Z. P. Zhang

Toward improved streamflow forecasts: value of semidistributed modeling

The focus of this study is to assess the performance improvements of semidistributed applications of the U.S. National Weather Service Sacramento Soil Moisture Accounting model on a watershed using radar-based remotely sensed precipitation data. Specifically, performance comparisons are made within an automated multicriteria calibration framework to evaluate the benefit of “spatial distribution” of the model input (precipitation), structural components (soil moisture and streamflow routing computations), and surface characteristics (parameters). A comparison of these results is made with those obtained through manual calibration. Results indicate that for the study watershed, there are performance improvements associated with semidistributed model applications when the watershed is partitioned into three subwatersheds; however, no additional benefit is gained from increasing the number of subwatersheds from three to eight. Improvements in model performance are demonstrably related to the spatial distribution of the model input and streamflow routing. Surprisingly, there is no improvement associated with the distribution of the surface characteristics (model parameters).

An enhanced and automated approach for deriving a priori SAC-SMA parameters from the soil survey geographic database

This paper presents an automated approach for processing the Soil Survey Geographic (SSURGO) Database and the National Land Cover Database (NLCD), and deriving gridded a priori parameters for the National Weather Service (NWS) Sacramento Soil Moisture Accounting (SAC-SMA) model from these data sets. Our approach considerably extends methods previously used in the NWS and offers automated and geographically invariant ways of extracting soil information, interpreting soil texture, and spatially aggregating SAC-SMA parameters. The methodology is composed of four components. The first and second components are SSURGO and NLCD preprocessors. The third component is a parameter generator producing SAC-SMA parameters for each soil survey area on an approximately 30-m grid mesh. The last component is a postprocessor creating parameters for user-specified areas of interest on the Hydrologic Rainfall Analysis Project (HRAP) grid. Implemented in open-source software, this approach was employed by creating a set of SAC-SMA parameter and related soil property grids spanning 25 states, wherein it was shown to greatly reduce the derivation time and meanwhile yield results comparable to those based on the State Soil Geographic Database (STATSGO). The broad applicability of the methodologies and associated intermediate products to hydrologic modeling is discussed.

Flash Flood Forecasting for Ungauged Locations with NEXRAD Precipitation Data, Threshold Frequencies, and a Distributed Hydrologic Model

