The discipline of hydrology has long focused on quantifying the water balance, which is frequently used to estimate unknown water fluxes or stores. While technologies for measuring water balance ...components continue to improve, all components of the balance have substantial uncertainty at the watershed scale. Watershed‐scale evapotranspiration, storage, and groundwater import or export are particularly difficult to measure. Given these uncertainties, analyses based on assumed water balance closure are highly sensitive to uncertainty propagation and errors of omission, where unknown components are assumed negligible. This commentary examines how greater insight may be gained in some cases by keeping the water balance open rather than applying methods that impose water balance closure. An open water balance can facilitate identifying where unknowns such as groundwater import/export are affecting watershed‐scale streamflow. Strategic improvements in monitoring networks can help reduce uncertainties in observable variables and improve our ability to quantify unknown parts of the water balance. Improvements may include greater spatial overlap between measurements of water balance components through coordination between entities responsible for monitoring precipitation, snow, evapotranspiration, groundwater, and streamflow. Measuring quasi‐replicate watersheds can help characterize the range of variability in the water balance, and nested measurements within watersheds can reveal areas of net groundwater import or export. Well‐planned monitoring networks can facilitate progress on critical hydrologic questions about how much water becomes evapotranspiration, how groundwater interacts with surface watersheds at varying spatial and temporal scales, how much humans have altered the water cycle, and how streamflow will respond to future climate change.
Plain Language Summary
The water balance is a fundamental concept in hydrology that underlies many tools for predicting streamflow, soil moisture, or groundwater availability. It is often expressed as an equation that relates water inputs, outputs, and storage for a watershed. Inputs can be rainfall, snowmelt, or water imports to the watershed. Outputs include water movement into the atmosphere (evaporation, transpiration, and sublimation), streamflow, and water exports through groundwater or human diversions. Water storage can be in snow or ice, surface water bodies, or underground. Each of these water balance components is difficult to measure, and some are rarely measured. Therefore, researchers often simplify the water balance, assuming that difficult to measure quantities, like groundwater imports/exports or changes in water storage, can be neglected. Such simplifying assumptions lead to missed opportunities for discovering where these unknowns in the water balance are important controls on streamflow. This commentary advocates strategically expanding watershed monitoring networks to coordinate monitoring of different water balance components, monitor multiple similar watersheds within each geographic region, and nest monitoring of tributary streams within larger watersheds. This can accelerate progress in understanding groundwater flow, plant water availability, streamflow generation, and human impacts to the water balance.
Key Points
Quantifying the watershed‐scale water balance remains elusive because all components are uncertain
Imposing water balance closure can lead to missed opportunities for identifying unknowns in the water balance
We need more strategic quasi‐replicate and nested watershed monitoring to improve water balance understanding
We use an inverse simulation strategy to estimate soil hydraulic parameter values for an extensively measured planar hillslope plot in Seattle, Washington, United States. Both the integrated ...(subsurface outflow) and internal (piezometer water levels, volumetric water contents) hydrologic responses are measured at the plot. Inverse simulation scenarios are configured in the physics‐based variably saturated hydrologic model, HYDRUS‐2D, for a nonhysteretic drainage scenario starting from saturated initial conditions. Multiple inverse simulations calibrate the model either to single‐measurement time series or to combinations of multiple types of measurements. Inverse simulations calibrated to different types of measurements give a wide range of parameter combinations, including over 2 orders of magnitude in predicted saturated hydraulic conductivity (Ks), in part because the calibrations to a single measurement type are poorly constrained and biased. Parameter values are better constrained with multiobjective inverse simulations (Ks from 30 to 55 cm h−1). All parameter combinations from inverse simulations were tested in 2‐month‐long continuous simulations of the plot flow response to natural precipitation and evapotranspiration. The long‐term outflow response was predicted best (Nash‐Sutcliffe E = 0.94) by the parameters from a multiobjective inverse simulation calibrated to both the outflow and the piezometer water levels. Overall results show that for an assumed nonhysteretic soil a physics‐based hydrologic response model can be calibrated using one short‐duration drainage‐from‐saturation event if both integrated (outflow) and internal (saturated water level) measurements are used as calibration objectives.
