Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds.

In this study, we assessed the effects of wildfires on soil using unmanned aerial vehicle (UAV) equipped with a MicaSense Altum sensor capable of capturing multispectral (red, green, blue, near infrared, red edge) and thermal images. Our study location was the Thompson Ridge Fire in Utah, USA. The aim of this study was to explore the potential of UAV-based thermal images with the integration of satellite-based remote sensing indices to create a burn severity map on a broader scale. We created a burn severity map from differenced normalized burn ratio (dNBR), which correlated with post-fire land surface temperature (LST) and the maximum infiltration rate of soil. We investigated the effects of burn severity on the maximum infiltration rate of the topsoil layer using field-based single-ring infiltrometer tests. These tests revealed a decrease in infiltration rate with fire effects. Our findings contribute to understanding the post-fire effects on watersheds, emphasizing the relationship between LST, burn severity, and infiltration rate using UAV based thermal and Landsat observations.

This book deals with Alfred Schutz's "On Multiple Realities" by describing the meaning of religious ritual, and African-American folkloric humor.

Evapotranspiration (ET) is a critical component of the water cycle, and an accurate prediction of ET is essential for water resource management, irrigation scheduling, and agricultural productivity. Traditionally, ET has been estimated using satellite-based remote sensing, which provides synoptic coverage but can be limited in spatial resolution and accuracy. Unmanned aerial vehicles (UAVs) offer improved ET prediction by providing high-resolution imagery of the Earth’s surface but are limited to a small area. Therefore, UAV and satellite images provide complementary data, but the integration of these two data for ET prediction has received limited attention. This paper presents a method that integrates UAV and satellite imagery for improved ET prediction and applies it to five crops (corn, rye grass, wheat, and alfalfa) from agricultural fields in the Walla Walla of eastern Washington State. We collected UAV and satellite data for five crops and used the combination of remote sensing models and statistical techniques to estimate ET. We show that UAV-based ET can be integrated with the Landsat-based ET with the application of integration factors. Our result shows that the Root Mean Square Error (RMSE) of daily ET for corn (Zea mays), rye grass (Lolium perenne), wheat (Triticum aestivum), peas (Pisum sativum), and alfalfa (Medicago sativa) can be improved by the application of the integration factor to the Landsat based ET in the range of (35.75–65.52%). We also explore the variability and effect of partial cloud on UAV-based ET estimation. Our findings have implications for the use of UAVs in water resource management and highlight the importance of considering multiple sources of data in ET prediction.

We conducted a field study to investigate the role of stringent response in cyanobacteria and coexisting bacterioplankton during nutrient-deprived periods at various stages of bloom in a freshwater lake (Utah Lake) for the first time. Using metagenomics and metatranscriptomics analyses, we examined the cyanobacterial ecology and expression of important functional genes related to stringent response, N and P metabolism, and regulation. Our findings mark a significant advancement in understanding the mechanisms by which toxic cyanobacteria survive and proliferate during nitrogen (N) and phosphorus (P) limitations. We successfully identified and analyzed the metagenome-assembled genomes (MAGs) of the dominant bloom-forming cyanobacteria, namely, Dolichospermum circinale, Aphanizomenon flos-aquae UKL13-PB, Planktothrix agardhii, and Microcystis aeruginosa. By mapping RNA-seq data to the coding sequences of the MAGs, we observed that these four prevalent cyanobacteria species activated multiple functions to adapt to the depletion of inorganic nutrients. During and after the blooms, the four dominant cyanobacteria species expressed high levels of transcripts related to toxin production, such as microcystins (mcy), anatoxins (ana), and cylindrospermopsins (cyr). Additionally, genes associated with polyphosphate (poly-P) storage and the stringent response alarmone (p)ppGpp synthesis/hydrolysis, including ppk, relA, and spoT, were highly activated in both cyanobacteria and bacterioplankton. Under N deficiency, the main N pathways shifted from denitrification and dissimilatory nitrate reduction in bacterioplankton toward N2-fixing and assimilatory nitrate reduction in certain cyanobacteria with a corresponding shift in the community composition. P deprivation triggered a stringent response mediated by spoT-dependent (p)ppGpp accumulation and activation of the Pho regulon in both cyanobacteria and bacterioplankton, facilitating inorganic and organic P uptake. The dominant cyanobacterial MAGs exhibited the presence of multiple alkaline phosphatase (APase) transcripts (e.g., phoA in Dolichospermum, phoX in Planktothrix, and Microcystis), suggesting their ability to synthesize and release APase enzymes to convert ambient organic P into bioavailable forms. Conversely, transcripts associated with bacterioplankton-dominated pathways like denitrification were low and did not align with the occurrence of intense cyanoHABs. The strong correlations observed among N, P, stringent response metabolisms and the succession of blooms caused by dominant cyanobacterial species provide evidence that the stringent response, induced by nutrient limitation, may activate unique N and P functions in toxin-producing cyanobacteria, thereby sustaining cyanoHABs.

