Evaluation of Reservoir Sediment Load under Climate Change

Climate Change, Adaptation and Long-Term Prediction

Use of Artificial Wetlands in Urban Storm Water Quality Management

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Stormwater Management Based on Resilient Urban Drainage Strategies

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Role of Resilience in Sustainable Urban Stormwater Management

Typically, best management practices (BMPs) are implemented to help sustainable stormwater management in urban areas. Over recent decades the selection of urban stormwater management measures for a site has been a challenge among urban planners where thecriterion based on flood volume no longer suffices for selecting urban drainage solutions. Therefore there is a need to consider a set of holistic criteria beyond runoff and inundation objectives by which it would be possible to evaluate sustainability of urban drainage projects. Frequent urban flooding events have justified the use of ‘resilience’ concept and pertaining criteria.  This paper proposes a methodology to verify the sustainability of BMPs projects alongside their resilience. The multi criteria decision making (MCDM) technique has been applied for BMPs ranking based on proposed criteria. The methodology has been applied to urban drainage system of a municipal district of Tehran (Iran). Results indicate the effects of manager’s preferences on selecting BMPs. The proposed methodology provides an effective tool for urban managers to adopt more resilient-sustainable decisions in urban areas

Flood Forecasting Using Artificial Neural Networks: an Application of Multi-Model Data Fusion technique

Floods are among the natural disasters that cause human hardship and economic loss. Establishing a viable flood forecasting and warning system for communities at risk can mitigate these adverse effects. However, establishing an accurate flood forecasting system is still challenging due to the lack of knowledge about the effective variables in forecasting. The present study has indicated that the use of artificial intelligence, especially neural networks is suitable for flood forecasting systems (FFSs). In this research, mathematical modeling of flood forecasting with the application of Artificial Neural Networks (ANN) and data fusion technique were used in estimating the flood discharge. Sensitivity analysis was performed to investigate the significance of each model input and the best MLP ANN architecture. The data used in developing the model comprise discharge at different time steps, precipitation and antecedent precipitation index for a major river basin. Application of model on a case study (Karun River in Iran) indicated that rainfall-runoff process using data fusion approach produces results with higher degrees of precision

An investigation into the effects of tidal barrier operation on the tidal asymmetry in the Arvand Estuary

The Arvand River forms the border between Iran and Iraq and is the only permanent river discharging into the Persian Gulf(PG). It is a tidal river adversely affected by sedimentation,which is more likely resulted from tidal asymmetry. The tidal barrier(TB) has remarkable effects on the tidal regime. To assess these effects on the tidal waves asymmetry,it is critical to study the two main factors of closure percentage(CP) and closure duration(CD). This manuscript aims to investigate the tidal barrier effect on the asymmetry of the tidal waves propagating through the estuary. To evaluate tidal asymmetry,Tidal Asymmetry Index(TAI) is introduced based on the relative phase angle of the M2 and M4 components through the river. A two-dimensional Delft3D hydrodynamic model is utilized. The tidal wave is flood dominant, and its relative phase angle increased slightly, from 90 to 135 degrees in Km 40 and then decreased to just under 90 degrees near the Abadan(65km) and constant along the Abadan to Khorramshahr. The tidal barrier has changed the tidal regime in the river which leads to relatively constant tidal asymmetry during the 45km upward. To reach the highest TAI, a closure percentage and duration of 55% and 180 minutes are estimated. The tidal barrier operation also adversely affects the amplitude of the M2 and M4 components. M4 component amplitude increases before reaching the TB and then decreases. The decrease is more elaborated from kilometer 45 onward. Increasing the closure percentage amplifies the changes described above,but it has little effect on the general trends

Statistical refinement of the North American Multi-Model Ensemble precipitation forecasts over Karoon basin, Iran

An effective postprocessing approach has been examined to improve the skill of North American Multi-Model Ensemble (NMME) precipitation forecasts in the Karoon basin, Iran. The Copula–Bayesian approach was used along with the Normal Kernel Density marginal distribution and the Kernel Copula function. This process creates more than one postprocessing precipitation value as results candidates (first pass). A similar process is used for a second pass to obtain preprocessed values based on the candidate inputs, which helps identify the most suitable postprocessed value. The application of the technique for order preference by similarity to the ideal solution method based on conditional probability distribution functions of the first and second passes leads to achieving final improved forecast data among the existing candidates. To validate the results, data from 1982–2010 and 2011–2018 were used for the calibration and forecast periods. The results show that while the GFDL and CFS2 models tend to overestimate precipitation, most other NMME models underestimate it. Postprocessing improves the accuracy of forecasts for most models by 20%–40%. Overall, the proposed Copula–Bayesian postprocessing approach could provide more reliable forecasts with higher spatial and temporal consistency, better detection of extreme precipitation values, and a significant reduction in uncertainties. HIGHLIGHTS The precipitation forecasts of Karoon river watershed in southwest Iran as a flood-prone area are investigated.; A new postprocessing approach is presented for North American Multi-Model Ensemble (NMME) precipitation estimations.; The proposed method is based on the Copula–Bayesian approach.; The method is desirable for detection of the extreme precipitation values.; Significant increases in forecast skill of improved NMME data are provided.