Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

AN EVALUATION OF WEEKLY AND MONTHLY TIME SERIES FORECASTS OF MUNICIPAL WATER USE1

ABSTRACT: A cascade model for forecasting municipal water use one week or one month ahead, conditioned on rainfall estimates, is presented and evaluated. The model comprises four components: long term trend, seasonal cycle, autocorrelation and correlation with rainfall. The increased forecast accuracy obtained by the addition of each component is evaluated. The City of Deerfield Beach, Florida, is used as the application example with the calibration period from 1976–1980 and the forecast period the drought year of 1981. Forecast accuracy is measured by the average absolute relative error (AARE, the average absolute value of the difference between actual and forecasted use, divided by the actual use). A benchmark forecast is calculated by assuming that water use for a given week or month in 1981 is the same as the average for the corresponding period from 1976 to 1980. This method produces an AARE of 14.6 percent for one step ahead forecasts of monthly data and 15.8 percent for weekly data. A cascade model using trend, seasonality and autocorrelation produces forecasts with AARE of about 12 percent for both monthly and weekly data while adding a linear relationship of water use and rainfall reduces the AARE to 8 percent in both cases if it is assumed that rainfall is known during the forecast period. Simple rainfall predictions do not increase the forecast accuracy for water use so the major utility of relating water use and rainfall lies in forecasting various possible water use sequences conditioned on sequences of historical rainfall data.     

Error Assessment for Height Above the Nearest Drainage Inundation Mapping

Real‐time flood inundation mapping is vital for emergency response to help protect life and property. Inundation mapping transforms rainfall forecasts into meaningful spatial information that can be utilized before, during, and after disasters. While inundation mapping has traditionally been conducted on a local scale, automated algorithms using topography data can be utilized to efficiently produce flood maps across the continental scale. The Height Above the Nearest Drainage method can be used in conjunction with synthetic rating curves (SRCs) to produce inundation maps, but the performance of these inundation maps needs to be assessed. Here we assess the accuracy of the SRCs and calculate statistics for comparing the SRCs to rating curves obtained from hydrodynamic models calibrated against observed stage heights. We find SRCs are accurate enough for large‐scale approximate inundation mapping while not as accurate when assessing individual reaches or cross sections. We investigate the effect of terrain and channel characteristics and observe reach length and slope predict divergence between the two types of rating curves, and SRCs perform poorly for short reaches with extreme slope values. We propose an approach to recalculate the slope in Manning’s equation as the weighted average over a minimum distance and assess accuracy for a range of moving window lengths.     

Book reviews

ANALYTICAL PLANNING: THE ORGANISATION OF SYSTEMS

Thomas L. Saaty and Kevin P. Kearns

Pergamon Press, 1985. Price: U.S. $40.00, U.K. £29.50.

THE GEOGRAPHER AT WORK

Peter Gould

Routledge and Kegan Paul, London 1985’ Price: £8.95.

UTOPIA ON TRIAL

Alice Colman

Hilary Shipman Ltd., 19 Framfield Road, Highbury, London. May 1985 (reprinted August, 1985). Price: £7.95.

SIR RAYMOND UNWIN: ARCHITECT, PLANNER AND VISIONARY

Frank Jackson

A. Zwemmer Ltd., London 1985. 187 pp. with maps and illustrations.

STRATEGIC PLANNING IN ACTION: THE IMPACT OF THE CLYDE VALLEY REGIONAL PLAN 1946–1982

R. Smith and U. Wannop (eds)

Gower, 1985, 272 pp. Price: £16.50.

LOCALITIES, CLASS AND GENDER

The Lancaster Regionalism Group (Linda Murgatroyd, Mike Savage, Dan Shapiro, John Urry, Sylvia Walby, Alan Warde, with Jane Mark‐Lawson)

Pion Ltd., 1985. Price: £12.00.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

River channel geometry is an important input to hydraulic and hydrologic models. Traditional approaches to quantify river geometry have involved surveyed river cross sections, which cannot be extended to ungaged basins. In this paper, we describe a method for developing a synthetic rating curve to relate flow to water level in a stream reach based on reach‐averaged channel geometry properties developed using the Height above Nearest Drainage (HAND) method. HAND uses a digital elevation model (DEM) of the terrain and computes the elevation difference between each land surface cell and the stream bed cell to which it drains. Taking increments in water level in the stream, HAND defines the inundation zone and a water depth grid within this zone, and the channel characteristics are defined from this water depth grid. We apply our method to the Blanco River (Texas) and the Tar River (North Carolina) using 10‐m terrain data from the United States Geological Survey (USGS) 3D Elevation Program (3DEP) dataset. We evaluate the method's performance by comparing the reach‐average stage‐river geometry relationships and rating curves to those from calibrated Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models and USGS gage observations. The results demonstrate that after some adjustment, the river geometry information and rating curves derived from HAND using national‐coverage datasets are comparable to those obtained from hydraulic models or gage measurements. We evaluate the inundation extent and show our approach is able to capture the majority of the Federal Emergency Management Agency (FEMA) 100‐year floodplain.     

Continental‐Scale River Flow Modeling of the Mississippi River Basin Using High‐Resolution NHDPlus Dataset

As a key component of the National Flood Interoperability Experiment (NFIE), this article presents the continental scale river flow modeling of the Mississippi River Basin (MRB), using high‐resolution river data from NHDPlus. The Routing Application for Parallel computatIon of Discharge (RAPID) was applied to the MRB with more than 1.2 million river reaches for a 10‐year study (2005‐2014). Runoff data from the Variable Infiltration Capacity (VIC) model was used as input to RAPID. This article investigates the effect of topography on RAPID performance, the differences between the VIC‐RAPID streamflow simulations in the HUC‐2 regions of the MRB, and the impact of major dams on the streamflow simulations. The model performance improved when initial parameter values, especially the Muskingum K parameter, were estimated by taking topography into account. The statistical summary indicates the RAPID model performs better in the Ohio and Tennessee Regions and the Upper and Lower Mississippi River Regions in comparison to the western part of the MRB, due to the better performance of the VIC model. The model accuracy also increases when lakes and reservoirs are considered in the modeling framework. In general, results show the VIC‐RAPID streamflow simulation is satisfactory at the continental scale of the MRB.