The use of numerical weather forecast model predictions as a source of data for irrigation modelling

Transcription

1 Meteorol. Appl. 1, () The use of numerical weather forecast model predictions as a source of data for irrigation modelling A. Venäläinen 1,T.Salo & C. Fortelius 1 1 Finnish Meteorological Institute, P.O. Box 3, FIN-11 Helsinki, Finland Agrifood Research Finland, MTT, FIN-31 Jokioinen ari.venalainen@fmi.fi doi:1.117/s1371x The use of numerical weather forecast model data as a source of data for soil moisture modelling was tested. Results show that the potential evaporation calculated using the Penman-Monteith equation can be estimated accurately using data obtained from the output of a high resolution numerical atmospheric model (HIRLAM, High Resolution Limited Area Model). The mean bias error was. mm for a 3-hour sum and the root mean square error was.1 mm. The evaporation obtained directly from HIRLAM was systematically smaller because this direct model output represents the real evaporation rather than the potential evaporation. The precipitation forecasts were less accurate. When the accuracy of parameters required for the calculation of potential evaporation were studied for one station, no serious bias was found. When two different irrigation models (AMBAV and SWAP) were run over one summer using either measured or HIRLAM data as the input, the results given by the models were quite similar regardless of input data source. The largest differences between the model outputs were caused by the formulation of crop and soil characteristics in the irrigation models. 1. Introduction Agricultural models need meteorological information. For example, the estimation of crop yields or forecasting the outbreaks of crop diseases or pests are largely based on meteorological conditions. To avoid gaps in reliable meteorological data that would limit the use and the further development of agricultural models, a continuous development in meteorological observational practices and spatialisation methods is also necessary. The availability of meteorological information for agricultural applications can be improved by further developing meteorological observing networks and spatial interpolation methods and by integrating more effectively weather radar and satellite information with traditional meteorological observations. The automation of meteorological observing stations may also have an impact on the availability of some meteorological parameters. Manual synoptic weather stations and also agrometeorological stations are being replaced with automatic weather stations (see for example, Automatic stations are typically equipped with air temperature, air pressure, air humidity and wind speed measuring instruments. Instruments that measure soil temperature, solar radiation soil moisture or surface state can also be installed. Instruments are also available that give an estimate of the present weather, visibility, precipitation amount and height of the cloud layer. The temporal resolution of data obtained from automatic stations is higher than that from the manual stations as the measurements can be made at minute intervals. The problem with automatic stations remains that in some cases observations such as cloudiness, present weather or visibility may be less reliable than in the case of manual observations. With automatic stations located in remote areas their maintenance and the quality control of the output data requires considerable efforts to ensure data reliability. Numerical atmospheric model output data form the backbone of modern weather forecasting services. Beside the direct use of forecast model data in daily weather services, these data are also used as the input for many applied models, as in the case of the road weather service (e.g. Thornes & Shao 199). Estimates of the impacts of climate change also rely on simulations made using numerical atmospheric models, but the time-span of those applications is tens of years rather than days or weeks (e.g. Tubiello et al. ). Data obtained from numerical atmospheric models is nowadays readily available. The data is in gridded format and its spatial and temporal coverage is good. Consequently the use of atmospheric models for 37

