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This paper introduces a collection of four articles describing the San Luis Basin Sustainability Metrics Project. The Project developed a methodology for evaluating regional sustainability. This introduction provides the necessary background information for the project, description of the region, overview of the methods, and summary of the results. Although there are a multitude of scientifically based sustainability metrics, many are data intensive, difficult to calculate, and fail to capture all aspects of a system. We wanted to see if we could develop an approach that decision-makers could use to understand if their system was moving toward or away from sustainability. The goal was to produce a scientifically defensible, but straightforward and inexpensive methodology to measure and monitor environmental quality within a regional system. We initiated an interdisciplinary pilot project in the San Luis Basin, south-central Colorado, to test the methodology. The objectives were: 1) determine the applicability of using existing datasets to estimate metrics of sustainability at a regional scale; 2) calculate metrics through time from 1980 to 2005; and 3) compare and contrast the results to determine if the system was moving toward or away from sustainability. The sustainability metrics, chosen to represent major components of the system, were: 1) Ecological Footprint to capture the impact and human burden on the system; 2) Green Net Regional Product to represent economic welfare; 3) Emergy to capture the quality-normalized flow of energy through the system; and 4) Fisher information to capture the overall dynamic order and to look for possible regime changes. The methodology, data, and results of each metric are presented in the remaining four papers of the special collection. Based on the results of each metric and our criteria for understanding the sustainability trends, we find that the San Luis Basin is moving away from sustainability. Although we understand there are strengths and limitations of the methodology, we argue that each metric identifies changes to major components of the system. [Copyright &y& Elsevier]

After peak snowmelt, baseflow is the primary contributor to streamflow in snow-dominated watersheds. These low flows provide important water for municipal, agricultural, and recreational purposes once peak flows have been allocated. This study examines the correlation between peak snow water equivalent (SWE), post-peak SWE precipitation, and baseflow characteristics, including any yearly lag in baseflow. To reflect the hydrologic processes that are occurring in snow-dominated watersheds, we propose using a melt year (MY) beginning with the onset of snowmelt contributions (the first deviation from baseflow) and ending with the onset of the following year's snowmelt contributions. We identified the beginning of an MY and extracted the subsequent baseflow values using flow duration curves (FDCs) for 12 watersheds of varying sizes across Colorado, USA. Based on the findings, peak SWE and summer rain both dictate baseflow, especially for the larger watersheds evaluated, as identified by higher correlations with the MY-derived baseflow. Lags in the correlation between baseflow and peak SWE are best identified when low-snow years are investigated separately from high-snow years. The MY is a different and more effective approach to calculating baseflow using FDCs in snow-dominated watersheds in Colorado. [ABSTRACT FROM AUTHOR]

The upper Colorado River basin (UCRB) is one of the primary sources of water for the western United States, and increasing temperatures likely will elevate the risk of reduced water supply in the basin. Although variability in water-year precipitation explains more of the variability in water-year UCRB streamflow than water-year UCRB temperature, since the late 1980s, increases in temperature in the UCRB have caused a substantial reduction in UCRB runoff efficiency (the ratio of streamflow to precipitation). These reductions in flow because of increasing temperatures are the largest documented temperature-related reductions since record keeping began. Increases in UCRB temperature over the past three decades have resulted in a mean UCRB water-year streamflow departure of −1306 million m3 (or −7% of mean water-year streamflow). Additionally, warm-season (April through September) temperature has had a larger effect on variability in water-year UCRB streamflow than the cool-season (October through March) temperature. The greater contribution of warm-season temperature, relative to cool-season temperature, to variability of UCRB flow suggests that evaporation or snowmelt, rather than changes from snow to rain during the cool season, has driven recent reductions in UCRB flow. It is expected that as warming continues, the negative effects of temperature on water-year UCRB streamflow will become more evident and problematic. [ABSTRACT FROM AUTHOR]

