scholarly journals NEXRAD Quantitative Precipitation Estimations for Hydrologic Simulation Using a Hybrid Hydrologic Model

2016 ◽  
Vol 18 (1) ◽  
pp. 25-47 ◽  
Author(s):  
Younghyun Cho ◽  
Bernard A. Engel
Keyword(s):  
Model Simulation ◽  
Model Performance ◽  
Stage Iv ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Feature Model ◽  
Coefficient Of Determination ◽  
Hydrologic Simulation ◽  
Direct Runoff ◽  
Spatially Distributed

Abstract A hybrid hydrologic model (lumped conceptual and distributed feature model), Distributed-Clark, is introduced to perform hydrologic simulations using spatially distributed NEXRAD quantitative precipitation estimations (QPEs). In Distributed-Clark, spatially distributed excess rainfall estimated with the Soil Conservation Service (SCS) curve number method and a GIS-based set of separated unit hydrographs are utilized to calculate a direct runoff flow hydrograph. This simple approach using few modeling parameters reduces calibration complexity relative to physically based distributed (PBD) models by only focusing on integrated flow estimation at watershed outlets. Case studies assessed the quality of NEXRAD stage IV QPEs for hydrologic simulation compared to gauge-only analyses. NEXRAD data validation against rain gauge observations and performance evaluation with model simulation result comparisons for inputs of spatially distributed stage IV and spatially averaged gauged data for four study watersheds were conducted. Results show significant differences in NEXRAD QPEs and gauged rainfall amounts, with NEXRAD data overestimated by 7.5% and 9.1% and underestimated by 15.0% and 11.4% accompanied by spatial variability. These differences affect model performance in hydrologic applications. Rainfall–runoff flow simulations using spatially distributed NEXRAD stage IV QPEs demonstrate relatively good fit [direct runoff: Nash–Sutcliffe efficiency ENS = 0.85, coefficient of determination R2 = 0.89, and percent bias (PBIAS) = 3.92%; streamflow: ENS = 0.91, R2 = 0.93, and PBIAS = 1.87%] against observed flow as well as better fit (ENS of 3.7% and R2 of 6.0% increase in direct runoff) than spatially averaged gauged rainfall for the same model calibration approach, enabling improved estimates of flow volumes and peak rates that can be underestimated in hydrologic simulations for spatially averaged rainfall.

scholarly journals Comparison of Hydrologic Model Performance Statistics Using Rain Gauge and NEXRAD Precipitation Input at Different Watershed Spatial Scales and Rainfall Return Frequencies for the Upper St. Johns River, Florida USA

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 11
Author(s):  
Amanda Bredesen ◽  
Christopher J. Brown
Keyword(s):  
Spatial Scale ◽  
Model Simulation ◽  
Numerical Models ◽  
Spatial Scales ◽  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Precipitation Data ◽  
Remotely Sensed Data ◽  
Model Input

Water resources numerical models are dependent upon various input hydrologic field data. As models become increasingly complex and model simulation times expand, it is critical to understand the inherent value in using different input datasets available. One important category of model input is precipitation data. For hydrologic models, the precipitation data inputs are perhaps the most critical. Common precipitation model input includes either rain gauge or remotely-sensed data such next-generation radar-based (NEXRAD) data. NEXRAD data provides a higher level of spatial resolution than point rain gauge coverage, but is subject to more extensive data pre and post processing along with additional computational requirements. This study first documents the development and initial calibration of a HEC-HMS model of a subtropical watershed in the Upper St. Johns River Basin in Florida, USA. Then, the study compares calibration performance of the same HEC-HMS model using either rain gauge or NEXRAD precipitation inputs. The results are further discretized by comparing key calibration statistics such as Nash–Sutcliffe Efficiency for different spatial scale and at different rainfall return frequencies. The study revealed that at larger spatial scale, the calibration performance of the model was about the same for the two different precipitation datasets while the study showed some benefit of NEXRAD for smaller watersheds. Similarly, the study showed that for smaller return frequency precipitation events, NEXRAD data was superior.

scholarly journals Impacts of Polarimetric Radar Observations on Hydrologic Simulation

2010 ◽  
Vol 11 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Scott E. Giangrande ◽  
Yang Hong ◽  
Zachary L. Flamig ◽  
Terry Schuur ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Model Parameters ◽  
Polarimetric Radar ◽  
Storm Events ◽  
Hydrologic Simulation ◽  
Radar Observations

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

scholarly journals Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change

2015 ◽  
Vol 19 (2) ◽  
pp. 857-876 ◽  
Author(s):  
S. Wi ◽  
Y. C. E. Yang ◽  
S. Steinschneider ◽  
A. Khalil ◽  
C. M. Brown
Keyword(s):  
Climate Change ◽  
High Performance ◽  
Climate Models ◽  
Optimization Problems ◽  
Model Simulation ◽  
Model Fitting ◽  
Hydrologic Model ◽  
Simulation Accuracy ◽  
Distributed Models ◽  
Spatially Distributed

Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

scholarly journals Multiobjective calibration of the MESH hydrological model on the Reynolds Creek Experimental Watershed

2010 ◽  
Vol 7 (2) ◽  
pp. 2121-2155 ◽  
Author(s):  
A. J. MacLean ◽  
B. A. Tolson ◽  
F. R. Seglenieks ◽  
E. Soulis
Keyword(s):  
Snow Water Equivalent ◽  
Model Simulation ◽  
Hydrologic Model ◽  
Simulation Accuracy ◽  
Mesh Model ◽  
Reynolds Creek ◽  
Spatially Distributed ◽  
Multiobjective Calibration ◽  
Computationally Intensive ◽  
Computational Budget

Abstract. The spatially distributed MESH hydrologic model (Pietroniro et al., 2007) was successfully calibrated and then validated for the prediction of snow water equivalent (SWE) and streamflow in the Reynolds Creek Experimental Watershed in Idaho, USA. The tradeoff between fitting to SWE versus streamflow data was assessed and showed that both could be simultaneously predicted with good quality by the MESH model. Not surprisingly, calibrating to only one objective (e.g. SWE) yielded poor simulation results for the other objective (e.g. streamflow). The multiobjective calibration problem in this study was efficiently solved via a simple weighted objective function approach and analyses showed that the approach yielded a balanced solution between the objectives. Our approach therefore eliminated the need to rely on a potentially more computationally intensive evolutionary multiobjective algorithm to approximate the entire tradeoff surface between objectives. Additional calibration experiments showed that for our calibration computational budget (2000 model evaluations), the autocalibration procedure would fail without being initialized to a model parameter set carefully determined for this specific case study. This study serves as a benchmark for MESH model simulation accuracy which can be compared with future versions of MESH.

scholarly journals Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

2005 ◽  
Vol 6 (4) ◽  
pp. 497-517 ◽  
Author(s):  
Koray K. Yilmaz ◽  
Terri S. Hogue ◽  
Kuo-lin Hsu ◽  
Soroosh Sorooshian ◽  
Hoshin V. Gupta ◽  
...  
Keyword(s):  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Soil Moisture Accounting ◽  
Time Period ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
System Maps ◽  
Accounting Model ◽  
Rain Gauge Network

Abstract This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

scholarly journals A Spatial–Dynamical Framework for Evaluation of Satellite Rainfall Products for Flood Prediction

2016 ◽  
Vol 17 (8) ◽  
pp. 2137-2154 ◽  
Author(s):  
Felipe Quintero ◽  
Witold F. Krajewski ◽  
Ricardo Mantilla ◽  
Scott Small ◽  
Bong-Chul Seo
Keyword(s):  
Multiple Scales ◽  
Stage Iv ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Evaluation Framework ◽  
Model Structure ◽  
Hydrologic Simulation ◽  
Flood Prediction ◽  
Spatial And Temporal Scales ◽  
Precipitation Estimates

Abstract Rainfall maps that are derived from satellite observations provide hydrologists with an unprecedented opportunity to forecast floods globally. However, the limitations of using these precipitation estimates with respect to producing reliable flood forecasts at multiple scales are not well understood. To address the scientific and practical question of applicability of space-based rainfall products for global flood forecasting, a data evaluation framework is developed that allows tracking the rainfall effects in space and time across scales in the river network. This provides insights on the effects of rainfall product resolution and uncertainty. Obtaining such insights is not possible when the hydrologic evaluation is based on discharge observations from single gauges. The proposed framework also explores the ability of hydrologic model structure to answer questions pertaining to the utility of space-based rainfall observations for flood forecasting. To illustrate the framework, hydrometeorological data collected during the Iowa Flood Studies (IFloodS) campaign in Iowa are used to perform a hydrologic simulation using two different rainfall–runoff model structures and three rainfall products, two of which are radar based [stage IV and Iowa Flood Center (IFC)] and one satellite based [TMPA–Research Version (RV)]. This allows for exploring the differences in rainfall estimates at several spatial and temporal scales and provides improved understanding of how these differences affect flood predictions at multiple basin scales. The framework allows for exploring the differences in peak flow estimation due to nonlinearities in the hydrologic model structure and determining how these differences behave with an increase in the upstream area through the drainage network. The framework provides an alternative evaluation of precipitation estimates, based on the diagnostics of hydrological model results.

scholarly journals Evaluation of the use of radar-derived precipitation in water balance simulations: A case study in small-medium catchments in Saxony, Germany