2 A. Venäläinen, T. Salo & C. Fortelius producing meteorological data for agro-meteorological applications is an alternative that is worth considering (e.g. Singh 1999). Soil moisture is an essential element for successful farming. The water stress and crop losses through drought can be eliminated by irrigation. The most common irrigation criteria are the soil moisture content and evapotranspiration that can either be measured or estimated. Irrigation models usually provide a rate of actual and potential evapotranspiration and most of them also calculate soil water content or soil water potential. There exists a variety of irrigation models with different levels of complexity. There are monthly-based models that are aimed at long-term irrigation planning (e.g. CROPWAT, Smith et al. 199). More complicated models include detailed crop and soil characteristics in order to simulate crop water use and soil hydrology (e.g. AMBAV: Braden 199; and SWAP: Kroes et al. 1999). These models can be used for prediction purposes. The aim of the current study is to examine whether the data obtained from numerical atmospheric models can be used to supplement or even to replace the measured meteorological data in soil moisture and irrigation modelling. The numerical atmospheric model used in the study is the HIRLAM high resolution limited area model (Undén et al. ). The key meteorological parameters in the soil moisture budget are precipitation and evaporation and in this study we have examined how accurate estimates of those parameters can be obtained based on HIRLAM data. Simulations were also made using two different irrigation models that have as input either measured values or data obtained from HIRLAM forecasts. AMBAV (Braden 199) and SWAP (Kroes et al. 1999) irrigation models were used in this comparison.. Material and methods The present study uses atmospheric parameters from the operational numerical weather forecasts of the Finnish Meteorological Institute. The Finnish forecasting system is based on the HIRLAM numerical weather prediction system, maintained jointly by the national meteorological services of Denmark, Finland, France, Iceland, Ireland, The Netherlands, Norway, Spain and Sweden. HIRLAM is a complete numerical weather prediction system containing modules for data analysis and a forecast model (Undén et al. ; Källén 199). In the Finnish configuration, the forecast model is a hydrostatic, two-time level, semi-lagrangian, semiimplicit limited area grid point model. Prognostic variables are surface pressure, horizontal wind, temperature, specific humidity, specific cloud condensate and the kinetic energy of small-scale turbulence. The model is run with a horizontal resolution of km and has 31 vertical 3 levels. The domain extends from Greenland to the Black Sea and from an area to the south-west of Great Britain to north-east of the Urals. Data on the edges of the domain are supplied by coarse-resolution ( km) HIRLAM corecasts, which, in turn, are nested into global forecasts provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Parameterised processes in the atmosphere include radiation, condensation and precipitation and vertical mixing by small-scale turbulence. At the surface, fluxes of momentum, sensible heat and latent heat are treated in the framework of a Monin-Obukhov similarity with drag coefficients depending on the Richardson number. At the surface each grid box contains a certain proportion of land, sea or ice. Fluxes are computed individually for each surface type, and averaged over the grid box. Temperature and moisture within the soil is computed by a dynamically coupled one-dimensional soil model having two active layers centred at 3 mm and mm below the surface and a passive layer centred at 7 mm. This model also handles melting and freezing of the snow cover and runoff. The soil moisture models applied here are a German model-type known as AMBAV (Braden 199) and a Dutch model-type SWAP (Kroes et al. 1999). They are both complex models including detailed parameters for crop and soil. AMBAV (Agrarmeteorologisches Modell zur Berechnung der aktuellen Verdunstung) is designed to produce recommendations for irrigation amounts and scheduling, and is used by the German Weather Service. The model simulates the water balance in the crop-soil system using the Penman-Monteith equation on an hourly basis. Soil water dynamics are simulated on the basis of Richards equation. Irrigation recommendations and soil water balance are given in data tables. SWAP (Soil, Water, Atmosphere and Plant) is designed by Alterra and Wageningen Agricultural University. The model simulates the vertical transport processes of water, solutes and heat at field-scale level and during the entire growing season. Daily potential evapotranspiration is calculated according to the Penman- Monteith equation and soil water movement according to the Richards equation. Irrigation demand is provided as a part of the water balance. In the current study we have made the following comparisons: (A) For the period May 3 August 1 the HIRLAM forecasted 3-hour precipitation sums were compared with measured values and a comparison was made for nine locations in Finland (Figure 1). A similar comparison was made for potential evaporation that was calculated using the Penman-Monteith equation (Eq. 1) (e.g. Monteith 191).

