Stochastic Environmental Research and Risk Assessment , 2017
Understanding hydrological processes at catchment scale through the use of hydrological model par... more Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to calibrate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential calibration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal differences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R 2) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed stream-flow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique out-performed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibration and validation, thereby taking into account the catchment spatial variability, the choice of each calibration method will depend on the application and spatial scale of implementation of the modelling results in the catchment.
Episodic Combined Sewer Overflow (CSO) discharges effectively control the ecological status of re... more Episodic Combined Sewer Overflow (CSO) discharges effectively control the ecological status of receiving water bodies. Hydrodynamic models like the Storm Water Management Model (SWMM) are often used to model the CSO events. However , such detailed models are computationally demanding especially when a long-term simulation of a complex system is required. Considering this, we developed an alternative simple continuous simulation model (COSIMAT) using the SIMULINK TM module in MATLAB TM as a means of solving the issue of computational time associated with the detailed models. The COSIMAT model was tested against a detailed model set up on the SWMM. The Paruck collector – one of the major Collector of the Brussels' sewer system was used as an example case. Results showed that the accuracy of the simplified COSIMAT model was comparable to that of the detailed hydrodynamic model (SWMM) with a significant reduction of computational time by a factor of 8. We believe such alternative approaches would be useful to replace a computationally demanding model component of an integrated modelling system of a complex sewer system.
This paper investigates the potential of using global reanalysis datasets as input for 7 hydrolog... more This paper investigates the potential of using global reanalysis datasets as input for 7 hydrological modelling in the data-scarce Sudano-Sahel region. To achieve this, two global 8 atmospheric reanalyses (Climate Forecasting System Reanalysis and ERA-Interim) datasets and one 9 global meteorological forcing dataset obtained from WATCH Forcing Data methodology applied to 10 ERA-Interim (WFDEI) were used to drive the Soil and Water Assessment Tool (SWAT) in the 11 Logone catchment, Lake Chad basin. Model performance indicators after calibration showed that at 12 daily and monthly time steps, only WFDEI produced Nash Sutcliff Efficiency (NSE) and Coefficient 13 of Determination (R 2) values above 0.50. Albeit a general underperformance compared to WFDEI; 14 CFSR performed better than ERA-Interim. Model uncertainty analysis after calibration showed that 15 more than 60% of all daily and monthly observed streamflow values at all hydrometric stations were 16 bracketed within the 95 percent prediction uncertainty (95PPU) range for all datasets. Results from 17 this study also show significant differences in simulated actual evapotranspiration estimates from 18 the datasets. Overall results showed that biased corrected WFDEI outperformed the two reanalysis 19 datasets; meanwhile CFSR performed better than ERA-Interim. We conclude that in the absence of 20 gauged hydro-meteorological data, WFDEI and CFSR could be used for hydrological modelling in 21 data-scarce areas such as the Sudano-Sahel region. 22
The socioeconomic consequences posed by climate change in Africa are giving increasing emphasis t... more The socioeconomic consequences posed by climate change in Africa are giving increasing emphasis to the need for trend analysis and detection of changes in hydro-climatic variables in data deficient areas. This study analyses rainfall data from 17 rain gauges unevenly distributed across the Logone catchment in the Lake Chad basin over a 50-year period (1951–2000). After quality control of the rainfall data using homogeneity tests, nonparametric Mann–Kendall and Spearman's rho tests were applied to detect the presence of trends. Trend magnitude was calculated using Sen's slope estimator. Results of the homogeneity test showed that rainfall was homogeneous across the catchment. Trend analysis revealed the presence of negative trends for annual rainfall at all the stations. Results of long-term trend analysis at a monthly time scale revealed the presence of statistically insignificant positive trends at 32% of the stations. Spatially, the analysis showed a clear distinction in rainfall magnitude between the semi-arid and Sudano zones. The slope of the trend lines for annual rainfall averaged over the respective zones was higher in the semi-arid zone (−4.37) compared to the Sudano zone (−4.02). However, the station with the greatest reduction in annual rainfall (−8.06 mm) was located in the Sudano zone.
In spite of the fact that advances in remote sensing technology have generated a wealth of potent... more In spite of the fact that advances in remote sensing technology have generated a wealth of potential data that can be used for climate studies and water resources management in data scarce areas, there are still some challenges relating to the use of satellite data. This is because remote sensing data is usually developed for application in large areas, limiting their application in moderate size catchments except the data is further downscaled, transformed or interpolated. Although multiyear global reanalysis datasets could be used, significant differences exist in precipitation estimates between them. Furthermore, some reanalysis products have very coarse resolution thus may fail to capture the spatial rainfall in a catchment. To overcome this challenge, high resolution products like CFSR could be used in small to medium size catchments. In this study, CFSR grid point precipitation estimates were compared with in situ measurements from a network of 25 rain gauge stations using standard statistical techniques and graphical plots. Results show that, CFSR estimated with reasonable accuracy and at different spatio-temporal scales, the precipitation amounts and its variation across the study area. It can be concluded that, CFSR precipitation data may be used for climate research and to enhance the management of water resources in such data scarce regions. However, additional work is needed to eliminate the systematic biases observed in the CFSR precipitation estimates.