Abstract: Groundwater is a critical input to agricultural production across the globe. Current groundwater pumping rates frequently exceed recharge, often by a substantial amount, leading to groundwater depletion and potential declines in agricultural profits over time. As a result, many regions reliant on irrigated agriculture have proposed policies to manage groundwater use. Even when gains from aquifer management exist, there is little information about how policies affect individual producers sharing the resource. In this paper, we investigate the variability of groundwater management policy impacts across heterogeneous agricultural producers. To measure these impacts, we develop a hydroeconomic model that captures the important role of well capacity, productivity of water, and weather uncertainty. We use the model to simulate the impacts of groundwater management policies on producers in the High Plains aquifer of eastern Colorado and compare outcomes to a no‐policy baseline. The management policies considered include a pumping fee, a quantity restriction, and an irrigated acreage fee. We find that well capacity and soil type affect policy impacts but in ways that can qualitatively differ across policy type. Model results have important implications for the distributional impacts and political acceptability of groundwater management policies. [ABSTRACT FROM AUTHOR]

Focus group discussions and a modeling approach were applied to determine policy and regulatory refinements for current water allocation practices in Kyrgyzstan. Lessons from the Lower Colorado River basin, Texas and New South Wales, Australia were taken into consideration. The paper analyzes the impact of adopting some of these interventions within the socio-environmental context that currently prevails in Kyrgyzstan. The optimization model for water distribution at the river-basin scale was developed using GAMS 2.25 software. Application of the model to the Akbura River basin indicated efficiencies in the proposed institutional rules especially in low water years. [ABSTRACT FROM AUTHOR]

Decision Making Under Deep Uncertainty often uses prohibitively large scenario ensembles to calculate robustness and rank policies' performance. This paper contributes a framework using subsampling algorithms and space-filling metrics to determine how smaller ensemble sizes impact the accuracy of robustness rankings. Subsampling methods create smaller scenario ensembles of varying sizes. We evaluate ranking sensitivity to the ensemble size and calculate accuracy relative to a baseline ranking. Then, metrics of scenario set quality predict ranking accuracy. Notably, the metrics and subsampling methods do not require additional model simulations. We demonstrate the framework with a case study of shortage policies for Lake Mead in the Colorado River Basin (CRB). Results suggest that fewer scenarios than previous studies can accurately rank Lake Mead policies, and results depend on the type of objective and robustness metric. Smaller ensembles could reduce the computational burden of robustness analyses in the ongoing CRB policy renegotiation. • Framework tests sensitivity of policy robustness rankings to scenario ensemble size. • Subsampling methods reevaluate policy rankings without additional simulations. • Model-free metrics of scenario ensemble quality predict rank accuracy. • Case study: shortage operation policies of Lake Mead in the Colorado River Basin. • Fewer scenarios than previous studies can accurately rank Lake Mead policies. [ABSTRACT FROM AUTHOR]

This paper examines the accuracy of high-resolution nested mesoscale model simulations of surface climate. The nesting capabilities of the atmospheric fifth-generation Pennsylvania State University (PSU)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) were used to create high-resolution, 5-yr climate simulations (from 1 October 1994 through 30 September 1999), starting with a coarse nest of 20 km for the western United States. During this 5-yr period, two finer-resolution nests (5 and 1.7 km) were run over the Yampa River basin in northwestern Colorado. Raw and bias-corrected daily precipitation and maximum and minimum temperature time series from the three MM5 nests were used as input to the U.S. Geological Survey’s distributed hydrologic model [the Precipitation Runoff Modeling System (PRMS)] and were compared with PRMS results using measured climate station data. The distributed capabilities of PRMS were provided by partitioning the Yampa River basin into hydrologic response units (HRUs). In addition to the classic polygon method of HRU definition, HRUs for PRMS were defined based on the three MM5 nests. This resulted in 16 datasets being tested using PRMS. The input datasets were derived using measured station data and raw and bias-corrected MM5 20-, 5-, and 1.7-km output distributed to 1) polygon HRUs and 2) 20-, 5-, and 1.7-km-gridded HRUs, respectively. Each dataset was calibrated independently, using a multiobjective, stepwise automated procedure. Final results showed a general increase in the accuracy of simulated runoff with an increase in HRU resolution. In all steps of the calibration procedure, the station-based simulations of runoff showed higher accuracy than the MM5-based simulations, although the accuracy of MM5 simulations was close to station data for the high-resolution nests. Further work is warranted in identifying the causes of the biases in MM5 local climate simulations and developing methods to remove them. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration. [ABSTRACT FROM AUTHOR]