A flash flood forecasting system has been developed which combines distributed modeling and statistical analyses to produce gridded forecasts of return periods. A distributed hydrologic model (DHM) coupled to a threshold frequency (TF) post-processor, DHM-TF, is currently being tested over a Maryland-centered domain, and has verified well against National Weather Service (NWS) flash flood warning areas and verification points. The prototype system is currently running in real-time at the NWS Office of Hydrologic Development (OHD), and efforts are underway to test DHM-TF at several NWS field offices. INTRODUCTION Flash floods are a devastating natural disaster, causing millions of dollars of damage each year and putting many lives in danger. With the exception of excessive heat, flooding leads to more weather-related fatalities than any other cause. In 2007, the last year for which statistics were available, flash flooding caused 70 fatalities, 51 injuries, and 1.2 billion dollars in damage (NWS, 2009a). Half of flood-related deaths were caused when victims were caught in vehicles and swept away. Given these statistics, accurate predictions of flash floods are essential for the protection of life and property. Unfortunately, the nature of these events makes them quite difficult to monitor and predict. Flash floods feature a fast onset—less than 6 hours from the causative event (NWS, 2002)—are local in scope, and depend greatly on fine scale weather and land surface conditions. Monitoring efforts are valuable but do not provide enough lead time for affected parties to take the action needed to prevent loss of life and property. Forecasts, which have the potential to increase warning lead time, can be produced by standard lumped hydrological modeling. However, these models are handicapped by the fact that they only provide information at basin outlets and cannot accurately represent the highly variable land surface and meteorological conditions that impact flash flooding. A promising alternative to lumped modeling is distributed modeling. Gridded distributed models more effectively represent the variable nature of meteorological forcing and land surface parameters and provide flood information at any grid point within the model domain. With this in mind, a method to use a distributed hydrologic model (DHM) in conjunction with a threshold frequency (TF) post processor (Reed et al., 2007) and NEXRAD precipitation data has been developed at NOAA’s Office of Hydrologic Development (OHD). Forced by Multisensor Precipitation Estimator (MPE) and High Resolution Precipitation Estimator (HPE) precipitation observations, and High Resolution Precipitation Nowcaster (HPN) precipitation forecasts, this modeling approach is focused on improving flash flood prediction capabilities by increasing forecast accuracy and usability (Kitzmiller et al., 2008). It also seeks to improve upon the current NWS flash flood warning lead time goal of 49 minutes through leveraging the two available hours of HPN precipitation forecasts (NWS, 2009b). Flash flood warning lead times have improved over the past several years (Figure 1), and DHM-TF forced by HPN output has the potential to lengthen lead times even more. DHM-TF OVERVIEW Operating on the Hydrologic Rainfall Analysis Project (HRAP) grid at a 4km resolution and hourly time step, DHM-TF produces gridded flow forecasts, from which gridded frequency forecasts are derived using historical simulations. These frequency forecasts are then compared against threshold frequency grids derived from local information for flash flood determination. DHM-TF relies on several hydrological modeling components to generate the required flow forecasts. These components, which include a gridded Sacramento hydrologic model, overland and channel routing algorithms, and a statistical post processor, are part of the research version of OHD’s distributed model (Koren et al., 2004). Hydrologic Modeling Components The Sacramento hydrologic model (Burnash et al., 1973) represents spatially heterogeneous runoff processes over river basins ranging from tens to a few thousand square kilometers. It accounts for processes in which the freeze and thaw of soil moisture can have significant effects on water balance and soil moisture dynamics (Koren et al., 1999). Runoff from Sacramento is routed via a kinematic wave channel router (Koren et al., 2004). Routing is a key component of the DHM-TF flash flood forecast approach. Flash floods may occur near the causative rain event, or may occur downstream from the rainfall. The latter case is especially dangerous, as the lack of heavy rain in a particular area may provide residents or forecasters with a false sense of security. With routing enabled, DHM-TF is able to transport water from channels in areas of heavy rainfall to downstream points, providing an accurate simulation of the potential for flash floods along an entire river network. Statistical Processing Distributed hydrologic models have the potential to provide valuable gridded flow information, and yet, as with other models, may be subject to biases which limit their applicability without calibration or post processing. To solve this problem, DHM-TF utilizes a threshold frequency post processing approach. Rather than assuming that the exact magnitudes of the simulated flows are correct, DHM-TF relies on the concept that the relative ranking of the flows are accurate. That is, even if the flows are persistently biased, they will be internally consistent and thus can be correctly ranked against each other. It is this assumption which allows for the reliable conversion of flow values to return period values without need for accompanying observations. Reed et al. (2007) demonstrated the effectiveness of this inherent bias correction for a simulation in the Dutch Mills basin of Arkansas. In particular, they showed that although raw model flow values may be biased, the probabilities of these model flows are accurate and able to support the calculation of return periods. The statistical package which accomplishes this task depends on a highquality long-term simulation of flow, which in turn requires high-quality hourly precipitation data as input. Flow values from this long term simulation are first passed through a routine which generates a grid of annual maximum peak flow values. A second routine fits the peaks to a log Pearson Type III distribution, and calculates a corresponding set of summary statistics which describe the distribution. Once the historical baseline simulation and associated computation of summary log Pearson Type III statistics are complete, real-time hourly simulations of discharge can then be ingested into a final DHM-TF routine. This routine utilizes the summary statistics to convert discharge into frequency and then return period for final display. Forecasters can compare these grids to locally derived threshold frequencies (and associated return periods) to aid in warning decisions. Local threshold frequencies may be derived from several sources of information such as known flood frequencies at selected river locations or frequencies associated with culvert designs. An in-depth discussion on this process can be found in Reed et al. (2007) Taken together, the various components of the DHM-TF modeling approach produce flash flood forecasts which feature many advantages over traditional flash flood guidance. These include the ability to predict flash flooding at ungauged locations, a high resolution 4 km product (versus basin scale for standard flash flood guidance), a rapid update ability (every 15 minutes), and the production of verifiable small basin flow estimates. NEXRAD PRECIPITATION DATA Three NEXRAD-based precipitation products are used as input to DHM-TF: MPE, HPE, and HPN. The MPE uses a combination of radar and gauge input data and is produced hourly within the AWIPS environment by each RFC on a 4 km grid (Kitzmiller et al., 2007). Rainfall estimates from Doppler radar, gauges, and satellites are automatically ingested, and bias correction factors are developed from a comparison of radar and gauge data. After automatic derivation of a gauge-only field, and a bias-corrected radar field, a blended radar/gauge product is produced through an automatic merging of the two fields. Since manual adjustments of input fields may occur repeatedly over several hours as additional gauge reports are received, the final MPE field may not be available for several hours (Kitzmiller et al., 2008). Thus, although the high quality of the MPE product makes it ideal for the long term baseline DHM-TF runs, the long lag times and slow updating characteristics of the product makes real-time use in flash flood forecasting impractical. Although not offering the rigorous manual quality control that defines MPE, HPE features a lower latency time (less than 1 hour), a more rapid update (every 15 minutes), and a higher resolution (1 km), and is thus better suited than MPE for realtime, flash flood operations. HPE leverages recent MPE gauge/radar bias information to automatically generate rainfall and rainrate products statistically corrected for bias. The process also ingests a user-defined radar mask which determines how overlapping radars will be blended for each pixel within the domain of interest. HPE is slated for implementation within AWIPS during 2009 (Kitzmiller et al., 2008). While MPE and HPE can be used by DHM-TF to bring model states up to the present, the most important aspect of the DHM-TF approach is its forecast capability which is powered by HPN data. Based on an updated extension of the Flash Flood Potential algorithm (Walton et al., 1985), the HPN process begins with the calculation of local motion vectors. These vectors are derived through a comparison of radar rain rates spaced 15 minutes apart, and are used to project current radar echoes forward in time out to two hours. Rain rates are then variably