Nonstructural Urban Stormwater Quality Measures: Building a Knowledge Base to Improve Their Use

This article summarizes a research project that investigated the use, performance, cost, and evaluation of nonstructural measures to improve urban stormwater quality. A survey of urban stormwater managers from Australia, New Zealand, and the United States revealed a widespread trend of increasing use of nonstructural measures among leading stormwater management agencies, with at least 76% of 41 types of nonstructural measures being found to be increasing in use. Data gathered from the survey, an international literature review, and a multicriteria analysis highlighted four nonstructural measures of greatest potential value: mandatory town planning controls that promote the adoption of low-impact development principles and techniques; development of strategic urban stormwater management plans for a city, shire, or catchment; stormwater management measures and programs for construction/building sites; and stormwater management activities related to municipal maintenance operations such as maintenance of the stormwater drainage network and manual litter collections. Knowledge gained on the use and performance of nonstructural measures from the survey, literature review, and three trial evaluation projects was used to develop tailored monitoring and evaluation guidelines for these types of measure. These guidelines incorporate a new evaluation framework based on seven alternative styles of evaluation that range from simply monitoring whether a nonstructural measure has been fully implemented to monitoring its impact on waterway health. This research helps to build the stormwater management industry’s knowledge base concerning nonstructural measures and provides a practical tool to address common impediments associated with monitoring and evaluating the performance and cost of these measures.     

Estimating the Impact of Temperature and Rainfall on Malaria Incidence in Ghana from 2012 to 2017

Environmental Modeling & Assessment - Malaria has a significant impact on the lives of many in Ghana. It is one of the key causes of mortality and morbidity, resulting in 32.5% of outpatient...     

Assessing conservation status with extensive but low-resolution data: Application of frequentist and Bayesian models to endangered Athabasca River rainbow trout

Use of extensive but low-resolution abundance data is common in the assessment of species at-risk status based on quantitative decline criteria under International Union for Conservation of Nature (IUCN) and national endangered species legislation. Such data can be problematic for 3 reasons. First, statistical power to reject the null hypothesis of no change is often low because of small sample size and high sampling uncertainty leading to a high frequency of type II errors. Second, range-wide assessments composed of multiple site-specific observations do not effectively weight site-specific trends into global trends. Third, uncertainty in site-specific temporal trends and relative abundance are not propagated at the appropriate spatial scale. A common result is the propensity to underestimate the magnitude of declines and therefore fail to identify the appropriate at-risk status for a species. We used 3 statistical approaches, from simple to more complex, to estimate temporal decline rates for a designatable unit (DU) of rainbow trout in the Athabasca River watershed in western Canada. This DU is considered a native species for purposes of listing because of its genetic composition characterized as >0.95 indigenous origin in the face of continuing introgressive hybridization with introduced populations in the watershed. Analysis of abundance trends from 57 time series with a fixed-effects model identified 33 sites with negative trends, but only 2 were statistically significant. By contrast, a hierarchical linear mixed model weighted by site-specific abundance provided a DU-wide decline estimate of 16.4% per year and a 3-generation decline of 93.2%. A hierarchical Bayesian mixed model yielded a similar 3-generation decline trend of 91.3% and the posterior distribution showed that the estimate had a >99% probability of exceeding thresholds for an endangered listing. We conclude that the Bayesian approach was the most useful because it provided a probabilistic statement of threshold exceedance in support of an at-risk status recommendation.