Paved surfaces, increased precipitation intensities in addition to limited capacity in the sewer ... more Paved surfaces, increased precipitation intensities in addition to limited capacity in the sewer systems, cause a higher risk of combined sewer overflows (CSOs). Sustainable drainage systems (SUDS) offer an alternative approach to mitigate CSO by managing the stormwater locally. Seven SUDS scenarios, developed based on the concept of effective impervious area reduction, have been implemented in the Grefsen catchment using the Mike Urban model. This study evaluated the hydrological performance of two SUDS controls (i.e. green roof (GR) and rain garden (RG)) modules of the model and the effect of the SUDS scenarios on the CSOs using event-based and continuous simulations. The Nash–Sutcliffe efficiency (NSE) along with flow duration curves (FDCs) has been used for evaluating the model performance. Event-based evaluations revealed the superior performance of the RG in reducing CSOs for larger precipitation events, while GRs were proven to have beneficial outcomes during smaller events. ...
Green roofs (GRs) have become a popular sustainable drainage system (SuDS) technology in urban ar... more Green roofs (GRs) have become a popular sustainable drainage system (SuDS) technology in urban areas. As many countries and regions experience political encouragement and substitution schemes in implementing GRs, there is a need for reliant models that can support designing purposes. The stormwater management model’s (SWMM) Low Impact Development Green Roof (LID-GR) control is used to simulate the hydrological detention performance of two GRs, GR1 and GR2, with different drainage properties located in Oslo, Norway. This study uses event-based data to replicate GR runoff. Accordingly, four event-models were calibrated using the Shuffled Complex Evolution algorithm with the Nash-Sutcliffe criteria (NSE) as the objective function. Eight events were used for model validation. Simulation results revealed that SWMM’s LID module can capture response of the GRs even though the adequacy varies among events. During calibration two GR1 (0.55 and 0.72) and three GR2 (0.73, 0.88 and 0.51) event-...
Hydrology and Earth System Sciences Discussions, 2014
Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resource... more Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an erro...
ABSTRACT Operationally the main purpose of hydrological models is to provide runoff forecasts. Th... more ABSTRACT Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality. Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be related to observable conditions in the catchment. Updating of these states, knowing their relation to observable catchment conditions, influence directly the forecast quality. Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize hydropower production to market demand and at the same time minimize spill of water and maximize available hydraulic head. The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the information they use. In this project the focus has been to improve the quality of the model states which the forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third directly changes the model states. The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual observation and to how well the gauging stations represent the catchment both in respect to temperatures and precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the result against observed response. Model uncertainty is reflected in a joint distribution of model parameters estimated using the Dream algorithm.
Paved surfaces, increased precipitation intensities in addition to limited capacity in the sewer ... more Paved surfaces, increased precipitation intensities in addition to limited capacity in the sewer systems, cause a higher risk of combined sewer overflows (CSOs). Sustainable drainage systems (SUDS) offer an alternative approach to mitigate CSO by managing the stormwater locally. Seven SUDS scenarios, developed based on the concept of effective impervious area reduction, have been implemented in the Grefsen catchment using the Mike Urban model. This study evaluated the hydrological performance of two SUDS controls (i.e. green roof (GR) and rain garden (RG)) modules of the model and the effect of the SUDS scenarios on the CSOs using event-based and continuous simulations. The Nash–Sutcliffe efficiency (NSE) along with flow duration curves (FDCs) has been used for evaluating the model performance. Event-based evaluations revealed the superior performance of the RG in reducing CSOs for larger precipitation events, while GRs were proven to have beneficial outcomes during smaller events. ...
Green roofs (GRs) have become a popular sustainable drainage system (SuDS) technology in urban ar... more Green roofs (GRs) have become a popular sustainable drainage system (SuDS) technology in urban areas. As many countries and regions experience political encouragement and substitution schemes in implementing GRs, there is a need for reliant models that can support designing purposes. The stormwater management model’s (SWMM) Low Impact Development Green Roof (LID-GR) control is used to simulate the hydrological detention performance of two GRs, GR1 and GR2, with different drainage properties located in Oslo, Norway. This study uses event-based data to replicate GR runoff. Accordingly, four event-models were calibrated using the Shuffled Complex Evolution algorithm with the Nash-Sutcliffe criteria (NSE) as the objective function. Eight events were used for model validation. Simulation results revealed that SWMM’s LID module can capture response of the GRs even though the adequacy varies among events. During calibration two GR1 (0.55 and 0.72) and three GR2 (0.73, 0.88 and 0.51) event-...
Hydrology and Earth System Sciences Discussions, 2014
Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resource... more Accuracy of reservoir inflow forecasts is instrumental for maximizing the value of water resources and benefits gained through hydropower generation. Improving hourly reservoir inflow forecasts over a 24 h lead-time is considered within the day-ahead (Elspot) market of the Nordic exchange market. We present here a new approach for issuing hourly reservoir inflow forecasts that aims to improve on existing forecasting models that are in place operationally, without needing to modify the pre-existing approach, but instead formulating an additive or complementary model that is independent and captures the structure the existing model may be missing. Besides improving forecast skills of operational models, the approach estimates the uncertainty in the complementary model structure and produces probabilistic inflow forecasts that entrain suitable information for reducing uncertainty in the decision-making processes in hydropower systems operation. The procedure presented comprises an erro...
ABSTRACT Operationally the main purpose of hydrological models is to provide runoff forecasts. Th... more ABSTRACT Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality. Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be related to observable conditions in the catchment. Updating of these states, knowing their relation to observable catchment conditions, influence directly the forecast quality. Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize hydropower production to market demand and at the same time minimize spill of water and maximize available hydraulic head. The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the information they use. In this project the focus has been to improve the quality of the model states which the forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third directly changes the model states. The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual observation and to how well the gauging stations represent the catchment both in respect to temperatures and precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the result against observed response. Model uncertainty is reflected in a joint distribution of model parameters estimated using the Dream algorithm.
Uploads
Papers by Ashenafi Gragne