Study region: The Utah Wasatch Mountains along the eastern Great Basin and the western Rocky Mountains. Study focus: Changes in seasonal precipitation patterns and summer evaporation rates due to increases in temperatures will have severe impacts on low flows of the mountainous watersheds affecting downstream water availability and thus impacting ecosystems and drinking water supplies under future climate change. Reliable prediction of climate change impacts on future low flows needs to consider both the extremity of climatic variables and the physical characteristics of the watersheds. This study analyzes low flows of small mountainous watersheds addressing nonstationarity of climatic parameters and estimates the impact of climate change on low flow, combining nonstationarity and outcome of a physically distributed hydrologic model. MACA statistically downscaled climate data have been used as inputs to the DHSVM model to estimate future low flows for both near term (2036-2044) and distant future (2091-2099) under RCP4.5 and 8.5 scenarios. New Hydrological Insights for the Region: Nonstationary models with time (5 watersheds) or temperature (2 watersheds) as the covariate in the location-scale provided the best performance. This study demonstrated that RCP4.5 has more severe impact on the low flow frequency and volume in the near future than RCP8.5. In contrast, the effect of RCP8.5 is more dominant on the low flow regimes in the distant future than RCP4.5.

Climate change and water diversions are putting the Great Salt Lake (GSL) at risk. Projections indicate a continued decrease in the GSL water surface elevation (WSE) would lead to several catastrophic consequences. An aspect of the GSL dynamics gaining importance, and not addressed in past studies, is how resilient the lake WSE will be to increasing diversions from contributing rivers, intensifying drought conditions, and more frequent hydrologic deficits caused by climate change. The objectives of the present study were to: (1) examine the impacts of historical drought and development on the GSL resilience and (2) determine future WSE resilience under a range of hydroclimate and development scenarios. The historical resilience was analyzed considering three periods with different development conditions: (1) less developed (1901–1950); (2) moderately developed (1951–2000); (3) highly developed (2001–2020). Furthermore, a range of hydroclimate and development conditions were introduced into a system dynamics-based water management model to simulate the future GSL WSE and corresponding resilience. The historical analysis showed a significant decline in resilience (45.4

Climate change effect on water quality performance of lakes, rivers and streams is a significant concern for watershed planning and management.Climate change characteristics may potentially increase the likelihood that waterbodies will fail to meet established water quality standards, often obligating watershed managers to undertake expensive monitoring and load allocation studies for possible remedies against such impairment.one such load allocation study involves the implementation of water quality trading (WQt), which often is proposed as a mechanism for improving surface water quality goals under a socially and economically feasible manner.however, while future growth and land use change is incorporated through a margin of safety, WQt markets do not typically incorporate the characteristics of climate change that have been suggested to exhibit strong linkages against achieving the desired levels of water quality benefit.Consequently, this modelling study evaluates the characteristics of climate change upon the levels of water quality benefit along a river system subject to distinct load removal exercises: a) removal upon point sources only and b) removal based on a point-nonpoint source trading mechanism under a theoretical WQt program.this study applies such assessments upon the load allocation exercises through carbonaceous biochemical oxygen demand reduction for addressing a recognized dissolved oxygen problem along the Jordan river in utah, conducting such analyses through selected climate change projections described by the representative concentration pathways.for achieving such tasks, separate simulations are conducted through the Water Quality Assessment simulation program, evaluating the performance of such trading mechanisms under observed meteorological data against modelled climate data through selected representative concentration pathway projections under a historical period from Water year 2007 to 2009.this exercise assesses the performance of such load allocation studies subject to climatic characteristics toward suggesting linkages among climate change, water quality benefit and the effectiveness of a theoretical WQt mechanism.