3 Numerical model data for irrigation modelling 7 N E Kilpisjärvi Inari Sodankylä Rautavaara Ilomantsi Kokemäki Mietoinen Piikkiö Jokioinen 3 E 1 km N Figure 1. Locations used for the estimation of the accuracy of evaporation and precipitation forecasts. (B) (C) E = Rn + ρ c p (1 + b r a /ρ c p ) (e s e)/r a, ( + γ (1 + b r a /ρ c p )) L (1) where L is the latent heat of vaporization (. 1 Jkg 1 ), is the slope of saturated vapour pressure vs. temperature curve (hpa K 1 ), Rn is the net radiation (Wm ), e s is the saturation vapour pressure (hpa), e is the vapour pressure (hpa), ρ is the density of air (1.93 kg m 3 ), γ is the psychrometer constant (. hpa K 1 ), r a is the aerodynamic resistance (sm 1 ), b is the measuring height correction multiplier (Wm K) and c p is the specific heat of air (1 J kg 1 K 1 ). Evaporation as obtained directly from HIRLAM was compared with the potential evaporation calculated from measured data for the same nine locations and time period as in (A). In the case of one station (Ilomantsi, Fig. 1) all the forecasted meteorological parameters required for the calculation of evaporation, i.e. global (D) radiation, long-wave radiation, relative humidity, air temperature and wind speed, were compared with the measured values. Comparisons were made for 1-, - and 3-hour forecasts and the time period studied was the same as in (A) and (B). For summer, irrigation models AMBAV and SWAP were utilised for one station location (Jokioinen, Figure 1). Soil moisture values were calculated using meteorological data obtained either from HIRLAM or based on measurements. The soil moisture values obtained using the models were also compared with measured soil moisture values. The measured meteorological data in all comparisons were for meteorological stations as well as 1 km 1 km grid interpolated values. The system used for obtaining gridded data is explained in detail in Venäläinen & Heikinheimo (). The HIRLAM 39

4 A. Venäläinen, T. Salo & C. Fortelius Table 1. Soil properties Particle size Pore size distribution (%, Saturated hydraulic distribution conductivity volume/volume) Organic Dry bulk (cm/d) based (%, weight/weight) carbon density Field Wilting on in situ Depth (cm) Clay Silt Sand (%) (kg/dm 3 ) Saturated capacity point measurements output is available in a coarser grid and thus all HIRLAM parameters in (A) were interpolated using liner interpolation onto the 1 km 1 km grid. In comparison (B) the HIRLAM evaporation values were not interpolated on the denser grid and so the nearest grid point value was used. The evaporation values taken directly from HIRLAM can be regarded as real evaporation. It is treated in the framework of a Monin- Obukhov surface layer with stability-dependent drag coefficients, for example; soil moisture or the surface type (water, vegetation, snow, etc.) are taken into account in the calculations. In all comparisons the forecasted data were obtained from the model run that was based on the UTC analyses. In the case of (A) (C) a forecast for up to 3 hours was used, and in the case of (D) a -hour forecast was used. Air temperature, wind speed, relative humidity, rainfall amount, global solar radiation and cloudiness parameters are needed for the running of the AMBAV model. The modelled data did not provide total cloudiness data and observed cloudiness was used instead. Both measured and observed cloudiness values were obtained for the synoptic moments, i.e. with three-hour intervals. The hourly values required were obtained by linearly interpolating the three-hourly values to hourly values. The SWAP model requires as daily input the minimum and maximum values of air temperature, wind speed, relative humidity, rainfall amount and global solar radiation. Soil moisture measurements were obtained from a site located km south-west of the Jokioinen Observatory ( 9 N, 3 3 E, elevation 1 m above sea level). The soil and crop properties were provided by the Agrifood Research Finland project Soil physics and crop yield: establishing the relationship between spatial variability of soil physical and chemical properties and crop yield by using soil and yield maps (Tables 1 &, Figure ). The crop on the site was barley which had been sown on 13 May and harvested on August. Irrigation models were not calibrated for the existing conditions, only the relevant measured and estimated inputs were added to the models. Irrigation was not used so the soil water content was dependent only on rainfall, evapotranspiration and soil water reserves. 31 Table. Dates of observed phenological stages and development stages (DVS) according to the SWAP model Date Julian day DVS (SWAP) Sowing Emergence leaves.. 1. Stem growth begins Spike outgrowth End of anthesis Late milk stage Harvest.. 3 Soil moisture measurements at Jokioinen were made by the Geological Survey of Finland using the TDRmethod (e.g. Noborio et al. 199). The sensors were installed at depths of 1, 7 and 7 1 cm. Daily average values were used for comparisons between the measurements and the models. The soil moisture measurements were made for the period June to August. 3. Results 3.1. The accuracy of precipitation and evaporation forecasts The results given here refer to comparisons between (A) and (B) as explained in section above. When evaporation forecasts are examined (Figures 3 & and Table 3), it can be seen that evaporation calculated by the Penman-Monteith formula and using input from HIRLAM is close to that obtained when using measured values as the input. The mean bias error is. mm and the RMS error.1 mm. The bias varied between.7 (Kilpisjärvi) and 1. mm (Rautavaara). Besides Kilpisjärvi, the other northern station (Inari) also has a large negative bias. The RMS error varied between 1.9 (Piikkiö) and 3.3 mm (Sodankylä). When the evaporation obtained directly from HIRLAM was compared with the value calculated using the Penman- Monteith equation and measured data it can be seen that the HIRLAM evaporation values are systematically lower than the measured ones (Figure ). The bias for

