Watershed Model Research Articles

Application of Different Weighting Schemes and Stochastic Simulations to Parameterization Processes Considering Observation Error: Implications for Climate Change Impact Analysis of Integrated Watershed Models

We investigated the potential impact of observation error on the calibration performance of an integrated watershed model. A three-dimensional integrated model was constructed using HydroGeoSphere and applied to the Sabgyo watershed in South Korea to assess the groundwater–surface water interaction process. During the model calibration, three different weighting schemes that consider observation error variances were applied to the parameter estimation tool (PEST). The applied weighting schemes were compared with the results from stochastic models, in which observation errors from surface discharges were considered a random variable. Based on the calibrated model, the interactions between groundwater and surface water were predicted under different climate change scenarios (RCP). Comparisons of calibration performance between the different models showed that the observation-error-based weighting schemes contributed to an improvement in the model parameterization. Analysis of the exchange flux between groundwater and surface water highlighted the significance of groundwater in delaying the hydrological response of integrated water systems. Predictions based on different RCP scenarios suggested the increasing role of groundwater in watershed dynamics. We concluded that the comparison of different weighting schemes for the determination of error covariance could contribute to an improved characterization of watershed processes and reduce the model uncertainty arising from observation errors.

Using 137Cs and 210Pbex to assess soil redistribution at different temporal scales along with lithogenic radionuclides to evaluate contrasted watersheds in the Uruguayan Pampa grassland

The use of environmental radionuclides in soil monitoring has made it possible to establish erosion rates. The combined use of 137Cs and 210Pbex is a common approach for determining mean annual erosion rates retrospectively in two different time windows; 137Cs in the past 50–60 years whereas 210Pbex provide a longer period up to 100 years. However, these radionuclides have contrasting sensitive behaviors, 210Pbex is able to be more sensitive to recent redistribution events of its last period of disintegration (22.3 yr) than 137Cs, being more sensitive to the past erosion and depositional events.This work studies soil redistribution rates at different temporal scales and the variability of lithogenic radionuclides by contrasting two watersheds with different land uses. The watersheds are Arroyo del Arbolito (AAW) and Cañada del Horno (CHW), both located near the Baygorria hydroelectric dam reservoir in Uruguay. Erosion rates obtained with 137Cs and 210Pbex using the MODERN conversion model for both watersheds showed that there are no significant differences for 210Pbex estimates, which suggests that the land management practices for both watersheds in the last twenty years do not differ and the erosion rates remained the same as the earlier period. However, significant differences were found for erosion estimated by using 137Cs in CHW vs AAW (p ≤ 0.05) and cultivated vs. natural pasture (p ≤ 0.001), which indicates that the main erosion events occurred further in the past. Furthermore, these changes in erosion estimated using both isotopes coincide with changes in land use and land management, from tillage-based practices in the past to the predominance of no-tillage since the early 2000 s. This change in soil management resulted in a reduction of approximately 50% in soil loss in those cultivated areas since 1965. In AAW, 51.7% of the sampled points exceeded the tolerable erosion rate (7 Mg.ha−1.yr−1) estimated using 137Cs, whereas for 210Pbex, 37% of the sampled points exceeded this limit. For CHW, 63.6% of the sampled points exceeded the tolerable erosion limit (9 Mg.ha−1.yr−1) estimated by using 137Cs, whereas for 210Pbex, 34.2% exceeded this limit. Our results suggest that the variation of lithogenic radionuclides by erosion is dependent on soil type and erosion magnitude. This study, which describes the use of two fallout soil radiotracers, provides new insights into the impact of land use conversion on soil degradation in the Pampas grassland ecosystem.

Spatiotemporal patterns and driving factors of evapotranspiration (natural and artificial) in the Ziya River Basin, China