The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet's dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United States drainage basins of varying acreage from smaller to very large (Bear, Colorado, Connecticut, and Mississippi). F2F combines model and ground observations of water-cycle fluxes in the form of surface runoff, subsurface baseflow, and gauged streamflow. We use these time series datasets to simulate, visualize, and analyze the watershed basin response to the varying climates and magnitudes of hydroclimatic fluxes in each river basin. Experiments modulating the time lag between remotely sensed and ground-truth measurements are performed to assess the metrological limits of forecasting with this platform. The resultant mean Nash–Sutcliffe and Kling–Gupta efficiency values are both greater than 90%. Our results show that a hydrological machine learning platform such as F2F can become a powerful resource to simulate and forecast hydroclimatic extremes and the resulting watershed responses and natural hazards in a changing global climate. [ABSTRACT FROM AUTHOR]

Current streamflow timing metrics, such as the center of volume, or COV, use flow days, which are days at which a specific total streamflow volume, such as 50% for COV, has passed a given point. These metrics have been used as indicators for changes in the timings of snowmelt contributions to streamflow, but they may not be indicating changes to snowmelt timings as they have a fixed volume. Using manually extracted start and end days for high-elevation, mountainous watersheds, which are regarded as true values, we developed a new method to estimate when snowmelt is entering streams. Based on RMSE and NSE values, this new method is a better model for snowmelt-driven streamflow than using flow days or the COV. In general, the trend analysis results from the different timing metrics indicate an earlier timing in the year. This method was suitable for 40 different-sized watersheds in Colorado, USA; these are all snow-dominated watersheds in a semi-arid climate. This method could be used to assess watersheds in different climates, including those that are not as snow-dominated. [ABSTRACT FROM AUTHOR]

Salinity can have major detrimental impacts on soil health, crop yield, and environmental ecosystem services. In this paper we introduce SWAT-MODFLOW-Salt, a version of SWAT-MODFLOW that simulates the fate and transport of major salt ions (SO 4 2−, Cl−, CO 3 2−, HCO 3 −, Ca2+, Na+, Mg2+, K+) in watershed systems, and focus on its development and application to irrigated stream-aquifer systems. The modeling code accounts for water and salt mass storage in soils, aquifer, and streams, and the movement of water and salt mass in surface runoff, soil lateral flow, percolation and recharge, groundwater flow, and groundwater-surface water exchange. The model is applied to a highly managed, irrigated 700 km2 region of the saline Lower Arkansas River Valley in Colorado, USA. The model is tested against groundwater salt concentrations and in-stream salt mass loadings, and then used to quantify the impact of soil salinity on crop yield. • We provide a new version of SWAT-MODFLOW that simulates salt ion transport in watershed systems • Salt ions are subject to all major hydrologic fluxes and precipitation-dissolution of salt minerals • The SWAT-MODFLOW-Salt code is applied to a saline stream-aquifer system in Colorado • The model can be used for salinity management scenario analysis [ABSTRACT FROM AUTHOR]

Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling assimilation to the basin scale is its performance at forested locations—where canopy‐snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) technique to assimilate intermittent snow depth observations into the Flexible Snow Model, and validate model outputs against snow density and SWE measurements across adjacent forest and open sites, at two locations with different climates and forest structures. Assimilation reduces snow depth error by 70%–90%, density error by 5%–30%, and SWE error by 50%–70% at forest locations (relative to controls). The PF simulates the seasonal evolution of the snowpack under forest canopy, including where interception losses decrease SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). Model outputs are sensitive to canopy‐related parameters, but DA reduces the range in snow depth and SWE estimates resulting from variations or uncertainties in these parameters by over 50%. These results demonstrate that the importance of accurately measuring, estimating, or calibrating canopy‐related parameters is reduced when snow depth observations are assimilated. Finally, we assimilate remotely sensed snow depth observations at 50 m resolution across the East River Basin, Colorado (1,000 km2). Across elevations, the PF increases estimated precipitation at forested sites by ∼15% relative to open sites, likely compensating for excessive sublimation of intercepted snow. Key Points: Data assimilation improves modeled snow depth, density, and SWE at forested sites in two contrasting climates and over multiple yearsAssimilation reduces apparent model output sensitivity to forest canopy parameters that are difficult to measure or calibrateAssimilation increases estimated precipitation in the forest relative to the open, likely due to excessive sublimation of intercepted snow [ABSTRACT FROM AUTHOR]