5 Numerical model data for irrigation modelling 1 cropheight (dm) 3 1 LAI Crop LAI Figure. Crop height and leaf area index (LAI) in summer at Jokioinen (see Figure 1). Table 3. The mean bias error (BIAS) and the root mean square error (RMS) at studied locations for the 3-hour precipitation and evaporation sum forecasts. The studied time period was May 3 August 1 Evaporation Precipitation Penman-Monteith HIRLAM BIAS RMS BIAS RMS BIAS RMS Piikkiö Mietoinen Jokioinen Kokemäki Ilomantsi Rautavaara Sodankylä Inari Kilpisjärvi All stations all stations is.1 mm, with the largest being for Mietoinen (.3 mm) and the smallest for Kilpisjärvi ( 1.3 mm). This result is expected as the evaporation calculated using the Penman-Monteith formula is the potential evaporation whereas the HIRLAM evaporation simulates real conditions where, for example, soil moisture influences the latent heat flux. The RMS error of the HIRLAM evaporation values is 3.79 mm. The largest RMS error is for Mietoinen (.1 mm) and the smallest for Kilpisjärvi (. mm). The precipitation estimation is more problematic than evaporation (see Figure and Table 3). The bias of the measured and the HIRLAM estimated precipitation values is. mm for all stations. The largest positive bias is for Sodankylä (1.9 mm) and the only negative bias is for Kokemäki (.39 mm). The RMS error for the whole dataset is. mm; the smallest for Jokioinen (3.3 mm) and the largest at Kilpisjärvi (9.9 mm). 3.. The accuracy of forecasts of meteorological parameters needed for the calculation of evaporation Ilomantsi, located in eastern Finland, will now be examined in detail. The results given here refer to comparison (B). The evaporation sum, calculated using the Penman-Monteith equation and the HIRLAM data, has a small positive bias.3 mm (see Figure 3 and Table 3). Examining the different components that are used for the calculation of potential evaporation (Figure and Table ) it can be seen that in both the 1- and 3-hour forecasts the highest global radiation values have been predicted relatively correctly. These are the mid-day cloudless situations. The 1-hour forecasts are slightly better than the 3-hour forecasts. The -hour forecasts are better because there is no solar radiation at night. In the case of daytime values (1- and 3-hour forecasts) the highest predicted upward long-wave radiation values are greater than the measured values. Remember that the long-wave radiation values, even in case of observations, are calculated using a parameterisation utilising air temperature, air humidity and cloudiness information (Venäläinen & Heikinheimo ). The network of stations making cloud observations is relatively sparse and so the long-wave radiation values will include inaccuracies. There is a tendency for the daytime predicted relative humidity values to be a little lower than the observed values. During the night, the values are close to 1%. Air temperature forecasts are good. The predicted daytime highest temperatures are a little higher that 311

