Joseph Santanello - Academia.edu (original) (raw)
Papers by Joseph Santanello
Http Dx Doi Org 10 1175 Jhm D 14 0203 1, Feb 17, 2015
Journal of Hydrometeorology, 2016
Journal of Hydrometeorology, 2015
Environmental Research Letters, 2015
We combine Landsat and MODIS data in a land model to assess the impact of urbanization on US surf... more We combine Landsat and MODIS data in a land model to assess the impact of urbanization on US surface climate. For cities built within forests, daytime urban land surface temperature (LST) is much higher than that of vegetated lands. For example, in Washington DC and Atlanta, daytime mean temperature differences between impervious and vegetated lands reach 3.3 and 2.0°C, respectively. Conversely, for cities built within arid lands, such as Phoenix, urban areas are 2.2°C cooler than surrounding shrubs. We find that the choice and amount of tree species in urban settings play a commanding role in modulating cities' LST. At continental and monthly scales, impervious surfaces are 1.9°C ± 0.6°C warmer than surroundings during summer and expel 12% of incoming precipitation as surface runoff compared to 3.2% over vegetation. We also show that the carbon lost to urbanization represents 1.8% of the continental total, a striking number considering urbanization occupies only 1.1% of the US land. With a small areal extent, urbanization has significant effects on surface energy, water and carbon budgets and reveals an uneven impact on surface climate that should inform upon policy options for improving urban growth including heat mitigation and carbon sequestration.
Journal of Geophysical Research: Atmospheres, 2015
In the long term, precipitation in the central U.S. decreases by about 25% during the seasonal 21... more In the long term, precipitation in the central U.S. decreases by about 25% during the seasonal 21 transition from June to July. This precipitation decrease is observed to have intensified since 22
Journal of Hydrometeorology, 2015
Journal of Hydrometeorology, 2015
Journal of Hydrometeorology, 2015
Environmental Modelling & Software, 2015
ABSTRACT With support from NASA's Modeling and Analysis Program, we have recently develop... more ABSTRACT With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. “Satellite-resolved” scales (roughly 1–25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the GOddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles.
Journal of Hydrometeorology, 2014
ABSTRACT Observing system simulation experiments (OSSEs) are often conducted to evaluate the wort... more ABSTRACT Observing system simulation experiments (OSSEs) are often conducted to evaluate the worth of existing data and data yet to be collected from proposed new missions. As missions increasingly require a broader “Earth systems” focus, it is important that the OSSEs capture the potential benefits of the observations on end-use applications. Toward this end, the results from the OSSEs must also be evaluated with a suite of metrics that capture the value, uncertainty, and information content of the observations while factoring in both science and societal impacts. This article presents a soil moisture OSSE that employs simulated L-band measurements and assesses its utility toward improving drought and flood risk estimates using the NASA Land Information System (LIS). A decision-theory-based analysis is conducted to assess the economic utility of the observations toward improving these applications. The results suggest that the improvements in surface soil moisture, root-zone soil moisture, and total runoff fields obtained through the assimilation of L-band measurements are effective in providing improvements in the drought and flood risk assessments as well. The decision-theory analysis not only demonstrates the economic utility of observations but also shows that the use of probabilistic information from the model simulations is more beneficial compared to the use of corresponding deterministic estimates. The experiment also demonstrates the value of a comprehensive modeling environment such as LIS for conducting end-to-end OSSEs by linking satellite observations, physical models, data assimilation algorithms, and end-use application models in a single integrated framework.