In this study, we develop a watershed zonation approach for characterizing watershed organization and functions in a tractable manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale heterogeneity of key bedrock-through-canopy properties and for quantifying the co-variability of these properties representing coupled ecohydrological and biogeochemical interactions, (2) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having the unique distributions of these properties relative to neighboring parcels, and (3) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought and solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesize airborne remote sensing data (lidar, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that (1) we can define the scale of hillslopes at which the hillslope-averaged metrics can capture the majority of the overall variability in key properties (such as elevation, net potential annual radiation, and peak snow-water equivalent – SWE), (2) elevation and aspect are independent controls on plant and snow signatures, (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are significantly correlated with surface topography and plan species distribution, and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, we show that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be applicable to other sites and generally useful for guiding the selection of hillslope-experiment locations and informing model parameterization. [ABSTRACT FROM AUTHOR]

This paper describes the theory, data, and methodology necessary for using Fisher information to assess the sustainability of the San Luis Basin (SLB) regional system over time. Fisher information was originally developed as a measure of the information content in data and is an important method in information theory. Our adaptation of Fisher information provides a means of monitoring the variables of a system to characterize dynamic order, and, therefore, its regimes and regime shifts. This work is part of the SLB Sustainability Metrics Project, which aimed to evaluate movement over time towards or away from regional sustainability. One of the key goals of this project was to use readily available data to assess the sustainability of the system including its environmental, social and economic aspects. For this study, Fisher information was calculated for fifty-three variables which characterize the consumption of food and energy, agricultural production, environmental characteristics, demographic properties and changes in land use for the SLB system from 1980 to 2005. Our analysis revealed that while the system displayed small changes in dynamic order over time with a slight decreasing trend near the end of the period, there is no indication of a regime shift. Therefore, the SLB system is stable with very slight movement away from sustainability in more recent years. [Copyright &y& Elsevier]

APEX (A gricultural P olicy/ E nvironmental e X tender) is an oft-used agroecosystem model but has limited use in groundwater-driven watersheds due to a simplistic representation of groundwater processes. This paper presents the linkage of APEX and the groundwater flow model MODFLOW into a single modeling code. The mapping of recharge, groundwater head, and groundwater-surface water interactions are handled internally via subroutines. The APEX-MODFLOW model is applied to three watersheds in the United States for testing code accuracy and hydrologic state variables and fluxes: the Animas River Watershed, Colorado and New Mexico (3543 km2); the Price River Watershed, Utah (4886 km2); and the Middle Bosque River Watershed, Texas (470 km2). Whereas the hydrology of the Animas River and Price River watersheds is driven by snowmelt and spring runoff, the hydrology of the Middle Bosque River Watershed is driven by summer thunderstorms. The model can be used for scenario analysis in groundwater-driven watersheds. • New integrated hydrologic model links APEX model to MODFLOW model. • Model passes recharge, water table elevation, and gw-sw flux rates internally. • APEX-MODFLOW applied to three watersheds in Utah, Colorado, and Texas, USA. • Model results show importance of including physically based groundwater flow. [ABSTRACT FROM AUTHOR]

As the potential for and scope of some types of disasters increases, so too does the need to build greater disaster resilience across the globe. Communities ideally begin building resilience prior to experiencing a disaster in order to reduce negative impacts and ease recovery processes; however, numerous environmental and sociopolitical factors can impede such efforts until a disaster occurs. While the disaster recovery period offers opportunities for communities to build resilience as they replace infrastructure and restore services, a host of new issues arise during this time that can further complicate or delay resilience building. In this study, we highlight the opportunities and challenges inherent at the intersection of disaster recovery and resilience building, which we term the "recovery–resilience nexus." To study this nexus, we analyze a first-of-its-kind disaster recovery program in Colorado, United States, that promotes resilience-building activities in disaster-affected communities by supporting the efforts of place-based watershed coalitions. Although the program faced numerous and interrelated technical, political, and fiscal hurdles, we argue that it provides an opportunity for drawing important lessons about how communities can navigate the recovery–resilience nexus via cross-boundary collaboration and creatively leveraging traditional disaster recovery funding sources to achieve resilience goals. [ABSTRACT FROM AUTHOR]