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Modeling Water Leasing Impacts on Instream Flows for River Ecosystem ProtectionAuthorsRajendraKhanaliDMichaelBarberSee all authors Rajendra KhanaliDCorresponding Author• Submitting AuthorUniversity of UtahiDhttps://orcid.org/0000-0002-3461-1075view email addressThe email was not providedcopy email addressMichael BarberUniversity of Utahview email addressThe email was not providedcopy email address

Estimation of satellite-based remotely sensed evapotranspiration (ET) as consumptive use has been an integral part of agricultural water management. However, less attention has been given to future predictions of ET at watershed-scales especially since with a changing climate, there are additional challenges to planning and management of water resources. In this paper, we used nine years of total seasonal ET derived using a satellite-based remote sensing model, Mapping Evapotranspiration at Internalized Calibration (METRIC), to develop a Random Forest machine learning model to predict watershed-scale ET into the future. This statistical model used topographic and climate variables in agricultural areas of Lower Yakima, Washington and had a prediction accuracy of 88% for the region. This model was then used to predict ET into the future with changed climatic conditions under RCP4.5 and RCP8.5 emission scenarios expected by 2050s. The model result shows increases in seasonal ET across some areas of the watershed while decreases in other areas. On average, growing seasonal ET across the watershed was estimated to increase by +5.69% under the low emission scenario (RCP4.5) and +6.95% under the high emission scenario (RCP8.5).

Balancing out‐of‐stream water demands and ecological instream flows is a difficult challenge in watershed‐scale management. Many watersheds already experience acute and chronic water shortages during average runoff years and may face more frequent and severe droughts in some locations due to climate and demographic change. Water markets may mitigate the economic consequences of shortages, but their potential is limited by the prevalence of all‐or‐nothing irrigate‐or‐fallow crop water use strategies. Irrigation water generally provides diminishing returns for crop productivity, so it may be possible to reduce water application at the margin with only a small loss in crop production, creating water savings that could be leased for other uses. We explore this scenario by combining a crop growth and hydrology (CropSyst) model with an economic model of farm profits and water trading, and apply it to the Walla Walla Basin in Washington State. Our results suggest that partial leasing of water rights through a deficit‐irrigation strategy could economically benefit annual crop growers while meaningfully increasing water availability for stream flow augmentation.

Climate change and population growth serve as fundamental problems in assessing potential impacts on future surface water quality. In addition to uncertainties in climate depicted in various representative concentration pathway (RCP) scenarios, futuristic population growth mimicking historical conditions is subject to uncertainties related to changing development patterns. The combination of climate change and population characteristics exacerbates concerns regarding the future water quality performance of river systems. Previous studies have established linkages among future climate, population impacts and watershed water quality performance. However, these linkages have not been specifically incorporated into water quality trading programs. Rather than temporally-variant adjustment factors, WQT programs use constant margins of safety for pollutant reduction credits resulting in trade ratios that do not explicitly account for futuristic climate and population uncertainties. Hence, this study proposes a conceptual framework for water quality trading establishing adjustment factors as margins of safety on trade ratios for pollutant reduction credits examining climate and population characteristics separately followed by evaluating them combined. This new framework is demonstrated using a programming script that calculates the margins of safety based on simulation results conducted through a water quality model of the Jordan River in Salt Lake City, UT, USA over a 3-year timeframe. With margins of safety over magnitudes of ±2 over the Jordan River simulations, this research introduces the framework as a foundation for developing adjustment factors for addressing climatic and population characteristics upon river systems.

Code and data used for partial leasing study

In 1998, the Pacific Northwest Water Resources Program was established in the four Pacific Northwest states (Alaska, Idaho, Oregon, Washington) with the ultimate goal of increasing public awareness about water quality and encouraging the public to take voluntary individual actions that would enhance water resources. Prevention of water pollution was the overall primary goal of this 20-year effort. The outreach arm of the land grant universities in the region led this effort. This effort was made possible by funding provided by the federal, state and local governments as well as non-governmental organizations. This project had short-term, medium-term and long-term goals. The short-term goals were to: (1) improve citizen knowledge about water issues, (2) improve awareness of the capabilities of land-grant universities to address water issues and (3) improve public awareness of and motivation to individually or collectively address water issues. The medium-term goals were to: (1) improve cooperation for educational programming between states, (2) improve partnerships between both public and private, and local, state and regional organizations, (3) encourage citizens to voluntarily make choices to protect water resources and (4) help policy makers enact legislation to enhance the quality and quantity of water resources. The long-term goal of the regional project was to improve the quality of water resources within the four-state region. Water quality education was delivered to local citizens using public meetings, workshops, printed pamphlets, Internet-based publications, video-conferences and mass media. There was a strong education-research partnership component to this program. Large regional surveys were conducted initially and then every 5 years to understand citizen attitudes, priorities and individual actions taken to protect water quality in the region. The survey results showed that over the term of the project the public had become more engaged in reducing water pollution. This regional program made the public both more aware of potential pollution problems and encouraged citizens to be a part of the solution to water pollution. Although it is difficult to directly measure water quality improvement on a regional basis, project surveys showed that compared to 2002 over 75% of the public have taken individual actions to improve water quality. Positive actions have included: (1) a change in the disposal of household chemicals, (2) a reduction in the use of pesticides and fertilizers in urban areas and (3) better maintenance of septic systems in rural areas.