2021 ◽  
Author(s):  
Thanh Thi Luong ◽  
Ivan Vorobevskii ◽  
Judith Pöschmann ◽  
Rico Kronenberg
Keyword(s):  
Water Balance ◽  
Spatial Information ◽  
Regional Scale ◽  
Model Performance ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Water Balance Components ◽  
Precipitation Estimation ◽  
Radar Imagery

<p>Water balance estimation/modeling is highly dependent on good-quality precipitation data and often lacks enough spatial information about it. Quantitative precipitation estimation (QPE) using radar data is recognized to have a good potential to significantly enhance the spatial depiction of precipitation compared to conventional rain gauge-based methods. However, precipitation measurements are often underestimated by wind drift or funnel evaporation, so that a correction such as Richter’s method is required before the data can be applied in the model. In this study, the Richter correction is applied for the first time to a radar-based QPE, namely RADKLIM-RW, to model water balance in ten selected catchments in Saxony, Germany. The modelled water balance components for the period 2001-2017 were evaluated by means of comparison of radar- and gauge-based precipitation inputs. The results showed that RADKLIM-RW was able to produce reliable simulations of discharge and water balance (KGE = 0.56 and 0.71 on the daily and monthly scales respectively). Application of the Richter correction improved the model performance by 5.5% and 8.9 % (for rain gauge-based and RADKLIM precipitation respectively). The study concluded that radar data as precipitation input to (pseudo)distributed hydrologic model shows immense potential to improve water balance simulations.</p><p><strong>Hightlights</strong>:</p><ul><li>Comparison of precipitation derived from sensor networks and radar imagery for small catchments</li> <li>Evaluation of potential application of radar precipitation in water balance simulation at regional scale</li> <li>Effect of wind correction (“Richter” correction) on radar precipitation products</li> <li>Evaluating corrected precipitation on water balance processes</li> </ul><p><strong>Keywords</strong>: HRU, radar climatology, RADKLIM RW (RADOLAN), Richter correction, Open sensor network, water balance simulation, BROOK90</p>

Hydrologic Evaluation of Rainfall Estimates from Radar, Satellite, Gauge, and Combinations on Ft. Cobb Basin, Oklahoma

2011 ◽  
Vol 12 (5) ◽  
pp. 973-988 ◽  
Author(s):  
Jonathan J. Gourley ◽  
Yang Hong ◽  
Zachary L. Flamig ◽  
Jiahu Wang ◽  
Humberto Vergara ◽  
...  
Keyword(s):  
Stage Iv ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Efficiency Score ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Rain Gauges ◽  
Distributed Hydrologic Model ◽  
Radar Satellite ◽  
Rainfall Estimates

Abstract This study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service’s (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM’s 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration.

scholarly journals Comprehensive Evaluation of the IFloodS Radar Rainfall Products for Hydrologic Applications

2018 ◽  
Vol 19 (11) ◽  
pp. 1793-1813 ◽  
Author(s):  
Bong-Chul Seo ◽  
Witold F. Krajewski ◽  
Felipe Quintero ◽  
Mohamed ElSaadani ◽  
Radoslaw Goska ◽  
...  
Keyword(s):  
Comprehensive Evaluation ◽  
Reference Product ◽  
Stage Iv ◽  
Statistical Error ◽  
Rain Gauge ◽  
Hydrologic Simulation ◽  
Radar Rainfall ◽  
Field Campaign ◽  
Error Structure ◽  
Using Data

Abstract This study describes the generation and testing of a reference rainfall product created from field campaign datasets collected during the NASA Global Precipitation Measurement (GPM) mission Ground Validation Iowa Flood Studies (IFloodS) experiment. The study evaluates ground-based radar rainfall (RR) products acquired during IFloodS in the context of building the reference rainfall product. The purpose of IFloodS was not only to attain a high-quality ground-based reference for the validation of satellite rainfall estimates but also to enhance understanding of flood-related rainfall processes and the predictability of flood forecasting. We assessed the six RR estimates (IFC, Q2, CSU-DP, NWS-DP, Stage IV, and Q2-Corrected) using data from rain gauge and disdrometer networks that were located in the broader field campaign area of central and northeastern Iowa. We performed the analyses with respect to time scales ranging from 1 h to the entire campaign period in order to compare the capabilities of each RR product and to characterize the error structure at scales that are frequently used in hydrologic applications. The evaluation results show that the Stage IV estimates perform superior to other estimates, demonstrating the need for gauge-based bias corrections of radar-only products. This correction should account for each product’s algorithm-dependent error structure that can be used to build unbiased rainfall products for the campaign reference. We characterized the statistical error structures (e.g., systematic and random components) of each RR estimate and used them for the generation of a campaign reference rainfall product. To assess the hydrologic utility of the reference product, we performed hydrologic simulations driven by the reference product over the Turkey River basin. The comparison of hydrologic simulation results demonstrates that the campaign reference product performs better than Stage IV in streamflow generation.

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