Years Of Weather Data Research Articles

The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

Published UK 2050 energy scenarios specify a range of decarbonised supply side technologies combined with electrification of transportation and heating. These scenarios are designed to meet CO2 reduction targets whilst maintaining reliability of supply. Current models of the UK energy system either make significant assumptions about the role of demand side management or do not carry out the analysis at sufficient resolution and hence determining the impact of heat electrification on the reliability of supply of the scenarios is not possible. This paper presents a new model that estimates national supply and demand, hour-by-hour. Calculations are based on 11 years of weather data which allows a probabilistic assessment of deficit frequency throughout the day. It is found that achieving demand reduction targets are far more important than meeting electrification targets and that significant adoption of CHP is most likely to deliver a viable energy future for the UK.

Re-evaluating sowing time of spring canola (Brassica napus L.) in south-eastern Australia—how early is too early?

Optimising the sowing date of canola (Brassica napus L.) in specific environments is an important determinant of yield worldwide. In eastern Australia, late April to early May has traditionally been considered the optimum sowing window for spring canola, with significant reduction in yield and oil in later sown crops. Recent and projected changes in climate, new vigorous hybrids, and improved fallow management and seeding equipment have stimulated a re-evaluation of early-April sowing to capture physiological advantages of greater biomass production and earlier flowering under contemporary conditions. Early–mid-April sowing generated the highest or equal highest yield and oil content in eight of nine field experiments conducted from 2002 to 2012 in south-eastern Australia. Declines in seed yield (–6.0% to –6.5%), oil content (–0.5% to –1.5%) and water-use efficiency (–3.8% to –5.5%) per week delay in sowing after early April reflected levels reported in previous studies with sowings from late April. Interactions with cultivar phenology were evident at some sites depending on seasonal conditions. There was no consistent difference in performance between hybrid and non-hybrid cultivars at the earliest sowing dates. Despite low temperatures thought to damage early pods at some sites (<−2°C), frost damage did not significantly compromise the yield of the early-sown crops, presumably because of greater impact of heat and water-stress in the later sown crops. A validated APSIM-Canola simulation study using 50 years of weather data at selected sites predicted highest potential yields from early-April sowing. However, the application of a frost-heat sensitivity index to account for impacts of temperature stress during the reproductive phase predicted lower yields and higher yield variability from early-April sowing. The frost–heat-limited yields predicted optimum sowing times of mid-April at southern sites, and late April to early May at the northern sites with lower median yield and higher yield variability in crops sown in early April. The experimental and simulation data are potentially compatible given that the experiments occurred during the decade of the Millennium drought in south-eastern Australia (2002–10), with dry and hot spring conditions favouring earlier sowing. However, the study reveals the need for more accurate and validated prediction of the frost and heat impacts on field-grown canola if simulation models are to provide more accurate prediction of attainable yield as new combinations of cultivar and sowing dates are explored.

Evaluation of the BlightPro Decision Support System for Management of Potato Late Blight Using Computer Simulation and Field Validation.

The objective of this study was to evaluate the utility of the BlightPro decision support system (DSS) for late blight management using computer simulation and field tests. Three fungicide schedules were evaluated: (i) calendar-based (weekly) applications, (ii) applications according to the DSS, or (iii) no fungicide. Simulation experiments utilized 14 years of weather data from 59 locations in potato-producing states. In situations with unfavorable weather for late blight, the DSS recommended fewer fungicide applications with no loss of disease suppression; and, in situations of very favorable weather for late blight, the DSS recommended more fungicide applications but with improved disease suppression. Field evaluation was conducted in 2010, 2011, 2012, and 2013. All experiments involved at least two cultivars with different levels of resistance. DSS-guided and weekly scheduled fungicide treatments were successful at protecting against late blight in all field experiments. As expected, DSS-guided schedules were influenced by prevailing weather (observed and forecast) and host resistance and resulted in schedules that maintained or improved disease suppression and average fungicide use efficiency relative to calendar-based applications. The DSS provides an interactive system that helps users maximize the efficiency of their crop protection strategy by enabling well-informed decisions.