Water Resources Research, 2008
1] The focus of this study is on the role of precipitation uncertainty in the estimation of soil ... more 1] The focus of this study is on the role of precipitation uncertainty in the estimation of soil texture and soil hydraulic properties for application to land-atmosphere modeling systems. This work extends a recent study by in which it was shown that soil texture and related physical parameters may be estimated using a combination of multitemporal microwave remote sensing, land surface modeling, and parameter estimation methods. As in the previous study, the NASA-GSFC Land Information System modeling framework, including the community Noah land surface model constrained with pedotransfer functions (PTF) for use with the Parameter Estimation Tool, is applied to several sites in the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona during the Monsoon '90 experiment period. It is demonstrated that the application of PTF constraints in the estimation process for hydraulic parameters provides accuracy similar to direct hydrologic parameter estimation, with the additional benefit of simultaneously estimated soil texture. Precipitation uncertainty is then represented with systematically varying sources, from the high-density precipitation gauge network in WGEW to lower quality sources, including spatially averaged precipitation, single gauges in and near the watershed, and results from the continental-scale North American Regional Reanalysis data set. It is demonstrated that the quality of the input precipitation data set, and particularly the accuracy of the data set, in both detection of convective (heavy) rainfall events and reproduction of the observed rainfall rate probabilities, is a critical determinant in the use of successive remote sensing results in order to establish and refine estimates of soil texture and hydraulic properties.
2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2013
ABSTRACT Irrigation represents the largest consumption of freshwater in the United States and has... more ABSTRACT Irrigation represents the largest consumption of freshwater in the United States and has been shown to modify local hydrology and regional climate. This study utilizes both the Land Information System (LIS) and the Weather Research and Forecasting model (WRF) to investigate changes in land-atmosphere interactions resulting from drip, flood, and sprinkler irrigation methods in the Southern Great Plains. Five-year irrigated LIS spin-ups are used to initialize two-day WRF forecasts in relatively dry and wet years (2006 and 2008, respectively). The offline and coupled simulation results show that both LIS spin-ups and LIS-WRF forecasts are sensitive to irrigation and irrigation methods, as exhibited by significant changes to temperature, soil moisture, boundary layer height, and the partitioning of latent and sensible heat fluxes. Dry year impacts are greater than those in the wet year suggesting that the magnitude of these changes is dependent on the existing precipitation regime. Sprinkler and flood irrigation schemes impact the LIS-WRF forecast the most, while drip irrigation has a comparatively small effect.
Water Resources Research, 2012
ABSTRACT Operational land surface models (LSMs) compute hydrologic states such as soil moisture t... more ABSTRACT Operational land surface models (LSMs) compute hydrologic states such as soil moisture that are needed for a range of important applications (e.g., drought, flood, and weather prediction). The uncertainty in LSM parameters is sufficiently great that several researchers have proposed conducting parameter estimation using globally available remote sensing data to identify best fit local parameter sets. However, even with in situ data at fine modeling scales, there can be significant remaining uncertainty in LSM parameters and outputs. Here, using a new uncertainty estimation subsystem of the NASA Land Information System (LIS) (described herein), a Markov chain Monte Carlo (MCMC) technique is applied to conduct Bayesian analysis for the accounting of parameter uncertainties. The Differential Evolution Markov Chain (DE-MC) MCMC algorithm was applied, for which a new parallel implementation was developed. A case study is examined that builds on previous work in which the Noah LSM was calibrated to passive (L-band) microwave remote sensing estimates of soil moisture for the Walnut Gulch Experimental Watershed. In keeping with prior related studies, the parameters subjected to the analysis were restricted to the soil hydraulic properties (SHPs). The main goal is to estimate SHPs and soil moisture simulation uncertainty before and after consideration of the remote sensing data. The prior SHP uncertainty is based on the original source of the standard SHP lookup tables for the Noah LSM. Conclusions are drawn regarding the value and viability of Bayesian analysis over alternative approaches (e.g., parameter estimation, lookup tables) and further research needs are identified.
Water Resources Research, 2012
1] Data assimilation is increasingly being used to merge remotely sensed land surface variables s... more 1] Data assimilation is increasingly being used to merge remotely sensed land surface variables such as soil moisture, snow, and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (1) parameter estimation to calibrate the land model to the climatology of the soil moisture observations and (2) scaling of the observations to the model's soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model's climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue. (2012), A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515,
Remote Sensing of Environment, 2007
Near-surface soil moisture is a critical component of land surface energy and water balance studi... more Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soil properties from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.
