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Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.
Said Mejia Manrique; Eric Harmsen; Reza Khanbilvardi; Jorge González. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María. Hydrology 2021, 8, 104 .
AMA StyleSaid Mejia Manrique, Eric Harmsen, Reza Khanbilvardi, Jorge González. Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María. Hydrology. 2021; 8 (3):104.
Chicago/Turabian StyleSaid Mejia Manrique; Eric Harmsen; Reza Khanbilvardi; Jorge González. 2021. "Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María." Hydrology 8, no. 3: 104.
Satellite-borne passive microwave radiometers provide brightness temperature (TB) measurements in a large spectral range which includes a number of frequency channels and generally two polarizations: horizontal and vertical. These TBs are widely used to retrieve several atmospheric and surface variables and parameters such as precipitation, soil moisture, water vapor, air temperature profile, and land surface emissivity. Since TBs are measured at different microwave frequencies with various instruments and at various incidence angles, spatial resolutions, and radiometric characteristics, a mere direct integration of them from different microwave sensors would not necessarily provide consistency. However, when appropriately harmonized, they can provide a complete dataset to estimate the diurnal cycle. This study first constructs the diurnal cycle of land TBs using the non-sun-synchronous Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations by utilizing a cubic spline fit. The acquisition times of GMI vary from day to day and, therefore, the shape (amplitude and phase) of the diurnal cycle for each month is obtained by merging several days of measurements. This diurnal pattern is used as a point of reference when intercalibrated TBs from other passive microwave sensors with daily fixed acquisition times (e.g., Special Sensor Microwave Imager/Sounder, and Advanced Microwave Scanning Radiometer 2) are used to modify and tune the monthly diurnal cycle to daily diurnal cycle at a global scale. Since the GMI does not cover polar regions, the proposed method estimates a consistent diurnal cycle of land TBs at global scale. Results show that the shape and peak of the constructed TB diurnal cycle is approximately similar to the diurnal cycle of land surface temperature. The diurnal brightness temperature range for different land cover types has also been explored using the derived diurnal cycle of TBs. In general, a large diurnal TB range of more than 15 K has been observed for the grassland, shrubland, and tundra land cover types, whereas it is less than 5K over forests. Furthermore, seasonal variations in the diurnal TB range for different land cover types show a more consistent result over the Southern Hemisphere than over the Northern Hemisphere. The calibrated TB diurnal cycle may then be used to consistently estimate the diurnal cycle of land surface emissivity. Moreover, since changes in land surface emissivity are related to moisture change and freeze–thaw (FT) transitions in high-latitude regions, the results of this study enhance temporal detection of FT state, particularly during the transition times when multiple FT changes may occur within a day.
Zahra Sharifnezhad; Hamid Norouzi; Satya Prakash; Reginald Blake; Reza Khanbilvardi. Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale. Remote Sensing 2021, 13, 817 .
AMA StyleZahra Sharifnezhad, Hamid Norouzi, Satya Prakash, Reginald Blake, Reza Khanbilvardi. Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale. Remote Sensing. 2021; 13 (4):817.
Chicago/Turabian StyleZahra Sharifnezhad; Hamid Norouzi; Satya Prakash; Reginald Blake; Reza Khanbilvardi. 2021. "Diurnal Cycle of Passive Microwave Brightness Temperatures over Land at a Global Scale." Remote Sensing 13, no. 4: 817.
Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA SWMM), MIKE URBAN-II and others, have been developed to simulate flooding events in cities. However, they require high accuracy mapping and a simulation of the underground storm drainage system. Sadly, this capability is usually not available for older or larger so-called megacities. Other hydrological model types are classified in the semi-physical category, like Cellular Automata (CA), require the incorporation of very fine resolution data. These types of data, in turn, demand massive computer power and time for analysis. Furthermore, available forecasting systems provide a way to determine total rainfall during extreme events, but they do not tell us what areas will be flooded. This work introduces an urban flooding tool that couples a rainfall-runoff model with a flood map database to expedite the alert process and estimate flooded areas. A 0.30-m Lidar Digital Elevation Model (DEM) of the study area (in this case Manhattan, New York City) is divided into 140 sub-basins. Several flood maps for each sub-basin are generated and organized into a database. For any forecasted extreme rainfall event, the rainfall-runoff model predicts the expected runoff volume at different times during the storm interval. The system rapidly searches for the corresponding flood map that delineates the expected flood area. The sensitivity analysis of parameters in the model show that the effect of storm inlet flow head is approximately linear while the effects of the threshold infiltration rate, the number of storm inlets, and the storm inlet flow reduction factor are non-linear. The reduction factor variation is found to exhibit a high non-linearity variation, hence requiring further detailed investigation.
