Phu Nguyen

Computational Earth Science: Big Data Transformed Into Insight

More than ever in the history of science, researchers have at their fingertips an unprecedented wealth of data from continuously orbiting satellites, weather monitoring instruments, ecological observatories, seismic stations, moored buoys, floats, and even model simulations and forecasts. With just an internet connection, scientists and engineers can access atmospheric and oceanic gridded data and time series observations, seismographs from around the world, minute-by-minute conditions of the near-Earth space environment, and other data streams that provide information on events across local, regional, and global scales. These data sets have become essential for monitoring and understanding the associated impacts of geological and environmental phenomena on society.

Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood

AbstractFloods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. This research applied the high-resolution coupled hydrologic–hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. HiResFlood-UCI was forced with the near-real-time Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. The model was run using the a priori hydrologic parameters and hydraulic Manning n values from lookup tables. The model results were evaluated in two aspects: point comparison using USGS streamflow and areal validation of inundation maps using USDA’s flood extent maps derived from Advanced Wide Field Sensor (AWiFS) 56-m resolution imagery. The results show that the PERSIANN-CCS simulation tends to capt...

Satellites Track Precipitation of Super Typhoon Haiyan

Typhoon Haiyan, which struck Southeast Asia in November 2013, might be the strongest storm on record, with a 10-minute sustained wind speed of 230 kilometers per hour. In the Philippines alone, the damage was immense—the storm killed more than 6000 and completely leveled cities and towns, particularly on the island of Leyte.

Assessing the Efficacy of High-Resolution Satellite-Based PERSIANN-CDR Precipitation Product in Simulating Streamflow

AbstractThis study aims to investigate the performance of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in a rainfall–runoff modeling application over the past three decades. PERSIANN-CDR provides precipitation data at daily and 0.25° temporal and spatial resolutions from 1983 to present for the 60°S–60°N latitude band and 0°–360° longitude. The study is conducted in two phases over three test basins from the Distributed Hydrologic Model Intercomparison Project, phase 2 (DMIP2). In phase 1, a more recent period of time (2003–10) when other high-resolution satellite-based precipitation products are available is chosen. Precipitation evaluation analysis, conducted against stage IV gauge-adjusted radar data, shows that PERSIANN-CDR and TRMM Multisatellite Precipitation Analysis (TMPA) have close performances with a higher correlation coefficient for TMPA (~0.8 vs 0.75 for PERSIANN-CDR) and almost the same root-mean-square deviati...

Using Densely Distributed Soil Moisture Observations for Calibration of a Hydrologic Model

AbstractCalibration is a crucial step in hydrologic modeling that is typically handled by tuning parameters to match an observed hydrograph. In this research, an alternative calibration scheme based on soil moisture was investigated as a means of identifying the potentially heterogeneous calibration needs of a distributed hydrologic model. The National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) was employed to carry out such a calibration, along with concentrated in situ soil moisture observations from the Iowa Flood Studies (IFloodS) field campaign in Iowa’s Turkey River basin. Synthetic, single-pixel experiments were conducted in order to identify parameters relevant to soil moisture dynamics and to test the ability of three calibration procedures (discharge, soil moisture, and hybrid based) to recapture prescribed parameter sets. It was found that three storage parameters of HL-RDHM could be consistently identified using soil moisture RMSE as the object...

Exploring Trends through “RainSphere”: Research data transformed into public knowledge

C limate is an area of interest that both influences and is influenced by the global population, and yet the transfer of critical basic knowledge about our climate system from scientists who specialize in the field to the general public is severely deficient. To bridge this gap and make understanding historical climate and climate projections accessible, an intuitive and user-friendly analysis tool called CHRS (Center for Hydrometeorology and Remote Sensing) RainSphere has been developed (hosted at http:// rainsphere.eng.uci.edu; a YouTube video tutorial on CHRS RainSphere is available at www.youtube.com /watch?v=eI2-f88iGlY&feature=youtu.be). CHRS RainSphere was designed as an educational tool that allows users to quickly and easily conduct analyses of historical and future precipitation at spatial scales that range from highly local to global and at daily, monthly, or annual time scales. With automatically generated time series, spatial plots, and basic trend analysis, users can swiftly explore historical precipitation estimates and future projections tailored to their specific interests. Allowing the data to speak for themselves in a way that the public can understand not only helps to spread the comprehension of climate and climate variability but also encourages independent inquisition and discovery of climate studies. At the heart of CHRS RainSphere is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) (Ashouri et al. 2015). This satellite-based precipitation product provides daily precipitation estimates from 60°S to 60°N latitude at a 0.25° spatial resolution. PERSIANN-CDR is derived from the parent PERSIANN algorithm (Hsu et al. 1997), which utilizes an artificial neural network to assign a surface rain rate based on brightness temperature retrievals of infrared information from geostationary Earth-orbiting satellites and passive microwave information from low-Earthorbiting satellites. Validation of the PERSIANN product has been performed in several studies (i.e., Sorooshian et al. 2000; Miao et al. 2015, Ashouri et al. 2016). PERSIANN-CDR provides the ability to study extreme hydrometeorological phenomena. The record begins on 1 January 1983 and continues to the present date. This 30+-year retrospective look at global precipitation lends itself to a host of historical precipitation-related questions such as “How does this June’s total precipitation compare to the average monthly total precipitation for June?” or “What is the trend in annual precipitation for my country?” These and countless other questions can be answered using PERSIANN-CDR facilitated through CHRS RainSphere. Complementing the past precipitation estimates from PERSIANN-CDR, CHRS RainSphere features global precipitation projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) based on three carbon emission scenarios (RCP2.6, RCP4.5, and RCP8.5 for low, stabilized, and high emissions scenarios, respectively) from the Intergovernmental Panel on Climate Change (IPCC). More details on CMIP5 can be found in Taylor et al. (2012). Ensemble mean IPCC projected precipitation data from 29 CMIP5 models were obtained from the Canadian Climate Data and Scenarios site (http:// ccds-dscc.ec.gc.ca). CMIP5 model results were interpolated to a common 1 × 1 degree grid (see more AFFILIATIONS: NguyeN—Center for Hydrometeorology and Remote Sensing (CHRS) and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California, and Nong Lam University, Ho Chi Minh City, Vietnam; SorooShiaN, ThorSTeNSeN, TraN, huyNh, Pham, aShouri, hSu, aghaKouchaK, aNd BraiThwaiTe—Center for Hydrometeorology and Remote Sensing (CHRS) and Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California

