scholarly journals Modelling catchment inflows into Lake Victoria: regionalisation of the parameters of a conceptual water balance model

2012 ◽  
Vol 44 (5) ◽  
pp. 789-808 ◽  
Author(s):  
Michael Kizza ◽  
Jose-Luis Guerrero ◽  
Allan Rodhe ◽  
Chong-yu Xu ◽  
Henry K. Ntale
Keyword(s):  
Water Balance ◽  
Model Calibration ◽  
Lake Victoria ◽  
Uncertainty Assessment ◽  
Uncertainty Estimation ◽  
Water Balance Model ◽  
Balance Model ◽  
Model Parameters ◽  
Lake Victoria Basin ◽  
Global Mean

The goal of this study was to evaluate regionalisation methods that could be used for modelling catchment inflows into Lake Victoria. WASMOD, a conceptual water balance model, was applied to nine gauged sub-basins in Lake Victoria basin in order to test the transferability of model parameters between the basins using three regionalisation approaches. Model calibration was carried out within the GLUE (generalised likelihood uncertainty estimation) framework for uncertainty assessment. The analysis was carried out for the period 1967–2000. Parameter transferability was assessed by comparing the likelihood values of regionalised simulations with the values under calibration for each basin. WASMOD performed well for all study sub-basins with Nash–Sutcliffe values ranging between 0.70 and 0.82. Transferability results were mixed. For the proxy-basin method, the best performing parameter donor basin was Mara with four proxy basins giving acceptable results. Sio, Sondu, Gucha and Duma also performed well. The global mean method gave acceptable performance for seven of the nine study basins. The ensemble regionalisation method provides the possibility to consider parameter uncertainty in the regionalisation. Ensemble regionalisation method performed best with an average departure of 40% from the observed mean annual flows compared to 48 and 60% for proxy-basin and global mean methods, respectively.

scholarly journals Estimation of flooded area in the Bahr El-Jebel basin using remote sensing techniques

2006 ◽  
Vol 3 (4) ◽  
pp. 1851-1877 ◽  
Author(s):  
M. A. H. Shamseddin ◽  
T. Hata ◽  
A. Tada ◽  
M. A. Bashir ◽  
T. Tanakamaru
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Lake Victoria ◽  
Open Water ◽  
Unsupervised Classification ◽  
Water Balance Model ◽  
Balance Model ◽  
Water Evaporation ◽  
Flooded Area ◽  
Remote Sensing Techniques

Abstract. In spite of the importance of Sudd (swamp) area estimation for any hydrological project in the southern Sudan, yet, no abroad agreement on its size, due to the inaccessibility and civil war. In this study, remote sensing techniques are used to estimate the Bahr El-Jebel flooded area. MODIS-Terra (Moderate Resolution Imaging Spectroradiometer) level 1B satellite images are analyzed on basis of the unsupervised classification method. The annual mean of Bahr El-Jebel flooded area has been estimated at 20 400 km2, which is 96% of Sutcliffe and Park (1999) estimation on basis of water balance model prediction. And only, 53% of SEBAL (Surface Energy Balance Algorithm for Land) model estimation. The accuracy of the classification is 71%. The study also found the swelling and shrinkage pattern of Sudd area throughout the year is following the trends of Lake Victoria outflow patterns. The study has used two evaporation methods (open water evaporation and SEBAL model) to estimate the annual storage volume of Bahr El-Jebel River by using a water balance model. Also the storage changes due time is generated throughout the study years.

Regionalization of lumped water balance model parameters based on multiple regression

2001 ◽  
Vol 246 (1-4) ◽  
pp. 209-222 ◽  
Author(s):  
Y Yokoo ◽  
S Kazama ◽  
M Sawamoto ◽  
H Nishimura
Keyword(s):  
Water Balance ◽  
Multiple Regression ◽  
Water Balance Model ◽  
Balance Model ◽  
Model Parameters

A performance assessment method for SAR satellite-derived surface soil moisture data using a soil-water balance model, meteorological observations, and soil pedotransfer functions.

2020 ◽  
Author(s):  
John Beale ◽  
Toby Waine ◽  
Ronald Corstanje ◽  
Jonathan Evans
Keyword(s):  
Regression Analysis ◽  
Soil Moisture ◽  
Water Balance ◽  
Surface Soil ◽  
Cosmic Ray ◽  
Water Balance Model ◽  
Balance Model ◽  
Model Parameters ◽  
Surface Soil Moisture

<p>The validation of surface soil moisture products derived from SAR satellites data is challenged by the difficulty of reliably measuring in-situ soil moisture at shallow soil depths of a few centimetres, consistent with the penetration depth of the microwave beam. Our analysis shows that the apparent accuracy of the remote sensing products is underestimated by comparison with inconsistent probe data or measurements at greater soil depths. Our alternative approach uses in-situ meteorological measurements to determine rainfall and potential evapotranspiration, to be used with soil hydrological properties as inputs to a water balance model to estimate surface soil moisture independently of the satellite data. In-situ soil moisture measurements are used to validate and refine the model parameters. The choice of appropriate soil hydrological parameters with which to convert remotely sensed surface soil moisture indices to volumetric moisture content is shown to have a significant impact on the bias and offset in the regression analysis. To illustrate this, Copernicus SSM data is analysed by this method for a number of COSMOS-UK soil moisture monitoring sites, showing a significant improvement in the coefficient of determination, bias and offset over regression analysis using in-situ measurements from soil moisture probes or the cosmic ray soil moisture sensor itself. This will benefit users of such products in agriculture, for example, in determining actual soil moisture deficit.</p>

scholarly journals Modelling the water balance of Lake Victoria (East Africa) – Part 1: Observational analysis

