<p> This research explores the benefits of conjunctively managed surface and groundwater resources in a volcanic aquifer system to reduce stream temperatures while valuing agricultural deliveries. The example problem involves advancing the understanding of flows, stream temperature, and groundwater dynamics in the Shasta Valley of Northern California. Three levels of interaction are explored from field data, to regional simulation, to regional management optimization. Stream temperature processes are explored using Distributed Temperature Sensing (DTS) data from the Shasta River and recalibrating an existing physically-based flow and temperature model of the Shasta River. DTS technology can collect abundant high resolution river temperature data over space and time to improve development and performance of modeled river temperatures. These data also identify and quantify thermal variability of micro-habitat that temperature modeling and standard temperature sampling do not capture. This helps bracket uncertainty of daily temperature variation in reaches, pools, side channels, and from cool or warm surface or subsurface inflows. The application highlights the influence of air temperature on stream temperatures, and indicates that physically-based numerical temperature models, using a heat balance approach as opposed to statistical models, may under-represent this important stream temperature driver. The utility of DTS to improve model performance and detailed evaluation of hydrologic processes is demonstrated. </p><p> Second, development and calibration of a numerical groundwater model of the Pluto's Cave basalt aquifer and Parks Creek valley area in the eastern portion of Shasta Valley helps quantify and organize the current conceptual model of this Cascade fracture flow dominated aquifer. Model development provides insight on system dynamics, helps identify important and influential components of the system, and highlights additional data needs. The objective of this model development is to reasonably represent regional groundwater flow and to explore the connection between Mount Shasta recharge, pumping, and Big Springs flow. The model organizes and incorporates available data from a wide variety of sources and presents approaches to quantify the major flow paths and fluxes. Major water balance components are estimated for 2008-2011. Sensitivity analysis assesses the degree to which uncertainty in boundary flow affects model results, particularly spring flow. </p><p> Finally, this work uses optimization to explore coordinated hourly surface and groundwater operations to benefit Shasta River stream temperatures upstream of its confluence with Parks Creek. The management strategy coordinates reservoir releases and diversions to irrigated pasture adjacent to the river and it supplements river flows with pumped cool groundwater from a nearby well. A basic problem formulation is presented with results, sensitivity analysis, and insights. The problem is also formulated for the Shasta River application. Optimized results for a week in July suggest daily maximum and minimum stream temperatures can be reduced with strategic operation of the water supply portfolio. These temperature benefits nevertheless have significant costs from reduced irrigation diversions. Increased irrigation efficiency would reduce warm tail water discharges to the river instead of reducing diversions. With increased efficiency, diversions increase and shortage costs decrease. Tradeoffs and sensitivity of model inputs are explored and results discussed.</p>

<p> Wastewater generated in the Tucson metropolitan region is conveyed to and treated at the Roger Road Wastewater Reclamation Facility (WRF) and Ina Road WRF. From 2005 to 2012, approximately 15,000 acre-feet per year of effluent was returned to the City of Tucson for additional filtration and reuse in the reclaimed water system. The remaining 48,000+ acre-feet per year of treated effluent was discharged to the Santa Cruz River, where a variable portion of the effluent infiltrates the streambed. The effluent that infiltrates the streambed contributes to recharge credits for participants invested in the Managed Underground Storage Facilities.</p><p> In the effluent-dependent river, physical, chemical, and biological processes work in combination to develop a clogging layer near the streambed surface, which reduces infiltration. Previous studies have shown that large storm events have the ability to scour away the clogging layer and are the most significant processes contributing to establishing infiltration rates. Without the occurrence of large storm events, other variables such as effluent discharge and effluent concentrations affect infiltration to a lesser degree.</p><p> Effluent discharge, biochemical oxygen demand, and total suspended solids are monitored and recorded daily at the outfalls of the WRFs. The parameters were investigated individually and in combination using statistical analyses to determine their correlations with streambed infiltration in the Santa Cruz River. The dry spring-early summer seasons from 2005 to 2012 were analyzed. A water balance was constructed for non-stormflow days during each time period. Evapotranspiration was calculated using riparian vegetation surveys and detailed delineations of aerial photography of the surface water and streamside herbaceous vegetation. Infiltration was derived as the residual of the water balance. </p><p> At the daily time scale, correlations among variables were unobtainable due to the extremely variable characteristics of infiltration. The seasonal time scale analyses demonstrated an inverse relationship between both the effluent concentrations of biochemical oxygen demand and total suspended solids with infiltration and a direct correlation between effluent discharge and infiltration under extreme conditions. Under normal conditions, the distribution of discharge between Roger Road WRF and Ina Road WRF had a critical effect on infiltration as a result of the different deposition and erosive regimes through the Santa Cruz River.</p>

