Evaluation of Operational National Weather Service Gridded Flash Flood Guidance over the Arkansas Red River Basin INTRODUCTION

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1 Evaluation of Operational National Weather Service Gridded Flash Flood Guidance over the Arkansas Red River Basin Dugwon Seo 1, Tarendra Lakhankar, Juan Mejia, Brian Cosgrove, and Reza Khanbilvardi ABSTRACT: National Oceanic and Atmospheric Administration s National Weather Service (NOAA/NWS) flash flood warnings are issued by Weather Forecast Offices (WFOs) and are underpinned by information from the Flash Flood Guidance (FFG) system operated by the River Forecast Centers (RFCs). This study focuses on the quantitative evaluation and limitations of the FFG system using reported flash flood cases in 2010 and The flash flood reports were obtained from the NWS Storm Event database for the Arkansas-Red Basin RFC (ABRFC). The current FFG system at the ABRFC provides gridded flash flood guidance (GFFG) System using the NWS Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) to translate the upper zone soil moisture to estimates of Soil Conservation Service Curve Numbers. Comparisons of the GFFG and real-time Multi-sensor Precipitation Estimator derived Quantitative Precipitation Estimate (QPE) for the same duration and location were used to analyze the success of the system. Typically, the 6-hr duration was characterized by higher Probability of Detection (POD) values than the 3-hr duration, which highlights the difficulty of hydrologic process estimation for shorter time scales. The current system does not take into account physical characteristics such as land use, including irrigated agricultural farm and urban areas, hence, overly dry soil moisture estimates over these areas can lower the success rate of the GFFG product. (KEY TERMS: flash flood; Gridded Flash Flood Guidance; soil moisture; Arkansas-Red Basin River Forecast Center; precipitation.) INTRODUCTION According to the United States National Hazard Statistics database, flooding and flash flooding have caused the largest number of deaths of any weather-related phenomenon over the last 30 years (Flash Flood Guidance Improvement Team, 2003). In the U.S., losses over this time have averaged 7.8 billion dollars in damage and 94 fatalities per year according to the flood loss data in the Hydrologic Information Center (HIC) database ( 1 Respectively, Ph.D. Candidate (Seo), Department of Civil and Environmental Engineering, The City College of New York, 160 Convent Ave. New York, NY 10031; Research Assistant Professor (Lakhankar), NOAA- Cooperative Remote Sensing Science and Technology Center; Undergraduate Student (Mejia), New York City College of Technology; Research Hydrologist (Cosgrove), NOAA-National Weather Service, Office of Hydrologic Development; Professor, Department of Civil and Environmental Engineering, The City College of New York, Director, NOAA-Cooperative Remote Sensing Science and Technology Center ( /Seo: dseo01@ccny.cuny.edu). 0

2 The deadly nature of flash floods stems from the rapid rise in water levels and devastating flow velocities associated with their sudden occurrence. As an example of their deadly impact, a storm producing 15 inches of rainfall per hour caused a flash flood and 237 deaths in Rapid City, South Dakota in 1972 (Ashley and Ashley, 2008). The fatalities and damage to property could have been reduced if advance notice of potential flash flooding had been provided. In spite of the deadly impact of flash floods, they are relatively poorly observed and forecasted compared to other natural hazards (Gruntfest, 2009). The NOAA NWS defines a flash flood as a rapid and extreme flow of high water into a normally dry area, or a rapid water level rise in a stream or creek above a predetermined flood level, beginning within six hours of the causative event (e.g., intense rainfall, dam failure, ice jam). However, the actual time threshold may vary in different parts of the country. Ongoing flooding can intensify to flash flooding in cases where intense rainfall results in a rapid surge of rising flood waters (National Weather Service Manual, 2010). Rainfall intensity is not the only factor that contributes to flash flooding, with duration of the rainfall, topography, land cover, slope of the basin, and soil moisture also serving as contributing factors (Sweeney, 1992). For example, urban areas are susceptible to flash flooding owing to the prevalence of impervious cover, from which rapid surface runoff is generated. Mountainous areas are also at risk of flash flooding more than flat areas because of the steep topography that accelerates runoff (Villarini et al., 2009). Soil moisture is important since it plays a major role in determining the rainfall-runoff response of a catchment (Dunne et al., 1975; Pietroniro et al., 2004; Western et al., 2002), which greatly influences the triggering of a flash flood. In particular, given the same amount of precipitation, saturated soils have a higher chance of supporting a flash flood than do dry soils. The NWS is responsible for providing flash flood watch and warning services to the Nation through its River Forecast Centers (RFCs) and Weather Forecast Offices (WFOs). The RFCs produce Flash Food Guidance (FFG) values based on the local hydrologic state of the watersheds. These fields are then passed to the WFOs which monitor rainfall and issue advisories and warnings. Different methodologies are used to develop FFG at each RFC based on their region s physical characteristics and local needs, with some RFCs calculating gridded FFG, and other RFCs calculating a lumped (basin or sub-basin) FFG and then mapping it to a grid. Soil moisture is typically estimated from a rainfall-runoff model such as the Sacramento- Soil Moisture Accounting Model (SAC-SMA) (Koren et al., 2000). Unfortunately, owing to verification challenges, relatively few studies have evaluated the current NWS flash flood guidance system. The lumped flash flood guidance and newly developed GFFG were evaluated as being only marginally skillful methods using the NWS Storm Event database, discharge measurements from the U.S. Geological Survey, and data from the Severe Hazard Analysis and Verification Experiment (Ortega et al., 2009; Gourley et al,. 2012). Gourley et al, showed that GFFG is more capable than lumped FFG at reproducing 1

