Elección del método multicriterio

Typical structure and materials of green roof systems were discussed in Chapter 3, using the ASLA green roof, Soka-Bau green roof, and Victoria desalination project green roof as case studies. These were selected to provide the state of the art in existing green roof technology around the world, with particular interest devoted to the construction material and installation techniques for the construction of extensive and intensive green roofs on existing and new buildings.

A comprehensive review of the current literature was completed to examine green roofs’

effectiveness in urban stormwater runoff retention and detention, and also in runoff water quality improvement. The key findings are that the responses given by the test green roofs to a single precipitation event varied widely from no rainfall retention to full retention. These results were not correlated to any particular season. Annual rainfall retention results revealed that green roofs can significantly help to reduce the volume of urban stormwater runoff with annual overall retention values ranging from 36% to 85% in different experimental green roofs.

The average values for peak flow rate reduction demonstrated that the test green roofs were able to reduce the peak outflow rate greatly when comparing with peak rainfall intensities (average reduction were between 44.3% and 88%). Comparing with peak runoff rates from impervious roofs, the green roofs also produced much lower peak outflow rates (average reduction were between 30.5% and 91.1%). The results related to runoff delay support the idea that green roofs in urban areas can help to alleviate stormwater runoff related issues. By releasing rainwater at later times, green roofs can relieve problems associated with large quantity of stormwater surging on impervious surfaces such as roads, car parks, etc.. With regard to the benefit of stormwater runoff quality improvement, the green roofs were not as effective as they were for stormwater runoff control benefits. In fact, green roofs can potentially

deteriorate rainwater quality. In most cases, for most of the elements used to represent the quality of green roof outflows and incoming water, the test green roofs increased the concentrations. However, one benefit consistently offered by the green roofs was that they could rapidly neutralize acidic rainfall.

International literature relating to hydrologic models of GI and green roof systems were also reviewed in detail to highlight the contribution of current work to the field of hydrologic modelling of GI. Estimation of evapotranspiration was identified to be the research gap.

Consequently, the FAO-56 method with the Tew Extension has been employed to estimate evapotranspiration. The method also allows to schedule irrigation. The FAO-56 method was modified so that it can be applied to agricultural green roofs. The method and the modifications have been implemented in a Matlab code. The code can be applied to estimate evaporation from soil and transpiration from crops for about 80 crops. Timing and depth for each irrigation event could also be determined. The code was written in such a way that user can choose to estimate evaporation only or both evaporation and transpiration. User can also choose simulation period, location (as long as the relevant weather parameters are available on the BoM website), field planting or rooftop planting, date of planting, soil type, irrigation method or no irrigation at all, percentage of dead vegetation coverage during non-growing periods, and amount of irrigation each time when irrigation is triggered. The code can be used as a stand-alone application to predict crop water use, and schedule irrigation, or can be incorporated into the existing GI hydrologic models for more accurate estimate of water loss through evapotranspiration.

The correct use of the relevant weather data provided by the BoM and the correct application of all the equations involved in the FAO-56 method were checked against the available examples in the FAO-56 document (Allen et al., 1998). The improvements provided by the Tew

Extension incorporated FAO-56 method was discussed in Chapter 5. In addition, the modifications made to the FAO-56 method with the Tew Extension for rooftop agriculture application were justified in Chapter 5.

The matlab code was then applied to urban agriculture and agricultural green roofs.

Evapotranspiration and irrigation needs of the ten popular crops planted in fields and on rooftops in Australia’s five major cities were calculated using historical weather data. These results signify water use of different crops in different urban areas and irrigation amounts that are to be expected if such practices are going to be widely adopted in urban centres.

Some common trend shared by the crops grown in fields and on rooftops were discussed.

Different crops planted in the same city resulted in significantly different irrigation requirements. If irrigation water availability in a city is an issue, beans, lettuce, maize and potatoes maybe the better choices than capsicums, carrots, groundnuts, onions, sweet melons and tomatoes since the former four crops required much less irrigation water. Capsicums, carrots, groundnuts, lettuce, onions, sweet melons and tomatoes maybe the best to be grown in Brisbane among the five cities where these crops required the least amounts of irrigation water.

For beans, maize and potatoes, Perth among the urban areas maybe the optimal place where they needed the least amounts of irrigation water.

For all crops in all five cities, the rooftop crops evapotranspired more water than field crops did with percentage increases all below 10%. Likewise, nearly in all cases, crops grown on rooftops demanded more irrigation than these crops grown in fields with the maximum percentage increase of about 27%.

Most of the water lost through evapotranspiration was transpiration in both field and rooftop cases; however, the percentages of transpiration to evapotranspiration on rooftops were smaller when compared with these values in the field case for all the crops in all five cites.

Capsicums, carrots, groundnuts, lettuce, onions, sweet melons and tomatoes grown in fields in Brisbane among the cities took up the least portions of the total water delivered to the sites, and were also the least irrigation dependent. Beans, maize and potatoes grown in Perth took up the least portions of the total water and also depended on irrigation water the least. In general, a city that is more irrigation water dependent is likely to use a larger portion of the total delivered water beneficially. Crops grown on rooftops are likely to take up less portions of the total delivered water than the same crops grown in fields are.

The same field crop grown in different cities can have significantly different ratios of yield to irrigation demand. The highest ratios of different crops occurred in difference places.

Averaging across all the cities, potatoes had the highest value for yield per millimetre of irrigation water, and groundnuts had the lowest yield per millimetre of irrigation water value.

The model limitations are described in Chapter 4 Section 4.4 and also discussed in Chapter 6 Section 6.5, and are briefly summarised in the following. Instead of using input values obtained from the literature, locally observed/derived Kcb values, crop growth stage lengths, crop average maximum height, rooting depth, weather parameters, ET0, and physical characteristics of soils should be used in the model. While the modified FAO-56 method with the Tew

Extension for rooftop applications considers the effects of shallow root depth on soil water balance, it does not take into account other relevant factors that would affect crop transpiration, soil evaporation and irrigation. For instance, extreme weather conditions and limited soil depth on a rooftop may have significant impact on crop growth and root development, and hence on crop transpiration. Model calibration was not possible due to the fact that measured evaporation, transpiration and irrigation data for the ten crops in the five cities were not available in the literature. Due to the lack of model calibration, it was uncertain if the estimated evaporation, transpiration and irrigation values were close to the real values. However, the current work focused on comparison purposes and providing indications (e.g. which crops are better be to grown in which cities) rather than calculating absolute values for evaporation, transpiration, and irrigation for a particular crop grown in a specific field or on a rooftop under particular management.