Prehistoric peoples chose farming locations based on environmental conditions, such as soil moisture, which plays a crucial role in crop production. Ancestral Pueblo communities of the central Mesa Verde region became increasingly reliant on maize agriculture for their subsistence needs by AD 900. Prehistoric agriculturalists (e.g., Ancestral Pueblo farmers) were dependent on having sufficient soil moisture for successful plant growth. To better understand the quality of farmland in terms of soil moisture, this study develops a static geospatial soil moisture model, the Soil Moisture Proxy Model, which uses soil and topographic variables to estimate soil moisture potential across a watershed. The model is applied to the semi-arid region of the Goodman watershed in the central Mesa Verde region of southwestern Colorado. We evaluate the model by comparing the Goodman watershed output to two other watersheds and to soil moisture sensor values. The simple framework can be used in other regions of the world, where water is also an important limiting factor for farming. The general outcome of this research is an improved understanding of potential farmland and human-environmental relationships across the local landscape. [ABSTRACT FROM AUTHOR]

Salinity is one of the most common water quality threats in river basins and irrigated regions worldwide. However, no available numerical models simulate all major processes affecting salt ion fate and transport at the watershed scale. This study presents a new salinity module for the SWAT model that simulates the fate and transport of eight major salt ions (SO42- , Ca2+ , Mg2+ , Na+ , K+ , Cl- , CO32- , HCO3-) in a watershed system. The module accounts for salt transport in surface runoff, soil percolation, lateral flow, groundwater, and streams, and equilibrium chemistry reactions in soil layers and the aquifer. The module consists of several new subroutines that are imbedded within the SWAT modelling code and one input file containing soil salinity and aquifer salinity data for the watershed. The model is applied to a 732 km 2 salinity-impaired irrigated region within the Arkansas River Valley in southeastern Colorado and tested against root zone soil salinity, groundwater salt ion concentration, groundwater salt loadings to the river network, and in-stream salt ion concentration. The model can be a useful tool in simulating baseline salinity transport and investigating salinity best management practices in watersheds of varying spatial scales. [ABSTRACT FROM AUTHOR]

Post-wildfire landscapes are highly susceptible to rapid geomorphic changes, and the resulting downstream effects, at both the hillslope and watershed scales due to increases in hillslope runoff and erosion. Numerous studies have documented these changes at the hillslope scale, but relatively few studies have documented larger-scale post-fire geomorphic changes over time. In this study we used five airborne laser scanning (ALS) datasets collected over 4 years to quantify erosion and deposition throughout the channel network in two ∼15 km 2 watersheds, Skin Gulch and Hill Gulch, in northern Colorado after a wildfire followed by a large, long-duration flood 15 months later. The objectives were to (1) quantify the volumes, spatial patterns, and temporal changes over time of erosion and deposition over a nearly 4-year period, and (2) evaluate the extent to which these spatially and temporally explicit changes are correlated to precipitation metrics, burn severity, and morphologic variables. The volumetric changes were calculated from a differencing of DEMs for 50 m long segments of the channel network and associated valley bottoms. The results showed net sediment accumulation after the wildfire in the valley bottoms of both watersheds, with greater accumulations in the wider and flatter valley bottoms in the first 2 years after burning. In contrast, the mesoscale flood caused large amounts of erosion, with higher erosion in those areas with more post-fire deposition. Only minor changes occurred over the 2 years following the mesoscale flood. Volume changes for the different time periods were weakly but significantly correlated to, in order of decreasing correlation, contributing area, channel width, percent burned at high and/or moderate severity, channel slope, confinement ratio, maximum 30 min precipitation, and total precipitation. These results suggest that morphometric characteristics, when combined with burn severity and a specified storm, can indicate the relative likelihood and locations for post-fire erosion and deposition. This information can help assess downstream risks and prioritize areas for post-fire hillslope rehabilitation treatments. [ABSTRACT FROM AUTHOR]

A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60 %. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETMC, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90mspatial resolution and another using theMRapproach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2% and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using ~100m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks. [ABSTRACT FROM AUTHOR]