3 the spatial variability of reported flash flooding, but features a slightly lower level of skill, as judged by the critical success index than does lumped FFG. This study is focused on quantitative evaluation of the current GFFG system at the ABRFC and an investigation into the cause of errors. Therefore, it is necessary to learn the challenges facing the operational system and to seek solutions that would lead to improvement of the model performance. In addition, we analyzed ABRFC s GFFG system which provides an advantage of relatively finer resolution forecasting over its lumped FFG counterpart, and addresses the sensitivity of flash floods to small-scale physical characteristics for the Arkansas and Red River basins. Gridded Flash Flood Guidance (GFFG) System The GFFG system was developed to generate spatially distributed flash flood guidance values in support of flash flood monitoring within ABRFC basins (Schmidt et al., 2007). In GFFG, a distributed grid of threshold runoff values are utilized in Flash Flood Guidance computations as opposed to a generalized grid of threshold runoff mapped to county boundaries. The GFFG system uses the 4 km Hydrologic Rainfall Analysis Project projection (HRAP) (Reed and Maidment, 1999) gridded input including estimated precipitation, soil moisture accounting from the distributed hydrologic model, and hydrographic data, and produces HRAP gridded flash flood guidance. The basic components of the GFFG model are threshold runoff (Thresh-R), soil moisture accounting from the distributed hydrologic model, and a rainfall-runoff model. Thresh- R is defined as the amount of runoff needed to initiate flooding on small headwater streams as shown in Equation (1). This value can be calculated by dividing bankfull flow by the peak of the unit hydrograph for a given duration. The bankfull flow (Q f ) estimate was derived from the Natural Resources Conservation Service (NRCS) Curve Number (CN) model with precipitation of a 2-year return period and 3-hour rainfall event design. The peak flow (Q p ) was calculated by using the NRCS Triangular Unit Hydrograph Method which takes into account the physical characteristics of basins such as their slope and Curve Number. The duration of rainfall in the Unit Hydrograph method is the element that determines the 1-, 3-, or 6-hour duration of GFFG. The ABRFC computes ThreshR values for all basins on a grid. Q f ThreshR = Equation (1) Qp The soil moisture accounting component of the GFFG process is important for the adjustment of antecedent soil moisture states used by the CN method to estimate available rainfall storage (initial abstraction). Curve Numbers are obtained from a lookup table of State Soil Geographic (STATSGO) hydrologic soil group data and the National Land Cover Data (NLCD) land useland cover data. These base CN values are then adjusted for normal, drier than normal, and 2

4 wetter than normal soil moisture conditions. Traditionally, the antecedent soil moisture state was determined by Antecedent Precipitation Index (API) rainfall-runoff relationship for producing flood forecast. The initial API value is based on time of the year, storm duration and storm rainfall to compute storm runoff (NWSRFS User Manual Documentation 2004). In the GFFG system, the antecedent soil moisture states are estimated by the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) (Koren et al., 2004) from the NWS Office of Hydrologic Development (OHD). The upper zone soil moisture saturation ratio obtained from HL-RDHM replaces the traditional method. This upper zone saturation ratio is interpolated to the adjusted CN value (CN adj ) and is used to calculate the available initial abstraction: 1000 S = 10 CN adj Equation (2) The next step is the application of a rainfall-runoff model to estimate the runoff Q (inches) associated with rainfall P (inches), and initial abstraction, S which are calculated from Equation (2): 2 ( P 0.2S) Q = ( P + 0.8S) Equation (3) Solving Equation (3) for precipitation, P yields P = 0.2S + Q x + 2Q 2 x S + Q 2 x Equation (4) From this equation, P (inches) will be computed given previously estimated Q x and S. Q x is the threshold runoff for x hours and P is the value that is used for GFFG, which is the rainfall depth (inches) in x hours required for flash flooding to occur. The GFFG values (P = GFFG) from this RFC-based process support the flash flood warning activities of WFOs. Generally, the WFO issues flash flood warnings to the public when anticipated precipitation is greater than the values generated by GFFG. However, occasionally warning and situational awareness decisions for watch products are made when an excessive rainfall product is issued by the Hydrometeorological Prediction Center (HPC). 3