Evapotranspiration (ET) accounts for 60%-70% of terrestrial precipitation. Accurately calculating ET has always been one of the hot issues in ecohydrological research. Watershed ET includes natural ET and artificial ET. Natural ET includes vegetation transpiration, bare land evaporation (the two called land ET) and water surface evaporation. Artificial ET mainly refers to irrigation ET, that is, ET generated by human activities. Estimating total ET is the premise of watershed ET management. In this paper, we used the Penman‒Monteith model to estimate land ET, the Dalton model to estimate water surface evaporation, and the AquaCrop model to estimate ET generated by agricultural irrigation water in the Ziya River Basin from 2005 to 2015. Based on the integrated watershed evapotranspiration model, the effects of driving factors on ET were quantitatively analysed. The results showed that the Penman‒Monteith model, Dalton model and AquaCrop model had high simulation accuracy. The total ET fluctuated between 418.4 and 452.3 mm/yr, and the annual average ET was 437.5 mm/yr. The ET of the main land use types was ranked as woodland (496.7 mm/yr) > arable land (468.9 mm/yr) > grassland (320.0 mm/yr). Spatially, the ET in mountainous areas was lower than that in plain areas. The distribution of ET in the total amount, mountainous and plain areas, woodland, grassland and arable land showed a significant Gaussian distribution. Temporally, the interannual variation was small, and the intra-annual difference was mainly caused by climate factors. The conversion of grassland and woodland into arable land had the greatest impact on the total ET. Temperature, precipitation and leaf area index had a positive impact on ET. The impact of land cover change on ET was more significant than that of climate factors. This study can provide an integrated approach for ET estimation in water resource management and land planning.

Fields of Application of SWAT Hydrological Model—A Review

Soil and Water Assessment Tool (SWAT) is a widely used model for runoff, non-point source pollution, and other complex hydrological processes under changing environments (groundwater flow, evapotranspiration, snow melting, etc.). This paper reviews the key characteristics and applications of SWAT. Since its inception in the 1990s, there has been a significant increase in the number of articles related to the SWAT model. In the last 10 years, the number of articles almost reached 4000. The range of applications varies between small and large scales; however, large watershed modelling dominates in North America and Asia. Moreover, the prevailing modelling is related to hydrological impacts in a changing environment, which is a global problem. The significant shortcoming of the SWAT model is the vast quantity of data necessary to run the model to generate accurate and reliable results, which is not accessible in some regions of the world. Apart from its accessibility, it has several advantages, including continuous development, which results in a slew of new interfaces and tools supporting the model. Additionally, it can simulate human activity and agricultural measures and adapt to new circumstances and situations. This article emphasizes weaknesses and strengths of SWAT model application on modelling of hydrological processes in changing climate and environment.

Costs of Reducing Nutrients from Riparian Buffers in Western Maryland

AbstractWe estimate the economic cost of nutrient reductions from riparian buffers in two counties in western Maryland. The costs and gains for farmers are estimated for establishing riparian buffers under the Conservation Reserve Enhancement Program. For nutrient reductions, we utilize nitrogen and phosphorus mitigation loads from the Chesapeake Bay Watershed Model and own estimates from our synoptic studies. We show that costs of reducing nutrients vary based on buffer type, its location, and the nutrient pollutant. We also show that installing riparian buffers alongside existing forests could be a more cost-effective practice for reducing nitrogen than other riparian buffers.

Impact assessment of land use/land cover changes on surface runoff characteristics in the Shetrunji River Basin using the SWAT model

Abstract Land use/land cover (LU/LC) change has a considerable effect on the runoff characteristics of any watershed. LU/LC change studies are necessary for policymakers to identify the challenges and adopt the course of action such as soil and water conservation measures for improvement. The current research is undertaken for the upper Shetrunji River basin using a hydrological model linked with GIS. Input data like LU/LC, meteorological, and soil elements are necessary to execute watershed modeling. The model was calibrated and validated in SWAT-CUP using the SUFI-2 algorithm. The data from 2000 to 2010 were utilized for calibration, whereas the data from 2011 to 2020 were used for validation with a three-year warm-up period from 1997 to 1999. LU/LC change detection has been accomplished using ERDAS IMAGINE 2015 via a supervised classification approach. Close agreement was obtained between simulated and observed streamflow data during model calibration (NSE = 0.74 and R2 = 0.78) and validation (NSE = 0.70 and R2 = 0.73). LU/LC change detection shows that agricultural, urban, and water body areas increased between 2000–2010 and 2010–2020, and forest areas increased in 2010 and decreased in 2020. Thus, it is concluded that the change in LU/LC significantly influenced the runoff of the upper Shetrunji River basin.

