Hydroinformatics and the Iowa Flood Information System

Witold F. Krajewski is a professor at the University of Iowa. This presentation was given as part of the GIS Day@KU symposium on November 16, 2016. For more information about GIS Day@KU activities, please see http://gis.ku.edu/gisday/2016/.Platinum Sponsors: KU Department of Geography and Atmospheric Science. Gold Sponsors: Enertech, KU Environmental Studies Program, KU Libraries. Silver Sponsors: Douglas County, Kansas, KansasView, State of Kansas Data Access & Support Center (DASC) and the KU Center for Global and International Studies

Compressed Sensing Applied to Weather Radar

We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information. Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets. The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains. We propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes. We use Iowa X-band Polarimetric (XPOL) radar data to test and illustrate our algorithms.Comment: 4 pages, 5 figrue

Optimal combining of ground-based sensors for the purpose of validating satellite-based rainfall estimates

Two problems related to radar rainfall estimation are described. The first part is a description of a preliminary data analysis for the purpose of statistical estimation of rainfall from multiple (radar and raingage) sensors. Raingage, radar, and joint radar-raingage estimation is described, and some results are given. Statistical parameters of rainfall spatial dependence are calculated and discussed in the context of optimal estimation. Quality control of radar data is also described. The second part describes radar scattering by ellipsoidal raindrops. An analytical solution is derived for the Rayleigh scattering regime. Single and volume scattering are presented. Comparison calculations with the known results for spheres and oblate spheroids are shown

Information Communication Using Knowledge Engine on Flood Issues

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Scale Dependence Of Radar Rainfall Uncertainty: Initial Evaluation Of NEXRAD\u27s New Super-resolution Data For Hydrologic Applications

This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008-August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data. © 2010 American Meteorological Society

On the Underestimation of Flood Risk for Locationsdownstream from Flood Control Reservoirs

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Multi-Scale Hydrologic Evaluation Of The National Water Model Streamflow Data Assimilation

Streamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model-simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF-Hydro® with the NWM modeling elements and assimilated 15-min streamflow data into the model, observed during 2016–2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as nudging, the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi-scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling–Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open-loop simulation, and the improvements increase with areal coverage of upstream assimilation points

Utility Of Vertically Integrated Liquid Water Content For Radar-Rainfall Estimation: Quality Control And Precipitation Type Classification

This study proposes a new estimation method for vertically integrated liquid water content (VIL) using radar reflectivity volume data and temperature sounding retrieved from the numerical weather model analysis. This method addresses uncertainty factors in conventional VIL estimation associated with the effects from the bright band (BB) and radar beam geometry near the radar site. The new VIL is then used for precipitation classification (convective/stratiform) and wind turbine clutter detection in the hope that the estimated VIL indicating vertical activities or development of precipitation systems will account for the two independent subjects together, in opposite ways. The non-precipitation radar echoes returned from wind turbines do not likely generate significant degree of VIL, compared to the one estimated from actual convective cells, which contain comparable reflectivity strength. We tested the proposed VIL estimation, precipitation classification, and wind turbine clutter detection methods using various Iowa cases and illustrated their successful application. We also performed a quantitative evaluation of precipitation classification using ground reference data from a dense rain gauge network over the Turkey River basin in Iowa. The evaluation results show improved performance for most non-convective event cases estimated by the stratiform estimator (Z = 200R1.6) because we applied the convective estimator (Z = 300R1.4) to all event cases without classification. In addition, we demonstrated the potential of the new classification to mitigate significant BB effects in quantitative precipitation estimation using a correction method based on the vertical profile of reflectivity

High-resolution QPF Uncertainty And Its Implications For Flood Prediction: A Case Study For The Eastern Iowa Flood Of 2016

This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling-Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1-3-h lead times and starts increasing with 4-6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction

Evaluation Of The Specific Attenuation Method For Radar-based Quantitative Precipitation Estimation: Improvements And Practical Challenges

This study demonstrates an implementation of the prototype quantitative precipitation R estimation algorithm using specific attenuation A for S-band polarimetric radar. The performance of R(A) algorithm is assessed, compared to the conventional algorithm using radar reflectivity Z, at multiple temporal scales. Because the factor a, defined as the net ratio of A to specific differential phase, is a key parameter of the algorithm characterized by drop size distributions (e.g., differential reflectivity Zdr dependence on Z), the estimation equations of a and a proper number of Zdr–Z samples required for a reliable a estimation are examined. Based on the dynamic estimation of a, the event-based evaluation using hourly rain gauge observations reveals that the performance of R(A) is superior to that of R(Z), with better agreement and lower variability. Despite its superiority, the study finds that R(A) leads to quite consistent overestimations of about 10%–30%. It is demonstrated that the application of uniform a over the entire radar domain yields the observed uncertainty because of the heterogeneity of precipitation in the domain. A climatological range-dependent feature of R(A) and R(Z) is inspected in the multiyear evaluation at yearly scale using rain totals for April–October. While R(Z) exposes a systematic shift and overestimation, each of which arise from the radar miscalibration and bright band effects, R(A) combining with multiple R(Z) values for solid/mixed precipitation shows relatively robust performance without those effects. The immunity of R(A) to partial beam blockage (PBB) based on both qualitative and quantitative analyses is also verified. However, the capability of R(A) regarding PBB is limited by the presence of the melting layer and its application requirement for the total span of differential phase (e.g., 38), which is another challenge for light rain