5 STUDY AREA AND DATASETS Study Area The Arkansas-Red river basin is located on the Central and Southern Plains which cover Oklahoma and part of Arkansas, Missouri, Kansas, Colorado, New Mexico and Texas as shown in Figure 1. The total drainage area of the basin is 538,718 square kilometers. Geomorphologic and climatic characteristics vary between the east and the west. The headwaters are located in the Rocky Mountains, to the west of the basin, and streams drain to the Mississippi River in Arkansas, east of the basin. Hence, the elevation of the basin decreases from 4200 meters in the west to 60 meters in the east. On the other hand, the average annual precipitation increases from the west (300 mm) to the east (1500 mm). The land use is mostly cropland, grassland pasture and range (Koren et al., 2004; Nickerson et al., 2011). Precipitation The Multi-sensor Precipitation Estimator (MPE) derived Quantitative Precipitation Estimate (QPE) product is used as an input of the HL-RDHM model. MPE-derived QPE consists of remotely sensed precipitation estimate algorithms which were developed by the NWS OHD for hydrological applications. These algorithms combine the radar rainfall estimates from the WSR- 88D, rain-gauge measurements and satellite precipitation estimates from the National Environmental Satellite, Data and Information Service (NESDIS) Hydro-Estimator (Nelson et al., 2010). The operational hydrologist also uses MPE to quality control the QPE data by removing bad gage reports, or anomalous propagation (AP) from the radar fields. MPE is executed operationally each hour to produce hourly QPE on a 4 km grid length. Flash flood events database The flash flood events database compiled for this study is based on locally collected reports, and there are uncertainties in the dataset related to population density, the time stamp of the flood, and the location. This flood event database draws on the NWS Storm Event Database which is collected by the local WFO and archives various types of storms. Data can be queried by state, county, or polygon for event time periods. The sources of flash flood reports are emergency management officials, local law enforcement officials, skywarn spotters, NWS damage surveys, newspaper clipping services, insurance companies and the general public. Each flash flood event in the dataset contains the latitude and longitude by bounding polygon along with the county name, estimated beginning and ending time, number of injuries and fatalities, any property and crop damage cost, and event narrative. Flood event data is available from 1993 to present. In this research, data from the years 2010 and 2011 were selected and searched for flood type events in the ABRFC domain. In spite of the uncertainties of the flash flood dataset, available QPE and GFFG data for the event dates were analyzed under the assumption that the 4

6 flash flood reports had a precise time and location information in this research. A total of 189 and 159 flash flood events were analyzed within the ABRFC domain for the year 2010 and 2011 respectively. Gridded Flash Flood Guidance Data GFFG values are calculated and their products are issued three or four times daily, usually at 12Z, 18Z, 00Z (Z refers to UTC) and also at 06Z in cases of high flood threats. One-, 3-, and 6- hour duration GFFG values are produced at each issuance time. In this study, only 3- and 6-hour GFFG were used to compare with the sum of the corresponding hourly precipitation data. The GFFG grids for 2010 and 2011 were obtained from the archive maintained by the ABRFC NWS. EVALUATION PROCEDURE AND ASSESSMENT The evaluation of flash flooding is highly sensitive to the accurate reporting of flash flood occurrence time after rain storms. Flash flood event data could have been collected by local reports an unknown time after the flash flood happened which increase the uncertainty of the event time. This uncertainty complicates the verification of the GFFG system. Therefore, quantitative assessment of the system was carried out carefully by taking into account the number of hours between the GFFG issuance time and the reported time of the flash flood, the duration of storm, and the amount of precipitation in each event. The GFFG system computes the minimum depth of rainfall necessary to cause a flash flood within a given time period. GFFG and QPE data pixels were selected based on the geographical location of reported flash-flood events. After determining the time and location, a comparison of archived 3- and 6-hour GFFG and accumulated 3- and 6-hour QPE was carried out. The evaluation of GFFG was carried out using QPE and flash flood reported data in the following methodological steps. 1) River and flash flood observations from NWS Storm Event Database in a GIS-compatible shape file format were acquired from the National Severe Storms Laboratory (NSSL). The list was combed for flash flood events within the ABRFC domain in 2010 and This dataset is referred to as flash flood event data hereafter in this article. 2) The reported flash flood locations in the NSSL data set were bounded by three to six corner points to form the polygon shapes shown in Figure 1. These points were averaged to compute a single middle point. However, if any side (line segment) of the polygon was longer than 4 km, each point was considered a single event, and therefore, was not averaged. For example, a single flash flood event in a large (line segment > 4 km) pentagon area is analyzed as five different events at each corner point location. 3) The central point of each flooded polygon area, computed above, was converted from latitude/longitude to the HRAP projection since the GFFG and QPE data are stored on an 5