Switchgrass procurement strategies for managing yield variability: Estimating the cost-efficient D (downtime cost) L (land to lease) frontier

Lignocellulosic biorefineries that plan to use switchgrass (Panicum virgatum L.) biomass exclusively will encounter both temporal (across years) and spatial (across locations within a given year) variability in feedstock production. Long term land leases could be employed to facilitate feedstock availability for the expected life of the biorefinery. If the quantity of land leased is based on average yields, in some years more biomass will be produced than can be processed. In other years feedstock production on the leased land may be insufficient to prevent biorefinery downtime. An optimal strategy for identifying which land to lease and seed to switchgrass, while considering yield variability and the opportunity cost of biorefinery downtime, is the focus of the research. The objective is to determine for a given biorefinery location the least-cost quantity, quality, and location of land to lease for alternative estimates of biorefinery downtime cost due to variable switchgrass yields. Fifty years of weather data are used to simulate switchgrass yield distributions for a case study region. An innovative mathematical programming model is developed and used to reveal the cost-efficient D (Downtime Cost) L (Land to Lease) frontier for a 2 Gg d−1 biomass capacity biorefinery. If interyear storage is not permitted, 60,492 ha would be required to insure that the biorefinery run at full capacity every year given the estimated yield distributions. However, for some circumstances, it would be optimal to produce switchgrass on only 49,464 ha and idle the biorefinery for some days in low biomass production years.

Features of a fully renewable US electricity system: Optimized mixes of wind and solar PV and transmission grid extensions

A future energy system is likely to rely heavily on wind and solar PV. To quantify general features of such a weather dependent electricity supply in the contiguous US, wind and solar PV generation data are calculated, based on 32 years of weather data with temporal resolution of 1h and spatial resolution of 40×40km2, assuming site-suitability-based and stochastic wind and solar capacity distributions. The regional wind-and-solar mixes matching load and generation closest on seasonal timescales cluster around 80% solar share, owing to the US summer load peak. This mix more than halves long-term storage requirements, compared to wind only. The mixes matching generation and load best on daily timescales lie at about 80% wind share, due to the nightly gap in solar production. Going from solar only to this mix reduces backup energy needs by about 50%. Furthermore, we calculate shifts in FERC (Federal Energy Regulatory Commission)-level LCOE (Levelized Costs Of Electricity) for wind and solar PV due to differing weather conditions. Regional LCOE vary by up to 29%, and LCOE-optimal mixes largely follow resource quality. A transmission network enhancement among FERC regions is constructed to transfer high penetrations of solar and wind across FERC boundaries, employing a novel least-cost optimization.

Comparison of simulated water, nitrate, and bromide transport using a Hooghoudt-based and a dynamic drainage model

For large-scale hydrological modeling, the accuracy of the models used is a trade-off with the computational requirements. The models that perform well on the daily/meter scale may not perform well when applied at the yearly/kilometer scale. We compare two models of water flow and nitrate and bromide transport in a tile drained soil. The first model is based on a 2-D grid with an explicit drain node, here called the Dynamic Drainage Model (DDM). The second and less computationally expensive model is based on an 1-D vertical discretization where the horizontal flow is included as a sink term based on the Hooghoudt theory, here called the Hooghoudt Drainage Model (HDM). Both are based on Finite Volume Method solutions to Richard's equation and to the advection-dispersion equation (ADE), and embedded within the Daisy agroecological model, which includes the nitrogen cycle. The two models are run with 10 years of weather data and three different lower-boundary conditions. Losses of water, nitrogen, and bromide to both drain pipes and deep percolation/leaching are compared between the models, at daily and yearly time scales. In no case do we find the discrepancy large enough to warrant a rejection of the use of the faster HDM instead of DDM. For the daily time scale, we find in general a higher Nash-Sutcliffe efficiency coefficient for water (0.98–1.00) than for nitrate (0.97–1.00), and the lowest for bromide (0.95–1.00). The results are explained with a low concentration gradient along the water flow pathway toward the drain.