ABSTRACT
This study develops a method for characterizing snow climatology in the Andes Mountains using the 8‐day maximum binary snow cover product from the Moderate Resolution Imaging ...Spectroradiometer sensor. The objectives are to: (1) identify regions with similar snow patterns and (2) identify snow persistence zones within these regions. Within a study area between 8° and 39°S, snow regions are defined using the (1) minimum elevation of snow cover, (2) rate of change of snow persistence with elevation, and (3) timing of the minimum elevation snow cover. In tropical latitudes (8°–23°S), snow cover is constrained to high elevations (>5000 m), and these areas have steep changes in snow persistence with elevation. Minimal differences in the elevation of snow on the east and west sides of the range suggest that temperature is a primary control on snow presence. Snow cover has minimal seasonal variability in the Tropics between 8° and 14°S, but it peaks in austral fall (March) after the wet season from 14° to 23°S. In mid‐latitudes (south of 23°S) snowline decreases in elevation with increasing latitude, and snow persistence changes with elevation are more gradual than in tropical regions. Snow cover peaks in the austral winter throughout the mid‐latitudes. Differences in elevations of snow accumulation between the east and west sides of the Andes are greatest between 28° and 37°S, where high mountain peaks produce a strong orographic effect and precipitation shadow. Within the snow regions, four snow zones are defined based on the average fraction of the year that snow persists: (1) little or no snow, (2) intermittent, (3) seasonal, and (4) permanent snow zones. Tropical latitudes have snow cover only on the highest peaks. Areas of seasonal and permanent snow zones are greatest between latitudes 28° and 37°S as a result of higher precipitation than mountains further north and higher elevations than mountains further south.
We study the relation of vector Proca field formalism and antisymmetric tensor-field formalism for spin-one resonances in the context of the large NC inspired chiral resonance Lagrangian ...systematically up to the order O(p6) and give a transparent prescription for the transition from vector to antisymmetric tensor Lagrangian and vice versa. We also discuss the possibility to describe the spin-one resonances using an alternative “mixed” first order formalism, which includes both types of fields simultaneously, and compare this one with the former two. We also briefly comment on the compatibility of the above lagrangian formalisms with the high-energy constraints for a concrete VVP correlator.
► HYDRUS simulations examine event response to hillslope-scale initial conditions. ► Hillslope storage exhibits preferred ranges for wet and dry seasons. ► Hillslope field capacity separates wet and ...dry season preferred storage ranges. ► Subsurface stormflow requires an initial storage threshold near field capacity. ► Total subsurface stormflow is a nonlinear function of initial storage.
Runoff response to rain events depends on the initial moisture conditions in the subsurface. This study explores subsurface stormflow response to initial conditions within the context of a continuous hillslope water balance. A hypothetical hillslope with three-dimensional variably saturated subsurface flow is developed using the HYDRUS model forced with a year-long sequence of hourly precipitation and transpiration from Seattle, WA, USA. Using six different soil hydraulic parameter sets, test simulations examine (1) variability of hillslope initial conditions prior to rain events, (2) persistence of initial conditions in a continuous simulation, and (3) effects of initial conditions on subsurface stormflow during rain events. Results show that hillslope initial conditions vary seasonally, producing bimodal distributions of storage values with preferred storage ranges for wet and dry seasons. Preferred storage ranges differ by soil texture and by hydraulic conductivity. Wet season initial conditions are most frequently at storage values above hillslope field capacity, with higher preferred ranges of storage for scenarios with lower saturated hydraulic conductivity. Dry season hillslope storage values converge to minimum values below field capacity, and these minimum values vary with soil texture but not with saturated hydraulic conductivity. Tests of initial condition convergence show that scenarios with different initial storages can eventually converge under either persistent wetting or persistent drying until hillslope storage reaches the wet or dry preferred states. Dry preferred states are unlikely to produce subsurface stormflow, as test rain events generate hillslope outflow only when initial storage is at or above an event and parameter-specific threshold near the hillslope field capacity. Above this flow threshold, all simulations show a nonlinear increase in subsurface stormflow with increasing initial storage, with the steepest rates of increase for scenarios with the highest values of saturated hydraulic conductivity. Simulation experiments present a quantitative approach for deriving functional relationships between event response, initial storage, and hillslope water retention.
A review of familiar results of the three-point Green functions of currents in the odd-intrinsic parity sector of QCD is presented. Such Green functions include very well-known examples of VVP, VAS ...or AAP correlators. We also shortly present some of the new results for VVA and AAA Green functions with a discussion of their high-energy behaviour and its relation to the QCD condensates.