The Lake Urmia environmental disaster in Iran: A look at aerosol pollution

Lake Urmia (LU) once was the second largest hypersaline lake in the world, covering up to 6000km2, but has undergone catastrophic desiccation in recent years resulting in loss of 90% of its area and extensive coverage by playas and marshlands that represent a source of salt and dust. This study examines daily Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2001 and 2015 over northwestern Iran, which encompasses LU. Intriguingly, salt emissions from the LU surface associated with ongoing desiccation do not drive the study regions AOD profile, whereas pollution transported from other regions and emissions around LU are more important. Signatures of increasing local crustal emissions are most evident outside of the peak dust season (January, February, and October) and on the periphery of LU. AOD has generally increased in the latter half of the study period with the onset of the AOD ramp-up starting a month earlier in the spring season when comparing 2009-2015 versus earlier years. Results indicate that suppression of emissions on the LU border is critical as the combined area of salt and salty soil bodies around LU have increased by two orders of magnitude in the past two decades, and disturbing these areas via activities such as grazing and salt harvesting on the lake surface can have more detrimental impacts on regional pollution as compared to benefits. These results have important implications for public health, climate, the hydrological cycle, and pollution control efforts.

Genesis, Pathways, and Terminations of Intense Global Water Vapor Transport in Association with Large-Scale Climate Patterns: IVT-CONNECT

Author(s): Sellars, SL; Kawzenuk, B; Nguyen, P; Ralph, FM; Sorooshian, S | Abstract: ©2017. The Authors. The CONNected objECT (CONNECT) algorithm is applied to global Integrated Water Vapor Transport data from the NASAs Modern-Era Retrospective Analysis for Research and Applications – Version 2 reanalysis product for the period of 1980 to 2016. The algorithm generates life-cycle records in time and space evolving strong vapor transport events. We show five regions, located in the midlatitudes, where events typically exist (off the coast of the southeast United States, eastern China, eastern South America, off the southern tip of South Africa, and in the southeastern Pacific Ocean). Global statistics show distinct genesis and termination regions and global seasonal peak frequency during Northern Hemisphere late fall/winter and Southern Hemisphere winter. In addition, the event frequency and geographical location are shown to be modulated by the Arctic Oscillation, Pacific North American Pattern, and the quasi-biennial oscillation. Moreover, a positive linear trend in the annual number of objects is reported, increasing by 3.58 objects year-over-year.

Global Precipitation Trends across Spatial Scales Using Satellite Observations

AbstractLittle dispute surrounds the observed global temperature changes over the past decades. As a result, there is widespread agreement that a corresponding response in the global hydrologic cycle must exist. However, exactly how such a response manifests remains unsettled. Here we use a unique recently developed long-term satellite-based record to assess changes in precipitation across spatial scales. We show that warm climate regions exhibit decreasing precipitation trends, while arid and polar climate regions show increasing trends. At the country scale, precipitation seems to have increased in 96 countries, and decreased in 104. We also explore precipitation changes over 237 global major basins. Our results show opposing trends at different scales, highlighting the importance of spatial scale in trend analysis. Furthermore, while the increasing global temperature trend is apparent in observations, the same cannot be said for the global precipitation trend according to the high-resolution dataset, P...

Developing Intensity‐Duration‐Frequency (IDF) Curves From Satellite‐Based Precipitation: Methodology and Evaluation

Author(s): Ombadi, M; Nguyen, P; Sorooshian, S; Hsu, KL | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Given the continuous advancement in the retrieval of precipitation from satellites, it is important to develop methods that incorporate satellite-based precipitation data sets in the design and planning of infrastructure. This is because in many regions around the world, in situ rainfall observations are sparse and have insufficient record length. A handful of studies examined the use of satellite-based precipitation to develop intensity-duration-frequency (IDF) curves; however, they have mostly focused on small spatial domains and relied on combining satellite-based with ground-based precipitation data sets. In this study, we explore this issue by providing a methodological framework with the potential to be applied in ungauged regions. This framework is based on accounting for the characteristics of satellite-based precipitation products, namely, adjustment of bias and transformation of areal to point rainfall. The latter method is based on previous studies on the reverse transformation (point to areal) commonly used to obtain catchment-scale IDF curves. The paper proceeds by applying this framework to develop IDF curves over the contiguous United States (CONUS); the data set used is Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR). IDFs are then evaluated against National Oceanic and Atmospheric Administration (NOAA) Atlas 14 to provide a quantitative estimate of their accuracy. Results show that median errors are in the range of (17–22%), (6–12%), and (3–8%) for one-day, two-day and three-day IDFs, respectively, and return periods in the range (2–100) years. Furthermore, a considerable percentage of satellite-based IDFs lie within the confidence interval of NOAA Atlas 14.