Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting

Bravo, Juan Martín; Paz, Adriano Rolim da; Collischonn, Walter; Uvo, Cíntia Bertacchi; Pedrollo, Olavo Correa; Chou, Sin Chan

doi:10.1061/(asce)he.1943-5584.0000014

Cited by 37 publications

(19 citation statements)

References 34 publications

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“…The model has been applied in several different South American river basins, like the Amazon (Paiva et al, 2012(Paiva et al, , 2013, the Grande River (Tucci et al, 2008;Bravo et al, 2009) and the Uruguay River (Collischonn et al, 2005). The model is also currently being used operationally for streamflow forecasts in the Paranaíba river basin (Collischonn et al, 2007a,b), the Pelotas river basin (Fan et al, 2014a), the Canoas river basin (Fan et al, 2014a), and for the three basins used as test-beds in the present study.…”

Section: Hydrological Modellingmentioning

confidence: 98%

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Fan

Schwanenberg

Collischonn

et al. 2015

Journal of Hydrology: Regional Studies

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a b s t r a c tStudy region: This paper describes a major ensemble-forecasts verification effort for inflows of three large-scale river basins of Brazil: Upper São Francisco, Doce, and Tocantins Rivers. Study focus: In experimental scenarios, inflow forecasts were generated forcing one hydrological model with quantitative precipitation forecasts (QPF) from three selected models of the TIGGE database. This study provides information on the regional ensemble performance and also evaluates how different QPF models respond for the different basins and what happens with the use of combined QPF in a greater ensemble. New hydrological insights for the region: This work presents one of the first extensive efforts to evaluate ensemble forecasts for large-scale basins in South America using TIGGE archive data. Results from these scenarios provide validation criteria and confirm that ensemble forecasts depend on the particular EPS used to run the hydrological model and on the basin studied. Furthermore, the use of the Super Ensemble seems to be a good strategy in terms of performance and robustness. The importance of the TIGGE database is also highlighted.

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Section: Hydrological Modellingmentioning

confidence: 98%

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Fan

Schwanenberg

Collischonn

et al. 2015

Journal of Hydrology: Regional Studies

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“…MGB-IPH has been employed in a range of large-scale river basins ranging from 6000 to more than 1 million km 2 , including applications for river flow forecasts based on quantitative precipitation forecasts (Tucci et al, 2003;Collischonn et al, 2005;Collischonn et al, 2007b;Tucci et al, 2008;Bravo et al, 2009), simulations of the impact of climate change on the Tocantins and Araguaia rivers (Tomasella et al, 2008) and tests of Tropical Rainfall Measurement Mission (TRMM) rainfall remote sensing data in the Tapajos river basin .…”

Section: The Mgb-iph Hydrological Modelmentioning

confidence: 99%

Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil

Nóbrega

Collischonn

Tucci

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract. We quantify uncertainty in the impacts of climate change on the discharge of Rio Grande, a major tributary of the Paraná River in South America and one of the most important basins in Brazil for water supply and hydroelectric power generation. We consider uncertainty in climate projections associated with the greenhouse-gas emission scenarios (A1b, A2, B1, B2) and increases in global mean air temperature of 1 to 6 • C for the HadCM3 GCM (Global Circulation Model) as well as uncertainties related to GCM structure. For the latter, multimodel runs using 6 GCMs (CCCMA CGCM31, CSIRO Mk30, IPSL CM4, MPI ECHAM5, NCAR CCSM30, UKMO HadGEM1) and HadCM3 as baseline, for a +2 • C increase in global mean temperature. Pattern-scaled GCM-outputs are applied to a large-scale hydrological model (MGB-IPH) of Rio Grande Basin. Based on simulations using HadCM3, mean annual river discharge increases, relative to the baseline or control run period , by +5% to +10% under the SRES emissions scenarios and from +8% to +51% with prescribed increases in global mean air temperature of between 1 and 6 • C. Substantial uncertainty in projected changes to mean river discharge (−28% to +13%) under the 2 • C warming scenario is, however, associated with the choice of GCM. We conclude that, in the case of Rio Grande Basin, the most important source of uncertainty derives from the GCM rather than the emission scenario or the magnitude of rise in mean global temperature.

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“…Watershed parameters such as infiltration coefficients, time of concentration, and base-flow were modified to produce a best fit between model and observations. The discharge output was calibrated with observed stream-flow [6,16,23,25,32,35]. Care was taken to achieve a good agreement between the average observed and simulated catchment runoff volume, the overall shape of the hydrographs and the root mean square error (RMSE) of the peak flows and low flows.…”

Section: Nam Model Calibration For Langat Basinmentioning

confidence: 99%

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

et al. 2011

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Observed rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3-12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R 2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R 2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R 2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through 'Nowcasting', thus increasing the response time for flood early warnings.

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Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting

Cited by 37 publications

References 34 publications

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil

Uncertainty in climate change impacts on water resources in the Rio Grande Basin, Brazil

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

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