<p> There is an undisputed need to increase accuracy of snow cover estimation in regions comprised of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their annual water supply, such as the Western United States, Central Asia, and the Andes. Presently, the most pertinent monitoring and research needs related to alpine snow cover area (SCA) are: (1) to improve SCA monitoring by providing detailed fractional snow cover (FSC) products which perform well in temporal/spatial heterogeneous forested and/or alpine terrains; and (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management. </p><p> To address the above, the presented research approach is based on Landsat Fractional Snow Cover (Landsat-FSC), as a measure of the temporal/spatial distribution of alpine SCA. A fusion methodology between remotely sensed multispectral input data from Landsat TM/ETM+, terrain information, and IKONOS are utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) are used to capture the multi-scale information content of the input data compositions by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat and terrain input data compositions. The ANN Landsat-FSC algorithm is validated (RMSE ~ 0.09; mean error ~ 0.001-0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. ANN input data selections are evaluated to determine the nominal data information requirements for FSC estimation. Snow/non-snow multispectral and terrain input data are found to have an important and multi-faced impact on FSC estimation. Constraining the ANN to linear modeling, as opposed to allowing unconstrained function shapes, results in a weak FSC estimation performance and therefore provides evidence of non-linear bio-geophysical and remote sensing interactions and phenomena in complex mountain terrains. The research results are presented for rugged areas located in the San Juan Mountains of Colorado, and the hilly regions of Black Hills of Wyoming, USA. </p>

<p> The Runoff Risk Advisory Forecast (RRAF) provides Wisconsin's farmers with an innovative decision support tool which communicates the threat of undesirable conditions for manure and nutrient spreading for up to 10 days in advance. The RRAF is a pioneering example of applying the National Weather Service's hydrologic forecasting abilities towards the Nation's water quality challenges. Relying on the North Central River Forecast Center's (NCRFC) operational Snow17 and Sacramento Soil Moisture Accounting Models, runoff risk is predicted for 216 modeled watersheds in Wisconsin. The RRAF is the first-of-its-kind real-time forecast tool to incorporate 5-days of future precipitation as well as 10-days of forecast temperatures to generate runoff risk guidance. The forecast product is updated three times daily and hosted on the Wisconsin Department of Agriculture, Trade, and Consumer Protection (DATCP) website. Developed with inter-agency collaboration, the RRAF model was validated against both edge-of-field observed runoff as well as small USGS gauged basin response. This analysis indicated promising results with a Bias Score of 0.93 and a False Alarm Ratio (FAR) of only 0.34 after applying a threshold method. Although the threshold process did dampen the Probability of Detection (POD) from 0.71 to 0.53, it was found that the magnitude of the events categorized as hits was 10-times larger than those classified as misses. The encouraging results from this first generation tool are aiding State of Wisconsin officials in increasing awareness of risky runoff conditions to help minimize contaminated agriculture runoff from entering the State's water bodies.</p>

