Rainfall-induced shallow landslides are one of the most frequent hazards on slanted terrains. Intense storms with high-intensity and long-duration rainfall have high potential to trigger rapidly moving soil masses due to changes in pore water pressure and seepage forces. Nevertheless, regardless of the intensity and/or duration of the rainfall, shallow landslides are influenced by antecedent soil moisture conditions. As of this day, no system exists that dynamically interrelates these two factors on large scales. This work introduces a Shallow Landslide Index (SLI) as the first implementation of antecedent soil moisture conditions for the hazard analysis of shallow rainfall-induced landslides. The proposed mathematical algorithm is built using a logistic regression method that systematically learns from a comprehensive landslide inventory. Initially, root-soil moisture and rainfall measurements modeled from AMSR-E and TRMM respectively, are used as proxies to develop the index. The input dataset is randomly divided into training and verification sets using the Hold-Out method. Validation results indicate that the best-fit model predicts the highest number of cases correctly at 93.2% accuracy. Consecutively, as AMSR-E and TRMM stopped working in October 2011 and April 2015 respectively, root-soil moisture and rainfall measurements modeled by SMAP and GPM are used to develop models that calculate the SLI for 10, 7, and 3 days. The resulting models indicate a strong relationship (78.7%, 79.6%, and 76.8% respectively) between the predictors and the predicted value. The results also highlight important remaining challenges such as adequate information for algorithm functionality and satellite based data reliability. Nevertheless, the experimental system can potentially be used as a dynamic indicator of the total amount of antecedent moisture and rainfall (for a given duration of time) needed to trigger a shallow landslide in a susceptible area. It is indicated that the SLI algorithm can be re-built for other regions where deterministic studies are not feasible. This represents a significant step towards rainfall-induced shallow landslide hazard readiness.
Sulaimania is a City located in Kurdistan region in the north of Iraq. The city is facing a lack of water, and it will reach a very critical condition shortly. One of the potential solutions is to reuse the treated wastewater for non-direct human uses such as irrigation, washing, firefighting, groundwater recharging, and others. There is no sewage treatment plant in the city. The wastewater flows into a stream through some sewer outlets, and that causes big environmental issues. Decentralized wastewater treatment units (DTUs) are suggested to solve the issue. The treated wastewater will be used for the irrigation of the green areas of the city. The selected plant type is Extended Aeration treatment system, which is recommended for residential areas. Specifying the locations of the treatment units is very important from environmental, social and technical aspects. The main objective of this study is to select the best suitable places for the DTUs. Preliminary selections of 134 nominated areas for DTU locations were made in different places in the city. The locations are distributed into 10 groups near the main sewer pipes of the city. A model is created to evaluate those selected locations and eliminate the non-suitable locations by using GIS software integrated with the Analytical Hierarchy Process (AHP). Five criteria were used in the model, which are, (1) The size of the available lands, (2) The distance from the decentralized units to the green areas (3) Population density around the decentralized treatment unit locations, (4) The slope of the land and (5) Depth of the main sewer pipe at the nominated area. In addition, the model adopted two restriction factors, which are: (1) The distance from the decentralized treatment unit to the buildings should not be less than 10 m and (2) The distance between the main sewer pipes and the treatment units are taken to be <50 m. The results of the suitability analysis produced six classes of suitability levels of the nominated areas started from restricted to extremely suitable. The suitability percentages of the 6 classes of the total nominated areas were found to be; 8.5% (6.95 ha) restricted, 0.4 % (0.23 ha) moderately suitable, 12.8% (10.50 ha) suitable, 38.8% very suitable (31.60 ha), 32.2% (26.33 ha) highly suitable and 7.3% (5.92 ha) extremely suitable. Each nominated area has more than one suitability class. Normalized Weighted Average (NWAV) of the suitability level percentage of each nominated area is found. The values of the NWAV are ranged from 0.0 to 1.0, and the selection of final DTUs locations will be for areas that have NWAV larger than 0.5. Optimum 30 suitable locations are selected out of the 134 nominated areas.
Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA SWMM), MIKE URBAN-II and others, have been developed to simulate flooding events in cities. However, they require high accuracy mapping and a simulation of the underground storm drainage system. Sadly, this capability is usually not available for older or larger so-called megacities. Other hydrological model types are classified in the semi-physical category, like Cellular Automata (CA), require the incorporation of very fine resolution data. These types of data, in turn, demand massive computer power and time for analysis. Furthermore, available forecasting systems provide a way to determine total rainfall during extreme events, but they do not tell us what areas will be flooded. This work introduces an urban flooding tool that couples a rainfall-runoff model with a flood map database to expedite the alert process and estimate flooded areas. A 0.30-m Lidar Digital Elevation Model (DEM) of the study area (in this case Manhattan, New York City) is divided into 140 sub-basins. Several flood maps for each sub-basin are generated and organized into a database. For any forecasted extreme rainfall event, the rainfall-runoff model predicts the expected runoff volume at different times during the storm interval. The system rapidly searches for the corresponding flood map that delineates the expected flood area. The sensitivity analysis of parameters in the model show that the effect of storm inlet flow head is approximately linear while the effects of the threshold infiltration rate, the number of storm inlets, and the storm inlet flow reduction factor are non-linear. The reduction factor variation is found to exhibit a high non-linearity variation, hence requiring further detailed investigation.
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