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To predict the phreatic surface and conduct flood routing of rockfill dams, the flow hydraulics and rating curve need to be simulated. In this research, one-dimensional flow hydraulics through a trapezoidal rockfill dam was modelled via the standard step method. Regarding the non-Darcy flow, the relationship between the hydraulic gradient and the bulk velocity in power form was adopted. Using the exit height as the boundary condition significantly improves the prediction of the phreatic surface. For this purpose, a modified Pavlovsky equation was introduced. The advantage of the proposed method is that, unlike in previous studies, the downstream water level is used to estimate the exit height instead of the upstream water level, which makes the model more grounded on steady gradually varying flow. The proposed model has the capability of predicting the reservoir head-outflow relationship and routed hydrographs with high accuracy comparable with that of two-dimensional models. [ABSTRACT FROM AUTHOR]

Flood mitigation measures are aimed at reducing adverse flooding impacts. Flood source control schemes mainly concentrate on biological soil and water conservation measures (SWCMs) including the revival and preservation of soil and land use. Such activities prevent splash erosion, reduce the velocity of surface runoff, increase surface roughness that in turn improve infiltration and control soil erosion. Nevertheless, based on technical and budget constraints, uniform implementation of biological SCWMs is neither practical nor optimal. Previous studies on spatial optimization of BMPs (best management practices) have mostly focused on urban structural and/or biological BMPs. Besides, the incorporation of land use/cover and slope maps in source control site selection has not been thoroughly studied. In this research, cover–slope zones are proposed as spatial units for spatial optimization of biological SCWMs. The objective of this study was to determine the optimal spatial distribution of SWCMs using coupled simulation–optimization models. A fully distributed event-based deterministic hydrologic rainfall–runoff model was developed that involved SCS-CN (soil conservation service curve number), ModClark and Muskingum as infiltration, rainfall–runoff and stream routing model, respectively. Genetic algorithm was adopted to search the optimized areas based on two objective functions: minimization of the outlet peak discharge and the cost. Nardin sub-catchment in northern Iran was selected as the case study. For different degrees of flood peak reduction under 100-year design rainfall, the optimal patterns identified by the optimization algorithm were a combination of less effective/costly options (exclusion) and the most effective option (transplant). Accordingly, biological SCWMs were assigned from the farthest zone to the outlet, stretching into downstream zones. [ABSTRACT FROM AUTHOR]

This paper compares and evaluates four principal methods of dust source and plume identification using MODIS data. The four MODIS methods used here are: (i) Roskovensky and Liou's dust identification algorithm, (ii) Ackerman's model, (iii) Normalized Difference Dust Index (NDDI), and (iv) Deep Blue algorithm. These techniques were applied to three recent significant events in the Middle East region. In addition, true color images and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used to evaluate the result of each technique. To optimize the result of dust detection for each technique, the published dust-nondust thresholds had to be considerably adjusted on an event-by-event basis. Results show that techniques that use brightness temperature (BT) difference are the most reliable techniques for dust source detection in several situations such as multiplume and multimineralogical conditions (unlike the NDDI index and other optical based algorithms). However, these techniques cannot effectively differentiate dust plume from the bright desert surfaces (due to the same thermal behavior of dust and desert surfaces in both BT31 and BT32). This weakness impelled us to develop a new model based on Ackerman's technique because of its more precise results in dust source identification. In this new presented model called Middle East Dust Index (MEDI), BT29 was involved to highlight the difference between dust and desert surfaces as the [(BT31-BT29)/(BT32-BT29)] equation. In this equation, the values of dusty pixels are less than 0.6 while nondusty pixels are greater than 0.6. Results indicate that the MEDI model is ideal in both identifying dust plume and sources and desert surfaces. Finally, due to some misclassification of the MEDI model in differentiating cirrus clouds from dust plumes, the NDDI index was added to the initial model to distinguish them more accurately. [ABSTRACT FROM AUTHOR]