scholarly journals Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

2012 ◽  
Vol 13 (1) ◽  
pp. 338-350 ◽  
Author(s):  
Menberu M. Bitew ◽  
Mekonnen Gebremichael ◽  
Lula T. Ghebremichael ◽  
Yared A. Bayissa
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Model Simulations ◽  
Mountainous Watershed ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

scholarly journals Experiences in using the TMPA-3B42R satellite data to complement rain gauge measurements in the Ecuadorian coastal foothills

2013 ◽  
Vol 17 (7) ◽  
pp. 2905-2915 ◽  
Author(s):  
M. Arias-Hidalgo ◽  
B. Bhattacharya ◽  
A. E. Mynett ◽  
A. van Griensven
Keyword(s):  
Satellite Data ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
New Technologies ◽  
Simple Procedure ◽  
Rain Gauge ◽  
Experimental Methodology ◽  
Rain Gauge Data ◽  
Gauge Data ◽  
Satellite Rainfall

Abstract. At present, new technologies are becoming available to extend the coverage of conventional meteorological datasets. An example is the TMPA-3B42R dataset (research – v6). The usefulness of this satellite rainfall product has been investigated in the hydrological modeling of the Vinces River catchment (Ecuadorian lowlands). The initial TMPA-3B42R information exhibited some features of the precipitation spatial pattern (e.g., decreasing southwards and westwards). It showed a remarkable bias compared to the ground-based rainfall values. Several time scales (annual, seasonal, monthly, etc.) were considered for bias correction. High correlations between the TMPA-3B42R and the rain gauge data were still found for the monthly resolution, and accordingly a bias correction at that level was performed. Bias correction factors were calculated, and, adopting a simple procedure, they were spatially distributed to enhance the satellite data. By means of rain gauge hyetographs, the bias-corrected monthly TMPA-3B42R data were disaggregated to daily resolution. These synthetic time series were inserted in a hydrological model to complement the available rain gauge data to assess the model performance. The results were quite comparable with those using only the rain gauge data. Although the model outcomes did not improve remarkably, the contribution of this experimental methodology was that, despite a high bias, the satellite rainfall data could still be corrected for use in rainfall-runoff modeling at catchment and daily level. In absence of rain gauge data, the approach may have the potential to provide useful data at scales larger than the present modeling resolution (e.g., monthly/basin).

Benchmarking High-Resolution Global Satellite Rainfall Products to Radar and Rain-Gauge Rainfall Estimates

2010 ◽  
Vol 48 (4) ◽  
pp. 1667-1683 ◽  
Author(s):  
E.N. Anagnostou ◽  
V. Maggioni ◽  
E.I. Nikolopoulos ◽  
T. Meskele ◽  
F. Hossain ◽  
...  
Keyword(s):  
High Resolution ◽  
Rain Gauge ◽  
Satellite Rainfall ◽  
Rainfall Estimates

scholarly journals Flash Flood Forecasting for Small Urban Watersheds in the Baltimore Metropolitan Region

2007 ◽  
Vol 22 (6) ◽  
pp. 1331-1344 ◽  
Author(s):  
Julie Rose N. Javier ◽  
James A. Smith ◽  
Katherine L. Meierdiercks ◽  
Mary Lynn Baeck ◽  
Andrew J. Miller
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Flash Flood ◽  
Flood Forecasting ◽  
Hydrologic Model ◽  
Urban Watersheds ◽  
Radar Rainfall ◽  
Flood Peaks ◽  
Rainfall Estimates ◽  
Flash Flood Forecasting