7 HRAP grid. Then, the eight adjacent HRAP pixels surrounding this central pixel were coselected for the analysis. Nine pixels (including the middle point) with a total footprint of 12 km X 12 km were selected to maximize the coverage of the polygon area from the original NWS Storm Data and to minimize the impact of possible spatial error of the QPE data. 4) Many event locations featured greatly fluctuating rainfall intensities within the 12 km 2 analysis areas (nine pixels). In particular, the average difference between the minimum and maximum QPE values within these nine pixel flash flood event locations is 1.3 inches. Given the importance of this variation on the overall analyses of the flood events, the statistics (mean, minimum and maximum) of the QPE data were calculated for each nine pixel area. By contrast, GFFG values were relatively stable within each analysis area. The coefficient of variation, which is defined as the ratio of the standard deviation to the mean, is computed to measure the degree of data dispersal. The average coefficient of variation for the GFFG data is Due to this low variability, it was thus, not necessary to compute and uses the minimum and maximum GFFG values in the comparison with corresponding QPE values. Each of the aforementioned QPE statistics (inches) is individually compared to the mean GFFG value (inches) for the same geographical area. Figure 2 provides a graphical description of the GFFG and QPE datasets and how they were compared to determine which values are greater. The nearest GFFG issuance time, t g(i), prior to the reported flash flood time is selected, where i can be 00Z, (06Z), 12Z, or 18Z. The flash flood event time, t f(j), is defined as the number of hours after the GFFG product is issued that the event took place, where j is from 1 to 6. The appropriate 3- and 6-hour duration GFFGs were selected and QPE hourly data matching the time period of the GFFG data was summed. The summation of hourly QPE was carried out as presented in Equations (5) and (6) so as to match the GFFG product: j P QPE 3 = P h if 1 j 3 which is t f1 flash flood event time t f3 Equation (5) h= 1 j P QPE 6 = P h if 4 j 6 which is t f4 flash flood event time t f6 Equation (6) h= 1 where P h = hourly QPE. As such, the 3 hour summation P QPE3 and the 6 hour summation P QPE6 are compared with P GFFG3 and P GFFG6 at t g(i) respectively, where P GFFGx refers to rainfall in x hours required for flash flooding to begin. Figure 3a shows a generic precipitation hyetograph, the GFFG issuance time, t g(i), hourly observed QPE, P h, and the reported flash flood event time, t f(j). Figure 3b lists the classified QPE and GFFG at each corresponding reported flash flood event time. For instance, if flash flood was reported at t f3 (from Figure 3a), previous hourly QPEs were added to yield total P QPE3. In that case, GFFG product issued at t g(i) (P GFFG3 ) is compared with P QPE3 (Figure 2). Figure 3c depicts an actual hyetograph from an event in Haskel, OK on September In this 6

