Can Remote Sensing Technologies Capture the Extreme Precipitation Event and Its Cascading Hydrological Response? A Case Study of Hurricane Harvey Using EF5 Modeling Framework

Abstract

A new generation of precipitation measurement products has emerged, and their performances have gained much attention from the scientific community, such as the Multi-Radar Multi-Sensor system (MRMS) from the National Severe Storm Laboratory (NSSL) and the Global Precipitation Measurement Mission (GPM) from the National Aeronautics and Space Administration (NASA). This study statistically evaluated the MRMS and GPM products and investigated their cascading hydrological response in August of 2017, when Hurricane Harvey brought historical and record-breaking precipitation to the Gulf Coast (>1500 mm), causing 107 fatalities along with about USD 125 billion worth of damage. Rain-gauge observations from Harris County Flood Control District (HCFCD) and stream-gauge measurements by the United States Geological Survey (USGS) were used as ground truths to evaluate MRMS, GPM and National Centers for Environmental Prediction (NCEP) gauge-only data by using statistical metrics and hydrological simulations using the Ensemble Framework for Flash Flooding Forecast (EF5) model. The results indicate that remote sensing technologies can accurately detect and estimate the unprecedented precipitation event with their near-real-time products, and all precipitation products produced good hydrological simulations, where the Nash-Sutcliff model efficiency coefficients (NSCE) were close to 0.9 for both the MRMS and GPM products. With the timeliness and seamless coverage of MRMS and GPM, the study also demonstrated the capability and efficiency of the EF5 framework for flash flood modeling over the United States and potentially additional international domains.Remote Sens. 2020, 12, 445 2 of 20 infrastructure, which leads to the intensification of the meteorological extremes [6,7] and increased surface runoff peaks [5]. Globally, the Gulf Coast of North America is one of many places that is heavily affected by tropical storms and their cascading floods in an urbanized area [8]. On August 25th, 2017, Hurricane Harvey made its first landfall at the northern end of San Jose Island, TX. Since then, Harvey stalled over the greater Houston area and produced over 1500 mm of rain in 4 days, which set the US record of total precipitation since the 1880s, when the reliable rainfall records started [9]. During this event, southeast Texas received 20 to 30 trillion tons of water with a return period exceeding 9000 years at some locations [10], interconnected the Colorado River and San Bernard River overland, and caused unprecedented flooding. Hurricane Harvey was estimated to cause about USD 125 billion worth of damage and 107 fatalities, and 127 flash flood warnings were issued during the event [11]. As much as technology has advanced, society is still searching for tools to improve prediction and mitigate the damage from floods.Over the past few decades, the scientific community has made great improvements in the capacity of flood modeling by combining climate models, weather models, hydrological models, river models, and hydrodynamic models [12]....

Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation

“…For example, NCEP differs from MRMS more in the Houston metropolitan areas, as marked in black circles in Figure 3b. This result is on a par with the investigation of Chen et al (2020) [53] in the Harvey event that MRMS has the highest CC (0.91) value and correspondingly lowest RMSE (5.75 mm/h) among NCEP gauge-only products and IMERG V06A in Hurricane Harvey. IMERG, on the other hand, exhibits better agreements with MRMS in the rainfall core regions than NCEP.…”

Can the re-infiltration process be ignored for flood inundation mapping and prediction during extreme storms?

“…Combined with our previous study that underlies the importance of infiltration and initial soil moisture for flood inundation modeling, we highly recommend taking into consideration the initial soil moisture state, as it has not been well-recognized in the hydraulic model community. This can be achieved via three ways: 1) warm up the model for a relatively long period prior to the simulation period (Chen et al, 2020); 2) parameterize the initial soil moisture and calibrate it, similar to the way we treat initial in-channel water depth (Xue et al, 2013); 3) approximate it using observations or other model simulations, like what has been done in the real case study in Section 3.2 (Flamig, Vergara, & Gourley, 2020). The first approach is ideal because it eliminates uncertainties in parameterization (such as equifinality) or error propagation from observations/simulations to models; it is, however, the most computationally expensive approach for hydraulic modeling compared to the other two.…”

“…[ INSERT FIGURE 1 HERE] During the 500-year Hurricane Harvey event, this region is largely inundated due to record-breaking 1600 mm rainfall over a one-week storm lifespan (Chen et al, 2020;Li et al, 2020). According to the Harris Country flood report, both Greens Bayou and Halls Bayou experienced a 500-year water level downstream and 50-year to 100-year in between upstream.…”

Advancing Satellite Precipitation Retrievals With Data Driven Approaches: Is Black Box Model Explainable?

“…Machine Learning (ML) or Deep Learning (DL) due to its powerful capacity to solve highly non-linear problems, is becoming ubiquitous across research fields. Some ML/DL based approaches in satellite precipitation retrievals, like Precipitation Estimates from Remotely Sensed Information using Artificial Neural Network (PERSIANN) family (Hsu et al 1999;Hong et al;2004;Behrangi et al 2009;Ashouri et al, 2015;Sadeghi et al, 2019) have demonstrated their potentials. Regarding AMSU platform, Surussavadee and Staelin (2009) first collected multiple channels along with information from Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) cloud resolving model to fit into a…”

“…It integrates 180 operational radars, including 146 S-band and 30 C-band radars, creating a seamless 3D radar mosaic across the CONUS and Southern Canada. The rigorous quality control steps have made this product been one of the most accurate Quantitative Precipitation Estimations (QPEs) in the CONUS (Chen et al, 2020;Li et al, 2020) and also made it intensively applied to flash flood monitoring (Gourley et al, 2017). The precipitation flags in the MRMS provides eight distinct features in total: missing, no precipitation, cool stratiform, warm stratiform, snow, overshooting, convective, hail, and warm rain.…”