Evaluation of climate adaptation options for Sudano-Sahelian cropping systems

In the Sudano-Sahelian region, smallholder agricultural production is dominated by rain-fed production of millet, sorghum and maize for food consumption and of cotton for the market. A major constraint for crop production is the amount of rainfall and its intra and inter-annual variability. We evaluated the effects of planting date on the yield of different varieties of four major crops (maize, millet, sorghum and cotton) over three contrasting growing seasons in 2009–2011 (with 842mm, 1248mm and 685mm of rainfall respectively) with the aim of identifying climate adaptation options in the Sudano-Sahelian region. Three planting dates (early, medium, and late) and three varieties of long, medium, and short duration of each crop were compared.For fertilized cereal crops, maize out yielded millet and sorghum by respectively 57% and 45% across the three seasons. Analysis of 40 years of weather data indicates that this finding holds for the longer time periods than the length of this trial. Late planting resulted in significant yield decreases for maize, sorghum and cotton, but not for millet. However, a short duration variety of millet was better adapted for late planting. When the rainy season starts late, sorghum planting can be delayed from the beginning of June to early July without substantial reductions in grain yield. Cotton yield at early planting was 28% larger than yield at medium planting and late planting gave the lowest yield with all three varieties. For all four crops the largest stover yields were obtained with early planting and the longer planting was delayed, the less stover was produced. There was an interaction between planting date and variety for millet and sorghum, while for maize and cotton the best planting date was more affected by the weather conditions. The findings of this study can support simple adaptation decisions: priority should be given to planting cotton early; maize is the best option if fertilizer is available; planting of maize and sorghum can be delayed by up to a month without strong yield penalties; and millet should be planted last.

Adapting to a New Reality—Strategies for Building Energy Design in a Changing Climate

ABSTRACTBuilding energy design is traditionally performed using retrogressive data sets (e.g., the past 30 years of weather data). The implied presumption has always been that this data will cycle back and forth around relatively static baseline averages. With increasing evidence that some level of climate change may be occurring, it is natural for building owners, developers, designers, and managers to question whether (and to what extent) these fundamental climate assumptions may be altered in future years. Depending on a building's locality, this could take the form of increasing or decreasing trends in seasonal average temperatures, daily maximum and minimum temperatures, relative humidity, barometric pressure, wind speed and direction, cloud cover, and total precipitation. These assumptions are crucial, because a typical building must remain habitable for 30 to 50 years (or longer) and provide its owner with the maximum possible return on a sizeable capital investment.This article will demonstrate ho...

Adapting to a New Reality—Strategies for Building Energy Design in a Changing Climate

ABSTRACT Building energy design is traditionally performed using retrogressive data sets (e.g., the past 30 years of weather data). The implied presumption has always been that this data will cycle back and forth around relatively static baseline averages. With increasing evidence that some level of climate change may be occurring, it is natural for building owners, developers, designers, and managers to question whether and to what extent these fundamental climate assumptions may be altered in future years. Depending on a building's locality, these could take the form of increasing or decreasing trends in seasonal average temperatures, daily maximum and minimum temperatures, relative humidity, barometric pressure, wind speed and direction, cloud cover, and total precipitation. These assumptions are crucial, because a typical building must remain habitable for 30–50 years (or longer) and provide its owner(s) with the maximum possible return on a sizeable capital investment. This article will demonstrate how building owners and developers can employ intelligent strategies to maximize energy efficiency while concurrently meeting building energy requirements and retaining significant flexibility to cope with potential variations in local climate. Data from existing buildings that currently exhibit outstanding energy performance (e.g., net zero energy buildings, Leadership in Energy and Environmental Design [LEED°] Gold- and Platinum-certified buildings, buildings with ENERGY STAR° ratings above 90) will be utilized to identify energy efficiency and renewable energy production technologies that can further improve energy performance and reduce risk. This article will demonstrate that, by implementing these types of adaptive strategies, the building sector can more nimbly respond to potential climate variations.