Journal of Hydrometeorology, 2011
Http Dx Doi Org 10 1175 Jhm D 14 0203 1, Feb 17, 2015
Journal of Hydrometeorology, 2016
Journal of Hydrometeorology, 2015
Environmental Research Letters, 2015
We combine Landsat and MODIS data in a land model to assess the impact of urbanization on US surf... more We combine Landsat and MODIS data in a land model to assess the impact of urbanization on US surface climate. For cities built within forests, daytime urban land surface temperature (LST) is much higher than that of vegetated lands. For example, in Washington DC and Atlanta, daytime mean temperature differences between impervious and vegetated lands reach 3.3 and 2.0°C, respectively. Conversely, for cities built within arid lands, such as Phoenix, urban areas are 2.2°C cooler than surrounding shrubs. We find that the choice and amount of tree species in urban settings play a commanding role in modulating cities' LST. At continental and monthly scales, impervious surfaces are 1.9°C ± 0.6°C warmer than surroundings during summer and expel 12% of incoming precipitation as surface runoff compared to 3.2% over vegetation. We also show that the carbon lost to urbanization represents 1.8% of the continental total, a striking number considering urbanization occupies only 1.1% of the US land. With a small areal extent, urbanization has significant effects on surface energy, water and carbon budgets and reveals an uneven impact on surface climate that should inform upon policy options for improving urban growth including heat mitigation and carbon sequestration.
Journal of Geophysical Research: Atmospheres, 2015
In the long term, precipitation in the central U.S. decreases by about 25% during the seasonal 21... more In the long term, precipitation in the central U.S. decreases by about 25% during the seasonal 21 transition from June to July. This precipitation decrease is observed to have intensified since 22
Journal of Hydrometeorology, 2015
Journal of Hydrometeorology, 2015
Journal of Hydrometeorology, 2015
Environmental Modelling & Software, 2015
ABSTRACT With support from NASA's Modeling and Analysis Program, we have recently develop... more ABSTRACT With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. “Satellite-resolved” scales (roughly 1–25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the GOddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles.
Journal of Hydrometeorology, 2014
ABSTRACT Observing system simulation experiments (OSSEs) are often conducted to evaluate the wort... more ABSTRACT Observing system simulation experiments (OSSEs) are often conducted to evaluate the worth of existing data and data yet to be collected from proposed new missions. As missions increasingly require a broader “Earth systems” focus, it is important that the OSSEs capture the potential benefits of the observations on end-use applications. Toward this end, the results from the OSSEs must also be evaluated with a suite of metrics that capture the value, uncertainty, and information content of the observations while factoring in both science and societal impacts. This article presents a soil moisture OSSE that employs simulated L-band measurements and assesses its utility toward improving drought and flood risk estimates using the NASA Land Information System (LIS). A decision-theory-based analysis is conducted to assess the economic utility of the observations toward improving these applications. The results suggest that the improvements in surface soil moisture, root-zone soil moisture, and total runoff fields obtained through the assimilation of L-band measurements are effective in providing improvements in the drought and flood risk assessments as well. The decision-theory analysis not only demonstrates the economic utility of observations but also shows that the use of probabilistic information from the model simulations is more beneficial compared to the use of corresponding deterministic estimates. The experiment also demonstrates the value of a comprehensive modeling environment such as LIS for conducting end-to-end OSSEs by linking satellite observations, physical models, data assimilation algorithms, and end-use application models in a single integrated framework.
