A computationally efficient framework for stochastic prediction of flood propagation

Wijesundera, Isuri; Halgamuge, Malka N.; Nirmalathas, Thas; Nanayakkara, Thrishantha

doi:10.1109/iciafs.2012.6420038

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, some parameter calibration is required to achieve accurate estimates of flow volumes and timing, particularly for applications requiring daily hydrographs. Inaccuracies in initial parameter estimates may arise from differences in the scale and quality of the input data or from the model itself (Gupta et al 1999, Wijesundera et al 2012, Blanc and Strobl 2013. Parameter adjustment can reduce such effects (Sahoo et al 2006)…”

Section: Model Calibrationmentioning

confidence: 99%

Estimating daily streamflow in the Congo Basin using satellite-derived data and a semi-distributed hydrological model

Munzimi

Hansen

Asante

2019

Hydrological Sciences Journal

View full text Add to dashboard Cite

The application of remotely-sensed data for hydrological modeling of the Congo Basin is presented. Satellite-derived data, including TRMM precipitation, are used as inputs to drive the USGS Geospatial Streamflow Model (GeoSFM) to estimate daily river discharge over the basin from 1998 to 2012. Physically-based parameterization was augmented with a spatially-distributed calibration that enables GeoSFM to simulate hydrological processes such as the slowing effect of the Cuvette Centrale. The resulting simulated long-term mean of daily flows and the observed flow at the Kinshasa gauge were comparable (40 631 and 40 638 m 3 /s respectively), in the 7-year validation period (2004)(2005)(2006)(2007)(2008)(2009)(2010), with no significant bias and a Nash-Sutcliffe model efficiency coefficient of 0.70. Modeled daily flows and aggregated monthly river outflows (compared to historical averages) for additional sites confirm the model reliability in capturing flow timing and seasonality across the basin, but sometimes fails to accurately predict flow magnitude. The results of this model can be useful in research and decisionmaking contexts and validate the application of satellite-based hydrological models driven for large, data-scarce river systems such as the Congo.

show abstract

Section: Model Calibrationmentioning

confidence: 99%

Estimating daily streamflow in the Congo Basin using satellite-derived data and a semi-distributed hydrological model

Munzimi

Hansen

Asante

2019

Hydrological Sciences Journal

View full text Add to dashboard Cite

show abstract

“…The significant role played by first encounters have made calculating the mean first passage time (MFPT) of great importance in numerous real world dynamic systems that can be modeled as random walks [1,2]. From exchange rate fluctuations [3], through natural disaster propagation [4,5], to the propagation of gossip [6], the MFPT to reach some special target state gives important information of the performance of the system. For this reason, random walks have been studied extensively for decades on one dimensional state space [7,8], regular lattices, fractal networks, and many other specialized networks [9].…”

Section: Introductionmentioning

confidence: 99%