6 A. Venäläinen, T. Salo & C. Fortelius 1 Ilomantsi 1 Inari 1 Jokioinen Kilpisjarvi Kokem ȧ ki Mietoinen Piikki ȯ Rautavaara Sodankyl ȧ Figure 3. The 3-hour potential evaporation sum based on forecasted (EFOREC) and measured data (EMEASURED) in May August 1 at nine locations (see Figure 1). The forecasted potential evaporation was obtained by using forecasts of air temperature, air humidity, wind speed, global and long-wave radiation values as input in the Penman-Monteith equation. the observed. The forecasted wind speed values contain no systematic error though the scatter is relatively large. It is important to notice that in all of these comparisons the forecasted values are compared with interpolated values that are not the same as measured values. Table. The mean bias error (BIAS) and root mean square errors (RMS) at Ilomantsi (see Fig. 1) for the 1-, - and 3-hour HIRLAM forecasts. The studied time period was May 3 August 1. The units are: global and long-wave radiation Wm, wind speed ms 1, relative humidity % and temperature C 1-hour forecast -hour forecast 3-hour forecast BIAS RMS BIAS RMS BIAS RMS Global radiation Long-wave radiation Wind speed Relative humidity Air temperature

7 Ilomantsi Inari Numerical model data for irrigation modelling Jokioinen Kilpisj ȧ rvi Kokem ȧ ki Mietoinen. ȧ Piikki ȯ Rautavaara Sodankyl ȧ Figure. The 3-hour evaporation sum taken directly from EHIRLAM forecasts and based on measured meteorological data (EMEASURED) in May August 1 at the nine locations Differences in the calculated soil moisture values The results given here refer to comparison (D). During the simulation period 1 May to August (Julian days 11 3) at Jokioinen (see Figure 1), the forecasted rainfall was 19 mm higher than measured (Figure 7 and Table ). The potential evapotranspiration was 7 mm higher in the SWAP than in the AMBAV model and both models forecast mm higher potential evapotranspiration than was measured. Actual evapotranspiration of the models was 1 3 mm. The low actual evapotranspiration forecasted by SWAP was due to water loss by runoff after a mm higher rainfall in the forecasted data compared to the measured data. The experimental site also had a runoff monitoring station but it did not measure any surface runoff or drainage during the simulation period. Thus runoff was overestimated due to the overestimation of the rainfall. Table. Water balance of simulations. ETP = evapotranspiration, runoff includes both surface runoff and drainage Potential Actual Change Rainfall ETP ETP Runoff of storage AMBAV measured AMBAV forecast SWAP measured SWAP forecast For the comparison of measured and modelled soil moisture values, data were available for the period June to August (Julian days 1 3) for Jokioinen (see Figure 1). At a depth of 1 cm, the SWAP model output and the forecasted AMBAV model corresponded quite closely with the measurements, although the level of soil moisture content was not exact (Figure ). The best fit with the measurements was not especially 313

8 A. Venäläinen, T. Salo & C. Fortelius 3 1 IIomantsi 3 1 Inari 3 1 Jokioinen Kilpisj ȧ rvi Kokem ȧ ki Mietoinen Piikki ȯ Rautavaara Sodankyl ȧ Figure. The 3-hour precipitation sum forecast (RRFOREC) and measurement (RROBS) in May August 1 at nine locations (see Figure 1). good (R =.) and was achieved using forecasted weather and the SWAP model. The AMBAV-model produced a weak fit, especially with observed values. One reason was that the AMBAV-model estimated a higher wilting point than either the SWAP-model or the actual measurements for the soil of the experimental site. The wilting point is the moisture content at which the capillary water in the soil becomes unavailable to plants. With the forecasted rainfall, the AMBAV-model output agreed with the measured soil water contents until the beginning of the rainfall period that began on Julian day. 31 The differences in measured and forecasted rainfall can be observed from modelled soil water content (see Figure 7). In the period between Julian days 179 and 1, the forecasted rainfall was mm higher than the measured rainfall, and this resulted in an increased soil water content especially in the AMBAVmodel. Later in the period, between Julian days and 13, the measured rainfall was mm higher than forecasted. This resulted in an increase in the soil water content in the SWAP model with the forecasted rainfall. In the case of the AMBAV-model, this water was assumed to have been lost through surface runoff and the soil water content did not increase. There was no measured runoff in the field, and so a more detailed study with soil parameters should have been made in order to fit the measured and simulated runoff. At a depth of 7 cm, both the AMBAV and the SWAP-models corresponded reasonably well with the measured soil water content (Figure 9). The best fit was found between the SWAP-model and the forecasted weather (R =.7). At a depth of 7 1 cm, both the measured and modelled soil water content were a good fit (Figure 1).