8 case, the flash flood event time, t f2 = 8 AM, and hourly precipitation data at 7 and 8 AM are summed up to calculate P QPE3 and compared with P GFFG3 at the GFFG issuance time of t g(i) = 6 UTC. The assessment of the GFFG product was carried out by comparing the GFFG and QPE values to define the hit or miss case (Table 1). If a forecasted GFFG value (inches) was less than or equal to the corresponding QPE total (inches) when the flash flood occurred (P QPEx / P GFFGx 1), then the guidance was defined as a hit. On the other hand, if the QPE sum was less than the GFFG value when a flash flood occurred (ratio less than 1), it was considered a miss. The same comparison in non-flood case defines false alarms and hits. However, we only analyzed flood occurrence cases in this study since verification reports of flood occurrence are available from the NWS Storm Database, while evidence of flood non-occurrence is less certain. The probability of detection (POD), which is defined as the fraction of observed flash floods that were correctly forecasted is used to evaluate the GFFG system. hits POD = Equation (7) hits + misses Where, a POD number close to 1 indicates the system is working with the QPE data to accurately indicate flash floods, whereas a POD number close to 0 indicates the system is functioning inaccurately. RESULTS AND DISCUSSION The statistics (mean, maximum, and minimum) of QPE values within each nine pixel area of all the selected flash flood events are compared to the mean values of the GFFG in Table 2. This table shows the number of hits and the POD for 3- and 6-hour GFFG durations in 2010 and The 6-hour GFFG product features a relatively higher probability of detection than the 3- hour GFFG product when mean and maximum QPE values are used as input into the analysis process. This difference in performance may be due to increased accuracy in the 6-hr versus 3-hr QPE used as forcing input. While highly accurate (Tesfagiorgis, 2011), the radar/gauge QPE data is still subject to spatial errors in the placement of precipitation, and to errors in timing and intensity. The longer precipitation accumulation period of the 6-hr GFFG window reduces the impact of these errors and supports a more realistic calculation of GFFG values. Likewise, accurate estimation of antecedent soil moisture differs between durations and impacts to the GFFG. Satellite based soil moisture incorporation to the GFFG algorithm can be expected to enhance the system in shorter period of forecast. The analyses using minimum QPE precipitation values show a very low POD. During the storms that caused flash flooding, the maximum variation of hourly precipitation within nine pixels (12 km x 12 km) was 5.27 inches. From this it is theorized that precipitation is variable within the 12 km x 12 km box and small spatial errors could impact the accuracy of flash flood forecast. Values of P QPEx / P GFFGx < 1, which represent flash flood miss cases are possibly due to 7

9 either overestimation of GFFG or overly low precipitation (QPE) (P QPEx < P GFFGx ). In this study, our focus is on the environmental conditions surrounding instances of GFFG overestimation. Soil moisture estimation is directly related to the GFFG product as can be seen in Equation 4. We thus investigate several missed flash flood events to determine the role of overly dry soil estimates as a cause of the missed cases. The role of urbanization is also examined. Analysis of Case Studies Several missed flash flood forecast events that resulted in a low P QPEx /P GFFGx ratio were selected to inspect the physical characteristics that the GFFG model does not take into account. Their precipitation history was reviewed, and the land use characteristics of event areas were explored. In this section, two representative case studies are analyzed. The flash flood in Potter County, Texas on September 16 th, 2010 occurred when 0.6 inches of precipitation fell over 2 hours, during a period in which 3- and 6-hour GFFG values were 3.3 and 4.2 inches. Similarly, the Sedgwick County, Kansas flash flood of June 9 th, 2011 arose from 0.6 inches of precipitation over 3 hours, when 3.4 inches of 3-hour and 4.1 inches of 6-hour GFFG values had been computed. For both cases, flash flooding occurred with only small amounts of precipitation while GFFG values were overestimated. The Google satellite images displayed in Figure 5 show that areas surrounding these flash flood locations are irrigated. According to the 2007 State Agriculture Overview from the National Agriculture Statistics Service (NASS), about 2,160 million square meters of land is irrigated farmland, and 3.4 million cubic meters of irrigated water are applied to the soil each day to assist in the growth of crops in Oklahoma (Vilsack and Clark, 2007). Additionally, the U.S. Geological Survey reports that 37 percent of total freshwater withdrawals from surface water or groundwater go towards irrigation uses in the United States (Hutson et al., 2004). Artificially managed water intake from surface water or groundwater will impact the natural water balance and estimates of hydrologic elements. For instance, hydrologic models not taking this into account will underestimate evaporation, evapotranspiration and soil moisture from irrigated lands (Oosterbaan, 1988). Overly dry soil moisture values within the GFFG system can result and lead to inaccurate assessments of the potential for flash flooding. In addition to irrigated areas, the images of these locations also show urban areas. Flash flooding in urban areas is usually caused by poor drainage systems rather than by saturated soils or overflows. Additionally, urban areas with large amounts of paved surfaces have low infiltration limits and thus are more susceptible to flash flooding (Carter, 1961; Bailey, 1989). The database of flash flood locations derived from flash flood event data shows urban and street flooding were reported in many other regions. As another example, many flash floods occurred in Mayes County, Oklahoma but the ratios of P QPEx / P GFFGx for all of the events were less than 1, which are missed cases as shown in Table 3. In this region, 1.5 inches of rain fell between 19:00 and 21:00 UTC on July 6 th, 2010 but 2.34 inches of P GFFG3 was issued at 18:00 UTC at HRAP coordinate (618, 360). Similarly, 0.88 inches 8

