Although development of low (extensive) and high (intensive) maintenance green roof systems has progressed significantly, studies on the function of the growing substrate as a living constituent are lacking. The objective of this review paper is to summarize current scientific knowledge on the components, composition, and characteristics of green roof substrates and to identify future research needs. Due to variations in climate and desired plant types, there is no universal growing substrate. An appropriate substrate is expected to provide permanent physical support for plants and possess a fine balance between free drainage and adequate plant available water and nutrient retention. Typical substrate components include minerals in natural or modified forms such as sand, lava rock, or expanded shale, clay and slate; recycled waste materials like crushed bricks or tiles, crushed or aerated concrete and subsoil; stabilized organic matter such as composts; and plastic materials and slow release fertilizers. Proportions of components vary among substrates based on target vegetation, green roof type, and other considerations. Better green roof management for maximum benefits will require characterizing, quantifying and understanding the impacts of plant species and building attributes such as aspect, slope, height and heating on substrate performance, and should be considered for future research.

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Abstract:The effects of Low Impact Development (LID) practices on urban runoff and pollutants have proven to be positive in many studies. However, the effectiveness of LID practices can vary depending on different urban patterns. In the present study, the performance of LID practices was explored under three land uses with different urban forms: (1) a compact high-density urban form; (2) a conventional medium-density urban form; and (3) a conservational medium-density urban form. The Soil and Water Assessment Tool (SWAT) was used and model development was performed to reflect hydrologic behavior by the application of LID practices. Rain gardens, permeable pavements, and rainwater harvesting tanks were considered for simulations, and a modeling procedure for the representation of LID practices in SWAT was specifically illustrated in this context. Simulations were done for each land use, and the results were compared and evaluated. The application of LID practices demonstrated a decrease in surface runoff and pollutant loadings for all land uses, and different reductions were represented in response to the land uses with different urban forms on a watershed scale. In addition, the results among post-LIDs scenarios generally showed lower values for surface runoff and nitrate in the compact high-density urban land use and for total phosphorus in the conventional medium-density urban land use compared to the other land uses. We suggest effective strategies for implementing LID practices.

Abstract:A disproportionate increase or decrease in water table in response to minor water input or drainage is observed in shallow water table conditions inside drainage lysimeters. This increase happens because the capillary fringe of the shallow water table reaches up to or near the surface (Wieringermeer effect). The correlations between water table level changes and rainfall, seepage irrigation, drip irrigation, and drainage were analysed. Correlations with rainfall, seepage irrigation, and drainage were high (R 2 ranged from 0Ð46 to 0Ð97). Drip irrigation had low correlations due to the low rates of application (R 2 ranged from 0Ð26 to 0Ð44). Conventional methods of calculating recharge, such as multiplying the specific yield with the water table fluctuations, cannot be used for Wieringermeer effect situations. A method using water balance data and soil moisture at different depths in the lysimeters was developed to estimate recharge and upflux. The recharge results were used to develop the apparent specific yield S ya , which could be used to calculate consequent recharge events from water table fluctuation data. Combining the water table fluctuation relationships developed with the S ya value will allow the prediction of recharge from rainfall and irrigation events without the need for soil moisture equipment.