Ground–atmosphere interaction modelling for long-term prediction of soil moisture and temperature

Land surface and subsurface variables, such as soil moisture–suction and temperature, are among the most important components to study the behaviour of expansive soil, geothermal energy, and climate change. A more accurate and long-term series of soil moisture and temperature prediction, due to ground–atmosphere interaction, is very important for real-time drought monitoring for understanding and improving the behaviour of soil, buried structures, and climate prediction. In this study, ground–atmosphere interaction is numerically modelled using Vadose/W software for two instrumented sites in Melbourne, Australia. Soil moisture and temperature down to 2 m depth were monitored over 2 years at discrete locations and the meteorological variables including air temperature, air humidity, wind speed, precipitation, and solar radiation were measured from a weather station installed at the sites. Further, laboratory and field tests were performed to establish initial conditions and soil characteristics such as the soil-water characteristic curve (SWCC ), hydraulic conductivity, and thermal conductivity functions. The numerical model results were calibrated with the field data, indicating good agreement between numerical and field results. The calibrated numerical model was used to compute the long-term moisture and temperature variations into the immediate future using the past 20 years of weather data in Melbourne.

창원시 학교 건축물의 냉난방부하에 대한 전력 소비량 추정에 관한 연구

This study was carried out in order to establish the estimation equation for school power consumption using regression analysis based on collected power consumption for two years of weather data and schools are located in Central Changwon and Masan district in Changwon city. (1) The power consumption estimation equation for Heating and cooling is calculated using power consumption per unit volume, the difference between actual power consumption and results of estimation equations is 4.1%. (2) The power consumption estimation equation for heating load is showed 2.6% difference compared to actual power consumption in Central Changwon and is expressed 2.9% difference compared to that in Masan district. Therefore, the power consumption prediction for each school using the power consumption estimation equation is possible. (3) The power consumption estimation equation for cooling load is showed 8.0% difference compared to actual power consumption in Central Changwon and is expressed 2.9% compared to that in Masan district. As the power consumption estimation equation for cooling load is expressed difference compared to heating load, it needs to investigate influence for cooling load.

Potential effects of individual versus simultaneous climate change factors on growth and water use in chickpea

It has been reported that in different regions and sowing dates, the response of crops to past climate change is not the same, due to different rates of decrease/increase in each climatic variable at different regions and months of year. This study was aimed to assess the effect of individual versus simultaneous changes in solar radiation (S), precipitation (P) and temperature (T) on rainfed- and irrigated-chickpea, using model CYRUS. The observed weather data for year 2004 in Kermanshah, Iran, was used as the control. Firstly, the responses of chickpea to individual changes in S and P (25, 50, 75, 125 and 150%) and T (±1, ±2 o C) with respect to control were studied. Secondly, S, P, and T were simultaneously changed by 50%, 150%, and ±2°C, and the interactions were analyzed. Results indicated that the value of change in biomass, harvest index (HI) and evapotranspiration (ET) was higher for S, compared to P and T. For irrigated-chickpea, the biomass and ET were directly, but HI (nearly) inversely affected by S. T had no impact on HI and ET. The biomass was slightly lower for warmer T. When T, S and P were simultaneously changed, P and T slightly interacted with huge effect of S. For rainfed-chickpea, it was found non-linear response to S for biomass, but linear response for HI. For high S levels, ET was same as control. The decreased levels of P positively affected biomass and HI. There was proportionally change in biomass and HI with changing T. It was found various considerable interactions between variables for biomass, HI and ET. For example, when T was cool, high values of S and P synergistically decreased biomass. These single- and interaction-based results would be adapted as one possible scenario of multi-parameter sensitivity analysis, and could be useful for identifying appropriate management/genotypes for future climate.