The 2013 federal Colorado River Basin Water Supply and Demand Study projected the water imbalance between future supply and demand to increase. The Colorado water supply (WS) exemplifies a pronounced quasi-decadal oscillation (QDO) of 10-20 years throughout its historical record; however, this QDO feature is unaccounted for in the climate models used to project the future WS. Adjacent to the Colorado River, the large watershed of the Great Salt Lake (GSL) in Utah records the hydrologicQDOsignal in its water surface, leading previous studies to explore the cause of decadal fluctuations in the lake elevation and assess predictability. This study reports a remarkable coherence between the Colorado WS and the GSL elevation at the 10-20-yr time scale. Analysis of precipitation and terrestrial water storage anomalies suggests a crossbasin connection in the climate and hydrometeorological variations of the Colorado WS and the GSL. The 160-yr-long and well-kept GSL elevation record makes it an effective indicator for the Colorado WS. [ABSTRACT FROM AUTHOR]

Although important for riverine solute and nutrient fluxes, the connections between biogeochemical processes and subsurface hydrology remain poorly characterized. We investigate these couplings in the East River, CO, a high-elevation shale-dominated catchment in the Rocky Mountains, using concentration-discharge (C-Q) relationships for major cations, anions, and organic carbon. Dissolved organic carbon (DOC) displays a positive C-Q relationship with clockwise hysteresis, indicating mobilization and depletion of DOC in the upper soil horizons and emphasizing the importance of shallow flow paths during snowmelt. Cation and anion concentrations demonstrate that carbonate weathering, which dominates solute fluxes, is promoted by both sulfuric acid derived from pyrite oxidation in the shale bedrock and carbonic acid derived from subsurface respiration. Sulfuric acid weathering dominates during base flow conditions when waters infiltrate below the inferred pyrite oxidation front, whereas carbonic acid weathering plays a dominant role during snowmelt as a result of shallow flow paths. Differential C-Q relationships between solutes suggest that infiltrating waters approach calcite saturation before reaching the pyrite oxidation front, after which sulfuric acid reduces carbonate alkalinity. This reduction in alkalinity results in CO2 outgassing when waters equilibrate to surface conditions, and reduces the riverine export of carbon and alkalinity by roughly 33% annually. Future changes in snowmelt dynamics that control the balance of carbonic and sulfuric acid weathering may substantially alter carbon cycling in the East River. Ultimately, we demonstrate that differential C-Q relationships between major solutes can provide unique insights into the complex subsurface flow and biogeochemical dynamics that operate at catchment scales. [ABSTRACT FROM AUTHOR]

This study analyzes spatial and temporal characteristics of multiyear droughts and pluvials over the southwestern United States with a focus on the upper Colorado River basin. The study uses two multiscalar moisture indices: standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) on a 36-month scale (SPEI36 and SPI36, respectively). The indices are calculated from monthly average precipitation and maximum and minimum temperatures from the Parameter-Elevation Regressions on Independent Slopes Model dataset for the period 1950-2012. The study examines the relationship between individual climate variables as well as large-scale atmospheric circulation features found in reanalysis output during drought and pluvial periods. The results indicate that SPEI36 and SPI36 show similar temporal and spatial patterns, but that the inclusion of temperatures in SPEI36 leads tomore extreme magnitudes in SPEI36 than in SPI36. Analysis of large-scale atmospheric fields indicates an interplay between different fields that yields extremes over the study region. Widespread drought (pluvial) events are associated with enhanced positive (negative) 500-hPa geopotential height anomaly linked to subsidence (ascent) and negative (positive) moisture convergence and precipitable water anomalies. Considering the broader context of the conditions responsible for the occurrence of prolonged hydrologic anomalies provides water resource managers and other decision-makers with valuable understanding of these events. This perspective also offers evaluation opportunities for climate models. [ABSTRACT FROM AUTHOR]