Hydro-meteorological data is an important asset that can enhance management of water resources. B... more Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.
Stochastic Environmental Research and Risk Assessment , 2017
Understanding hydrological processes at catchment scale through the use of hydrological model par... more Understanding hydrological processes at catchment scale through the use of hydrological model parameters is essential for enhancing water resource management. Given the difficulty of using lump parameters to calibrate distributed catchment hydrological models in spatially heterogeneous catchments, a multiple calibration technique was adopted to enhance model calibration in this study. Different calibration techniques were used to calibrate the Soil and Water Assessment Tool (SWAT) model at different locations along the Logone river channel. These were: single-site calibration (SSC); sequential calibration (SC); and simultaneous multi-site calibration (SMSC). Results indicate that it is possible to reveal differences in hydrological behavior between the upstream and downstream parts of the catchment using different parameter values. Using all calibration techniques, model performance indicators were mostly above the minimum threshold of 0.60 and 0.65 for Nash Sutcliff Efficiency (NSE) and coefficient of determination (R 2) respectively, at both daily and monthly time-steps. Model uncertainty analysis showed that more than 60% of observed stream-flow values were bracketed within the 95% prediction uncertainty (95PPU) band after calibration and validation. Furthermore, results indicated that the SC technique out-performed the other two methods (SSC and SMSC). It was also observed that although the SMSC technique uses streamflow data from all gauging stations during calibration and validation, thereby taking into account the catchment spatial variability, the choice of each calibration method will depend on the application and spatial scale of implementation of the modelling results in the catchment.
Episodic Combined Sewer Overflow (CSO) discharges effectively control the ecological status of re... more Episodic Combined Sewer Overflow (CSO) discharges effectively control the ecological status of receiving water bodies. Hydrodynamic models like the Storm Water Management Model (SWMM) are often used to model the CSO events. However , such detailed models are computationally demanding especially when a long-term simulation of a complex system is required. Considering this, we developed an alternative simple continuous simulation model (COSIMAT) using the SIMULINK TM module in MATLAB TM as a means of solving the issue of computational time associated with the detailed models. The COSIMAT model was tested against a detailed model set up on the SWMM. The Paruck collector – one of the major Collector of the Brussels' sewer system was used as an example case. Results showed that the accuracy of the simplified COSIMAT model was comparable to that of the detailed hydrodynamic model (SWMM) with a significant reduction of computational time by a factor of 8. We believe such alternative approaches would be useful to replace a computationally demanding model component of an integrated modelling system of a complex sewer system.
This paper investigates the potential of using global reanalysis datasets as input for 7 hydrolog... more This paper investigates the potential of using global reanalysis datasets as input for 7 hydrological modelling in the data-scarce Sudano-Sahel region. To achieve this, two global 8 atmospheric reanalyses (Climate Forecasting System Reanalysis and ERA-Interim) datasets and one 9 global meteorological forcing dataset obtained from WATCH Forcing Data methodology applied to 10 ERA-Interim (WFDEI) were used to drive the Soil and Water Assessment Tool (SWAT) in the 11 Logone catchment, Lake Chad basin. Model performance indicators after calibration showed that at 12 daily and monthly time steps, only WFDEI produced Nash Sutcliff Efficiency (NSE) and Coefficient 13 of Determination (R 2) values above 0.50. Albeit a general underperformance compared to WFDEI; 14 CFSR performed better than ERA-Interim. Model uncertainty analysis after calibration showed that 15 more than 60% of all daily and monthly observed streamflow values at all hydrometric stations were 16 bracketed within the 95 percent prediction uncertainty (95PPU) range for all datasets. Results from 17 this study also show significant differences in simulated actual evapotranspiration estimates from 18 the datasets. Overall results showed that biased corrected WFDEI outperformed the two reanalysis 19 datasets; meanwhile CFSR performed better than ERA-Interim. We conclude that in the absence of 20 gauged hydro-meteorological data, WFDEI and CFSR could be used for hydrological modelling in 21 data-scarce areas such as the Sudano-Sahel region. 22
The socioeconomic consequences posed by climate change in Africa are giving increasing emphasis t... more The socioeconomic consequences posed by climate change in Africa are giving increasing emphasis to the need for trend analysis and detection of changes in hydro-climatic variables in data deficient areas. This study analyses rainfall data from 17 rain gauges unevenly distributed across the Logone catchment in the Lake Chad basin over a 50-year period (1951–2000). After quality control of the rainfall data using homogeneity tests, nonparametric Mann–Kendall and Spearman's rho tests were applied to detect the presence of trends. Trend magnitude was calculated using Sen's slope estimator. Results of the homogeneity test showed that rainfall was homogeneous across the catchment. Trend analysis revealed the presence of negative trends for annual rainfall at all the stations. Results of long-term trend analysis at a monthly time scale revealed the presence of statistically insignificant positive trends at 32% of the stations. Spatially, the analysis showed a clear distinction in rainfall magnitude between the semi-arid and Sudano zones. The slope of the trend lines for annual rainfall averaged over the respective zones was higher in the semi-arid zone (−4.37) compared to the Sudano zone (−4.02). However, the station with the greatest reduction in annual rainfall (−8.06 mm) was located in the Sudano zone.