Modelled yield and water use efficiency of maize in response to crop management and Southern Oscillation Index in a soil-climate transect in Argentina

Maize responses to individual management factors have been widely investigated but studies of higher-order interactions involving multiple factors are rare. This paper investigates the responses of grain yield and water use efficiency of rainfed maize to sowing date, stubble condition, hybrids of contrasting cycle length, soil depth and their interactions, and how these responses are affected by El Niño – Southern Oscillation (ENSO) phenomenon. Grain yield and water balance were modelled with CropSyst using 33 years of weather data for eight locations in an east–west transect in the Southern Pampas of Argentina. Modelled grain yield decreased westward, in parallel with decreasing rainfall. Southern Oscillation Index (SOI) discriminated crop season rainfall and grain yield, with lower rainfall and grain yield for SOI phase Consistently Positive (CP) and higher rainfall and grain yield for SOI phase Consistently Negative (CN). Differences in grain yield between stubble and bare soil were constant across location. High available water, as related to soil depth, increased grain yield across the transect. The effect of sowing date and stubble varied with SOI phase; for CN, highest grain yields were obtained with early sowing whereas for CP grain yield showed no correlation with sowing date. Yield differences between bare soil and stubble conditions were higher under CP than under CN, reflecting the positive effect of stubble in years with rainfall below average. Water use efficiency (WUE=yield per unit evapotranspiration) averaged 19.0kgha−1mm−1 for soils with stubble and 16.5kgha−1mm−1 for bare soil. We conclude that SOI provides an agronomically meaningful predictor of seasonal conditions that can be used to estimate crop production and manage risk by adjusting key management practices.

Hydrologic Effects of Size and Location of Fields Converted from Drained Pine Forest to Agricultural Cropland

Hydrological effects of land-use change are of great concern to ecohydrologists and watershed managers, especially in the Atlantic coastal plain of the southeastern United States. The concern is attributable to rapid population growth and the resulting pressure to develop forested lands. Many researchers have studied these effects in various scales, with varying results. An extended watershed-scale forest hydrologic model, calibrated with 1996–2000 data, was used to evaluate long-term hydrologic effects of conversion to agriculture (corn–wheat–soybean cropland) of a 29.5-km2 intensively managed pine-forested watershed in Washington County in eastern North Carolina. Fifty years of weather data (1951–2000) from a nearby weather station were used for simulating hydrology to evaluate effects on outflows, evapotranspiration, and water table depth compared with the baseline scenario. Other simulation scenarios were created for each of five different percentages (10, 25, 50, 75, and 100%) of land-use conversion occurring at upstream and downstream locations in the pine-forest watershed. Simulations revealed that increased mean annual outflow was significant (α=0.05) only for 100% conversion from forest (261 mm) to agricultural crop (326 mm), primarily attributed to a reduction in evapotranspiration. Although high flow rates >5 mm day−1 increased from 2.3 to 2.6% (downstream) and 2.6 to 4.2% (upstream) for 25 to 50% conversion, the frequency was higher for the upstream location than the downstream. These results were attributed to a substantial decrease in soil hydraulic conductivity of one of the dominant soils in the upstream location, which is expected after land-use conversion to agriculture. As a result, predicted subsurface drainage decreased, and surface runoff increased as soil hydraulic conductivity decreased for the soil upstream. These results indicate that soil hydraulic properties resulting from land-use conversion have a greater influence on hydrologic components than the location of land use conversion.