We develop a novel risk assessment approach that integrates complementary, yet distinct, spatial modeling approaches currently used in wildfire risk assessment. Motivation for this work stems largely from limitations of existing stochastic wildfire simulation systems, which can generate pixel-based outputs of fire behavior as well as polygon-based outputs of simulated final fire perimeters, but due to storage and processing limitations do not retain spatially resolved information on intensity within a given fire perimeter. Our approach surmounts this limitation by merging pixel- and polygon-based modeling results to portray a fuller picture of potential wildfire impacts to highly valued resources and assets (HVRAs). The approach is premised on using fire perimeters to calculate fire-level impacts while explicitly capturing spatial variation of wildfire intensity and HVRA susceptibility within the perimeter. Relative to earlier work that generated statistical expectations of risk, this new approach can better account for the range of possible fire-level or season-level outcomes, providing far more comprehensive information on wildfire risk. To illustrate the utility of this new approach, we focus on a municipal watershed on the Pike and San Isabel National Forests in Colorado, USA. We demonstrate a variety of useful modeling outputs, including exceedance probability charts, conditional distributions of watershed area burned and watershed impacts, and transmission of risk to the watershed based on ignition location. These types of results can provide more information than is otherwise available using existing assessment frameworks, with significant implications for decision support in pre-fire planning, fuel treatment design, and wildfire incident response. [ABSTRACT FROM AUTHOR]

In snowmelt-driven river systems, it is critical to enable reliable predictions of the spatiotemporal variability in seasonal snowpack to support local and regional water management. Previous studies have shown that assimilating satellite-station blended snow depth data sets can lead to improved snow predictions, which however do not always translate into improved streamflow predictions, especially in complex mountain regions. In this study, we explore how an existing optimal interpolation-based blending strategy can be enhanced to reduce biases in satellite snow depth products for improving streamflow predictions. Two major new considerations are explored, including: (1) incorporating terrain aspect and (2) incorporating areal snow coverage information. The methodology is applied to the bias reduction of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth estimates, which are then assimilated into the Noah land surface model via the ensemble Kalman Filtering (EnKF) for streamflow predictions in the Upper Colorado River Basin. Our results indicate that using only observations from low-elevation stations such as the Global Historical Climatology Network (GHCN) in the bias correction can lead to underestimation in streamflow, while using observations from high-elevation stations (e.g., the Snow Telemetry (SNOTEL) network) along with terrain aspect is critically important for achieving reliable streamflow predictions. Additionally incorporating areal snow coverage information from the Moderate Resolution Imaging Spectroradiometer (MODIS) can slightly improve the streamflow results further. [ABSTRACT FROM AUTHOR]

The impact of projected 21st century climate conditions on streamflow in the Upper Colorado River Basin was estimated using a multi-model ensemble approach wherein the downscaled outputs of 112 future climate projections from 16 global climate models (GCMs) were used to drive a macroscale hydrology model. By the middle of the century, the impacts on streamflow range, over the entire ensemble, from a decrease of approximately 30% to an increase of approximately the same magnitude. Although prior studies and associated media coverage have focused heavily on the likelihood of a drier future for the Colorado River Basin, approximately 25 to 35% of the ensemble of runs, by 2099 and 2039, respectively, result in no change or increases in stream- flow. The broad range of projected impacts is primarily the result of uncertainty in projections of future precipitation, and a relatively small part of the variability of precipitation across the projections can be attributed to the effect of emissions pathways. The simulated evolution of future temperature is strongly influenced by emissions, but temperature has a smaller influence than precipitation on flow. Period change statistics (i.e., the change in flow from one 30-yr period to another) vary as much within a model ensemble as between models and emissions pathways. Even by the end of the current century, the variability across the projections is much greater than changes in the ensemble mean. The relatively large ensemble analysis described herein provides perspective on earlier studies that have used fewer scenarios, and suggests that impact analyses relying on one or a few climate scenarios are unacceptably influenced by the choice of projections. [ABSTRACT FROM AUTHOR]

This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005-2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using "percentage of bare soil" ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005-2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions. [ABSTRACT FROM AUTHOR]

Elevated selenium concentrations have been observed in several rivers in the western United States and are a concern for healthy aquatic systems. Water quality modeling is a valuable tool for quantifying the importance of sources and assessing management alternatives for stream standards compliance in basins with impaired water quality. This modeling study uses a relatively simple approach to describe a water distribution system and processes that drive loading of dissolved selenium and salts from the naturally occurring Cretaceous shale soils in the extensively irrigated Uncompahgre and Gunnison River Basins in Western Colorado. Calibrated model output characterizes processes that load an average total of 4,130 kg(9,100 lb) of selenium and 2.9×108 kg(316,000 t) of salts per year from agricultural subbasins to the Uncompahgre River. Simulations of best management practices predict that extensive implementation of methods to minimize seepage and constituent loading would be required to reduce selenium concentrations to comply with the current water quality standards. [ABSTRACT FROM AUTHOR]

