Green roof stormwater retention: effects of roof surface, slope, and media depth

Urban areas generate considerably more stormwater runoff than natural areas of the same size due to a greater percentage of impervious surfaces that impede water infiltration. Roof surfaces account for a large portion of this impervious cover. Establishing vegetation on rooftops, known as green roofs, is one method of recovering lost green space that can aid in mitigating stormwater runoff. Two studies were performed using several roof platforms to quantify the effects of various treatments on stormwater retention. The first study used three different roof surface treatments to quantify differences in stormwater retention of a standard commercial roof with gravel ballast, an extensive green roof system without vegetation, and a typical extensive green roof with vegetation. Overall, mean percent rainfall retention ranged from 48.7% (gravel) to 82.8% (vegetated). The second study tested the influence of roof slope (2 and 6.5%) and green roof media depth (2.5, 4.0, and 6.0 cm) on stormwater retention. For all combined rain events, platforms at 2% slope with a 4-cm media depth had the greatest mean retention, 87%, although the difference from the other treatments was minimal. The combination of reduced slope and deeper media clearly reduced the total quantity of runoff. For both studies, vegetated green roof systems not only reduced the amount of stormwater runoff, they also extended its duration over a period of time beyond the actual rain event.     

Macroinvertebrate communities in agriculturally impacted southern Illinois streams: patterns with riparian vegetation, water quality, and in-stream habitat quality

Relationships between riparian land cover, in-stream habitat, water chemistry, and macroinvertebrates were examined in headwater streams draining an agricultural region of Illinois. Macroinvertebrates and organic matter were collected monthly for one year from three intensively monitored streams with a gradient of riparian forest cover (6, 22, and 31% of riparian area). Bioassessments and physical habitat analyses were also performed in these three streams and 12 other nearby headwater streams. The intensively monitored site with the least riparian forest cover had significantly greater percent silt substrates than the sites with medium and high forest cover, and significantly higher very fine organics in substrates than the medium and high forested sites. Macroinvertebrates were abundant in all streams, but communities reflected degraded conditions; noninsect groups, mostly oligochaetes and copepods, dominated density and oligochaetes and mollusks, mostly Sphaerium and Physella, dominated biomass. Of insects, dipterans, mostly Chironomidae, dominated density and dipterans and coleopterans were important contributors to biomass. Collector-gatherers dominated functional structure in all three intensively monitored sites, indicating that functional structure metrics may not be appropriate for assessing these systems. The intensively monitored site with lowest riparian forest cover had significantly greater macroinvertebrate density and biomass, but lowest insect density and biomass. Density and biomass of active collector-filterers (mostly Sphaerium) decreased with increasing riparian forest. Hilsenhoff scores from all 15 sites were significantly correlated with in-stream habitat scores, percent riparian forest, and orthophosphate concentrations, and multiple regression indicated that in-stream habitat was the primary factor influencing biotic integrity. Our results show that these "drainage ditches" harbor abundant macroinvertebrates that are typical of degraded conditions, but that they can reflect gradients of conditions in and around these streams.     

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

SUMMER LOW FLOWS IN NEW ENGLAND DURING THE 20TH CENTURY1

ABSTRACT: High springtime river flows came earlier by one to two weeks in large parts of northern New England during the 20th Century. In this study it was hypothesized that late spring/early summer recessional flows and late summer/early fall low flows could also be occurring earlier. This could result in a longer period of low flow recession and a decrease in the magnitude of low flows. To test this hypothesis, variations over time in the magnitude and timing of low flows were analyzed. To help understand the relation between low flows and climatic variables in New England, low flows were correlated with air temperatures and precipitation. Analysis of data from 23 rural, unregulated rivers across New England indicated little evidence of consistent changes in the timing or magnitude of late summer/early fall low flows during the 20th Century. The interannual variability in the timing and magnitude of the low flows in northern New England was explained much more by the interannual variability in precipitation than by the interannual variability of air temperatures. The highest correlation between the magnitude of the low flows and air temperatures was with May through November temperatures (r =?0.37, p= 0.0017), while the highest correlation with precipitation was with July through August precipitation (r = 0.67, p > 0.0001).     