Assessing stormwater control measure inventories from 23 cities in the United States

Since the 1987 Clean Water Act Section 319 amendment, the US Government has required and funded the development of nonpoint source pollution programs with about $5 billion dollars. Despite these expenditures, nonpoint source pollution from urban watersheds is still a significant cause of impaired waters in the United States. Urban stormwater management has rapidly evolved over recent decades with decision-making made at a local or city scale. To address the need for a better understanding of how stormwater management has been implemented in different cities, we used stormwater control measure (SCM) network data from 23 US cities and assessed what physical, climatic, socioeconomic, and/or regulatory explanatory variables, if any, are related to SCM assemblages at the municipal scale. Spearman’s correlation and Wilcoxon rank-sum tests were used to investigate relationships between explanatory variables and SCM types and assemblages of SCMs in each city. The results from these analyses showed that for the cities assessed, physical explanatory variables (e.g. impervious percentage and depth to water table) explained the greatest portion of variability in SCM assemblages. Additionally, it was found that cities with combined sewers favored filters, swales and strips, and infiltrators over basins, and cities that are under consent decrees with the Environmental Protection Agency tended to include filters more frequently in their SCM inventories. Future work can build on the SCM assemblages used in this study and their explanatory variables to better understand the differences and drivers of differences in SCM effectiveness across cities, improve watershed modeling, and investigate city- and watershed-scale impacts of SCM assemblages.

Robustness of storm water management model parameter sets for dry and wet hydroclimatic conditions

The Storm Water Management Model (SWMM) is an urban watershed model for simulating urban runoff quantity and quality. Although SWMM has been widely used, little to no research has explored the effect of fitting the same SWMM dataset to multiple hydrologic conditions. This paper assesses the impact of best-fit parameter estimates on the hydrologic responses of dry and wet hydroclimatic conditions, evaluates parameter uncertainty, and identifies the physical processes that govern parameter changes during dry and wet conditions. We compared the ability of SWMM to predict flows when independently calibrated to a dry and a wet year, respectively, using an automatic calibration program for SWMM, OSTRICH-SWMM (Optimization Software Toolkit for Research Involving Computational Heuristics). The model developed using SWMM calibrated to a wet year performed better during the model assessment period. The best fit estimates of SWMM parameters differed significantly between dry and wet years. For instance, Manning's roughness coefficient for overland flow was higher in a dry year, as less runoff meant less flow depth on surfaces. Some parameters, e.g., % effective imperviousness, exhibited an expanded posterior probability distribution, increasing the uncertainty of the parameter estimate. However, other parameters, such as Manning's roughness coefficient for streams were the least sensitive and well-defined as the width of their posterior distribution only spanned a relatively narrow range which did not change drastically between the two cases. The robust assessment conducted in this study demonstrated that the hydrologic behavior of SWMM was different between models developed and calibrated for dry and wet hydroclimatic conditions. The different optimal parameter values suggest that although the processes and mechanisms driving hydrologic processes in dry and wet years are similar, their magnitude differs greatly. This study highlights that precipitation patterns affect the selection of optimal parameter values and the water budgets differed markedly between dry and wet years, as expected. The results of this study can assist urban watershed planners in selecting the most appropriate model to use for prediction purposes or when there is a paucity of data available for model calibration. This study recommends changing SWMM model parameters on an annual basis when projecting the effects of new conditions outside of current experience (i.e., land use and climate change) on urban runoff quantity and quality.

Dynamically coupling system dynamics and SWAT+ models using Tinamït: application of modular tools for coupled human–water system models

Abstract. Participatory water resource management requires modeling techniques that are accurate and flexible yet stakeholder-friendly. While different modeling frameworks offer advantages and disadvantages, system dynamics (SDs) models have seen sustained use as a stakeholder-friendly approach for participatory water resource modeling. Physically based models (e.g., SWAT+) have seen sustained use to model the hydrological components of water systems. Proposed as a way to combine the relative strengths of both modeling paradigms, model coupling allows researchers to, for example, build participatory SD models with stakeholders, while delegating the hydrological components of the overall model to an external hydrological model. Recently developed to facilitate model coupling, the Tinamït Python package presents an extensible, outward-facing application programming interface (API). It allows for the development of extensions (wrappers) that expand compatibility with different physically based models. However, no watershed hydrological model has yet been connected to this API. In the present study, a socket and JavaScript Object Notation (JSON)-based communication protocol was developed with the goal of facilitating the coupling of models written in languages such as Fortran. This novel protocol served to develop a Tinamït-compatible wrapper for the hydrological model SWAT+, allowing it to be coupled to human–water SD models. The novel coupling protocol was then applied to a case study of Tanzania's Usa river catchment. This approach provides the modeler with the benefits of both physically based and SD models, thereby allowing the detection of potentially far-reaching effects of policy-makers' decisions.

