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While the rain-driven evapotranspiration (ET) process has been well-studied in the humid climate, the mixed irrigation and rain-driven ET process is less understood for green roof implementations in dry regions, where empirical observations and model parameterizations are lacking. This paper presents an effort of monitoring and simulating the ET process for an irrigated green roof in a rain-scarce environment. Annual ET rates for three weighing lysimeter test units with non-vegetated, sedums, and grass covers were 2.01, 2.52, and 2.69 mm d −1 , respectively. Simulations based on the three Penman-Monteith equation-derived models achieved accuracy within the reported range of previous studies. Compared to the humid climate, the overestimation of high ET rates by existing models is expected to cause a larger error in dry environments, where the enhanced ET process caused by repeated irrigations overlapped with hot, dry conditions often occurs during summer. The studied sedum species did not show significantly lower ET rates than native species, and could not effectively take advantage of the deep moisture storage. Therefore, native species, instead of the shallow-rooted species commonly recommended in humid climates, might be a better choice for green roofs in rain-scarce environments.

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Flooding is a prevalent natural disaster with both short and long-term social, economic, and infrastructure impacts. Changes in intensity and frequency of precipitation (including rain, snow, and rain on snow) events create challenges for the planning and management of resilient infrastructure and communities. While there is general acknowledgement that new infrastructure design should account for future climate change, no clear methods or actionable information is available to community planners and designers to ensure resilient design considering an uncertain climate future. This research used climate projections to drive high-resolution hydrology and flood models to evaluate social, economic, and infrastructure resilience for the Snohomish Watershed, WA, U.S.A. The proposed model chain has been calibrated and validated. Based on the established model chain, the peaks of precipitation and streamflows were found to shift from spring and summer to earlier winter season. The nonstationarity of peak discharges was discovered with more frequent and severe flood risks projected. The peak discharges were also projected to decrease for a certain period in the near future, which might be due to the reduced rain-on-snow events. This research was expected to provide a clear method for the incorporation of climate science in flood resilience analysis and to also provide actionable information relative to the frequency and intensity of future precipitation events.

Flooding is a prevalent natural disaster with both short and long-term social, economic, and infrastructure impacts. Changes in intensity and frequency of precipitation (including rain, snow, and rain-on-snow) events create challenges for the planning and management of resilient infrastructure and communities. While there is general acknowledgment that new infrastructure design should account for future climate change, no clear methods or actionable information are available to community planners and designers to ensure resilient designs considering an uncertain climate future. This research demonstrates an approach for an integrated, multi-model, and multi-scale simulation to evaluate future flood impacts. This research used regional climate projections to drive high-resolution hydrology and flood models to evaluate social, economic, and infrastructure resilience for the Snohomish Watershed, WA, USA. Using the proposed integrated modeling approach, the peaks of precipitation and streamflows were found to shift from spring and summer to the earlier winter season. Moreover, clear non-stationarities in future flood risk were discovered under various climate scenarios. This research provides a clear approach for the incorporation of climate science in flood resilience analysis and to also provides actionable information relative to the frequency and intensity of future precipitation events.

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Soil evaporation (E) and plant transpiration (T) dominate the water budget of semi‐arid sparse steppe, and they are crucial for vegetation restoration, as shadows cast by sparse vegetation may shade the sunlight and thereby reduce near‐ground radiation – which could affect biotic process of plants and land‐atmosphere flux transport dominated by E and evapotranspiration (ET). Currently, there are few studies on the effects of shadow on E and ET in semi‐arid sparse steppe because of the lack of appropriate research tools. However, high‐resolution thermal cameras provide an opportunity to meet this challenge. This study aims to detect the shadow‐affected soil area in sparse steppe and determine its effects on E and ET by using the thermal imaging with classification algorithm and the extended three‐temperature model (3T model) with the introduction of a new calibration coefficient, which considers the influence of shadows on E and ET. A series of field experiments were carried out in a semi‐arid restored grassland in Taipus Banner, Inner Mongolia, China, in summer 2008. The Bowen ratio method was used to validate the ET estimates. Results showed that the importance of shading effects on E and ET tends to increase along with the plant coverage as the restoration prolongs. And shadow‐affected soil zone around the plant could be larger than plant area. And ET and E may be 4–28% and 10–66% overestimated if the effect of shadow is not considered. Implication of this study on other ET estimation methods in remote sensing is further discussed. Copyright © 2014 John Wiley & Sons, Ltd.

No abstract