<p> This study investigated two important aspects of hydrologic effects of low impact development (LID) practices at the watershed scale by (1) examining the potential benefits of scaling up of LID design, and (2) evaluating downstream effects of LID design and its spatial translation within a watershed. The Personal Computer Storm Water Management Model (PCSWMM) was used to model runoff reduction with the implementation of LID practices in Deer Creek watershed (DCW), Missouri. The model was calibrated from 2003 to 2007 (R² = 0.58 and NSE = 0.57), and validated from 2008 to 2012 (R² = 0.64 and NSE = 0.65) for daily direct runoff. Runoff simulated for the study period, 2003 to 2012 (NSE = 0.61; R² = 0.63), was used as the baseline for comparison to LID scenarios. Using 1958 areal imagery to assign land cover, a predevelopment scenario was constructed and simulated to assess LID scenarios' ability to restore predevelopment hydrologic conditions. The baseline and all LID scenarios were simulated using 2006 National Land Cover Dataset.</p><p> The watershed was divided in 117 subcatchments, which were clustered in six groups of approximately equal areas and two scaling concepts consisting of incremental scaling and spatial scaling were modelled. Incremental scaling was investigated using three LID practices (rain barrel, porous pavement, and rain garden). Each LID practice was simulated at four implementation levels (25%, 50%, 75%, and 100%) in all subcatchments for the study period (2003 to 2012). Results showed an increased runoff reduction, ranging from 3% to 31%, with increased implementation level. Spatial scaling was investigated by increasing the spatial extent of LID practices using the subcatchment groups and all three LID practices (combined) implemented at 50% level. Results indicated that as the spatial extent of LID practices increased the runoff reduction at the outlet also increased, ranging from 3% to 19%. Spatial variability of LID implementation was examined by normalizing LID treated area to impervious area for each subcatchment group. The normalized LID implementation levels for each group revealed a reduction in runoff at the outlet of the watershed, ranging from 0.6% to 3.7%. This study showed that over a long-term period LID practices could restore pre-development hydrologic conditions. The optimal location for LID practice implementation within the study area was found to be near the outlet; however, these results cannot be generalized for all watersheds. </p>

<p> Releases of perchloroethylene (PCE) from dry cleaners pose a threat to groundwater quality. An assessment was performed of the Niles Cone Groundwater Basin to determine its vulnerability to PCE contamination from both historic and more recently operating dry cleaners. Sensitivity assessments of the Basin's two subbasins were performed using a modification of the DRASTIC Index Method, whereby the hydrogeological variables of depth to water, aquifer media, vadose zone media, and soil drainage classification were represented by a range of sensitivity categories and ratings assigned to each range. A source assessment was performed by identifying the locations of historic and presently operating dry-cleaning plants and assigning a threat ranking to each based on the approximate years in which the four generations of dry-cleaning machinery were introduced. Using ArcGIS, the sensitivity assessments and the source assessment were mapped, and the source assessment was superimposed over the sensitivity maps to create vulnerability maps of the two subbasins. The most sensitive area of the Below Hayward Fault subbasin in the forebay area near the Hayward Fault is due to a higher proportion of coarse-grained aquifer and vadose zone media and a thinner to absent aquitard due to deposition from the Alameda Creek. The existence of dry cleaners of higher threat makes this an area that is vulnerable to PCE contamination.</p>

<p> The Sierra Nevada is estimated to provide over 66% of California's water supply, which is largely derived from snowmelt. Global climate warming is expected to result in a decrease in snow pack and an increase in melting rate, making the attenuation of snowmelt by any means, an important ecosystem service for ensuring water availability. Montane meadows are dispersed throughout the mountain range providing wildlife habitat, water filtration, and water storage. Despite the important role of meadows in the Sierra Nevada, the majority are degraded from stream incision, which increases volume outflows and reduces overbank flooding, thus reducing infiltration and potential water storage. Restoration of meadow stream channels would therefore improve hydrological functioning, including increased water storage. The potential water holding capacity of restored meadows has yet to be quantified, thus this research seeks to address this knowledge gap by estimating the restorable water volume due to stream incision. More than 17,000 meadows were analyzed by categorizing their erosion potential using channel slope and soil texture, ultimately resulting in six general erodibility types. Field measurements of over 100 meadows, stratified by latitude, elevation, and geologic substrate, were then taken and analyzed for each erodibility type to determine average depth of incision. Restorable water volume was then quantified as a function of water holding capacity of the soil, meadow area and incised depth. Total restorable water volume across meadows in the Sierra Nevada was found to be 120 x 10⁶m³, or approximately 97,000 acre-feet. Using 95% confidence intervals for incised depth, the upper and lower bounds of the total restorable water volume were found to be 107 x 10⁶m³ &ndash; 140 x 10⁶m³. Though this estimate of restorable water volume is small in regards to the storage capacity of typical California reservoirs, restoration of Sierra Nevada meadows remains an important objective. Storage of water in meadows benefits California wildlife, potentially attenuates floods, and elevates base flows, which can ease effects to the spring snowmelt recession from the expected decline in Sierran snowpack with atmospheric warming.</p>