GEOMORPHOLOGY OF STEEPLAND HEADWATERS: THE TRANSITION FROM HILLSLOPES TO CHANNELS1

Headwater streams comprise 60 to 80 percent of the cumulative length of river networks. In hilly to mountainous terrain, they reflect a mix of hillslope and channel processes because of their close proximity to sediment source areas. Their morphology is an assemblage of residual soils, landslide deposits, wood, boulders, thin patches of poorly sorted alluvium, and stretches of bedrock. Longitudinal profiles of these channels are strongly influenced by steps created by sediment deposits, large wood, and boulders. Due to the combination of small drainage area, stepped shallow gradient, large roughness elements, and cohesive sediments, headwater streams typically transport little sediment or coarse wood debris by fluvial processes. Consequently, headwaters act as sediment reservoirs for periods spanning decades to centuries. The accumulated sediment and wood may be episodically evacuated by debris flows, debris floods, or gully erosion and transported to larger channels. In mountain environments, these processes deliver significant amounts of materials that form riverine habitats in larger channels. In managed steepland forests, accelerated rates of landslides and debris flows resulting from the harvest of headwater forests have the potential to seriously impact the morphology of headwater streams and downstream resources.     

ACCURACY OF LAKE AND STREAM TEMPERATURES ESTIMATED FROM THERMAL INFRARED IMAGES1

Emitted thermal infrared radiation (TIR, λ= 8 to 14 μm) can be used to measure surface water temperatures (top approximately 100 μm). This study evaluates the accuracy of stream (50 to 500 m wide) and lake (300 to 5,000 m wide) radiant temperatures (15 to 22°C) derived from airborne (MASTER, 5 to 15 m) and satellite (ASTER 90 m, Landsat ETM+ 60 m) TIR images. Applied atmospheric compensations changed water temperatures by ?0.2 to +2.0°C. Atmospheric compensation depended primarily on atmospheric water vapor and temperature, sensor viewing geometry, and water temperature. Agreement between multiple TIR bands (MASTER ‐ 10 bands, ASTER ‐ 5 bands) provided an independent check on recovered temperatures. Compensations improved agreement between image and in situ surface temperatures (from 2.0 to 1.1°C average deviation); however, compensations did not improve agreement between river image temperatures and loggers installed at the stream bed (from 0.6 to 1.6°C average deviation). Analysis of field temperatures suggests that vertical thermal stratification may have caused a systematic difference between instream gage temperatures and corrected image temperatures. As a result, agreement between image temperatures and instream temperatures did not imply that accurate TIR temperatures were recovered. Based on these analyses, practical accuracies for corrected TIR lake and stream surface temperatures are around 1°C.     

DEVELOPMENT OF EMPIRICAL,GEOGRAPHICALLY SPECIFIC WATER QUALITY CRITERIA: A CONDITIONAL PROBABILITY ANALYSIS APPROACH1

The need for scientifically defensible water quality standards for nonpoint source pollution control continues to be a pressing environmental issue. The probability of impact at differing levels of nonpoint source pollution was determined using the biological response of instream organisms empirically obtained from a statistical survey. A conditional probability analysis was used to calculate a biological threshold of impact as a function of the likelihood of exceeding a given value of pollution metric for a specified geographic area. Uncertainty and natural variability were inherently incorporated into the analysis through the use of data from a probabilistic survey. Data from wadable streams in the mid‐Atlantic area of the U.S. were used to demonstrate the approach. Benthic macroinvertebrate community index values (EPT taxa richness) were used to identify impacted stream communities. Percent fines in substrate (silt/clay fraction, > 0.06 mm) were used as a surrogate indicator for sedimentation. Thresholds of impact due to sedimentation were identified by three different techniques, and were in the range of 12 to 15 percent fines. These values were consistent with existing literature from laboratory and field studies on the impact of sediments on aquatic life in freshwater streams. All results were different from values determined from current regulatory guidance. Finally, it was illustrated how these thresholds could be used to develop criterion for protection of aquatic life in streams.     

Techniques for accuracy assessment of tree locations extracted from remotely sensed imagery

Remotely sensed imagery is becoming a common source of environmental data. Consequently, there is an increasing need for tools to assess the accuracy and information content of such data. Particularly when the spatial resolution of imagery is fine, the accuracy of image processing is determined by comparisons with field data. However, the nature of error is more difficult to assess. In this paper we describe a set of tools intended for such an assessment when tree objects are extracted and field data are available for comparison. These techniques are demonstrated on individual tree locations extracted from an IKONOS image via local maximum filtering. The locations of the extracted trees are compared with field data to determine the number of found and missed trees. Aspatial and spatial (Voronoi) analysis methods are used to examine the nature of errors by searching for trends in characteristics of found and missed trees. As well, analysis is conducted to assess the information content of found trees.