Testing and Improving Temperature Thresholds for Snow and Rain Prediction in the Western United States

The phase of precipitation at the land surface is critical to determine the timing and amount of water available for hydrological and ecological systems. However, there are few techniques to directly observe the precipitation phase and many prediction tools apply a single temperature threshold (e.g., 0°C) to determine phase. In this paper, we asked two questions: (1) what is the accuracy of default and station optimized daily temperature thresholds for predicting precipitation phase and (2) what are the regions and conditions in which typical temperature‐based precipitation phase predictions are most suited. We developed a ground truth dataset of rain vs. snow using an expert decision‐making system based on precipitation, snow depth, and snow water equivalent observations. This dataset was used to evaluate the accuracy of three temperature‐threshold‐based techniques of phase classification. Optimizing the temperature threshold improved the prediction of precipitation phase by 34% compared to using 0°C threshold. Developing a temperature threshold based on station elevation improved the error by 12% compared with using the 0°C temperature threshold. We also found the probability of snow as a function of temperature differed among ecoregions, which suggests a varied response to future climate change. These results highlight a current weakness in our ability to predict the effects of regional warming that could have uneven impacts on water and ecological resources.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Variation in root density along stream banks

While it is recognized that vegetation plays a significant role in stream bank stabilization, the effects are not fully quantified. The study goal was to determine the type and density of vegetation that provides the greatest protection against stream bank erosion by determining the density of roots in stream banks. To quantify the density of roots along alluvial stream banks, 25 field sites in the Appalachian Mountains were sampled. The riparian buffers varied from short turfgrass to mature riparian forests, representing a range of vegetation types. Root length density (RLD) with depth and aboveground vegetation density were measured. The sites were divided into forested and herbaceous groups and differences in root density were evaluated. At the herbaceous sites, very fine roots (diameter < 0.5 mm) were most common and more than 75% of all roots were concentrated in the upper 30 cm of the stream bank. Under forested vegetation, fine roots (0.5 mm < diameter < 2.0 mm) were more common throughout the bank profile, with 55% of all roots in the top 30 cm. In the top 30 cm of the bank, herbaceous sites had significantly greater overall RLD than forested sites (alpha = 0.01). While there were no significant differences in total RLD below 30 cm, forested sites had significantly greater concentrations of fine roots, as compared with herbaceous sites (alpha = 0.01). As research has shown that erosion resistance has a direct relationship with fine root density, forested vegetation may provide better protection against stream bank erosion.     

Harvesting of microalgae biomass from the phycoremediation process of greywater

The wide application of microalgae in the field of wastewater treatment and bioenergy source has improved research studies in the past years. Microalgae represent a good source of biomass and bio-products which are used in different medical and industrial activities, among them the production of high-valued products and biofuels. The present review focused on greywater treatment through the application of phycoremediation technique with microalgae and presented recent advances in technologies used for harvesting the microalgae biomass. The advantages and disadvantages of each method are discussed. The microbiological aspects of production, harvesting and utilization of microalgae biomass are viewed.