10 of QPE were estimated for two hours on July 8 th, 2010 while 2.37 inches of P GFFG3 was issued at 18:00 UTC for the neighboring HRAP cell (617, 360). Stream flow direction data supplied by NWS OHD in the form of a routing connectivity file was overlaid for the location (HRAP x, HRAP y = 618, 360) with QPE data at 19:00 and 20:00 UTC for the flash flood event on July 6 th as shown in Figure 4. The connectivity network is based on the slope and the elevation of each HRAP cell (Koren, 2004). It was discovered that water flows into this cell (618, 360) from surrounding upstream cells and then exits, passing downstream to cell (617, 360). As such, accumulated runoff from upstream precipitation flowed into this particular pixel area and caused the flash flood. Since the GFFG system does not route water from cell to cell and only focuses on soil moisture deficits, it was unable to account for this stream-based accumulation of water and the resulting flash flood. Analysis of GFFG and Precipitation It is expected that the value of GFFG would change from the previously issued GFFG if any precipitation occurred in between two issuance times (usually 6 hours apart). Figure 6 shows the plot of the difference of forecasted GFFG values (inches) and the precipitation (inches) between two sequential issuance times. The horizontal axis is not a representation of the time series but represents independent flash flood events. This analysis confirms that the precipitation after one GFFG issuance is being taken into account to the next issuance. Most of the traces depicting GFFG differences values share a shape similar to that of the precipitation amount between the two issuance times, which confirms the strong connection between the two factors. However, there were a few cases that featured very little or no change in forecasted GFFG value between the two issuance times, in spite of the occurrence of precipitation. Little or no change in GFFG values verifies that the soil wetness after the precipitation was not considered at the next GFFG issuance calculation which leads to the errors. The next analysis was aimed at investigating the relationship between hits and the total amount of precipitation. The area-average QPE from each nine pixel study area was summed for the period preceding the flood (up to a maximum of 18 hours) to calculate the total amount of rainfall event by event. The duration of the storm was recorded along with rainfall amount. The scatter plot in Figure 7 shows the relationship of total precipitation to the status of the forecast which is represented by the fraction of QPE to GFFG from reported flash flood event data. The fractions that are greater than one and less than one correspond to hits and misses respectively. The circles in the missed case section of the plot are concentrated in the lower precipitation region, with most of these events featuring short storm durations of 1 hour (orange color). On the other hand, large values of precipitation (more than 3 inches) and longer periods of duration (more than 5 hours) tend to result in hits. 9

11 SUMMARY AND CONCLUSIONS In this study, the accuracy of the operational GFFG product and the causes behind misforecasts were evaluated over the ABRFC domain in an attempt to optimize the existing NWS GFFG system. Nine pixels of gridded data (GFFG and QPE) were selected based on their proximity to the flash flood report, and the mean value of GFFG was compared against the mean, maximum and minimum QPE value for the same duration. Each flash flood event was analyzed in a case-by-case fashion, since the time of occurrence, location, and duration of rainfall varied. The probability of detection was computed as the number of hits over the sum of hits and misses (total cases). A case was defined as a hit when the forecasted amount of precipitation (P GFFGx ) needed to cause a flash flood during 3 hours (or 6 hours) was less than or equal to observed precipitation (P QPEx ) for the same duration when the flash flood occurred. The PODs using the maximum QPE values obtained from pixels surrounding the event location for 3-hr and 6-hr GFFG durations are 0.23 and 0.33 respectively in 2010, and 0.34 and 0.54 in Using mean QPE values from the same pixels yielded PODs of 0.08 for 3-hr duration and 0.14 for 6-hr duration in 2010, and 0.21 and 0.22 in Typically, 6-hr duration GFFG values were characterized by higher PODs than their 3-hr duration counterparts, which highlight the difficulty of hydrologic process estimation over shorter time scales. In addition to the overall analyses above, two representative flash flood cases that feature a missed forecast (P QPEx / P GFFGx < 1) were analyzed against possible causal factors. First, it is difficult to produce accurate GFFG values over large irrigated agricultural farm areas, which feature artificial irrigation given that GFFG does not represent such irrigated soil moisture. We verified the land surface characteristics of two of these types of flash flood locations using Google satellite images. These images focused on the locations of the Potter County, TX flood of September 16 th, 2010 and the Sedgwick County, KS flood of June 9 th, Overly low soil moisture values due to a lack of irrigation in the GFFG system led to overly large GFFG values and a miss of flooding conditions. The satellite images of these regions additionally indicate the presence of urban areas. As the GFFG system was designed for small, natural, headwater streams, these areas are ill-suited for the use and testing of GFFG. An analysis of two sequential GFFG issuance times was carried out by focusing on the timeto-change of GFFG values alongside the depth of precipitation which fell between the two forecasts. The results indicate that intervening precipitation is usually directly reflected in the next GFFG issuance. The correlation values among the total precipitation amount from the past 18 hours to the flash flood report time, duration of the storm and the accuracy of the forecast were not significant. It is noteworthy that a flash flood with little precipitation and short storm duration is relatively more difficult to predict utilizing GFFG. In conclusion, missed forecasts of flash floods can be due to several local factors which are linked to the estimation of antecedent soil moisture and streamflow routing. Inclusion of the impacts of stream flow routing into the GFFG process and/or use of another NWS technique such as DHM-TF (Reed et al., 2007) which 10