Water Resources Research, 2008
1] The focus of this study is on the role of precipitation uncertainty in the estimation of soil ... more 1] The focus of this study is on the role of precipitation uncertainty in the estimation of soil texture and soil hydraulic properties for application to land-atmosphere modeling systems. This work extends a recent study by in which it was shown that soil texture and related physical parameters may be estimated using a combination of multitemporal microwave remote sensing, land surface modeling, and parameter estimation methods. As in the previous study, the NASA-GSFC Land Information System modeling framework, including the community Noah land surface model constrained with pedotransfer functions (PTF) for use with the Parameter Estimation Tool, is applied to several sites in the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona during the Monsoon '90 experiment period. It is demonstrated that the application of PTF constraints in the estimation process for hydraulic parameters provides accuracy similar to direct hydrologic parameter estimation, with the additional benefit of simultaneously estimated soil texture. Precipitation uncertainty is then represented with systematically varying sources, from the high-density precipitation gauge network in WGEW to lower quality sources, including spatially averaged precipitation, single gauges in and near the watershed, and results from the continental-scale North American Regional Reanalysis data set. It is demonstrated that the quality of the input precipitation data set, and particularly the accuracy of the data set, in both detection of convective (heavy) rainfall events and reproduction of the observed rainfall rate probabilities, is a critical determinant in the use of successive remote sensing results in order to establish and refine estimates of soil texture and hydraulic properties.
2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 2013
ABSTRACT Irrigation represents the largest consumption of freshwater in the United States and has... more ABSTRACT Irrigation represents the largest consumption of freshwater in the United States and has been shown to modify local hydrology and regional climate. This study utilizes both the Land Information System (LIS) and the Weather Research and Forecasting model (WRF) to investigate changes in land-atmosphere interactions resulting from drip, flood, and sprinkler irrigation methods in the Southern Great Plains. Five-year irrigated LIS spin-ups are used to initialize two-day WRF forecasts in relatively dry and wet years (2006 and 2008, respectively). The offline and coupled simulation results show that both LIS spin-ups and LIS-WRF forecasts are sensitive to irrigation and irrigation methods, as exhibited by significant changes to temperature, soil moisture, boundary layer height, and the partitioning of latent and sensible heat fluxes. Dry year impacts are greater than those in the wet year suggesting that the magnitude of these changes is dependent on the existing precipitation regime. Sprinkler and flood irrigation schemes impact the LIS-WRF forecast the most, while drip irrigation has a comparatively small effect.
Water Resources Research, 2012
ABSTRACT Operational land surface models (LSMs) compute hydrologic states such as soil moisture t... more ABSTRACT Operational land surface models (LSMs) compute hydrologic states such as soil moisture that are needed for a range of important applications (e.g., drought, flood, and weather prediction). The uncertainty in LSM parameters is sufficiently great that several researchers have proposed conducting parameter estimation using globally available remote sensing data to identify best fit local parameter sets. However, even with in situ data at fine modeling scales, there can be significant remaining uncertainty in LSM parameters and outputs. Here, using a new uncertainty estimation subsystem of the NASA Land Information System (LIS) (described herein), a Markov chain Monte Carlo (MCMC) technique is applied to conduct Bayesian analysis for the accounting of parameter uncertainties. The Differential Evolution Markov Chain (DE-MC) MCMC algorithm was applied, for which a new parallel implementation was developed. A case study is examined that builds on previous work in which the Noah LSM was calibrated to passive (L-band) microwave remote sensing estimates of soil moisture for the Walnut Gulch Experimental Watershed. In keeping with prior related studies, the parameters subjected to the analysis were restricted to the soil hydraulic properties (SHPs). The main goal is to estimate SHPs and soil moisture simulation uncertainty before and after consideration of the remote sensing data. The prior SHP uncertainty is based on the original source of the standard SHP lookup tables for the Noah LSM. Conclusions are drawn regarding the value and viability of Bayesian analysis over alternative approaches (e.g., parameter estimation, lookup tables) and further research needs are identified.
Water Resources Research, 2012
1] Data assimilation is increasingly being used to merge remotely sensed land surface variables s... more 1] Data assimilation is increasingly being used to merge remotely sensed land surface variables such as soil moisture, snow, and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (1) parameter estimation to calibrate the land model to the climatology of the soil moisture observations and (2) scaling of the observations to the model's soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model's climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue. (2012), A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515,
Remote Sensing of Environment, 2007
Near-surface soil moisture is a critical component of land surface energy and water balance studi... more Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soil properties from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating within a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.
Journal of Hydrometeorology, 2011