Rafea Al-Suhili; Cheila Cullen; Reza Khanbilvardi. An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA. Hydrology 2019, 6, 56 .
AMA StyleRafea Al-Suhili, Cheila Cullen, Reza Khanbilvardi. An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA. Hydrology. 2019; 6 (2):56.
Chicago/Turabian StyleRafea Al-Suhili; Cheila Cullen; Reza Khanbilvardi. 2019. "An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA." Hydrology 6, no. 2: 56.
We present the output data of Robust Principal Component Analysis (RPCA) applied to global crop yield variability of maize, rice, sorghum and soybean (MRSS) as presented in the publication “Climate drives variability and joint variability of global crop yields” (Najafi et al., 2019). Global maps of the correlation between all the principal components (PCs) acquired from the low rank matrix (L) of MRSS and Palmer Drought Severity Index (PDSI), air temperature anomalies (ATa) and sea surface temperature anomalies (SSTa) are provided in this article. We present co-varying countries, impacted cropland areas across global countries, and 10 global regions by climate and the association between PCs and multiple atmospheric and oceanic indices. Moreover, the joint dependency between PCs of MRSS yields are presented using two different approaches.
Ehsan Najafi; Indrani Pal; Reza Khanbilvardi. Data of variability and joint variability of global crop yields and their association with climate. Data in Brief 2019, 23, 103745 .
AMA StyleEhsan Najafi, Indrani Pal, Reza Khanbilvardi. Data of variability and joint variability of global crop yields and their association with climate. Data in Brief. 2019; 23 ():103745.
Chicago/Turabian StyleEhsan Najafi; Indrani Pal; Reza Khanbilvardi. 2019. "Data of variability and joint variability of global crop yields and their association with climate." Data in Brief 23, no. : 103745.
Drought is one of the most devastating environmental disasters. Analyzing historical changes in climate extremes is critical for mitigating its adverse impacts in the future. In the present study, the spatial and temporal characteristics of the global severe droughts using Palmer Drought Intensity Index (PDSI) from 1900 to 2014 are explored. K-means clustering is implemented to partition the extreme negative PDSI values. The global extreme droughts magnitude around the world from 1950 to 1980 were less intense compared to the other decades. In 2012, the largest areas around the world, especially Canada, experienced their most severe historical droughts. Results show that the most recent extreme droughts occurred in some regions such as the North of Canada, central regions of the US, Southwest of Europe and Southeast Asia. We found that after 1980, the spatial extent of the regions that experienced extreme drought have increased substantially.
Ehsan Najafi; Saman Armal; Reza Khanbilvardi. Clustering and Trend Analysis of Global Extreme Droughts from 1900 to 2014. 2018, 1 .
AMA StyleEhsan Najafi, Saman Armal, Reza Khanbilvardi. Clustering and Trend Analysis of Global Extreme Droughts from 1900 to 2014. . 2018; ():1.
Chicago/Turabian StyleEhsan Najafi; Saman Armal; Reza Khanbilvardi. 2018. "Clustering and Trend Analysis of Global Extreme Droughts from 1900 to 2014." , no. : 1.
Accurate estimation of passive microwave land surface emissivity (LSE) is crucial for numerical weather prediction model data assimilation, for microwave retrievals of land precipitation and atmospheric profiles, and for a better understanding of land surface and subsurface characteristics. In this study, global instantaneous LSE is estimated for a 9-yr period from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and for a 5-yr period from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensors. Estimates of LSE from both sensors were obtained by using an updated algorithm that minimizes the discrepancy between the differences in penetration depths from microwave and infrared remote sensing observations. Concurrent ancillary datasets such as skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) and profiles of air temperature and humidity from the Atmospheric Infrared Sounder are used. The latest collection 6 of MODIS skin temperature is used for the LSE estimation, and the differences between collections 6 and 5 are also comprehensively assessed. Analyses reveal that the differences between these two versions of infrared-based skin temperatures could lead to approximately a 0.015 difference in passive microwave LSE values, especially in arid regions. The comparison of global mean LSE features from the combined use of AMSR-E and AMSR2 with an independent product—Tool to Estimate Land Surface Emissivity from Microwave to Submillimeter Waves (TELSEM2)—shows spatial pattern correlations of order 0.92 at all frequencies. However, there are considerable differences in magnitude between these two LSE estimates, possibly because of differences in incidence angles, frequencies, observation times, and ancillary datasets.