12 includes stream flow routing may address some of the noted issues. In addition, timely estimation of accurate soil moisture through satellite remote sensing techniques would likely help to improve the current flash flood guidance system through correction of the underlying HL-RDHM s soil moisture states. ACKNOWLEDGMENTS This study was supported and monitored by National Oceanic and Atmospheric Administration (NOAA) under Grant NA06OAR The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision. The authors thank to Anthony Anderson from National Weather Service, Arkansas-Red Basin River Forecast Center and Mike Smith from NOAA-National Weather Service, Office of Hydrologic Development for useful comments and suggestions in preparation of this manuscript. The authors also acknowledge Dr. Jonathan J. Gourley from The National Severe Storms Laboratory in The National Weather Center for providing the flash flood observation database and helpful advice. LITERATURE CITED Ashley, S.T. and W.S. Ashley, Flood Fatalities in the United States. Journal of Applied Meteorology and Climatology 47: Bailey, J.F.. T.W.O.. W.K.L. and R.T.J., Estimation of Flood-frequency Characteristics and the Effects of Urbanization for Streams in the Philadelphia, Pennsylvania Area: U.S. Geological Survey Water-Resources Investigations Report. Carter, R.W., Magnitude and Frequency of Floods in Suburban Areas: U.S. Geological Survey Professional Paper 424-B. :9 11. Dunne, T., T.R. Moore, and C.H. Taylor, Recognition and Prediction of Runoff-producing Zones in Humid Regions. Hydrological Sciences Bulletin 20: Flash Flood Guidance Improvement Team, River Forecast Center (RFC) Development Management Team: Final Report. Gourley, J.J., J.M. Erlingis, Y. Hong, and E.B. Wells, Evaluation of Tools Used for Monitoring and Forecasting Flash Floods in the United States. Weather and Forecasting 27: Gruntfest, E., Editorial. Journal of Flood Risk Management 2: Hutson, S.S., N.L. Barber, J.F. Jenny, K.S. Linsey, D.S. Lumia, and M.A. Maupin, Estimated Use of Water in the United States in Koren, V., S. Reed, M. Smith, Z. Zhang, and D.J. Seo, Hydrology Laboratory Research Modeling System (HL-RMS) of the US National Weather Service. Journal of Hydrology 291: Koren, V.I., M. Smith, D. Wang, and Z. Zhang, Use of Soil Property Data in the Derivation of Conceptual Rainfall-runoff Model Parameters. 11

13 National Weather Service Manual , 2010., accessed January Nelson, B.R., D.J. Seo, and D. Kim, Multisensor Precipitation Reanalysis. Journal of Hydrometeorology 11: Nickerson, C., R. Ebel, A. Borchers, and F. Carriazo, Major Uses of Land in the United States, NWSRFS User Manual Documentation: Continuous Incremental API Operation, accessed March Oosterbaan, R.J., Effectiveness and Social/Environmental Impacts of Irrigation Projects: a Criticial Review. Wageninge, The Netherlands. Ortega, K.L., T.M. Smith, K.L. Manross, A.G. Kolodziej, K.A. Scharfenberg, A. Witt, and J.J. Gourley, The Severe Hazards Analysis and Verification Experiment. Bulletin of the American Meteorological Society 90: Pietroniro, A., E.D. Soulis, and N. Kouwen, Scaling Soil Moisture for Hydrological Models. Scale in Hydrology and Water Management. International Association of Hydrological Sciences, pp Reed, S.M. and D.R. Maidment, Coordinate Transformations for Using NEXRAD Data in GIS-based Hydrologic Modeling. Journal of Hydrologic Engineering 4: Reed, S., J. Schaake, and Z. Zhang, A Distributed Hydrologic Model and Threshold Frequency-based Method for Flash Flood Forecasting at Ungauged Locations. : Schmidt, J., A. J. Anderson, and J. H. Paul, Spatially-variable, Physically-derived Flash Flood Guidance. Conference on Hydrology, San Antonio, Amer. Meteor. Soc., 6B.2. Sweeney, T.L., Modernized Areal Flash Flood Guidance NOAA Technical Memorandum NWS. Tesfagiorgis, K. Mahani, S. E., Krakauer, N.Y., K.R., Bias Correction of Satellite Rainfall Estimates Using a Radar-qauge Product - a Case Study in Oklahoma (USA). Hydrology and Earth System Sciences 15: Villarini, G., F. Serinaldi, J.A. Smith, and W.F. Krajewski, On the Stationarity of Annual Flood Peaks in the Continental United States During the 20th Century. Water Resources Research 45:1 17. Vilsack, T. and C.Z.F.. Clark, Census of Agriculture, Oklahoma State and County Data. dex.asp, accessed December Western, A.W., R.B. Grayson, and G. Blöschl, Scaling of Soil Moisture: A Hydrologic Perspective. Annual Review of Earth and Planetary Sciences 30:

