Browsing by Author "Kleiner, Joseph"

Now showing 1 - 3 of 3
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    Application of a New Species-Richness Based Flow Ecology Framework for Assessing Flow Reduction Effects on Aquatic Communities
    Rapp, Jennifer L.; Burgholzer, Robert; Kleiner, Joseph; Scott, Durelle T.; Passero, Elaina M. (2020)
    Water-resources managers are challenged with maintaining a balance among beneficial uses throughout river networks and need robust means of assessing potential risks to aquatic life resulting from flow alterations. This study generated ecological limit functions from species-streamflow relations to quantify potential fish richness response to flow alteration and compared results to currently accepted streamflow management guidelines. Modeled responses of absolute richness change were watershed specific and varied among sample sets derived from hydrologic unit classifications of different sizes (large HUC 6 basins to regional scale HUC 8). With a 20% flow reduction, 10% of HUC 8 predicted a richness decrease in one or more taxa. While absolute richness change was consistent across streams within a HUC, percent richness change was stream size dependent. Comparisons with Instream Flow Incremental Methodology habitat models predicted habitat loss greater than percent richness change; however, predictions for habitat and richness decreased similarly as stream size decreased. Watershed-specific responses from flow reductions could allow water-resources management decisions to be made locally based on the predicted richness change for certain sized streams. Quantitative results highlight the utility of a richness-based framework for generating watershed-specific risk assessments that validate and inform currently employed water-resources management practices.
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    elfgen: A New Instream Flow Framework for Rapid Generation and Optimization of Flow-Ecology Relations
    Kleiner, Joseph; Passero, Elaina; Burgholzer, Robert; Rapp, Jennifer; Scott, Durelle T. (2020-09-06)
    Effective water resource management requires practical, data-driven determination of instream flow needs. Newly developed, high-resolution flow models and aquatic species databases provide enormous opportunity, but the volume of data can prove challenging to manage without automated tools. The objective of this study was to develop a framework of analytical methods and best practices to reduce costs of entry into flow-ecology analysis by integrating widely available hydrologic and ecological datasets. Ecological limit functions (ELFs) describing the relation between maximum species richness and stream size characteristics (streamflow or drainage area) were developed. Species richness is expected to increase with streamflow through a watershed up to a point where it either plateaus or transitions to a decreasing trend in larger streams. Our results show that identifying the location of this "breakpoint" is critical for producing optimal ELF model fit. We found that richness breakpoints can be estimated using automated low-supervision methods, with high-supervision providing negligible improvement in detection accuracy. Model fit (and predictive capability) was found to be superior in smaller hydrologic units. The ELF model ("elfgen" R package available on GitHub: ) can be used to generate ELFs using built-in datasets for the conterminous United States, or applied anywhere else streamflow and biodiversity data inputs are available.
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    Estimating Facility-Level Monthly Water Consumption of Commercial, Industrial, Municipal, and Thermoelectric Users in Virginia
    McCarthy, Morgan; Brogan, Connor; Shortridge, Julie; Burgholzer, Robert; Kleiner, Joseph; Scott, Durelle T. (American Water Resources Association, 2022-06-16)
    Understanding water consumption is an important component of water management. However, water consumption data are limited and consumption coefficients do not account for variability through time and across users. This study combines federally maintained discharge data with state-maintained withdrawal data at monthly time steps to estimate facility-level and spatially aggregated water consumption in Virginia between 2010 and 2016. We evaluate (1) the feasibility of using discharge and withdrawal datasets to estimate sub-annual water consumption, (2) how these consumption estimates vary depending on the level of spatial aggregation, and (3) what patterns of seasonality exist in consumption estimates. We find that a combined process of text matching and geospatial analysis is effective in matching facilities and yielding monthly time-series of water consumption. Our results suggest that median consumption in industrial (17%) and commercial (19%) facilities may be higher than median consumption coefficients in the literature (10%). Consumption estimates also demonstrated more variability across facilities and seasons than aggregate coefficients in the literature suggest. Combining this approach with institutional knowledge can assist in quantifying issues such as inter-basin transfers and infiltration that impact consumption estimates, ultimately allowing for more accurate accounts of water use and availability.