Efficiency of Three Wind Turbines Installed on High Plains Region of Algeria

A Monthly Weibull parameter k and c and a variance for three stations located in the west of high plains of Algeria (Tiaret, Djelfa and El Bayadh) have been estimated using ten years of weather data. The Weibull parameters have been extrapolated to 45 meters taken as height of wind machine rotor. The corresponding mean cubic speeds of the selected sites and the mean monthly power density have been calculated. The most interesting wind potential is obtained for Tiaret region.Using a cut-in speed and a rated speed of a given generators, with 100kW, 600kW and 850kW of nominal power, the monthly mean usable cubic wind speed for the selected sites has been computed.The results show that a 600kW and 850kW wind machine are quite equivalent, in useful power density sense. The choice of an appropriate wind machine will depend of local energy needs. A big decrease noticed during hot season can be completed by a supply source or solar system.

Applying CSM-CERES-Maize to define a sowing window for irrigated maize crop – The Riacho´s Farm case study

Irrigation use constitutes an alternative to improve maize production in Central Minas Gerais State, Brazil. However, even under adequate water supply conditions, other environmental factors may influence maize crop growth and development and may, ultimately, affect grain yield. This study aimed to establish a sowing window for irrigated maize crop, based on simulation results obtained with the decision support model CSM- CERES-Maize. Simulations were made for crop management conditions of Riachos Farm, located in Matozinhos, Minas Gerais State, Brazil. It was employed the models seasonal tool, along with a data set containing 46 years of weather data records, to simulate maize yield for weekly sowing scenarios, starting on August 1 st and ending on July 24 th of each year. One defined an irrigated maize sowing window, taking into account the yield break risk that a farmer would be willing to take. The model proved to be an interesting tool to assist in decision making, regarding crop and irrigation management, for an irrigated maize production system. Assuming a 10% yield break in the expected average maximum maize yield, it was defined as sowing window, the period from January 23 rd to March 6 th , with February 20 th as the best sowing date. Other sowing windows may be established according to the risk that the farmer would be willing to take.

Net Irrigation Requirements for Florida Turfgrass Lawns: Part 1 - Report of Gathered Weather Data and Quality Check

Urban water users need specific recommendations to irrigate efficiently and for planning and regulatory programs. The first step in the process of estimating net irrigation requirements is to check the quality of 30 years of weather data at ten different locations in Florida and one in Alabama. This 9-page fact sheet was written by Consuelo C. Romero and Michael D. Dukes and published by the UF Department of Agricultural and Biological Engineering, August 2011. (AP photo by Milt Putnam, University of Florida/IFAS) 
 http://edis.ifas.ufl.edu/ae480

Aeration Strategy Simulations for Wheat Storage in the Sub-Tropical Region of North India

Ambient aeration is typically used in temperate climates to preserve grains by cooling, preventing moisture migration, and maintaining temperature as low and uniform throughout the grain mass as possible. However, the climatic factors for wheat bulk storage in the sub-tropical climate of north India are challenging for the grain preservation process. Low relative humidity with high temperature during daytime and high relative humidity with low temperature during nighttime is the common phenomenon. Five years of weather data were analyzed using the modified Chung-Pfost EMC equation for wheat. Fifteen different aeration strategies were formulated based on the weather data during the maintenance period (June to September) and cooling period (October to March). Strategies were selected based on combinations of the following parameters: temperature control; EMC control; 1 h, 2 h, or 4 h morning and/or evening aeration; and 0.11, 0.34, and 0.67 m3 min-1 t-1 airflow rate. These 15 aeration strategies for the sub-tropical weather conditions of north India were studied using the 2D PHAST-FEM model developed at Purdue University. The initial grain temperature and moisture were assumed as 27°C and 12% (w.b.), respectively, based on typical harvest conditions. Dry matter loss (DML), insect development, fan run hours, and average grain temperature and moisture content for these aeration strategies were quantified based on weather data from 2000-2001 to 2004-2005. Aeration strategies were selected based on the optimum combination of low values of DML, insect development, grain temperature, moisture content, and fan run hours during both the maintenance and cooling periods . The best strategy was to operate fans 4 h during the morning and evening with an airflow of 0.11 m3 min-1 t-1, with EMC control but without temperature control during the maintenance period and with both EMC and temperature control during the cooling period (strategy 13).

