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Modeling and Remote Sensing of Urban Land-Atmosphere Interactions with a Focus on Urban Irrigation


Urbanization is a demographic trend worldwide. It is found that half of all cities with populations greater than 100,000 are located in water stressed watersheds and are heavily dependent on imported water. Urban irrigation can exceed natural precipitation in these cities and is an important component of the water cycle. For instance, 14-30% of municipal water consumption in California is used for irrigation. Hence, understanding and quantifying the potential influence of urban anthropogenic soil moisture contribution on local and regional hydrological cycle is an imperative step toward sustainable and better managed water resources in water scarce regions.

The first part of current work examines the influence of irrigation on urban hydrological cycles through development of an irrigation scheme within the Noah Land Surface Model (LSM) - Urban Canopy Model (UCM) system. The model is run at a 30-m resolution for a two year period over a study domain in the Los Angeles metropolitan area. A sensitivity analysis indicates significant sensitivity relative to both the amount and timing of irrigation on diurnal and monthly energy budgets, hydrological fluxes and state variables. Monthly residential water use data and three estimates of outdoor water consumption are used to calibrate the developed irrigation scheme. Model performance is evaluated using a previously developed MODIS-(Moderate Resolution Imaging Spectroradiometer)-Landsat evapotranspiration (ET) and Landsat Land Surface Temperature (LST) products as well as hourly ET observations through the California Irrigation Management Information System (CIMIS). Results show that the Noah LSM-UCM realistically simulates the diurnal and seasonal variations of ET when the irrigation module is incorporated. However, without irrigation, the model produces large biases in ET simulations. The ET errors for the non-irrigation simulation are -56 and -90 mm/month for July 2003 and 2004, respectively, while these values reduce to -6 and -11 mm/month over the same two months when the proposed irrigation scheme is adopted. Results also show that the irrigation-induced increase in latent heat flux leads to a decrease in LST of about 2 °C in urban parks. The developed modeling framework can be utilized for a number of applications, ranging from outdoor water use estimation to climate change impact assessments.

In the second part of this work we investigate the utility of remote sensing based surface parameters in the Noah-UCM for a more accurate representation of developed surfaces in this modeling framework. Landsat and fused Landsat-MODIS data are utilized to generate high resolution (30 m) monthly spatial maps of green vegetation fraction (GVF), impervious surface area (ISA), albedo, leaf area index (LAI), and emissivity in the Los Angeles metropolitan area. The gridded remotely sensed parameter datasets are directly substituted for the land-use/lookup table based values in the Noah-UCM modeling framework. Model performance in reproducing ET and LST fields is evaluated utilizing Landsat based LST and ET estimates from CIMIS stations as well as in-situ measurements. Our assessment shows that the large deviations between the spatial distributions and seasonal fluctuations of the default and measured parameter sets lead to significant errors in the model predictions of monthly ET fields (RMSE= 22.06 mm/month). Results indicate that implemented satellite derived parameter maps, particularly GVF, enhance the Noah-UCM capability to reproduce observed ET patterns over vegetated areas in the urban domains (RMSE= 11.77 mm/month). GVF plays the most significant role in reproducing the observed ET fields, likely due to the interaction with other parameters in the model. Our analysis also shows that remotely sensed GVF and ISA improve the model capability to predict the LST differences between fully vegetated pixels and highly developed areas.

In the third part we explicitly address the impacts of urban irrigation by integrating the developed irrigation scheme within the coupled framework of the WRF-UCM over the semi-arid Los Angeles metropolitan area. We focus on the impacts of irrigation on the urban water cycle and atmospheric feedback in arid and semi-arid cities. Our objective is to build upon previous work, focusing on improving the representation of irrigated urban vegetated in the numerical weather prediction models which are now standard tools to study urban-atmosphere interactions. Our results demonstrate a significant sensitivity of WRF-UCM simulated surface turbulent fluxes to the incorporation of irrigation. Introducing anthropogenic moisture, the vegetated pixels show increased latent heat fluxes and decreased sensible and ground heat fluxes confirming irrigation induced shift in the energy partitioning toward elevated latent heat fluxes. The evaluation of the model performance via comparison against CIMIS based reference ET indicates that WRF-UCM, after adding irrigation, performs reasonably during the course of the month, tracking day to day variability of ET with notable fidelity. In the absence of irrigation, simulated ET fluctuations are similar to the CIMIS based ET and irrigated case, in the first few days of simulations. Toward the end of the month, however, the differences between simulated ET and CIMIS based ET0 become more significant in the not irrigated case. This is due to fact that, in the not irrigated simulation, the soil moisture is the only source of water in the absence of irrigation and significant precipitation. In the course of simulation, the soil moisture sorted in the soil layers is consumed, resulting in considerable decreases in the soil moisture levels in all layers. The soil moisture depletion leads to reduced latent heating and cooling effects of urban vegetation. Analysis of these results indicates the importance of accurate representation of urban irrigation in water scarce regions such as Los Angeles metropolitan area. Moreover, it is found that the initial soil moisture level plays a principal role in disguising the real impacts of irrigation in urban domains and to see the actual impacts of urban irrigation the simulations should be conducted for a longer period of time than one month which is rare in WRF-UCM studies.

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