Dynamics of runoff quality associated with an urban park and WSUD treatment train in a tropical climate.

A combination of discrete sampling using autosamplers triggered by area-velocity metres and continuous water quality monitoring using YSI datasondes enabled us to evaluate the dynamics of both stormwater runoff and dry weather water quality associated with an urban park and WSUD treatment train in Singapore. The WSUD treatment train consisted of a raingarden and sedimentation ponds. A first flush phenomenon was observed for total suspended solids (TSS) in three of four discrete-sampled events, but there was a less pronounced first flush characteristic for nutrients. Mean TN concentrations associated with runoff from the park (upstream of the WSUD treatment train) were in the range that would be associated with eutrophic conditions, while mean TP levels were in the mesotrophic/eutrophic boundary range. The raingarden reduced nutrient and TSS levels by 7-62% and loads by 65-93%, although nitrate levels were higher in the effluent compared to the influent. The sedimentation ponds successfully reduced sediment levels by 56%, based on continuous turbidity measurements. The dissolved oxygen levels in the sedimentation ponds were lower during drought periods, which has implications for aquatic organisms. The WSUD treatment train generally improved runoff quality from the park and such features can be easily integrated into the park design.

Inferring floodplain bathymetry using inundation frequency.

This study proposes a new method to retrieve the bathymetry of turbid-water floodplains from the inundation frequency (IF) data derived from over 32 years of composite optical remote sensing data. The new method was tested and validated over the Curuai floodplain in the lower Amazon River, where the entire bathymetry was surveyed in 2004, and water level gauge data has been available since 1960. The depth was estimated based on the relationship derived from IF and surveyed depth data, and the results were compared to those retrieved from bare-Earth DEM. We further assessed the sensitivity of the approach by analyzing the deepest part of the lake (i.e., permanent water body ~ 8m) with high IF, as well as the effect of gradual sedimentation in the lake over time. The results showed that the model is highly accurate and sensitive to IF changes even in the permanent water body areas, suggesting that this model can be used in other seasonal lakes worldwide with turbid-waters, where large-scale bathymetry surveys are not feasible due to high operation costs.

Dramatic decrease of flood frequency in the Mekong Delta due to river-bed mining and dyke construction.

Here we present a proof of concept evaluation of the impacts of riverbed-mining on river-wetland connectivity by analyzing the temporal trends of the flood frequencies in the Vietnamese Mekong Delta (VMD), while accounting for the effect of dyke constructions. We focus on the Long Xuyen Quadrangle (LXQ), which is significant in terms of biodiversity and economic contribution to the VMD as it is one of the most important food baskets of Southeast Asia that depends on seasonal flooding. Our results indicate that the flood frequency in LXQ has decreased significantly over the past 20 years (1995-2015). Time-series analyses of water level data at Chau Doc, Tan Chau, and Can Tho stations confirmed that the overall descending trend is statistically significant (p-value < 0.001 and tau ~ 0.1). However, the river discharge at Kratie showed no significant trend (p-value = 0.98) over the same period. This indicates that the flood frequency is associated with the lowering of the riverbed (incision) other than climatic factors. The connectivity analysis also revealed a remarkable drop in the inundation duration after early 2000, which corresponds to the previous observations of the shifting shoreline of the VMD from construction to shrinking. Finally, regression and principal component analyses underpinned the strong causality between the riverbed-mining and the decreased seasonal flooding patterns in LXQ, while accounting for the effect of the dyke system over the last decades (R2 = 0.75). This study offers compelling evidences on the relationship between sand-mining in the river and the disrupted flood regimes in VMD. The reduction in water and sediments that is necessary for sustaining current rates of agricultural production in the long term would endanger the livelihoods of millions of VMD inhabitants.

Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis.

In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter "Cc". Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R2). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil.