The importance of flow variability and floodplain water table recharge for the establishment and long-term survival of riparian vegetation has been well-documented. However, temporal and spatial variation in floodplain aquifers has received less attention, although native species can have narrow tolerances for groundwater decline. Our observations of decreased cottonwood cover on floodplains and increased willow cover on river banks since dam completion on the Dolores River led to comparisons between three long-term study sites above and below McPhee Dam. We summarize 5 years (2010-2014) of shallow groundwater well data from transects of three wells per site. Vegetation cover data were collected from quadrats and line-intercept transects. In the willow zone, groundwater well levels mirror in-channel flows and rarely drop below 0.6 m from ground surface. Willow cover and stem counts on point bars are higher at dammed sites. Wells in the cottonwood zone indicate that alluvial recharge happens only during prolonged peak discharge during spring snowmelt or dam release. Years with no dam spill reduced connectivity between surface flows and groundwater, and groundwater depth dropped to between 2 and >2.5 m. Long-term data below the dam indicate that canopy cover of the dominant cottonwoods has declined over time (48% in 1995, 19% in 2003), especially in the wake of severe drought. Mature cottonwood cover is significantly higher at the undammed site (p = 0.025). Our results indicate that floodplain habitats below dams exist under artificially extreme drought and inform how biologically diverse riparian systems will be impacted by a drying climate.
Core Ideas Computationally inexpensive, objective method for recommending sensor type/depth. Comprehensive information about potential measurement networks. Network design for user‐defined uncertainties before sensor installation. Limited monitoring budgets restrict the type and number of sensors that can be installed for field‐based studies. Therefore, sensor selection should be both informative and efficient. We propose a method to optimize sensor network design, prior to data collection, by combining multiple linear regression (MLR) and robust decision‐making (RDM). Multiple linear regression inherently considers the strength of the relationship between observations and predictions of interest and correlations among proposed observations. In our approach, we use universal Multiple Linear Regression (uMLR) to quantify the explanatory power of all possible combinations of model‐simulated candidate observations (of different sensor types and locations). A model‐ensemble approach allows for network design in the context of user‐defined uncertainties, including expected measurement error and parameter and structural uncertainty. Application of uMLR with RDM produces a comprehensive assessment of the likely value of many observation sets. These results can be used to design sensor networks to address specific experimental objectives and to balance the cost and effort of installing sensors to the expected value of the data for model testing and decision support.
Irrigation agriculture is the primary consumer of water worldwide. Because groundwater resources are being depleted, there is an increasing need to measure subsurface flux to quantify consumptive use and return flow. This study used HYDRUS-1D to simulate one-dimensional vertical movement of water and heat. Temperature time series were analyzed for different materials, depths, and applied fluxes to understand the limitations of temperature methods for monitoring steady-state water flux under unsaturated conditions. The conditions used in this analysis were intended to explore the minimum standards needed for unsaturated, steady-state flux estimation. If these conditions cannot be met, then it is unnecessary to look at more challenging conditions (e.g., heterogeneity, transient flux, and unknown hydraulic and thermal properties). Previous work has suggested that there is a minimum flux that is measurable with temperature methods. The low saturated hydraulic conductivities of fine-grained materials limit the applicability of temperature methods to high fluxes that lead to nearsaturated conditions for these materials. Although coarser soils have higher hydraulic conductivities, thermal diffusivity increases significantly with decreasing water content and may still complicate flux measurements for unsaturated conditions. As a result, we conclude that temperature-based methods in unsaturated conditions will most likely fail even with the most simplifying assumptions for most soil types. Therefore temperature methods should be restricted to monitoring steady-state fluxes in coarse soils and/or conditions near saturation.Worldwide, irrigation agriculture is the primary consumer of water (?90%), accounting for approximately 70% of freshwater withdrawals, 49% of which is irrigation with either solely groundwater or a combination of surface and groundwater (Siebert et al., 2010). The high demand for groundwater use is dramatically altering the quality and quantity of available groundwater. Groundwater storage is being exploited at higher rates than groundwater recharge, although the exact rates of withdrawal and return flow are poorly understood (Siebert et al., 2010).Interest in reducing irrigation must be balanced by the need to provide irrigation excess to avoid soil salinization. This practice results in some water percolating through the root zone, with some of it recharging underlying aquifers as return flow. To better manage irrigated agriculture, measurement of downward flux at depth is required to quantify this flow. There are several methods used to understand aquifer recharge and directly measure percolation rates (Kalbus et al., 2006). However, the level of uncertainty in groundwater recharge estimates is difficult to determine (Healy, 2010;Siebert et al., 2010). As the global population rises, we will need practical methods to quantify agricultural consumption and return flow.The focus of this study was to explore the potential and limitations of using subsurface temperature signals to infer return flow. For b...
Surface barriers are commonly installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model‐simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2‐m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down‐sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We also added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We found that uMLR, combined with robust decision‐making, provides a simple, flexible, and high‐quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.
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