(2009) and are located inside the interval reported by the Jucar River Basin Authority (2013), it can be concluded that the model has been well calibrated with respect to the groundwater recharge. 9.7: VARIABLE SWAT SANZ ET AL (2010) JUCAR RIVER BASIN (2013) Groundwater recharge 310 Mm3 320 Mm3 238-334 Mm3 After that, the crop behavior provided by SWAT through the irrigation demands has been compared with the ones expected. First SWAT simulation crop irrigation demands were lower than expected, and therefore the crop yields parameters have been modified in order to reproduce as close as possible the expected irrigation demands. As can be seen in Table 9.8, the final SWAT irrigation demand values closely resemble the expected ones, and therefore the SWAT model is adequately reproducing the crop yields. 9.8: SWAT CROP TYPE IRRIGATION (mm) YIELD (Tn/ha) SWAT EXPECTED SWAT EXPECTED Wheat 363 350 – 420 7.2 7 – 8 Onion 572 550 – 620 5.1 4.5 – 5.5 Corn 552 500 – 600 12.2 12 – 13 Sugarbee 827 800 – 900 12.8 12.5 – 13.5 Barley 282 250 – 350 8.6 8 – 9 Alfalfa 782 750 – 850 12.1 11.5 – 12.5 With regard to nitrate leaching, there is no possible comparison with historical data, given that there are no historical records. However, as this output is essential for the MT3D calculations, SWAT results must be checked before introducing them into one different model. In order to proceed to that comparison, the nitrate leaching values obtained by the SWAT model, for each crop type, have been compared with the ones provided in previous studies about nitrate leaching associated to different crop types, in which the GEPIC software was used (Martin-Benlloch, 2012) to estimate their leaks. As can be seen in Table 9.9, the SWAT results are located inside the confidence intervals that correspond to each crop, meaning that the SWAT model is adequately reproducing the behaviour associated to the previously validated GEPIC model. 9.9: SWAT GEPIC CROP TYPE LEACHING (Kg NO3/ha) SWAT 53 45 – 85 Onion 114 100 – 120
Highlights and concluions Summary of the case studies The following section summarizes all the information provided in the case studies with regard to land use and land management and to adaptation measures, which are the main strategies to confront climate change. Land use and land management change The relevance of land use change in the performance of groundwater and dependent ecosystems is not similar in all the case studies. In fact, it ranges from important (see 8.3 section), to nearly insignificant (see 7.3 and A1.3 sections), depending on the role played by land use and management. In several case studies the relevance of changes in land management within current land uses has been found to be higher than the one caused by land use changes (see 6.3, 8.3, 9.3 and 10.3 sections). It can be concluded that, even regarding the fact that each case study shows unique features, all of them point out that the potential effects caused by climate change in current land uses, due to changes in water requirements and/or changes in land use management strategies, have potentially higher relevance than land use changes. This is caused by the fact that agricultural and/or forested areas dominate the case studies’ landscape, with no future threats expected to that dominance (see 6.3, 8.3, 9.3 and 10.3 sections), and therefore, changes in water requirements due to climate change (see 6.3, 8.3 and 9.3 sections) are likely to overcome the impacts caused by land use changes. A summary of each case study land use and management scenarios features is showed in Table 14.1. 14.1: Case study Current main land uses Potential land use and management changes impact Main drivers of land use change Expected land use changes Expected land management changes Greece Agriculture Medium Photovoltaic park replacing agriculture Increase in industrial area, decrease in agriculture None Austria Agriculture High Changes in maize crop cycle Insignificant Changes in maize crops irrigation practices Norway Agriculture Low None None None Netherlands Agriculture, grassland and forest High Grassland replacing forest, changes in grassland and forest cycle Increase in grassland, decrease in forest (caused by the necessity to balance the increasing forest water requirements with the grassland)
Spain Agriculture High Agriculture replacing forest and grassland Small increase in agriculture, small decrease in forest and grassland Changes in crops irrigation practices Finland Forest logging and peatlands High Changes in land management practices None Forest logging intensity changes and peatlands water discharge management changes Maize and meadow Low Rotation patterns between maize and meadow None Promotion of rain irrigation Turkey Forest and agriculture Low Urban areas replacing forest and agriculture Very increase in urban areas decreasing the others None France Agricultural experimental area High Different organic amendments accoriding to the experiment development Adaptation measures The results obtained and the adaptation measures considered are summarized below: No need of further adaptation measures due to negligible impacts (see 7.4). Impacts obtained, but not enough high to need additional adaptation measures (see 6.4 and 0 sections). Impacts obtained that require more aggressive application of water management measures, consisting of water usage efficiency improvement (sections 5.4, 9.4, 11.4 and 12.4), new small dams building (section 5.4), and resource origin replacement from ground to surface water (section 9.4). Impacts obtained that require more aggressive application of land management measures, consisting of replacing forested areas with grassland and agricultural ones (sections 8.3 and 8.4), or replacing some crops with others whose water demand is lower (sections 5.4 and 12.4). Impacts obtained that can compromise the application of land management measures consisting of land restoration (section 10.4).
application amount was set to 300 kg N ha-1. Elemental nitrogen and elemental phosphors were selected as the main fertilizer. The major crops heat units to reach maturity were initially based on Figure A2.13 and then calibrated. Remaining crop parameters and parameters for non-crop land covers, originate from SWAT default database (Neitsche et al., 2009). Parameters sensitive to model outputs were also subsequently calibrated to the local conditions. A2.11: A2.12: A2.3: A2.13: The nitrogen stress threshold, which is a fraction of potential plant growth, was set to 0.95 which ensures that there is almost no reduction of plant growth due to nutrient stress. Any time actual plant growth falls below this threshold fraction due to nitrogen stress, the model will automatically apply fertilizer to the HRU. The nitrogen stress factor is calculated by dividing the growth of the plant undergoing nitrogen stress by the growth of the plant if there was no nitrogen stress. The application efficiency was set to 1.3 to allow the model to apply fertilizer to meet harvest removal plus an extra amount to make up for nitrogen losses due to surface runoff/leaching. Fraction of fertilizer applied to top 10 mm of soil is 0.8. Irrigation was based on plant-water-stressed automatic scheduling, and withdrawn from outside sources so that the model will add water to the soil until its field capacity. The automatic irrigation option is triggered by water demand. Water stress threshold which is a fraction of plant growth is assumed to be 0.95 to ensure that there is no reduction of plant growth