Characterizing the multi–scale spatial structure of remotely sensed evapotranspiration with information theory

A more thorough understanding of the multi-scale spatial structure of land surface heterogeneity will enhance understanding of the relationships and feedbacks between land surface conditions, mass and energy exchanges between the surface and the atmosphere, and regional meteorological and climatological conditions. The objectives of this study were to (1) quantify which spatial scales are dominant in determining the evapotranspiration flux between the surface and the atmosphere and (2) to quantify how different spatial scales of atmospheric and surface processes interact for different stages of the phenological cycle. We used the ALEXI/DisALEXI model for three days (DOY 181, 229 and 245) in 2002 over the Ft. Peck Ameriflux site to estimate the latent heat flux from Landsat, MODIS and GOES satellites. We then applied a multiresolution information theory methodology to quantify these interactions across different spatial scales and compared the dynamics across the different sensors and different periods. We note several important results: (1) spatial scaling characteristics vary with day, but are usually consistent for a given sensor, but (2) different sensors give different scalings, and (3) the different sensors exhibit different scaling relationships with driving variables such as fractional vegetation and near surface soil moisture. In addition, we note that while the dominant length scale of the vegetation index remains relatively constant across the dates, the contribution of the vegetation index to the derived latent heat flux varies with time. We also note that length scales determined from MODIS are consistently larger than those determined from Landsat, even at scales that should be detectable by MODIS. This may imply an inability of the MODIS sensor to accurately determine the fine scale spatial structure of the land surface. These results aid in identifying the dominant cross-scale nature of local to regional biosphere-atmosphere interactions.National Science Foundation grant number DEB-1021095 and subaward G214-11-W333

Automated Detection Algorithm for SACZ, Oceanic SACZ, and Their Climatological Features

The South Atlantic Convergence Zone (SACZ) is responsible for a large amount of the total summer precipitation over Brazil and is related to severe droughts and extreme floods over the southeast of Brazil. This paper aims to demonstrate the feasibility of an objective, simplified and automated method based on satellite outgoing longwave radiation (OLR) for South Atlantic Convergence Zone (SACZ) and oceanic SACZ (SACZOCN) detection, and characterize their climatological features. Here we developed an automated algorithm and made available the SACZ and SACZOCN dates and characteristics (intensity and size) for the first time in the literature. The method agreed with 77% of SACZ occurrences compared with 21 years of SACZ observations. The temporal criterion of permanency of the SACZ convective activity for at least 4 days was essential to differentiate the SACZ from the transient frontal systems over the Brazilian Southeast. About 30% of the SACZ days occurred in November and March, therefore the December to February period is not sufficient to fully represent its activity. A barotropic trough near the Uruguay coast influences the intensity and position of the coastal and oceanic SACZ portions. When this trough closes into a cyclonic vortex Southwest of the SACZ (CVSS) cloud band it characterizes a SACZOCN episode. SACZOCN episodes were objectively identified, being characterized by a more intense convective activity and shifted to the north. We show that some oceanic SACZ episodes are associated with extreme floods and severe droughts over Brazil, therefore its identification is important to the Brazilian society. Besides, oceanic surface currents and temperature over the Southwestern Atlantic Ocean are modified during the SACZOCN active phase. The method presented here is a viable alternative to objectively classify SACZ and SACZOCN episodes, it can be implemented operationally and used to SACZ studies in the context of climate change

Understanding the Effects of Fire on Net Radiation and Evapotranspiration Patterns in a Mature Amazonian Forest Using MODIS Remote Sensing Data

This presentation was given as part of the GIS Day@KU symposium on November 18, 2015. For more information about GIS Day@KU activities, please see http://www.gis.ku.edu/gisday/2015/.Platinum Sponsors: KU Department of Geography and Atmospheric Science; KU School of Business. Gold Sponsors: Bartlett & West; Kansas Biological Survey; KU Environmental Studies Program; KU Institute for Policy & Social Research; KU Libraries. Silver Sponsors: State of Kansas Data Access and Support Center (DASC). Bronze Sponsors: KU Center for Remote Sensing of Ice Sheets (CReSIS); TREKK Design Group, LLC; Wilson & Company, Engineers and Architects

Greenspace Pattern and the Surface Urban Heat Island: A Biophysically-Based Approach to Investigating the Effects of Urban Landscape Configuration