Abstract The utility of distributed hydrologic models in combination with high-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) rainfall estimates for flash flood forecasting in urban drainage basins is examined through model simulations of 10 flood events in the 14.3 km2 Dead Run watershed of Baltimore County, Maryland. The hydrologic model consists of a simple infiltration model and a geomorphological instantaneous unit hydrograph–based representation of hillslope and channel response. Analyses are based on high-resolution radar rainfall estimates from the Sterling, Virginia, WSR-88D and observations from a nested network of 6 stream gauges in the Dead Run watershed and a network of 17 rain gauge stations in Dead Run. For the three largest flood peaks in Dead Run, including the record flood on 7 July 2004, hydrologic model forecasts do not capture the pronounced attenuation of flood peaks. Hydraulic controls imposed by valley bottom constrictions associated with bridges and bridge abutments are a dominant element of the extreme flood response of small urban watersheds. Model analyses suggest that a major limitation on the accuracy of flash flood forecasting in urban watersheds is imposed by storm water management infrastructure. Model analyses also suggest that there is potential for improving model forecasts through the utilization of information on initial soil moisture storage. Errors in the rainfall field, especially those linked to bias correction, are the largest source of uncertainty in quantitative flash flood forecasting. Bias correction of radar rainfall estimates is an important element of flash flood forecasting systems.

scholarly journals Extreme runoff response to short-duration convective rainfall in South-West Germany

2011 ◽  
Vol 8 (6) ◽  
pp. 10739-10780
Author(s):  
V. Ruiz-Villanueva ◽  
M. Borga ◽  
D. Zoccatelli ◽  
L. Marchi ◽  
E. Gaume ◽  
...  
Keyword(s):  
High Resolution ◽  
Flash Flood ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
West Germany ◽  
Structure Evolution ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Precipitation Frequency ◽  
South West

Abstract. The 2 June 2008 flood-producing storm on the Starzel river basin in South-West Germany is examined as a prototype for organized convective systems that dominate the upper tail of the precipitation frequency distribution and are likely responsible for the flash flood peaks in this region. The availability of high-resolution rainfall estimates from radar observations and a rain gauge network, together with indirect peak discharge estimates from a detailed post-event survey, provides the opportunity to study the hydrometeorological and hydrological mechanisms associated with this extreme storm and the ensuing flood. Radar-derived rainfall, streamgauge data and indirect estimates of peak discharges are used along with a distributed hydrologic model to reconstruct hydrographs at multiple locations. The influence of storm structure, evolution and motion on the modeled flood hydrograph is examined by using the "spatial moments of catchment rainfall" (Zoccatelli et al., 2011). It is shown that downbasin storm motion had a noticeable impact on flood peak magnitude. Small runoff ratios (less than 20%) characterized the runoff response. The flood response can be reasonably well reproduced with the distributed hydrological model, using high resolution rainfall observations and model parameters calibrated at a river section which includes most of the area impacted by the storm.

scholarly journals Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

2007 ◽  
Vol 11 (4) ◽  
pp. 1373-1390 ◽  
Author(s):  
D. Sharma ◽  
A. Das Gupta ◽  
M. S. Babel
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Hydrological Modeling ◽  
Correction Method ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Model Parameters ◽  
Bias Correction Method ◽  
Spatial Disaggregation ◽  
Grid Nodes

Abstract. Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.

scholarly journals Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

2007 ◽  
Vol 8 (6) ◽  
pp. 1204-1224 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens ◽  
E. J. Pebesma ◽  
R. Uijlenhoet
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Added Value ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Gauge Data ◽  
Event Based ◽  
Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

scholarly journals Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

2014 ◽  
Vol 15 (6) ◽  
pp. 2347-2369 ◽  
Author(s):  
Matthew P. Young ◽  
Charles J. R. Williams ◽  
J. Christine Chiu ◽  
Ross I. Maidment ◽  
Shu-Hua Chen
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Shallow Convection ◽  
Rain Gauges ◽  
The Mean ◽  
Satellite Rainfall ◽  
Convective Cells ◽  
Rainfall Estimates

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

scholarly journals Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1340
Author(s):  
Woodson ◽  
Adams ◽  
Dymond
Keyword(s):  
Hydrological Modeling ◽  
Mean Squared Error ◽  
Uniform Estimate ◽  
Mean Field ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Urban Watersheds ◽  
Precipitation Estimation

Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.