Harmful cyanobacterial blooms produce lethal toxins in many aquatic ecosystems experiencing eutrophication. This manuscript presents results on the effects of cyanotoxins on the aerobic microbial communities residing at the interface of sediments and water columns with the ammonia-oxidizing bacteria (AOB) as the model microbial community. Microcystin-LR (MC-LR), a heavily researched cyanotoxin variant, was used as the model cyanotoxin. To measure cyanotoxin influence on the activity of nitrifying microbial communities, an enriched culture of AOBs collected from an ongoing partial nitrification-nitritation reactor was examined for its exposure to 1, 5 and 10 μg/L of MC-LR. The nitritation kinetics experiment demonstrated MC-LR’s ability at 1, 5, and 10 μg/L concentrations to prevent ammonium oxidation with statistically significant differences in nitritation rates between the blanks and spiked samples (One-way ANOVA, p < 0.05). Significantly decreased dissolved oxygen (DO) consumption during oxygen update batch tests demonstrated toxin’s influence on AOB’s oxidizing capabilities when exposed to even lower concentrations of 0.75, 0.5, and 0.25 μg/L of MC-LR in a separate set of experiments. Based on competitive kinetics, the MC-LR inhibition coefficient-the concentration needed to produce half-maximum inhibition of the mixed community AOBs was determined to be 0.083 μg/L. The stress tests proved the recovery of nitritation to some extent at lower MC-LR concentrations (1 and 5 μg/L), but significant irreversible inhibition was recorded when the AOB population was exposed to 10 μg/L MC-LR. The comparisons of amoA gene expressions corresponded well with nitrifying kinetics. All concentrations of MC-LR spiking were determined to produce a discernible impact on the AOB nitritation rate by either destroying the bacterial cell or immediately inhibiting the amoA gene expression.

Mountainous forested regions are the major sources of water for meeting downstream water demands in many parts of the world, including the United States, where two-thirds of the freshwater supply is estimated to originate from these areas.Wildfires pose significant threats to downstream urban infrastructure and livelihoods by impacting the timing, quantity and quality of waters emerging from these forested ecosystems.Wildfires affect key processes of the water cycle by reducing infiltration and interception, resulting in higher runoff volumes.Predicting post-fire flood events is important for proper water management and planning, including the safety of downstream communities.The objective of the study is to determine how changes in the locations of wildfire events coupled with the type (severity) of fire events affect peak flow regimes of high mountain watersheds.American Fork, a high mountain forest watershed in Utah with an area of 60 sq.miles (155 sq.km) and elevations ranging from 5,000 ft. to 11,700 ft.(1550 m to 3600 m), is taken as the study area.A historical fire event that took place in a neighbouring watershed was superimposed on three different locations of the studying watershed with varying severity.A hydrologic model named Distributed Hydrologic Soil vegetation Model was used to predict the flows due to changes in land cover and hydrologic processes for different wildfire events.Changes in peak flow due to different wildfire events at different locations of the watershed are analysed to estimate how location and type of wildfire events affect the peak flow regimes of the watershed and how thus it affects the overall downstream water supply.This study also identifies the critical location in the watershed for which the peak flow regime of the watershed will be most vulnerable due to a certain extent of wildfire.