This work is licensed under a Creative Commons Attribution 4.0 International License.Surface urban heat islands (SUHIs) are influenced by the spatial distribution of green space, which in turn can be influenced by urban planning. When studying the relationship between structure and function it is critical that the scale of observation reflects the scale of the phenomenon being measured. To investigate the relationship between green space pattern and the SUHI in the Kansas City metropolitan area, we conducted a multi-resolution wavelet analysis of land surface temperature (LST) to determine the dominant length scales of LST production. We used these scales as extents for calculating landscape metrics on a high-resolution land cover map. We built regression models to investigate whether–controlling for the percent vegetated area–patch size, fragmentation, shape, complexity, and/or proximity can mitigate SUHIs. We found that while some of the relationships between landscape metrics and LST are significant, their explanatory power would be of little use in planning for green infrastructure. We also found that the relationships often reported between landscape metrics and LST are artifacts of the relationship between the percent of vegetation and LST. By using the dominant length scales of LST we provide a methodology for robust biophysically-based analysis of urban landscape pattern and demonstrate that the contributions of green space configuration to the SUHI are negligible. The simple result that increasing green space can lower LST regardless of configuration allows the prioritization of resources towards benefiting neighborhoods most vulnerable to the negative impacts of urban heat

Assessing Regional Climate and Local Landcover Impacts on Vegetation with Remote Sensing

This is the published version, also available here: http://dx.doi.org/10.3390/rs5094347.Landcover change alters not only the surface landscape but also regional carbon and water cycling. The objective of this study was to assess the potential impacts of landcover change across the Kansas River Basin (KRB) by comparing local microclimatic impacts and regional scale climate influences. This was done using a 25-year time series of Normalized Difference Vegetation Index (NDVI) and precipitation (PPT) data analyzed using multi-resolution information theory metrics. Results showed both entropy of PPT and NDVI varied along a pronounced PPT gradient. The scalewise relative entropy of NDVI was the most informative at the annual scale, while for PPT the scalewise relative entropy varied temporally and by landcover type. The relative entropy of NDVI and PPT as a function of landcover showed the most information at the 512-day scale for all landcover types, implying different landcover types had the same response across the entire KRB. This implies that land use decisions may dramatically alter the local time scales of responses to global climate change. Additionally, altering land cover (e.g., for biofuel production) may impact ecosystem functioning at local to regional scales and these impacts must be considered for accurately assessing future implications of climate change

Energy Balance Partitioning and Net Radiation Controls on Soil Moisture – Precipitation Feedbacks

A series of model runs using the University of Oklahoma’s Advanced Regional Prediction System (ARPS) were conducted to investigate the relative impacts of energy balance partitioning and net radiation on soil moisture–precipitation feedbacks in the U.S. central plains and to examine how the dominant physical processes are affected by changes in mean soil moisture and spatial resolution. Soil temperature and Bowen ratio are influenced nonlinearly by soil moisture, and by varying the mean soil moisture in the model it was possible to examine the relationship between soil moisture and the scaling characteristics of these fields using the statistical moments. Information theory metrics were used to provide an indication of the uncertainty associated with varying model resolutions. It was determined that energy balance partitioning plays a dominant role in the occurrence of soil moisture–precipitation feedback, while net radiation was not impacted by mean soil moisture. A strong relationship was seen between soil moisture and the scaling properties of Bowen ratio, while soil moisture did not appear to influence the scaling characteristics of soil temperature. Spatial resolution had a large effect on the representation of boundary layer turbulence, with coarser resolutions unable to capture turbulent motions, which are necessary for convective processes. The ability of the model to capture boundary layer turbulence will alter the dynamics of soil moisture–precipitation feedback as the horizontal transport of moisture by turbulent motions will affect the spatial and temporal scales over which feedback occurs. Higher-resolution runs are generally associated with a higher information content. This may provide a methodology for monitoring land– atmosphere feedbacks via remotely sensed soil moisture and vegetation fields through statistical knowledge of the dependency of the resulting precipitation signal on soil moisture and vegetation fields at the resolution they were observed.National Science Foundation EPSCOR Grant NSF EPS 055372

Multiscale Interactions between Water and Carbon Fluxes and Environmental Variables in A Central U.S. Grassland

This is the authors accepted manuscript. The published version can be found here: http://dx.doi.org/10.3390/e15041324.The temporal interactions between water and carbon cycling and the controlling environmental variables are investigated using wavelets and information theory. We used 3.5 years of eddy covariance station observations from an abandoned agricultural field in the central U.S. Time-series of the entropy of water and carbon fluxes exhibit pronounced annual cycles, primarily explained by the modulation of the diurnal flux amplitude by other variables, such as the net radiation. Entropies of soil moisture and precipitation show almost no annual cycle, but the data were collected during above average precipitation years, which limits the role of moisture stress on the resultant fluxes. We also investigated the information contribution to resultant fluxes from selected environmental variables as a function of time-scale using relative entropy. The relative entropy of latent heat flux and ecosystem respiration show that the radiation terms contribute the most information to these fluxes at scales up to the diurnal scale. Vapor pressure deficit and air temperature contribute to the most information for the gross primary productivity and net ecosystem exchange at the daily time-scale. The relative entropy between the fluxes and soil moisture illustrates that soil moisture contributes information at approximately weekly time-scales, while the relative entropy with precipitation contributes information predominantly at the monthly time-scale. The use of information theory metrics is a relatively new technique for assessing biosphere-atmosphere interactions, and this study illustrates the utility of the approach for assessing the dominant time-scales of these interactions

