous section. During the period 1995-2001, water deficits compared with the respective water supplies pointed out both critical years and, for each of these years, the irrigation systems particularly interested by drought phenomena.

Deficit estimates have been carried out on each district considering the whole irrigable area; then total water that would be supplied by irrigation (Mm3) could assume in some cases over- estimated values. In fact, if the whole district is considered, also in the middle of irrigation season some dry areas subsist; as well as in some hill areas slope, soil condition and difficulty in cultivating involve a reduction in the effective irrigated area. With the aim of taking into account these aspects, calculated water deficit values have not been increased as normally happens according to the typology and efficiency of the considered systems (water losses during the transport, losses for evaporation and distribution irregularities).

Figure 4 shows for each year, and for each irrigation system analysed, the difference between water supply and water deficit. Specifically, the years 1997, 1998, 2000 and 2001 appear to be the most critical period. In these years a sensible spatial variability of the deficit is observed, that becomes smaller in the southern irrigation systems. This is not due to climatic conditions, but it depends on the reduced irrigable areas of the same systems.

Some calculated deficit values appear to be over-estimated with respect to those normally adopted in planning irrigation systems. It is necessary to point out that in the proposed soil water balance volume of reservoirs not yet operating or in construction were neglected, focusing the analysis just on the existing water resources and on the currently irrigable areas. Furthermore, the use of the more reliable evapotranspiration equations (1) and (6) produces an increasing in water deficit with respect to commonly used methods, such as the one proposed by Thornthwaite (1948), that systematically under-estimates the phenomenon. Finally, at the better estimate of the climatic drivers, which are constantly compared with data locally recorded by meteorological stations operating on the whole Calabrian region, has to be also underlined the spatially distributed approach used into the water balance procedure that, in particular, allows a detailed description of the soil surface layer (and of its water content), with levels of efficiency greater than the ones obtained with methods based only on the use of simple descriptive coefficients.

6 CONCLUSIONS In this paper water deficit on the Calabrian irrigation systems has been analysed during the period 1995-2001. The analysis has been carried out trying to take into account all the information and technologies necessary for a detailed description of the reality, mainly considering the importance of this datum in the calculation of the water demand for agricultural uses.

Results obtained contribute to a more correct and rational use of the Calabrian water resources, especially related to drought periods which appear more frequent and intense.

Water deficit variability observed on each system during the analysed period, in many cases distant from values commonly used for irrigation planning, does not allow to carry out deterministic hydrological forecasts. Further probabilistic analyses will be necessary at different time scales to correctly forecast the water balance between resources and deficit.

Critical conditions observed in many irrigation systems point out that modernisation and restructuring of the current systems, together with the completion of the constructing reservoirs, represents the main strategy to be followed to improve the efficiency and the covering of the irrigation service. Furthermore, this last strategy has to be accompanied by interventions aimed at improving the cooperative management of the systems, rationalising in this way the use of water resources.

Specifically, it is necessary to proceed to a reorganisation of the irrigation service, introducing measurement systems and qualitative and quantitative control systems for the supplied water, privileging the introduction of low water resource consumption systems. This requires a different organisation of the consortium structure and differentiated taxation systems depending on the effective water consumption, but it also makes possible a decreasing in tax evasion and wasted water resources. Water taxation together with acquisition of new entries, considering the scenarios of enlargement of networks and of consumptions, is possible only considering the total reorganisation of the water distribution system.

IDENTIF. IRRIGABLE WATER SUPPLIED DURING REGION AGIENCY NUMBER IRRIGATION SYSTEM AREA IRRIGATION SEASON (*) (Ha) (Mmc) NORTHERN Consorzio di Bonifica del Pollino 1 Pollino 4'002 27.22 Consorzio di Bonifica del Lao 2 Lao 3'830 14.26 Consorzio di Bonifica Ferro e Sparviero 3 Ferro Sparviero e Caldanello 4'950 13.93 Consorzio di Bonifica Piana di Sibari e Media Valle Crati 4 Coscile-Raganello 6'000 21.77 5 Apollinara - Mordillo - Q. 40 1'158 7.62 6Esaro Basso 4'463 21.00 7Destra Crati 8'655 43.92 8 Cino 3'257 5.44 9Garga 1'500 5.44 10 Mucone 3'000 17.74 11 Mavigliano 300 2.18

CENTRAL Consorzio di Bonifica Valle Neto 12 Bassa Valle Neto 6'425 50.87 Consorzio di Bonifica Capo Colonna 13 Isola Capo Rizzuto 8'900 42.50 Fondo Valle Tacina - Cerasara - Consorzio di Bonifica Alli Punta delle Castella 14 Camporaso 2'500 14.80 15 Alli Tacina 3'220 30.55 Consorzio di Bonifica Alli Copanello 16 Alli Alessi 2'460 15.13 Consorzio di Bonifica Assi Soverato 17 Assi Soverato 920 4.68 Savuto - Angintola - S. Ippolito Badia - Consorzio di Bonifica Piana di S. Eufemia 18 Bagni Cantagalli 4'369 12.73

Mesima - Budello - Metramo B - SOUTHERN Consorzio di Bonifica Piana di Rosarno 19 Metramo C - Vena - Petrace 6'075 36.47 Consorzio di Bonifica di Caulonia 20 Allaro Precariti 1'019 3.81 21 Amus a 221 1.36 22 Torbido 715 5.33 23 Stilaro 156 1.63 Consorzio di Bonifica Versante Ionico Meridionale 24 Gelsi Bianchi 300 0.27 25 Amendolea S. Pasquale 440 3.27 26 Palizzi 504 0.39 27 La Verde 270 0.65 28 Careri 120 0.54 29 Condoianni 200 1.42