The performance of dynamically downscaled climate fields with respect to observed historical stream runoff has been assessed at basin scale using a physically distributed hydrologic model (DHSVM). The dynamically downscaled climate fields were generated by running the Weather Research & Forecasting (WRF) model at 4-km horizontal resolution with boundary conditions derived from the Climate Forecast System Reanalysis. Six hydrologic models were developed using DHSVM for six mountainous tributary watersheds of the Jordan River basin at hourly time steps and 30-m spatial resolution. The size of the watersheds varies from 19 km2 to 130 km2. The models were calibrated for a 6-year period from water year (WY) 1999–2004, using the observed meteorological data from the nearby Snow Telemetry (SNOTEL) sites of the Natural Resources Conservation Services (NRCS). Calibration results showed a very good fit between simulated and observed streamflow with an average Nash-Sutcliffe Efficiency (NSE) greater than 0.77, and good to very good fits in terms of other statistical parameters like percent bias (PBIAS) and coefficient of determination (R2). A 9-year period (WY 2001–2009) was selected as the historical baseline, and stream discharges for this period were simulated using dynamically downscaled climate fields as input to the calibrated hydrologic models. Historical baseline results showed a satisfactory fit of simulated and observed streamflow with an average NSE greater than 0.45 and a coefficient of determination above 0.50. Using volumetric analysis, it has been found that the total volume of water simulated using downscaled climate projections for the entire historical baseline period for all six watersheds is 4% less than the observed amount representing a very good estimation in terms of percent error volume (PEV). However, in the case of individual watersheds, analysis of total annual water volumes showed that estimated total annual water volumes were higher than the observed for Big Cottonwood, City Creek, Millcreek and lower than the observed total annual volume of water for Little Cottonwood, Red Butte Creek, and Parleys Littledell, demonstrating similar characteristics obtained from the calibration results. Seasonal analysis showed that the models can capture the flow volume observed for Big Cottonwood, City Creek and Red Butte Creek during the peak season, and the models can capture the flow volume observed for all the watershed satisfactorily except Big Cottonwood during the dry season. Study results indicated that the dynamically downscaled climate projections used in this study performed satisfactorily in terms of stream runoff, total flow volume, and seasonal flow analyses based on different statistical tests, and can satisfactorily capture flow patterns and flow volume for most of the watersheds considering the uncertainties associated with the study.

The US Army Corps of Engineers Hydrologic Engineering Center recently released version of the River Analysis System (HEC-RAS) has added two-dimensional (2D) modeling capabilities to a decade old one-dimensional (1D) model dating back to HEC-2 developed in the 1970s. Several recent studies have indicated that 2D flood modeling is preferable in urban environments to better account for the complex topography caused by infrastructure. The newest version of HEC-RAS also allows users to simulate unsteady flow using either the Saint Venant equations or the diffusion wave (DW) equations using an implicit finite volume algorithm. The Saint Venant solution allows for turbulence and Coriolis effects to be accounted for with momentum additions. While applicable to a wider range of flood problems, the Saint Venant solution is slower and inherently less stable than the DW approach. We evaluate the similarities and differences between both 1D and 2D solution techniques using the lower Provo River in Utah as a prototypical urban river. Furthermore, since the 2D version of HEC-RAS is relatively new, we compared the HEC-RAS simulations to the 2D sedimentation and river hydraulics (SRH-2D) model developed by the U.S. Bureau of Reclamation. The method uses high resolution light detection and ranging imagery to determine floodplain topography and cross-section information for the channel properties. While no single river reach can adequately answer the question of whether 2D flood modeling produces superior results compared to 1D solutions, in this study the 1D unsteady flow model struggled to predict meandering stream phenomenon particularly because it was difficult to identify active flow versus storage areas as a function of flow depth. We conclude that temporal variations in most complex flow regimes will not be well modeled in 1D and that 2D modeling will produce superior results.

Water quality trading (WQT) has been proposed as a mechanism for improving surface water quality goals in an economically and socially responsible manner.however, to date, successful markets for WQT have been slow to develop with many interested parties pointing to the need for aggressive regulatory enforcement of standards as a key requirement in the trading process.As regulations in the United States and many other countries typically apply to impaired waterways, the inherent problem with this as the only driver for trades is that little to no value is prescribed to raising water quality to above minimum standards.Because numerous studies have shown the economic value of improved ecosystem services and our own work with public surveys that demonstrated the importance of water quality, we hypothesize that an informed public (as well as aquatic ecosystem managers) will place additional value on water quality conditions that exceed minimum values.We present a framework for incorporating this concept into the WQT process that already includes essential elements such as trading ratios, uncertainty, and evaluation.We demonstrate the framework approach using a Streeter-Phelps dissolved oxygen (DO) model to address a recognized DO problem in the Jordan River in Utah, USA.It is recognized that this work represents the initial discussion of the process and that adaptive management of the complex processes will be needed in order to maximize the sustainable of water resources.