High-resolution pan-European multi-model simulations of hydrologic states and fluxes

2020 ◽  
Author(s):  
Bibi S Naz ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Flow ◽  
Hydrologic Model ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Time Step ◽  
European Continent ◽  
Model Simulations ◽  
Community Land Model

<p> <span>High-resolution large-scale predictions of hydrologic states and fluxes are important for many regional-scale applications and water resource management. However, because of uncertainties related to forcing data, model structural errors arising from simplified representations of hydrological processes or uncertain model parameters, model simulations remain uncertain. To quantify this uncertainty, multi-model simulations were performed at 3km resolution over the European continent using the Community Land Model (CLM3.5) and the ParFlow hydrologic model. While Parflow uses a similar approach as CLM in simulating the snow, vegetation and land-atmosphere exchange processes, it simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation. </span><span>The </span><span>CLM</span><span>3.5</span><span> uses a simple groundwater model to account for groundwater recharge and discharge processes. Both models were driven with the COSMO-REA6 reanalysis dataset at 6km resolution for the time period from 2000 to 2006 at an hourly time step, and both used the same datasets for the static input variables (such as topography, vegetation and soil properties). The performance of both models was analyzed through comparisons with independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, river discharge, water table depth and total water storage datasets. Overall, both models capture the interannual variability in the hydrologic states and fluxes well, however differences in performance between models showed the uncertainty associated with the representation of hydrological processes, such as groundwater flow and soil moisture and its control on latent and sensible heat fluxes at the surface.</span></p>

Evolving Multisensor Precipitation Estimation Methods: Their Impacts on Flow Prediction Using a Distributed Hydrologic Model

2011 ◽  
Vol 12 (6) ◽  
pp. 1414-1431 ◽  
Author(s):  
David Kitzmiller ◽  
Suzanne Van Cooten ◽  
Feng Ding ◽  
Kenneth Howard ◽  
Carrie Langston ◽  
...  
Keyword(s):  
High Resolution ◽  
Positive Impact ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Freezing Level ◽  
Distributed Hydrologic Model ◽  
Precipitation Estimation ◽  
Precipitation Estimates

Abstract This study investigates evolving methodologies for radar and merged gauge–radar quantitative precipitation estimation (QPE) to determine their influence on the flow predictions of a distributed hydrologic model. These methods include the National Mosaic and QPE algorithm package (NMQ), under development at the National Severe Storms Laboratory (NSSL), and the Multisensor Precipitation Estimator (MPE) and High-Resolution Precipitation Estimator (HPE) suites currently operational at National Weather Service (NWS) field offices. The goal of the study is to determine which combination of algorithm features offers the greatest benefit toward operational hydrologic forecasting. These features include automated radar quality control, automated Z–R selection, brightband identification, bias correction, multiple radar data compositing, and gauge–radar merging, which all differ between NMQ and MPE–HPE. To examine the spatial and temporal characteristics of the precipitation fields produced by each of the QPE methodologies, high-resolution (4 km and hourly) gridded precipitation estimates were derived by each algorithm suite for three major precipitation events between 2003 and 2006 over subcatchments within the Tar–Pamlico River basin of North Carolina. The results indicate that the NMQ radar-only algorithm suite consistently yielded closer agreement with reference rain gauge reports than the corresponding HPE radar-only estimates did. Similarly, the NMQ radar-only QPE input generally yielded hydrologic simulations that were closer to observations at multiple stream gauging points. These findings indicate that the combination of Z–R selection and freezing-level identification algorithms within NMQ, but not incorporated within MPE and HPE, would have an appreciable positive impact on hydrologic simulations. There were relatively small differences between NMQ and HPE gauge–radar estimates in terms of accuracy and impacts on hydrologic simulations, most likely due to the large influence of the input rain gauge information.

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