Satya Prakash; Hamid Norouzi; Marzi Azar; Reginald Blake; Catherine Prigent; Reza Khanbilvardi. Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations. Journal of Applied Meteorology and Climatology 2018, 57, 907 -919.
AMA StyleSatya Prakash, Hamid Norouzi, Marzi Azar, Reginald Blake, Catherine Prigent, Reza Khanbilvardi. Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations. Journal of Applied Meteorology and Climatology. 2018; 57 (4):907-919.
Chicago/Turabian StyleSatya Prakash; Hamid Norouzi; Marzi Azar; Reginald Blake; Catherine Prigent; Reza Khanbilvardi. 2018. "Estimation of Consistent Global Microwave Land Surface Emissivity from AMSR-E and AMSR2 Observations." Journal of Applied Meteorology and Climatology 57, no. 4: 907-919.
This study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.
Saman Armal; Naresh Devineni; Reza Khanbilvardi. Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes. Journal of Climate 2017, 31, 369 -385.
AMA StyleSaman Armal, Naresh Devineni, Reza Khanbilvardi. Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes. Journal of Climate. 2017; 31 (1):369-385.
Chicago/Turabian StyleSaman Armal; Naresh Devineni; Reza Khanbilvardi. 2017. "Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes." Journal of Climate 31, no. 1: 369-385.
This article presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration Terra and Aqua Earth Observing System satellites using in-situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January–April) and 2014 (February–April). A total of 314 day-and-night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. CREST-SAFE is a synoptic ground station, located in the cold county of Caribou in Maine, USA, with a distinct advantage over most meteorological stations because it provides automated and continuous LST observations via an Apogee Model SI-111 Infrared Radiometer. This article also attempts to answer the question of whether a single pixel (1 km2) or several spatially averaged pixels should be used for satellite LST validation by increasing the MODIS window size to 5 × 5, 9 × 9, and 25 × 25 windows. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy (about 1°C), both suggesting that MODIS LST retrievals are reliable for similar land-cover classes and atmospheric conditions. Increasing the MODIS window size showed an overestimation of in-situ LST and some improvement in the daytime Terra and night-time Aqua biases, with the highest accuracy achieved with the 5 × 5 window. A comparison between MODIS emissivity from bands 31, 32, and in-situ emissivity showed that emissivity errors (relative error = −0.30%) were insignificant.
Carlos L. Pérez-Díaz; Tarendra Lakhankar; Peter Romanov; Jonathan Muñoz; Reza Khanbilvardi; Yunyue Yu. Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE. International Journal of Remote Sensing 2017, 38, 4722 -4740.
AMA StyleCarlos L. Pérez-Díaz, Tarendra Lakhankar, Peter Romanov, Jonathan Muñoz, Reza Khanbilvardi, Yunyue Yu. Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE. International Journal of Remote Sensing. 2017; 38 (16):4722-4740.
Chicago/Turabian StyleCarlos L. Pérez-Díaz; Tarendra Lakhankar; Peter Romanov; Jonathan Muñoz; Reza Khanbilvardi; Yunyue Yu. 2017. "Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE." International Journal of Remote Sensing 38, no. 16: 4722-4740.
Extreme rainfall events, specifically in urban areas, have dramatic impacts on society and can lead to loss of life and property. Despite these hazards, little is known about the city-scale variability of heavy rainfall events. In the current study, gridded stage IV radar data from 2002 to 2015 are employed to investigate the clustering and the spatial variability of simultaneous rainfall exceedances in the greater New York area. Multivariate clustering based on partitioning around medoids is applied to the extreme rainfall events’ average intensity and areal extent for the 1- and 24-h accumulated rainfall during winter (December–February) and summer (June–August) seasons. The atmospheric teleconnections of the daily extreme event for winter and summer are investigated using compositing of ERA-Interim. For both 1- and 24-h durations, the winter season extreme rainfall events have larger areal extent than the summer season extreme rainfall events. Winter extreme events are associated with deep and organized circulation patterns that lead to more areal extent, and the summer events are associated with localized frontal systems that lead to smaller areal extents. The average intensities of the 1-h extreme rainfall events in summer are much higher than the average intensities of the 1-h extreme rainfall events in winter. A clear spatial demarcation exists within the five boroughs in New York City for winter extreme events. Resultant georeferenced cluster maps can be extremely useful in risk analysis and green infrastructures planning as well as sewer systems’ management at the city scale.