In spite of the fact that advances in remote sensing technology have generated a wealth of potent... more In spite of the fact that advances in remote sensing technology have generated a wealth of potential data that can be used for climate studies and water resources management in data scarce areas, there are still some challenges relating to the use of satellite data. This is because remote sensing data is usually developed for application in large areas, limiting their application in moderate size catchments except the data is further downscaled, transformed or interpolated. Although multiyear global reanalysis datasets could be used, significant differences exist in precipitation estimates between them. Furthermore, some reanalysis products have very coarse resolution thus may fail to capture the spatial rainfall in a catchment. To overcome this challenge, high resolution products like CFSR could be used in small to medium size catchments. In this study, CFSR grid point precipitation estimates were compared with in situ measurements from a network of 25 rain gauge stations using standard statistical techniques and graphical plots. Results show that, CFSR estimated with reasonable accuracy and at different spatio-temporal scales, the precipitation amounts and its variation across the study area. It can be concluded that, CFSR precipitation data may be used for climate research and to enhance the management of water resources in such data scarce regions. However, additional work is needed to eliminate the systematic biases observed in the CFSR precipitation estimates.
Hydro-meteorological data is an important asset that can enhance management of water resources. B... more Hydro-meteorological data is an important asset that can enhance management of water resources. But existing data often contains gaps, leading to uncertainties and so compromising their use. Although many methods exist for infilling data gaps in hydro-meteorological time series, many of these methods require inputs from neighbouring stations, which are often not available, while other methods are computationally demanding. Computing techniques such as artificial intelligence can be used to address this challenge. Self-organizing maps (SOMs), which are a type of artificial neural network, were used for infilling gaps in a hydro-meteorological time series in a Sudano-Sahel catchment. The coefficients of determination obtained were all above 0.75 and 0.65 while the average topographic error was 0.008 and 0.02 for rainfall and river discharge time series, respectively. These results further indicate that SOMs are a robust and efficient method for infilling missing gaps in hydro-meteorological time series.
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are still some challenges relating to the use of satellite data. This is because remote sensing data is usually developed for application in large areas, limiting their application in moderate size catchments except the data is further downscaled, transformed or interpolated.
Although multiyear global reanalysis datasets could be used, significant differences exist in precipitation estimates between them. Furthermore, some reanalysis products have very coarse
resolution thus may fail to capture the spatial rainfall in a catchment. To overcome this challenge, high resolution products like CFSR could be used in small to medium size catchments.
In this study, CFSR grid point precipitation estimates were compared with in situ measurements from a network of 25 rain gauge stations using standard statistical techniques and graphical plots.
Results show that, CFSR estimated with reasonable accuracy and at different spatio-temporal scales, the precipitation amounts and its variation across the study area. It can be concluded that, CFSR precipitation data may be used for climate research and to enhance the
management of water resources in such data scarce regions. However, additional work is needed to eliminate the systematic biases observed in the CFSR precipitation estimates.
are still some challenges relating to the use of satellite data. This is because remote sensing data is usually developed for application in large areas, limiting their application in moderate size catchments except the data is further downscaled, transformed or interpolated.
Although multiyear global reanalysis datasets could be used, significant differences exist in precipitation estimates between them. Furthermore, some reanalysis products have very coarse
resolution thus may fail to capture the spatial rainfall in a catchment. To overcome this challenge, high resolution products like CFSR could be used in small to medium size catchments.
In this study, CFSR grid point precipitation estimates were compared with in situ measurements from a network of 25 rain gauge stations using standard statistical techniques and graphical plots.
Results show that, CFSR estimated with reasonable accuracy and at different spatio-temporal scales, the precipitation amounts and its variation across the study area. It can be concluded that, CFSR precipitation data may be used for climate research and to enhance the
management of water resources in such data scarce regions. However, additional work is needed to eliminate the systematic biases observed in the CFSR precipitation estimates.