9 GLOBOBS (Wm - ) GLOB1 (Wm - ) GLOBOBS (Wm - ) GLOB (Wm - ) Numerical model data for irrigation modelling GLOBOBS (Wm - ) GLOB3 (Wm - ) LWOBS (Wm - ) - LWOBS (Wm - ) - LWOBS (Wm - ) RHOBS (%) LW1 (Wm - ) 1 TOBS ( C) RH1 (%) WOBS (ms -1 ) T1 ( C) W1 (ms -1 ) RHOBS (%) LW (Wm - ) 1 TOBS ( C) RH (%) 3 WOBS (ms -1 ) T ( C) W (ms -1 ) RHOBS (%) LW3 (Wm - ) 1 TOBS ( C) WOBS (ms -1 ) RH3 (%) T3 ( C) W3 (ms -1 ) Figure. Global radiation (GLOB), long-wave radiation balance (LW), relative humidity (RH), air temperature (T) and wind speed (W) forecasts ( UTC + 1 h, UTC + h and UTC + 3 h) at Ilomantsi compared with the measured (OBS) values in May August 1.. Discussion and conclusions The modelled soil moisture content deviated more between the different irrigation models than between the same model using different weather data (see Table, Figures 1). Although the model input parameters were set up to be as close to each other as possible in both models, the models simulated the potential 31

10 A. Venäläinen, T. Salo & C. Fortelius Rainfal(mm) Julian days Measured Hirlam Figure 7. Measured cumulative precipitation data and data from HIRLAM obtained during summer 1, at Jokioinen M1_ AM1_ AH1_ SM1_.3 SH1_ Julian days Figure. Measured and modelled soil water contents (volume/volume) in the depth of 1 cm (M = measured soil water content, AM = AMBAV, measured weather, AH = AMBAV, HIRLAM modelled weather, SM = SWAP, measured weather, SH = SWAP, HIRLAM modelled weather). evapotranspiration and soil water fluxes in a slightly different way. In the case of the weather input data, the differences in rainfall created the greatest differences. As mm rainfall can often increase soil water content by 1% in the ploughing layer, errors of this magnitude in forecasted rainfall data can have a major effect on irrigation demand. The sensitivity of irrigation models on the crop and especially on the soil characteristics is probably the main challenge to the use of models in irrigation scheduling. The selection of parameters that are used in irrigations models seems to be more crucial than the source of meteorological input data. According to this study, numerical weather forecast model data seem to be a serviceable alternative when input data for a soil moisture model are needed. There were no major systematic differences when potential evaporation was calculated using either modelled or measured data as input. Unlike evaporation, there can be large differences when daily measured and forecasted precipitation values are compared. However, when the precipitation sum over the whole growing season is calculated then the differences between the measured and modelled values become much smaller and this makes the use of modelled meteorological 31