Investigation of Urban Air Temperature and Humidity Patterns during Extreme Heat Conditions Using Satellite-Derived Data

Extreme heat is a leading cause of weather-related human mortality. The urban heat island (UHI) can magnify heat exposure in metropolitan areas. This study investigates the ability of a new MODIS-retrieved near-surface air temperature and humidity dataset to depict urban heat patterns over metropolitan Chicago, Illinois, during June–August 2003–13 under clear-sky conditions. A self-organizing mapping (SOM) technique is used to cluster air temperature data into six predominant patterns. The hottest heat patterns from the SOM analysis are compared with the 11-summer median conditions using the urban heat island curve (UHIC). The UHIC shows the relationship between air temperature (and dewpoint temperature) and urban land-use fraction. It is found that during these hottest events 1) the air temperature and dewpoint temperature over the study area increase most during nighttime, by at least 4 K relative to the median conditions; 2) the urban–rural temperature/humidity gradient is decreased as a result of larger temperature and humidity increases over the areas with greater vegetation fraction than over those with greater urban fraction; and 3) heat patterns grow more rapidly leading up to the events, followed by a slower return to normal conditions afterward. This research provides an alternate way to investigate the spatiotemporal characteristics of the UHI, using a satellite remote sensing perspective on air temperature and humidity. The technique has potential to be applied to cities globally and provides a climatological perspective on extreme heat that complements the many case studies of individual events

The Effects of Great Plains Irrigation on the Surface Energy Balance, Regional Circulation, and Precipitation

This is the published version, also available here: http://dx.doi.org/10.3390/cli2020103.Irrigation provides a needed source of water in regions of low precipitation. Adding water to a region that would otherwise see little natural precipitation alters the partitioning of surface energy fluxes, the evolution of the planetary boundary layer, and the atmospheric transport of water vapor. The effects of irrigation are investigated in this paper through the employment of the Advanced Research (ARW) Weather Research and Forecasting Model (WRF) using a pair of simulations representing the extremes of an irrigated and non-irrigated U.S. Great Plains region. In common with previous studies, irrigation in the Great Plains alters the radiation budget by increasing latent heat flux and cooling the surface temperatures. These effects increase the net radiation at the surface, channeling that energy into additional latent heat flux, which increases convective available potential energy and provides downstream convective systems with additional energy and moisture. Most noteworthy in this study is the substantial influence of irrigation on the structure of the Great Plains Low-level Jet (GPLLJ). The simulation employing irrigation is characterized by a positive 850-mb geopotential height anomaly, a result interpreted by quasi-geostrophic theory to be a response to low-level irrigation-induced cooling. The modulation of the regional-scale height pattern associated with the GPLLJ results in weaker flow southeast of the 850-mb anomaly and stronger flow to the northwest. Increased latent heat flux in the irrigated simulation is greater than the decrease in regional transport, resulting in a net increase in atmospheric moisture and a nearly 50% increase in July precipitation downstream of irrigated regions without any change to the number of precipitation events

Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: evaluation and implications

Understanding the impacts of future climate change in Kansas is important for agricultural and other socioeconomic sectors in the region. To quantify these impacts, seasonal trends in air temperature and precipitation patterns from decadally averaged monthly output of 21 global climate models under the Special Report on Emissions Scenarios A1B scenario used in the Intergovernmental Panel of Climate Change Assessment Report 4 are examined for six grid cells representing Kansas. To ascertain the performance of the models, we compared model output to kriged meteorological data from stations in the Global Historical Climate Network for the period from 1950 to 2000. Agreement between multimodel ensemble mean output and observations is very good for temperature (r2 all more than 0.99, root mean square errors range from 0.84 to 1.48°C) and good for precipitation (r2 ranging between 0.64 and 0.89, root mean square errors range from 322 to 1144 mm). Seasonal trends for the second half of the 20th century are generally not observed except in modelled temperature trends. Linear trends for the 21st century are significant for all seasons in all grid cells for temperature and many for precipitation. Results indicate that temperatures are likely to warm in all seasons, with the largest trends being on the order of 0.04 °C/year in summer and fall. Precipitation is likely to increase slightly in winter and decrease in summer and fall. These changes have profound implications for both natural ecosystems and agricultural land uses in the region. Copyright 2009 Royal Meteorological SocietyLand Institute Climate and Energy Project (NFP #49780-720) and the National Science Foundation EPSCoR program (NSF EPS #0553722