Impacts of the climate change on runoff and diffuse phosphorus load to Lake Balaton (Hungary)

Development of an autonomous flow-proportional water sampler for the estimation of pollutant loads in urban runoff

Implementation of an extensive urban runoff monitoring program, targeting the quantification of heavy metal and organic micropollutant loads, necessitated the development of an autonomous water sampler. The design requirements for the device were to fulfill flow-proportional continuous composite sampling of urban runoff events in a widely customizable, relatively inexpensive, and simple way. In this paper, we introduce the concept along with the experiences gained from the first several months of field tests at seven pilot areas in Hungary that represent a wide range of urban environments. During the test period, prototype samplers were placed in natural (urban) streams as well as stormwater drainage pipes, resulting in a total of 97 automatic composite runoff samples. At two sites, an additional 28 manual grab samples were collected to represent time series from five distinct runoff events. Sampling efficiency was checked by comparing collected volumes with the theoretical ones (derived from pump mileage data). Ranges and ratios of concentrations measured from composite and grab samples were graphically interpreted in order to evaluate their representativeness. It has been shown that the concept is suitable for conducting cost-effective urban runoff characterization surveys targeting inter-event variability.

An uncertainty-based framework to quantifying climate change impacts on coastal flood vulnerability: case study of New York City

The continued development efforts around the world, growing population, and the increased probability of occurrence of extreme hydrologic events have adversely affected natural and built environments. Flood damages and loss of lives from the devastating storms, such as Irene and Sandy on the East Coast of the USA, are examples of the vulnerability to flooding that even developed countries have to face. The odds of coastal flooding disasters have been increased due to accelerated sea level rise, climate change impacts, and communities' interest to live near the coastlines. Climate change, for instance, is becoming a major threat to sustainable development because of its adverse impacts on the hydrologic cycle. Effective management strategies are thus required for flood vulnerability reduction and disaster preparedness. This paper is an extension to the flood resilience studies in the New York City coastal watershed. Here, a framework is proposed to quantify coastal flood vulnerability while accounting for climate change impacts. To do so, a multi-criteria decision making (MCDM) approach that combines watershed characteristics (factors) and their weights is proposed to quantify flood vulnerability. Among the watershed characteristics, potential variation in the hydrologic factors under climate change impacts is modeled utilizing the general circulation models' (GCMs) outputs. The considered factors include rainfall, extreme water level, and sea level rise that exacerbate flood vulnerability through increasing exposure and susceptibility to flooding. Uncertainty in the weights as well as values of factors is incorporated in the analysis using the Monte Carlo (MC) sampling method by selecting the best-fitted distributions to the parameters with random nature. A number of low impact development (LID) measures are then proposed to improve watershed adaptive capacity to deal with coastal flooding. Potential range of current and future vulnerability to flooding is estimated with and without consideration of climate change impacts and after implementation of LIDs. Results show that climate change has the potential to increase rainfall intensity, flood volume, floodplain extent, and flood depth in the watershed. The results also reveal that improving system resilience by reinforcing the adaptation capacity through implementing LIDs could mitigate flood vulnerability. Moreover, the results indicate the significant effect of uncertainties, arising from the factors' weights as well as climate change, impacts modeling approach, on quantifying flood vulnerability. This study underlines the importance of developing applicable schemes to quantify coastal flood vulnerability for evolving future responses to adverse impacts of climate change.

Storm Water Management Model: Performance Review and Gap Analysis

The storm water management model (SWMM) is a widely used tool for urban drainage design and planning. Hundreds of peer-reviewed articles and conference proceedings have been written describing applications of SWMM. This review focuses on collecting information on model performance with respect to calibration and validation in the peer-reviewed literature. The major developmental history and applications of the model are also presented. The results provide utility to others looking for a quick reference to gauge the integrity of their own unique SWMM application. A gap analysis assesses the model's ability to perform water-quality simulations considering green infrastructure (GI)/low impact development (LID) designs and effectiveness. It is concluded that the level of detail underlying the conceptual model of SWMM versus its overall computational parsimony is well balanced-making it an adequate model for large and medium-scale hydrologic applications. However, embedding a new mechanistic algorithm or providing user guidance for coupling with other models will be necessary to realistically simulate diffuse pollutant sources, their fate and transport, and the effectiveness of GI/LID implementation scenarios.