Ali Hamidi; Naresh Devineni; James F. Booth; Amana Hosten; Ralph R. Ferraro; Reza Khanbilvardi. Classifying Urban Rainfall Extremes Using Weather Radar Data: An Application to the Greater New York Area. Journal of Hydrometeorology 2017, 18, 611 -623.
AMA StyleAli Hamidi, Naresh Devineni, James F. Booth, Amana Hosten, Ralph R. Ferraro, Reza Khanbilvardi. Classifying Urban Rainfall Extremes Using Weather Radar Data: An Application to the Greater New York Area. Journal of Hydrometeorology. 2017; 18 (3):611-623.
Chicago/Turabian StyleAli Hamidi; Naresh Devineni; James F. Booth; Amana Hosten; Ralph R. Ferraro; Reza Khanbilvardi. 2017. "Classifying Urban Rainfall Extremes Using Weather Radar Data: An Application to the Greater New York Area." Journal of Hydrometeorology 18, no. 3: 611-623.
Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness.
Cheila Avalon Cullen; Rafea Al-Suhili; Reza Khanbilvardi. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sensing 2016, 8, 866 .
AMA StyleCheila Avalon Cullen, Rafea Al-Suhili, Reza Khanbilvardi. Guidance Index for Shallow Landslide Hazard Analysis. Remote Sensing. 2016; 8 (10):866.
Chicago/Turabian StyleCheila Avalon Cullen; Rafea Al-Suhili; Reza Khanbilvardi. 2016. "Guidance Index for Shallow Landslide Hazard Analysis." Remote Sensing 8, no. 10: 866.
Groundwater Dependent Ecosystems (GDEs) are increasingly threatened by humans’ rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. Probabilities are obtained by modeling the relationship between the known locations of GDEs and factors influencing groundwater dependence, namely water table depth and climatic aridity index. Probabilities are derived for the state of Nevada, USA, using modeled water table depth and aridity index values obtained from the Global Aridity database. The model selected results from the performance comparison of classification trees (CT) and random forests (RF). Based on a threshold-independent accuracy measure, RF has a better ability to generate probability estimates. Considering a threshold that minimizes the misclassification rate for each model, RF also proves to be more accurate. Regarding training accuracy, performance measures such as accuracy, sensitivity, and specificity are higher for RF. For the test set, higher values of accuracy and kappa for CT highlight the fact that these measures are greatly affected by low prevalence. As shown for RF, the choice of the cutoff probability value has important consequences on model accuracy and the overall proportion of locations where GDEs are found.
Isabel C. Pérez Hoyos; Nir Y. Krakauer; Reza Khanbilvardi. Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models. Environments 2016, 3, 9 .
AMA StyleIsabel C. Pérez Hoyos, Nir Y. Krakauer, Reza Khanbilvardi. Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models. Environments. 2016; 3 (4):9.
Chicago/Turabian StyleIsabel C. Pérez Hoyos; Nir Y. Krakauer; Reza Khanbilvardi. 2016. "Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models." Environments 3, no. 4: 9.
Groundwater Dependent Ecosystem (GDE) protection is increasingly being recognized as essential for the sustainable management and allocation of water resources. GDE services are crucial for human well-being and for a variety of flora and fauna. However, the conservation of GDEs is only possible if knowledge about their location and extent is available. Several studies have focused on the identification of GDEs at specific locations using ground-based measurements. However, recent progress in remote sensing technologies and their integration with Geographic Information Systems (GIS) has provided alternative ways to map GDEs at a much larger spatial extent. This paper presents a review of the geospatial methods that have been used to map and delineate GDEs at spatial different extents. Additionally, a summary of the satellite sensors useful for identification of GDEs and the integration of remote sensing data with ground-based measurements in the process of mapping GDEs is presented.
Isabel C. Pérez Hoyos; Nir Y. Krakauer; Reza Khanbilvardi; Roy A. Armstrong. A Review of Advances in the Identification and Characterization of Groundwater Dependent Ecosystems Using Geospatial Technologies. Geosciences 2016, 6, 17 .
AMA StyleIsabel C. Pérez Hoyos, Nir Y. Krakauer, Reza Khanbilvardi, Roy A. Armstrong. A Review of Advances in the Identification and Characterization of Groundwater Dependent Ecosystems Using Geospatial Technologies. Geosciences. 2016; 6 (2):17.
Chicago/Turabian StyleIsabel C. Pérez Hoyos; Nir Y. Krakauer; Reza Khanbilvardi; Roy A. Armstrong. 2016. "A Review of Advances in the Identification and Characterization of Groundwater Dependent Ecosystems Using Geospatial Technologies." Geosciences 6, no. 2: 17.
This work proposes an approach to automatically adjust the Curve Number (CN) to account for changes in vegetation density. Precipitation-runoff pairs from the MOdel Parameter Estimation EXperiment (MOPEX) dataset were used to estimate monthly simulated CNs (CNsim). Remotely sensed Greenness Fraction (GF) was used as a proxy for vegetation density. A relationship was established between CNsim and GF values and an adjustment factor was introduced. The coefficients of determination (R2) between the simulated and observed runoff when using the unadjusted and adjusted CNs were 0.63 and 0.80, respectively. Likewise, a Nash–Sutcliffe Coefficient (NSC) of –0.17 and 0.67 and Root Mean Square Error (RMSE) of 5.22 and 2.75 were also obtained for the unadjusted and adjusted CNs, respectively. The results evidence how the adjustments compensate the runoff overestimation when using the standard CN (CNstd) and also imply that the adjustment is crucial for improved hydrological modeling, particularly, for flood and flash flood monitoring and forecasting.
Álvaro González-Álvarez; Marouane Temimi; Reza Khanbilvardi. Adjustment to the curve number (NRCS-CN) to account for the vegetation effect on hydrological processes. Hydrological Sciences Journal 2015, 60, 591 -605.
AMA StyleÁlvaro González-Álvarez, Marouane Temimi, Reza Khanbilvardi. Adjustment to the curve number (NRCS-CN) to account for the vegetation effect on hydrological processes. Hydrological Sciences Journal. 2015; 60 (4):591-605.
Chicago/Turabian StyleÁlvaro González-Álvarez; Marouane Temimi; Reza Khanbilvardi. 2015. "Adjustment to the curve number (NRCS-CN) to account for the vegetation effect on hydrological processes." Hydrological Sciences Journal 60, no. 4: 591-605.
Hamid Norouzi; Marouane Temimi; Amir AghaKouchak; Marzi Azar; Reza Khanbilvardi; Gerarda Shields; Kibrewossen Tesfagiorgis. Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures. Physics and Chemistry of the Earth, Parts A/B/C 2015, 83-84, 28 -35.
AMA StyleHamid Norouzi, Marouane Temimi, Amir AghaKouchak, Marzi Azar, Reza Khanbilvardi, Gerarda Shields, Kibrewossen Tesfagiorgis. Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures. Physics and Chemistry of the Earth, Parts A/B/C. 2015; 83-84 ():28-35.
Chicago/Turabian StyleHamid Norouzi; Marouane Temimi; Amir AghaKouchak; Marzi Azar; Reza Khanbilvardi; Gerarda Shields; Kibrewossen Tesfagiorgis. 2015. "Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures." Physics and Chemistry of the Earth, Parts A/B/C 83-84, no. : 28-35.
Marouane Temimi; Tarendra Lakhankar; Xiwu Zhan; Michael H. Cosh; Nir Krakauer; Ali Fares; Victoria Kelly; Reza Khanbilvardi; Laetitia Kumassi. Soil Moisture Retrieval Using Ground-Based L-Band Passive Microwave Observations in Northeastern USA. Vadose Zone Journal 2014, 13, 1 .
AMA StyleMarouane Temimi, Tarendra Lakhankar, Xiwu Zhan, Michael H. Cosh, Nir Krakauer, Ali Fares, Victoria Kelly, Reza Khanbilvardi, Laetitia Kumassi. Soil Moisture Retrieval Using Ground-Based L-Band Passive Microwave Observations in Northeastern USA. Vadose Zone Journal. 2014; 13 (3):1.
Chicago/Turabian StyleMarouane Temimi; Tarendra Lakhankar; Xiwu Zhan; Michael H. Cosh; Nir Krakauer; Ali Fares; Victoria Kelly; Reza Khanbilvardi; Laetitia Kumassi. 2014. "Soil Moisture Retrieval Using Ground-Based L-Band Passive Microwave Observations in Northeastern USA." Vadose Zone Journal 13, no. 3: 1.
Jonathan Murioz; Jonathan Munoz; José Infante; Tarendra Lakhankar; Reza Khanbilvardi; Peter Romanov; Nir Krakauer; Al Powell. Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation. British Journal of Environment and Climate Change 2013, 3, 1 .
AMA StyleJonathan Murioz, Jonathan Munoz, José Infante, Tarendra Lakhankar, Reza Khanbilvardi, Peter Romanov, Nir Krakauer, Al Powell. Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation. British Journal of Environment and Climate Change. 2013; 3 (4):1.