11 Numerical model data for irrigation modelling M_7 AM_7 AH_7 SM 7.3 SH Julian days Figure 9. Measured and modelled soil water contents (volume/volume) in the depth of 1 7 cm (M = measured soil water content, AM = AMBAV, measured weather, AH = AMBAV, HIRLAM modelled weather, SM = SWAP, measured weather, SH = SWAP, HIRLAM modelled weather) M7_1 AM7_1 AH7_1 SM_7_1.3 SH_7_ Julian days Figure 1. Measured and modelled soil water contents (volume/volume) in the depth of 71 1 cm (M = measured soil water content, AM = AMBAV, measured weather, AH = AMBAV, HIRLAM modelled weather, SM = SWAP, measured weather, SH = SWAP, HIRLAM modelled weather). data for soil moisture simulations a more tempting option. Naturally, if good quality measured meteorological data are available then there is no need to use modelled data. However, it is possible to supplement measured data with respect to spatial or temporal coverage with the modelled data if the former are unavailable. Numerical model data can also be used to supplement some parameters that are seldom measured, such as radiation components. Acknowledgements We would like to thank Dr Laura Alakukukku and Antti Ristolainen for providing data on crop and soil characteristics from the project Soil physics and crop yield: establishing the relationship between spatial variability of soil physical and chemical properties and crop yield by using soil and yield maps of Agrifood Research Finland. We also would like to thank Dr Pekka Hänninen from the Geological Survey of Finland for providing measured soil moisture 317

12 A. Venäläinen, T. Salo & C. Fortelius data, as well for his valuable comments during this study. References Braden, H The model AMBETI- A detailed description of a soil-plant-atmosphere model. Berichte des Deutschen Wetterdienstes. 19: 117. Eumetnet, AWS (Automatic Weather Stations) eumetnet.eu.org/contaws.html (accessed September 3). Kroes, J. G., van Dam, J. C., Huygen, J. & Vervoort, R. W. (1999) SWAP.: User s Guide. Simulation of water flow, solute transport and plant growth in the Soil-Water- Atmosphere-Plant environment. Technical Document 3. DLO Winand Staring Centre, Wageningen Report 1, Department of Water Resources, Wageningen Agricultural University. Källén, E. ed. (199) HIRLAM Documentation Manual. System.. June 199, available from SMHI, S-17, Norrköping, Sweden. Monteith, J. L. (191) Evaporation and surface temperature. Q. J. R. Meteorol. Soc. 1 (1): 1 7. Noborio, K., McInnes, K. J. & Heilman, J. L. (199) Measurement of soil water content, heat capacity, and thermal conductivity with a single TDR probe. Soil Science 11 (1):. Singh, S. (1999). Use of medium range weather forecast for agricultural operations and crop weather modelling. In: A. Bhatnagar (ed.) Proceedings of TROPMET-99, Chennai (India), 1 19 February 1999, pp Smith, M., Clarke, D. & El-Askari, K. (199) CropWat for Windows: User Guide. faoinfo/agricult/agl/aglw/cropwat.stm Thornes, J. & Shao, J. (199) Objective method for improving the operational performance of road ice prediction model using interpolated mesoscale output and a template for correcting systematic error. Meteorol. Mag.11 (1): 197. Tubiello, F., Rosenzweig, C., Goldberg, R., Jagtap, S. & Jones, J. () Effects of climate change on US crop production: simulation results using two different GCM scenarios. Part I: Wheat, potato, maize, and citrus. Clim. Res. (3): 9 7. Undén, P., Rontu, L., Järvinen, H., Lynch, P., Calvo, J., G. Cats, Cuxart, J., Eerola, K., Fortelius, C., Garcia- Moya, J. A., Jones, C., Lenderlink, G., McDonald, A., McGrath, R., Navascues, B., Woetman Nielsen, N., Ødegaard, V., Rodriguez, E., Rummukainen, M., Rõõm, R., Sattler, K., Hansen Sass, B., Savijärvi, H., Wichers Schreur, B., Sigg, R., The, H. & Tijm, A. () HIRLAM- Scientific Documentation. HIRLAM- project, available from the Swedish Meteorological and Hydrological Institute, SE-1 7 Norrköping, Sweden. Venäläinen, A. & Heikinheimo, M. () Meteorological data for agricultural applications. Physics & Chemistry of the Earth. 7 (3 ):