14 Figure 1. Geographical Map of Arkansas Red River Basin (shade) with Observed Flash Flood Locations Bounded by Polygon Obtained from NWS Storm Event Database in 2010 and Flash flood events within the basin is analyzed in this study. Figure 2. Description of Comparison Pixels. The values of GFFG and QPE (mean, min or max) within nine pixels where flash flood was reported are compared to determine if the case is hit or miss. 13

15 (a) (b) Flash flood event time Sum of hourly QPE (inches) GFFG issuance time GFFG Duration (inches) t f1 P QPE3 = P h1 t g(i) P GFFG3 t f2 P QPE3 = P h1 +P h2 t g(i) P GFFG3 t f3 P QPE3 = P h1 +P h2 +P h3 t g(i) P GFFG3 t f4 P QPE6 = P h1 +P h2 +P h3 +P h4 t g(i) P GFFG6 t f5 P QPE6 = P h1 +P h2 +P h3 +P h4 +P h5 t g(i) P GFFG6 t f6 P QPE6 = P h1 +P h2 +P h3 +P h4 +P h5 +P h6 t g(i) P GFFG6 (c) Figure 3. (a) General Description of GFFG Issuance and the Flash Flood Report Time Line (b) Strategy of Selecting GFFG (3- or 6-hr) Based on Observed Flood Event after the Precipitation: Six scenarios take place at one GFFG issuance time (t g(i ) ). If flood occurs within next three hours after GFFG is issued, then 3-hr GFFG used, else 6-hr GFFG used in this analysis (c) Example of Hyetograph with Time Description at GFFG Issuanes and the Flash Flood Event in Haskel, OK on September 9 th

16 Figure 4. Stream Flow Direction in Relation to Flooded Pixel Location (X and Y axes are HRAP coordinates) is Overlaid Precipitation (inches) Images at 19:00 and 20:00 UTC. 15

17 (a) (b) Figure 5. Satellite Image of the Flash Flooded Locations. (a) Potter County, TX, (b) Sedgwick County, KS in near of the agricultural farm and urban areas acquired from Google Earth. 16

18 Figure 6. Comparison of Precipitation (QPE) Amount of That Fell in between the Two Sequential GFFG Issuance Times and the Differences in Two Sequential GFFG (3 -hour and 6 - hour) Values. The horizontal axis is not a representation of the time series but represents independent flash flood events. This analysis is to confirm the GFFG system takes the precipitation into account between issuance times. 17

19 Figure 7. Distribution of Ratio, P QPEx /P GFFGx, which Defines Hit or Miss Cases against Precipitation and Storm Duration for all Flood Events during 2010 and

20 Table 1. Strategy for Comparative Analysis between Precipitation (QPE) and GFFG Values. GFFG (inch) QPE (inch) Comparison Analysis Event Define P GFFGx P QPEx P GFFGx P QPEx P QPEx / P GFFGx 1 Flood Hit P GFFGx P QPEx P GFFGx > P QPEx P QPEx / P GFFGx < 1 Flood Miss P GFFGx P QPEx P GFFGx < P QPEx P QPEx / P GFFGx > 1 No Flood False Alarm P GFFGx P QPEx P GFFGx P QPEx P QPEx / P GFFGx 1 No Flood Hit Table 2. Accuracy Analysis of 3- and 6-hr GFFG and QPE within Nine Pixels Using Number of Hits and Probability of Detection. Mean GFFG values were compared with mean, maximum, and minimum QPE for 2010 and Year QPE Statistics 3-hr GFFG 6-hr GFFG MEAN Number of hits 7 14 POD MAX Number of hits POD MIN Number of hits 5 5 POD MEAN Number of hits POD MAX Number of hits POD MIN Number of hits 9 2 POD Table 3. Flash Flood Events List with HRAP Coordinates, QPE, GFFG Values and the Ratio in Mayes County, OK Date HRAPx HRAPy P QPE P GFFGx P QPE /P GFFGx Mar 25 th (x=3) 0.26 Jul 6 th (x=3) 0.67 Jul 8 th (x=3) 0.37 Aug 12 th (x=6)