Third Fork Creek is a historically impaired urban stream that flows through the city of Durham, North Carolina. Caenorhabditis elegans (C. elegans) are non-parasitic, soil and aquatic dwelling nematodes that have been used frequently as a biological and ecotoxicity model. We hypothesize that exposure to Third Fork Creek surface water will inhibit the growth and chemotaxis of C. elegans. Using our ring assay model, nematodes were enticed to cross the water samples to reach a bacterial food source which allowed observation of chemotaxis. The total number of nematodes found in the bacterial food source and the middle of the plate with the water source was recorded for 3 days.
Our findings suggest a reduction in chemotaxis and growth on day three in nematodes exposed to Third Fork Creek water samples when compared to the control (p value < 0.05). These exploratory data provide meaningful insight to the quality of Third Fork Creek located near a Historically Black University.
Further studies are necessary to elucidate the concentrations of the water contaminants and implications for human health. The relevance of this study lies within the model C. elegans that has been used in a plethora of human diseases and exposure research but can be utilized as an environmental indicator of water quality impairment.
Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.
Runoff in urban streams is the most important factor influencing urban inundation. It also affects inundation in other areas as various urban streams and rivers are connected. Current runoff predictions obtained using a multi-layer perceptron (MLP) exhibit limited accuracy. In this study, the runoff of urban streams was predicted by applying an MLP using a harmony search (MLPHS) to overcome the shortcomings of MLPs using existing optimizers and compared with the observed runoff and the runoff predicted by an MLP using a real-coded genetic algorithm (RCGA). Furthermore, the results of the MLPHS were compared with the results of the MLP with existing optimizers such as the stochastic gradient descent, adaptive gradient, and root mean squared propagation. The runoff of urban steams was predicted based on the discharge of each pump station and rainfall information. The results obtained with the MLPHS exhibited the smallest error of 39.804 m3/s when compared to the peak value of the observed runoff. The MLPHS gave more accurate runoff prediction results than the MLP using the RCGA and that using existing optimizers. The accurate prediction of the runoff in an urban stream using an MLPHS based on the discharge of each pump station is possible.
Fluvial reclamation to facilitate urban development leads to culverting, hence, a loss of urban streams. Using the palimpsest analogy, we examine how the Amman Stream in Amman (Jordan) historically provided regulatory and socio-cultural ecosystem services through its socio-spatial (longitudinal, lateral, and vertical) connections. We then explore the impact of the stream's culverting, partially in 1967 then completely in 1997, on these connections and, consequently, on ecosystem services. To overcome data paucity, our methodology relied on constructing spatial data by georeferencing and digitizing aerial photos and satellite images (from 1953, 1975, 1992, and 2000) using ArcGIS. We augmented our data with archival research (historic and contemporary documents and maps), an online survey among Amman's residents, and in situ observations and photography. The results reveal striking contrasts between the historic and contemporary configuration of urban form vis-à-vis the Amman Stream. Throughout its early urban history during the Classical and early Islamic periods, the urban form elements reflected reverence and prudence toward the Amman Stream as manifested in the investment in water infrastructure and the alignment of thoroughfares, civic monuments, and bridges that collectively capitalized on the land relief (the strath) and established strong connections with the Amman Stream, maximizing, in the process, its regulatory and socio-cultural services. In contrast, the contemporary urban form replaced the stream with car-oriented roads, hence, eradicated its regulatory services and replaced its socio-spatial connections with urban socio-economic and cultural fissures. Accordingly, we propose to daylight (de-culvert) the Amman Stream to restore its regulatory and socio-cultural services and its socio-spatial connections. We substantiate the feasibility of daylighting through: (1) morphological analysis that reveals that roads cover most of the stream; (2) the survey's findings that indicate public support; and (3) the cascading benefits for the larger watershed in a water insecure region.