2018 ◽  
Vol 22 (10) ◽  
pp. 5509-5525 ◽  
Author(s):  
Inne Vanderkelen ◽  
Nicole P. M. van Lipzig ◽  
Wim Thiery
Keyword(s):  
Water Balance ◽  
Lake Level ◽  
Lake Victoria ◽  
Balance Model ◽  
Lake Victoria Basin ◽  
Lake Level Fluctuations ◽  
Lake Evaporation ◽  
In Situ Observations ◽  
The Individual

Abstract. Lake Victoria is the largest lake in Africa and one of the two major sources of the Nile river. The water level of Lake Victoria is determined by its water balance, consisting of precipitation on the lake, evaporation from the lake, inflow from tributary rivers and lake outflow, controlled by two hydropower dams. Due to a scarcity of in situ observations, previous estimates of individual water balance terms are characterized by substantial uncertainties, which means that the water balance is often not closed independently. In this first part of a two-paper series, we present a water balance model for Lake Victoria, using state-of-the-art remote sensing observations, high-resolution reanalysis downscaling and outflow values recorded at the dam. The uncalibrated computation of the individual water balance terms yields lake level fluctuations that closely match the levels retrieved from satellite altimetry. Precipitation is the main cause of seasonal and interannual lake level fluctuations, and on average causes the lake level to rise from May to July and to fall from August to December. Finally, our results indicate that the 2004–2005 drop in lake level can be about half attributed to a drought in the Lake Victoria Basin and about half to an enhanced outflow, highlighting the sensitivity of the lake level to human operations at the outflow dam.

scholarly journals Modelling the water balance of Lake Victoria (East Africa) – Part 1: Observational analysis

2018 ◽  
Author(s):  
Inne Vanderkelen ◽  
Nicole P. M. van Lipzig ◽  
Wim Thiery
Keyword(s):  
Water Balance ◽  
Lake Level ◽  
Lake Victoria ◽  
Balance Model ◽  
Lake Victoria Basin ◽  
Lake Level Fluctuations ◽  
Lake Evaporation ◽  
In Situ Observations ◽  
The Individual

Abstract. Lake Victoria is the largest lake in Africa and one of the two major sources of the Nile River. The water level of Lake Victoria is determined by its water balance, consisting of precipitation on the lake, evaporation from the lake, inflow from tributary rivers and lake outflow, controlled by two hydropower dams. Due to scarcity of in-situ observations, previous estimates of individual water balance terms are characterised by substantial uncertainties, which makes that the water balance is often not closed independently. Here we present a water balance model for Lake Victoria, using state-of-the-art remote sensing observations, high resolution reanalysis downscaling and outflow values recorded at the dam. The uncalibrated computation of the individual water balance terms yield lake level fluctuations that closely match the levels retrieved from satellite altimetry. Precipitation is the main cause of seasonal and inter-annual lake level fluctuations, and on average causes the lake level to rise from May to July and to fall from August to December. Finally, our results indicate that the 2004–2005 drop in lake level can be attributed about half to a drought in the Lake Victoria Basin and about half to an enhanced outflow, highlighting the sensitivity of the lake level to human operations at the outflow dam.

scholarly journals Evaluating the bio-hydrological impact of a cloud forest in Central America using a semi-distributed water balance model

2013 ◽  
Vol 61 (1) ◽  
pp. 9-20b ◽  
Author(s):  
Luis A. Caballero ◽  
Zachary M. Easton ◽  
Brian K. Richards ◽  
Tammo S. Steenhuis
Keyword(s):  
Water Balance ◽  
Central America ◽  
Forest Cover ◽  
Cloud Forest ◽  
Water Balance Model ◽  
Population Pressure ◽  
Balance Model ◽  
Annual Rainfall ◽  
Management Decision ◽  
Model Parameters

Abstract Water scarcity poses a major threat to food security and human health in Central America and is increasingly recognized as a pressing regional issues caused primarily by deforestation and population pressure. Tools that can reliably simulate the major components of the water balance with the limited data available and needed to drive management decision and protect water supplies in this region. Four adjacent forested headwater catchments in La Tigra National Park, Honduras, ranging in size from 70 to 635 ha were instrumented and discharge measured over a one year period. A semi-distributed water balance model was developed to characterize the bio-hydrology of the four catchments, one of which is primarily cloud forest cover. The water balance model simulated daily stream discharges well, with Nash Sutcliffe model efficiency (E) values ranging from 0.67 to 0.90. Analysis of calibrated model parameters showed that despite all watersheds having similar geologic substrata, the bio-hydrological response the cloud forest indicated less plantavailable water in the root zone and greater groundwater recharge than the non cloud forest cover catchments. This resulted in watershed discharge on a per area basis four times greater from the cloud forest than the other watersheds despite only relatively minor differences in annual rainfall. These results highlight the importance of biological factors (cloud forests in this case) for sustained provision of clean, potable water, and the need to protect the cloud forest areas from destruction, particularly in the populated areas of Central America.

Sign in / Sign up

Export Citation Format

Share Document