River managers often need estimates of streamflow for ungauged streams. These estimates can be used in water rights acquisitions, in‐stream flow management, habitat assessment, water quality ...planning, and stream hazard identification. This publication describes new regression models for predicting mean annual and mean monthly streamflow in Colorado. Unlike previous regional regression studies, the new models incorporate snow persistence (SP), the fraction of time a watershed remains snow covered. Models were developed using streamflow data from 131 watersheds with drainage areas <1,500 km2, no transbasin diversions, and <10% urban area. In addition to SP, topographic, climate, geologic, and hydrologic region variables were used in model predictions. All new models had very good performance, with <6% absolute bias and stronger performance compared to current regional regression models in StreamStats. The mean annual model had the strongest performance, with Nash‐Sutcliffe efficiency coefficient (NSE) of 0.93 and <2% absolute bias. Mean monthly models had best performance during snowmelt runoff months (May‐Jul; NSE ≥0.79; absolute % bias ≤ 4) and weaker performance during low flow months (Aug‐Apr; NSE ≥ 0.59; % bias ≤ 5). Tests of the mean annual model using decadal average streamflow from 1910s to 2000s show very good performance (NSE > 0.75), but predictions were biased low by 14–28% in wetter decades. All equations and coefficients needed to run the models are presented in the publication appendix, and the associated data release includes the spatial data and model code, which can be applied using R or within an R‐based Shiny web app.
Streamflow duration is important for aquatic ecosystems and assigning stream protection status. This study predicts streamflow duration, represented as the fraction of time with flow each year, using ...a combination of sensor data and crowd‐sourced visual observations for a study area in northern Colorado, USA. We used 11 stream stage sensors and 177 visual monitoring points to examine how frequently streams should be sampled to compute flow fractions accurately. This showed that the number of visual observations needed to compute accurate flow fractions increases with decreasing flow duration. We then developed random forest models to predict mean annual flow fractions using climate, topographic, and land cover predictors and found that snow persistence, summer precipitation, and drainage area were important predictors. Model performance was best when using sites with ≥10 visual observations. Our model predicts that almost all (98%) of streams in the study region are non‐perennial, about 10% more than the amount of non‐perennial streams in the National Hydrography Dataset. Stream type maps are sensitive to the time period of data collection and to thresholds used to represent perennial versus non‐perennial flow. To improve maps of non‐perennial streams, we recommend moving beyond categorical classification of streams to a continuous variable like flow fraction. These efforts can be best supported with frequent observations in time that span streams with a wide range of flow fractions and drainage area attributes.
Plain Language Summary
Most small streams in the world are not monitored, so we know little about when they are flowing or dry. Yet, the amount of time streams flow can determine whether they are protected by water quality legislation and streamside management plans. In this study we used visual observations of stream flow/no flow and stream sensors to develop a model that predicts the fraction of time that streams flow. At a study area in northern Colorado, volunteer observers documented stream flow/no flow at 177 stream segments, and we placed sensors in 11 headwater streams at different elevations. We found that streams needed to be visited approximately weekly to determine how long they flow each year. Streams that rarely flow needed to be visited more often than those that flow most of the time. Our model shows that most (98%) of the streams in the study area do not flow continuously. The amount of time that streams flow is sensitive to changing climate and water demands. Ongoing monitoring of these streams will help us track and predict the range of flow conditions that are possible throughout the vast networks of small streams that feed larger rivers and lakes.
Key Points
Predicted April–September fraction of time with flow using sensors, crowd‐sourced observations, and statistical models in Colorado streams
Snow persistence, summer precipitation, and drainage area are dominant predictors of flow fractions in the Northern Colorado study area
Developing a reliable model of flow fraction requires sampling diverse streams that span the full spectrum of flow fractions (0–1)
A small but growing number of watershed investment programs in the western United States focus on wildfire risk reduction to municipal water supplies. This paper used return on investment (ROI) ...analysis to quantify how the amounts and placement of fuel treatment interventions would reduce sediment loading to the Strontia Springs Reservoir in the Upper South Platte River watershed southwest of Denver, Colorado following an extreme fire event. We simulated various extents of fuel mitigation activities under two placement strategies: (a) a strategic treatment prioritization map and (b) accessibility. Potential fire behavior was modeled under each extent and scenario to determine the impact on fire severity, and this was used to estimate expected change in post-fire erosion due to treatments. We found a positive ROI after large storm events when fire mitigation treatments were placed in priority areas with diminishing marginal returns after treating >50–80% of the forested area. While our ROI results should not be used prescriptively they do show that, conditional on severe fire occurrence and precipitation, investments in the Upper South Platte could feasibly lead to positive financial returns based on the reduced costs of dredging sediment from the reservoir. While our analysis showed positive ROI focusing only on post-fire erosion mitigation, it is important to consider multiple benefits in future ROI calculations and increase monitoring and evaluation of these benefits of wildfire fuel reduction investments for different site conditions and climates.
•We estimated potential return on investment from wildfire risk mitigation.•Simulated fuel treatments were effective at reducing simulated post-fire erosion.•Return on investment varied by placement of fuel treatment and extent treated.•Return on investment varied with the size of simulated storms.•Maximum financial return occurred with 50–80% of forested area treated.
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