Chicago/Turabian StyleJonathan Murioz; Jonathan Munoz; José Infante; Tarendra Lakhankar; Reza Khanbilvardi; Peter Romanov; Nir Krakauer; Al Powell. 2013. "Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation." British Journal of Environment and Climate Change 3, no. 4: 1.
Naira Chaouch; Robert Leconte; Ramata Magagi; Marouane Temimi; Reza Khanbilvardi. Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin. Vadose Zone Journal 2013, 12, 1 .
AMA StyleNaira Chaouch, Robert Leconte, Ramata Magagi, Marouane Temimi, Reza Khanbilvardi. Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin. Vadose Zone Journal. 2013; 12 (3):1.
Chicago/Turabian StyleNaira Chaouch; Robert Leconte; Ramata Magagi; Marouane Temimi; Reza Khanbilvardi. 2013. "Multi-Stage Inversion Method to Retrieve Soil Moisture from Passive Microwave Measurements over the Mackenzie River Basin." Vadose Zone Journal 12, no. 3: 1.
Hamid Norouzi; William Rossow; Marouane Temimi; Catherine Prigent; Marzi Azar; Sid Ahmed Boukabara; Reza Khanbilvardi. Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land. Remote Sensing of Environment 2012, 123, 470 -482.
AMA StyleHamid Norouzi, William Rossow, Marouane Temimi, Catherine Prigent, Marzi Azar, Sid Ahmed Boukabara, Reza Khanbilvardi. Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land. Remote Sensing of Environment. 2012; 123 ():470-482.
Chicago/Turabian StyleHamid Norouzi; William Rossow; Marouane Temimi; Catherine Prigent; Marzi Azar; Sid Ahmed Boukabara; Reza Khanbilvardi. 2012. "Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land." Remote Sensing of Environment 123, no. : 470-482.
In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed.
Christine Chen; Tarendra Lakhankar; Peter Romanov; Sean Helfrich; Al Powell; Reza Khanbilvardi. Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States. Remote Sensing 2012, 4, 1134 -1145.
AMA StyleChristine Chen, Tarendra Lakhankar, Peter Romanov, Sean Helfrich, Al Powell, Reza Khanbilvardi. Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States. Remote Sensing. 2012; 4 (5):1134-1145.
Chicago/Turabian StyleChristine Chen; Tarendra Lakhankar; Peter Romanov; Sean Helfrich; Al Powell; Reza Khanbilvardi. 2012. "Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States." Remote Sensing 4, no. 5: 1134-1145.
This work proposes a method for detecting inundation between semi‐diurnal low and high water conditions in the northern Gulf of Mexico using high‐resolution satellite imagery. Radarsat 1, Landsat imagery and aerial photography from the Apalachicola region in Florida were used to demonstrate and validate the algorithm. A change detection approach was implemented through the analysis of red, green and blue (RGB) false colour composites image to emphasise differences in high and low tide inundation patterns. To alleviate the effect of inherent speckle in the SAR images, we also applied ancillary optical data. The flood‐prone area for the site was delineated a priori through the determination of lower and higher water contour lines with Landsat images combined with a high‐resolution digital elevation model. This masking technique improved the performance of the proposed algorithm with respect to detection techniques using the entire Radarsat scene. The resulting inundation maps agreed well with historical aerial photography as the probability of detection reached 83%. The combination of SAR data and optical images, when coupled with a high‐resolution digital elevation model, was shown to be useful for inundation mapping and have a great potential for evaluating wetting/drying algorithms of inland and coastal hydrodynamic models. Copyright © 2011 John Wiley & Sons, Ltd.
Naira Chaouch; Marouane Temimi; Scott Hagen; John Weishampel; Stephen Medeiros; Reza Khanbilvardi. A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico. Hydrological Processes 2011, 26, 1617 -1628.
AMA StyleNaira Chaouch, Marouane Temimi, Scott Hagen, John Weishampel, Stephen Medeiros, Reza Khanbilvardi. A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico. Hydrological Processes. 2011; 26 (11):1617-1628.
Chicago/Turabian StyleNaira Chaouch; Marouane Temimi; Scott Hagen; John Weishampel; Stephen Medeiros; Reza Khanbilvardi. 2011. "A synergetic use of satellite imagery from SAR and optical sensors to improve coastal flood mapping in the Gulf of Mexico." Hydrological Processes 26, no. 11: 1617-1628.