Empirical modeling and spatio-temporal patterns of urban evapotranspiration for the Phoenix metropolitan area, Arizona

In this study, an empirical model for predicting urban evapotranspiration (ET) is examined for the Phoenix metropolitan area that is in a subtropical desert climate using in situ ET measurements from a local flux tower and remotely sensed moderate-resolution imaging spectroradiometer land products. Annual ET maps of Phoenix are then created for the period from 2001 to 2015 using the empirical model developed. A time-series trend analysis is finally performed using predicted ET maps to discover the spatio-temporal patterns of ET changes during the study period. Results suggest that blue-sky albedo and land surface temperature are two statistically significant variables explanatory to model urban ET for Phoenix. Areas that have experienced significant increases of ET are highly spatially clustered, and are mainly found on the outskirts of the city, while areas of decreasing ET are generally associated with highly developed areas, such as downtown Phoenix.

[1]  A. Brazel,et al.  Assessing xeriscaping as a sustainable heat island mitigation approach for a desert city , 2012 .

[2]  R. Crago,et al.  Analytical Land–Atmosphere Radiometer Model , 2002 .

[3]  R. Balling,et al.  The impact of rapid urbanization on pan evaporation in phoenix. Arizona , 1987 .

[4]  Michael E. Schaepman,et al.  Algorithm theoretical basis document , 2009 .

[5]  J. Smith,et al.  A coupled energy transport and hydrological model for urban canopies evaluated using a wireless sensor network , 2013 .

[6]  Kevin W. Manning,et al.  The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements , 2011 .

[7]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[8]  Lin Sun,et al.  Validation of the land surface temperature derived from HJ-1B/IRS data with ground measurements , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[9]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[10]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[11]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[12]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[13]  Yang Hong,et al.  Actual evapotranspiration estimation for different land use and land cover in urban regions using Landsat 5 data , 2010 .

[14]  Kenneth Belitz,et al.  A remote sensing approach for estimating the location and rate of urban irrigation in semi-arid climates , 2012 .

[15]  F. I. Morton Operational estimates of lake evaporation , 1983 .

[16]  K. Seto,et al.  A Meta-Analysis of Global Urban Land Expansion , 2011, PloS one.

[17]  P. Cox,et al.  The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes , 2011 .

[18]  Toby N. Carlson,et al.  On estimating total daily evapotranspiration from remote surface temperature measurements , 1989 .

[19]  R. Balling,et al.  Recent changes in Phoenix, Arizona summertime diurnal precipitation patterns , 1987 .

[20]  Xiangao Xia,et al.  Impacts of urban expansion and future green planting on summer precipitation in the Beijing metropolitan area , 2009 .

[21]  Soe W. Myint,et al.  Enhancing Hydrologic Modelling in the Coupled Weather Research and Forecasting–Urban Modelling System , 2015, Boundary-Layer Meteorology.

[22]  Zhao-Liang Li,et al.  Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .

[23]  T. Stockdale,et al.  Population estimates. , 2004, Health statistics quarterly.

[24]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[25]  Zhihua Wang,et al.  Spatio-Temporal Modeling of the Urban Heat Island in the Phoenix Metropolitan Area: Land Use Change Implications , 2016, Remote. Sens..

[26]  R. G. Mein,et al.  Modelling the urban water cycle , 2001, Environ. Model. Softw..

[27]  R. Granger,et al.  Modelling hourly rates of evaporation from small lakes , 2011 .

[28]  T. Oke Advectively-assisted evapotranspiration from irrigated urban vegetation , 1979 .

[29]  E. Vivoni,et al.  Observed relation between evapotranspiration and soil moisture in the North American monsoon region , 2008 .

[30]  Akihiko Kondoh,et al.  Changes in hydrological cycle due to urbanization in the suburb of Tokyo Metropolitan area, Japan , 2000 .

[31]  C. Woodcock,et al.  Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods , 2014 .

[32]  E. Vivoni,et al.  Seasonal dynamics of a suburban energy balance in Phoenix, Arizona , 2014 .

[33]  A. Comrie,et al.  The North American Monsoon , 1997 .

[34]  S. Myint,et al.  Using Watered Landscapes to Manipulate Urban Heat Island Effects: How Much Water Will It Take to Cool Phoenix? , 2009 .

[35]  T. Carlson,et al.  An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization , 1998 .

[36]  Eric Vermote,et al.  Atmospheric correction for the monitoring of land surfaces , 2008 .

[37]  M. Georgescu,et al.  Seasonal hydroclimatic impacts of Sun Corridor expansion , 2012 .

[38]  Luis A. Garcia,et al.  Surface Energy Balance-Based Model for Estimating Evapotranspiration Taking into Account Spatial Variability in Weather , 2008 .

[39]  Timothy R. Oke,et al.  Evapotranspiration rates in urban areas , 1999 .