Climate Change Impact on Bioclimatic Deficiency, Using MicroLEIS DSS in Ahar Soils, Iran

Regional impact studies of the future climate change effects are necessary because projected changes in meteorological variables differ from one region to another, and different climate systems can react in varied ways to the same changes. In this study, the effects of climate change on bioclimatic deficiency were compared in two cultivation methods (irrigated and rainfed) in a semi-arid region, Ahar (East Azarbaijan, IRAN). The agricultural land uses selected for evaluation were wheat ( Triticum aestivum ), alfalfa ( Medicago sativa ), sugar beet (Beta vulgaris ), potato ( Solanum tuberosum ), and maize ( Zea mays ). In this way, Terraza model included in the land evaluation decision support system, called MicroLEIS DSS, was used. Terraza gives a quantitative prediction of a site bioclimatic deficiency. Soil morphological and analytical data were obtained from 44 sampling points based on a grid survey. Agro-climatic data, referred to temperature and precipitation, were collected from weather stations located in Ahar region, which benefits from more than 20 consecutive years of weather data. A future scenario of climate change was calculated according to the Intergovernmental Panel on Climate Change (IPCC) on regions of Asia under scenario A1FI (highest future emission) for 2080s. Although, increasing of precipitation being available by climate change in the future scenario, humidity index will be reduced because of high temperature. The results showed that climate change is likely to cause severe water stress in irrigated cultivation of alfalfa, sugar beet, potato, and maize, the use of irrigation methods being essential to maintain agricultural productivity. Although irrigation is indicated as very important in this regime of semi-arid agriculture, cultivation of rainfed wheat can be possible instead of the irrigated one. Also, it is revealed that climate perturbation effects on rainfed conditions are more serious than those on the irrigated conditions in the area.

Design of intercrop management plans to fulfil production and environmental objectives in vineyards

Designing intercrop management plans (IMP) to meet objectives related to both crop production and environmental impacts is a challenge for farmers. A multiple criteria decision analysis is thus needed to evaluate and rate various cropping systems in different soil and climate conditions. Different intercrop management plans in vineyards were analysed in this study so as to classify them in relation to their ability to fulfil a particular set of objectives and to deal with climatic variability. The method included five steps. A set of intercrop management plans was defined by combining the type of grass, the covered soil surface ratio, and the intercropping duration. Four evaluation criteria were chosen: grapevine vegetative development, yield, product quality and runoff. Corresponding indicators were identified and the range of values that would be desirable or not were defined. A water balance model designed for row crops was run to simulate the behaviour of the grapevine–intercrop–soil system under different management plans and at various soil depths. The model was used to calculate, for each management plan and soil depth, the four indicators and evaluate the overall agreement and discordance with various weights assigned to the four criteria. A frequency analysis on 30 years of weather data was carried out to estimate the robustness of the most satisfactory intercrop management plans. The most satisfactory intercrop management plans differed according to the priority given to managing production or reducing environmental impacts and depended on the soil depth. This confirms the conclusions drawn on the basis of various experiments assessing a limited range of intercropping policies. Overall, giving priority to the environmental criterion was favourable for cropping systems with a long intercrop period regardless of the soil type. Few management plans were suitable for all years. The observed yearly rainfall varied from 330 to 1200 mm during the 1975–2003 period, which generated marked variations in the water balance of the grapevine–intercrop–soil system. The lack of robustness of the explored intercrop management plans could be a consequence of the poor description of the management plans due to the limited combinations of technical options considered. Strategic or tactical adjustments could be introduced by farmers. We assume that more robust intercrop management plans could be developed by introducing such rules in a decision model combined with the present biophysical model.