(Water supply - Water deficit) 1995 (Water supply - Water deficit) 1996

8000 8000

6000 6000

4000 4000

2000 2000 -1 -1 ha

0 ha 3 0 3 m m

-2000 -2000

-4000 -4000

-6000 -6000

-8000 -8000 1234567891011121314151617181920212223242526272829 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Irrigation system (Identif. number in table 2) Irrigation system (Identif. number in table 2)

(Water supply - Water deficit) 1997 (Water supply - Water deficit) 1998

8000 8000

6000 6000

4000 4000

2000 2000 -1 -1

ha 0 3 ha 0 3 m m

-2000 -2000

-4000 -4000

-6000 -6000

(Water supply - Water deficit) 1999 (Water supply - Water deficit) 2000

8000 8000

6000 6000

4000 4000

2000 2000 -1 -1

ha 0 3 ha 0 3 m m

-2000 -2000

-4000 -4000

-6000 -6000

(Water supply - Water deficit) 2001 8000

6000

4000

2000 -1

ha 0 3 m -2000

-4000

-6000 Figure 4. Annual differences between

-8000 1234567891011121314151617181920212223242526272829 water supplied in each irrigation system Irrigation system (Identif. number in table 2) and the corresponding water deficit.

BIBLIOGRAFY

AA. VV., FAO Penman-Monteith Formula, FAO Expert consultation on revision of FAO methodologies for crop water requirements, Rome, 1990.

Allen, R.G., Pereira, L. S., Raes, D., & Smith, M., Crop Evapotranspiration, FAO Irrigation and Drainage Paper 56, 1998.

Brutsaert, W., Evaporation into the atmosphere: Theory, history, and applications, D. Reidel Publishing Company, Dordrecht, Holland, 1982.

Cassa per il Mezzogiorno, Progetto Speciale per gli Schemi Idrici Intersettoriali della Calabria, Quaderno n. 6, Roma, 1984. Davies, J.A. & Allen, C.D., Equilibrium, potential, and actual evaporation from cropped surfaces in southern Ontario, Journal of Applied Meteorology, 1973, 12, 649-657.

Hatfield, J.L. & Allen, R.G., Evapotranspiration estimates under deficient water supplies, Journal of Irrigation and Drainage Engineering, 1996, 122(5), 301-308.

Mendicino, G., Senatore, A., Versace, P., Stima dell’evapotraspirazione potenziale in regioni meridionali attraverso l’analisi spaziale dei dati altimetrici, di temperatura e radiazione solare, XXVIII Convegno di Idraulica e Costruzioni Idrauliche, Potenza, Italy, 2002, vol. 2, 445-454.

Mendicino, G., Spatially distributed prediction of water deficit periods, Proceedings of the NATO Advanced Research Workshop on Integrated technologies for environmental monitoring and information production, Marmaris, Turkey, 2001.

Mendicino, G., Versace, P., Aspetti metodologici nella ricostruzione del bilancio idrologico a scala interregionale, in “Analisi del fenomeno siccitoso e delle possibilità di mitigazione”, Quaderni di Idrotecnica, BIOS, Cosenza, 2002, vol. 14, 45-94.

Mendicino, G., Versace, P., Siccità: monitoraggio, analisi e previsione, Convegno Interreg Siccità: Monitoraggio, Mitigazione, Effetti, Cagliari, Italy, 21-23 September, 2000

Meyer, S.J., Hubbard, K.G. & Wilhite, D.A., Estimating potential evapotranspiration: the effect of random and systematic errors, Agric. and Forest Meteorology, 1989, 46(4), 285-296.

Monteith, J.L., Evaporation and the environment. In: The state and movement of water in living organisms, XIXth Symp. Soc. Exp. Biol., Swansea. Cambridge University Press, 1965, 205-234.

Moore, I.D., Norton, T.W. & Williams, J.E., Modelling environmental heterogeneity in forested landscapes, Journal of Hydrology, 1993, 150, 717-747.

Penman, H.L., Natural evaporation from open water bare soil and grass, Proc. R. Soc. London, Ser. A, 1948, 193, 120-145.

Priestley, C.H.B. & Taylor e R.J., On the assessment of surface heat flux and evaporation using large-scale parameters, Monthly Weather Review, 1972, 100(2), 81-92.

Principato, G., Stato di attuazione delle grandi infrastrutture idrauliche in Calabria con particolare riferimento al comprensorio Sibari-Crati, 20° Corso di Aggiornamento in “Tecniche per la difesa dall’inquinamento”, 1999.

Principato, G., Le grandi infrastrutture idrauliche in Calabria alla luce dell’Accordo di Programma Quadro per il Ciclo Integrato delle Acque, Associazione Idrotecnica Italiana – Sezione Calabra, 2000.

Principato, G., Il sistema irriguo in Calabria, Conferenza Internazionale “Acqua e Irrigazione”, Cremona, Italia, 2001.

Shuttleworth, J.W., Evaporation, In: D.R. Maidment (Ed.), Handbook of Hydrology, McGraw Hill, Inc., 1993, 4.1-4.53.

Soil Conservation Service, Hydrology, Supplement A to Section 4, National Engineering Handbook. Washington, D.C.: U.S. Department of Agriculture, 1968.

Stannard, D.I., Comparison of Penman-Monteith, Shuttleworth-Wallace, and modified Priestley-Taylor evapotranspiration models for wildland vegetation in semiarid rangeland, Water Resources Research, 1993, 29(5), 1379-1392. Thornthwaite, C. W., & Mather, J.R., The water balance, Climatology, Drexel Inst. Of Technology, Centeron, New Jersey, 1955.

Thornthwaite, C.W., An approach toward a rational classification of climate, Geographic Review, 1948, 38(1), 55-94.