Three major disturbances affect subalpine forests in the Rocky Mountains: blowdown, insect outbreaks, and fire. These disturbances may influence one another temporally and spatially, creating a mosaic of disturbance patches. In 1997, a severe windstorm blew down trees on over 10,000 ha of subalpine forest in northern Colorado. Did previous disturbances influence the spatial pattern of blowdown in a representative part of the blowdown in the Middle Fork Elk River watershed? Dendrochronological evidence and a geographic information system were used to reconstruct the disturbance history, and the relationship between disturbance history (i.e., patch age) and the 1997 blowdown was examined. The fire regime varies temporally and spatially, with an area-weighted mean fire interval/fire rotation of 108 to 195 years. Stand-replacing fires appear to have had the most impact on the fire regime, but evidence of nonstand-replacing fires was also found. South-facing, lower-elevation slopes were the settings most likely to burn. Lowseverity spruce-beetle disturbance was found in the mid-1700s and mid- to late 1800s, possibly related to regional outbreaks. Sheep-grazing and fire suppression may have influenced tree-regeneration and fire-frequency patterns. Each disturbance event occurred with varying severity across the landscape, influenced, in part, by the patterns of severity of previous disturbances. Patch age contributed to the pattern of the 1997 blowdown, but the relationship is complicated. The vegetation mosaic influenced spatial patterns of blowdown, resulting in new complexity and maintaining landscape heterogeneity. Key Words: blowdown; disturbance interactions; fire ecology; subalpine forest. [ABSTRACT FROM AUTHOR]

Due to increasing water use, diversion and salinization, along with subsidence and sea-level rise, deltas in arid regions are shrinking worldwide. Some of the most ecologically important arid deltas include the Colorado, Indus, Nile, and Tigris-Euphrates. The primary stressors vary globally, but these deltas are threatened by increased salinization, water storage and diversion, eutrophication, and wetland loss. In order to make these deltas sustainable over time, some water flow, including seasonal flooding, needs to be re-established. Positive impacts have been seen in the Colorado River delta after flows to the delta were increased. In addition to increasing freshwater flow, collaboration among stakeholders and active management are necessary. For the Nile River, cooperation among different nations in the Nile drainage basin is important. River flow into the Tigris-Euphrates River delta has been affected by politics and civil strife in the Middle East, but some flow has been re-allocated to the delta. Studies commissioned for the Indus River delta recommended re-establishment of some monthly water flow to maintain the river channel and to fight saltwater intrusion. However, accelerating climate impacts, socio-political conflicts, and growing populations suggest a dire future for arid deltas. [ABSTRACT FROM AUTHOR]

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR© Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m3/s) (2.5%); 62.1 m3/s (−10.4%); 3.42 m3/s (7.3%), and 1579 m3/s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available. [ABSTRACT FROM AUTHOR]

Elevated concentrations of dissolved-solids (salinity) including calcium, sodium, sulfate, and chloride, among others, in the Colorado River cause substantial problems for its water users. Previous efforts to reduce dissolved solids in upper Colorado River basin (UCRB) streams often focused on reducing suspended-sediment transport to streams, but few studies have investigated the relationship between suspended sediment and salinity, or evaluated which watershed characteristics might be associated with this relationship. Are there catchment properties that may help in identifying areas where control of suspended sediment will also reduce salinity transport to streams? A random forests classification analysis was performed on topographic, climate, land cover, geology, rock chemistry, soil, and hydrologic information in 163 UCRB catchments. Two random forests models were developed in this study: one for exploring stream and catchment characteristics associated with stream sites where dissolved solids increase with increasing suspended-sediment concentration, and the other for predicting where these sites are located in unmonitored reaches. Results of variable importance from the exploratory random forests models indicate that no simple source, geochemical process, or transport mechanism can easily explain the relationship between dissolved solids and suspended sediment concentrations at UCRB monitoring sites. Among the most important watershed characteristics in both models were measures of soil hydraulic conductivity, soil erodibility, minimum catchment elevation, catchment area, and the silt component of soil in the catchment. Predictions at key locations in the basin were combined with observations from selected monitoring sites, and presented in map-form to give a complete understanding of where catchment sediment control practices would also benefit control of dissolved solids in streams. [ABSTRACT FROM AUTHOR]