Humberto Vergara

Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma

This study evaluates rainfall estimatesfrom the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km 2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research DistributedHydrologicModel (HL-RDHM) at 4-km/l-hand 0.258/3-h resolutions. The unadjustedradarproduct wastheoverallworstproduct,whilethestageIVradarproductwithhourlyraingaugeadjustmenthadthebest hydrologic skill with a Micronet relative efficiency score of 20.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANNCCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the modelwascalibratedat TRMM’s0.258/3-hresolution,thushighlightingtheimportanceofconsideringrainfall product resolution during model calibration.

Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administrations Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash-Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world.

Effects of Resolution of Satellite-Based Rainfall Estimates on Hydrologic Modeling Skill at Different Scales

Uncertainty due to resolution of current satellite-based rainfall products is believed to be an important source of error in applications of hydrologic modeling and forecasting systems. A method to account for the input’s resolution and to accurately evaluate the hydrologic utility of satellite rainfall estimates is devised and analyzed herein. A radar-based Multisensor Precipitation Estimator (MPE) rainfall product (4km, 1h) was utilized to assess the impact of resolution of precipitation products on the estimation of rainfall and subsequent simulation of streamflow on a cascade of basins ranging from approximately 500 to 5000km 2 . MPE data were resampled to match the Tropical Rainfall Measuring Mission’s (TRMM) 3B42RT satellite rainfall product resolution (25km, 3h) and compared with its native resolution data to estimate errors in rainfall fields. It was found that resolution degradation considerably modifies the spatial structure of rainfall fields. Additionally, a sensitivity analysis was designed to effectively isolate the error on hydrologic simulations due to rainfall resolution using a distributed hydrologic model. These analyses revealed that resolution degradation introduces a significant amount of error in rainfall fields, which propagated to the streamflow simulations as magnified bias and dampened aggregated error (RMSEs). Furthermore, the scale dependency of errors due to resolution degradation was found to intensify with increasing streamflow magnitudes. The hydrologic model was calibrated with satellite- and original-resolution MPE using a multiscale approach. The resulting simulations had virtually the same skill, suggesting that the effects of rainfall resolution can be accounted for during calibration of hydrologic models, which was further demonstrated with 3B42RT.

Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations

This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks‐Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluateensemblestreamflowsimulationsobtainedbyforcingthemodelwithsatelliterainfallcorrectedusing stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500‐5000km 2 ). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

The FLASH project: improving the tools for flash flood monitoring and prediction across the United States

This study introduces the Flooded Locations and Simulated Hydrographs (FLASH) project. FLASH is the first system to generate a suite of hydrometeorological products at flash flood scale in real-time across the conterminous United States, including rainfall average recurrence intervals, ratios of rainfall to flash flood guidance, and distributed hydrologic model–based discharge forecasts. The key aspects of the system are 1) precipitation forcing from the National Severe Storms Laboratory (NSSL)’s Multi-Radar Multi-Sensor (MRMS) system, 2) a computationally efficient distributed hydrologic modeling framework with sufficient representation of physical processes for flood prediction, 3) capability to provide forecasts at all grid points covered by radars without the requirement of model calibration, and 4) an open-access development platform, product display, and verification system for testing new ideas in a real-time demonstration environment and for fostering collaborations. This study assesses the FLASH system’s ability to accurately simulate unit peak discharges over a 7-yr period in 1,643 unregulated gauged basins. The evaluation indicates that FLASH’s unit peak discharges had a linear and rank correlation of 0.64 and 0.79, respectively, and that the timing of the peak discharges has errors less than 2 h. The critical success index with FLASH was 0.38 for flood events that exceeded action stage. FLASH performance is demonstrated and evaluated for case studies, including the 2013 deadly flash flood case in Oklahoma City, Oklahoma, and the 2015 event in Houston, Texas—both of which occurred on Memorial Day weekends.

Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

AbstractAn extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using a minimum of 6 years (within the period of 2000–13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cevennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.25° spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimatio...

The CI-Flow Project: A System for Total Water Level Prediction from the Summit to the Sea

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface. Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and techn...

Mapping Flash Flood Severity in the United States

AbstractFlash floods, a subset of floods, are a particularly damaging natural hazard worldwide because of their multidisciplinary nature, difficulty in forecasting, and fast onset that limits emergency responses. In this study, a new variable called “flashiness” is introduced as a measure of flood severity. This work utilizes a representative and long archive of flooding events spanning 78 years to map flash flood severity, as quantified by the flashiness variable. Flood severity is then modeled as a function of a large number of geomorphological and climatological variables, which is then used to extend and regionalize the flashiness variable from gauged basins to a high-resolution grid covering the conterminous United States. Six flash flood “hotspots” are identified and additional analysis is presented on the seasonality of flash flooding. The findings from this study are then compared to other related datasets in the United States, including National Weather Service storm reports and a historical floo...

Hydrological Modeling and Capacity Building in the Republic of Namibia

AbstractThe Republic of Namibia, located along the arid and semiarid coast of southwest Africa, is highly dependent on reliable forecasts of surface and groundwater storage and fluxes. Since 2009, the University of Oklahoma (OU) and National Aeronautics and Space Administration (NASA) have engaged in a series of exercises with the Namibian Ministry of Agriculture, Water, and Forestry to build the capacity to improve the water information available to local decision-makers. These activities have included the calibration and implementation of NASA and OU’s jointly developed Coupled Routing and Excess Storage (CREST) hydrological model as well as the Ensemble Framework for Flash Flood Forecasting (EF5). Hydrological model output is used to produce forecasts of river stage height, discharge, and soil moisture.To enable broad access to this suite of environmental decision support information, a website, the Namibia Flood Dashboard, hosted on the infrastructure of the Open Science Data Cloud, has been developed...

Hydrologic Evaluation of NWP-Adjusted CMORPH Estimates of Hurricane-Induced Precipitation in the Southern Appalachians

AbstractSatellite-retrieved precipitation has the potential to support flood modeling in mountainous areas. However, to reach this potential satellite estimates need to be corrected for the severe underestimation exhibited in orography-induced heavy precipitation events (HPEs). This paper assesses an existing satellite precipitation error correction technique driven by high-resolution numerical weather prediction (NWP) simulations of HPEs in complex terrain. The study is based on NOAA Climate Prediction Center morphing technique (CMORPH) high-resolution precipitation estimates of six such events induced by hurricane landfalls in the southern Appalachian mountainous region. A distributed hydrological model (Coupled Routing and Excess Storage model) is applied to evaluate the impact of the proposed satellite precipitation error correction on flood simulations for 20 basins of various sizes in this mountainous region. The results demonstrate significant improvements due to the NWP-based adjustment technique ...