Multi-objective synchronous calibration and Pareto optimality of runoff and sediment parameters in an arid and semi-arid watershed.

Calibration methodologies must extract as much information as possible from available data, but it is not well understood in investigating the multi-objective synchronous calibration strategy by using multiple sources of information and by exploiting the data in better ways. The non-dominated sorting genetic algorithm II (NSGA-II) is introduced to study the calibration performance of runoff and sediment parameters under nine targeted scenarios, which considers the best choice to obtain high-cost performance results for decision makers through multi-objective optimization and the calculation of Pareto-optimal front with a high precision. (i) SWAT model has good adaptability in the runoff simulation of the Yanhe River watershed. Both the calibration results of NSGA-II and sequential uncertainty fitting approach-version 2 (SUFI-2) can meet the accuracy requirements of runoff simulation. Particularly, the NSGA-II based on multiple objective functions not only has strong applicability but also can better constrain the parameter process, making the calibrated model more in line with the physical conditions of the watershed. (ii) The two-site synchronous calibration of runoff or sediment can make full use of data information of different sites, reduce the impact of spatial heterogeneity on model parameters, and improve the calibration efficiency of the model. The single-site synchronous calibration of runoff and sediment based on NSGA-II not only has high calibration efficiency but also can avoid the tedious steps of calibrating runoff and sediment separately. (iii) The two-site synchronous calibration of runoff and sediment based on NSGA-II combines the advantages of the above synchronous calibration strategies, which can get Pareto-optimal front and represent the best trade-offs among different objectives, and its applicability is stronger than the traditional single-site or single-element calibration strategy. This study provides new and competing ways to evaluate hydrological models and their performance, and the multiple criteria approach for watershed modeling is one of the focuses in future research extensions.

New distributed model for predicting erosion-type pollution by integrating sediment connectivity and watershed model

New distributed model for predicting erosion-type pollution by integrating sediment connectivity and watershed model

Identification and Quantification of Surface Depressions on Grassy Land Surfaces of Different Topographic Attributes Using High-Resolution Terrestrial Laser Scanning Point Cloud and Triangulated Irregular Network

The objective of this study was to identify and quantify surface depressions on grass-covered land surfaces using a high-resolution terrestrial laser scanning (TLS) point cloud, and a triangulated irregular network (TIN). The entire grassy land surface in the study area was divided into five subwatersheds of different topographic attributes (i.e., depression depth and surface slope). Surface depressions were identified and quantified using a TIN generated from a high-resolution TLS point cloud. The results indicated that microtopography of the grassy land surface was well-characterized within each subwatershed in comparison with field observations. With the terrestrial light detection and ranging (LIDAR) point cloud of 15-mm point spacing and the TIN method, surface depression storage depths of the five subwatersheds ranged from 1.73 to 14.28 mm in the study area. The surface depression storage depth, as expected, increased with the maximum depth of surface depression. It was also found to increase when the land surface slope became milder. A sensitivity analysis indicated that a point cloud with a point spacing of 30 mm was sufficient to obtain an accurate representation of the terrain surface in the study area. This study also indicated the TIN method can represent the ground surface and the surface depression more realistically than the commonly used digital elevation model (DEM) method due to the TIN method’s higher capability of identifying and filtering out surface obstructions such as blades of grass. Moreover, by using the high-resolution TLS technology and the TIN method, our study provides an important and broad range of reference data on the surface depression storage depth commonly needed in application of the Storm Water Management Model (SWMM) and other watershed models.

Probabilistic Sensitivity Analysis With Dependent Variables: Covariance‐Based Decomposition of Hydrologic Models

AbstractVariance‐based analysis has emerged as method of choice for quantifying the sensitivity of the output, y, of a scalar‐valued square‐integrable function, f ∈ L2(), to its d ≥ 1 input variables, x = {x1, …, xd}, with support . The prototype of this approach, Sobol's method is a generalization of the analysis of variance (ANOVA) to d > 2 independent input variables and decomposes y, as sum of elementary functions of zeroth‐, first‐, second‐, up to dth‐order. This independence assumption is mathematically convenient but may not be borne out of the causal or correlational relationships between the x's. This paper is concerned with variance‐based sensitivity analysis (SA) for correlated input variables, for example, multivariate dependencies in a posterior parameter distribution. We use high‐dimensional model representation (HDMR) of Li et al. (2010, https://doi.org/10.1021/jp9096919), Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0) and replace Sobol's elementary functions with so‐called component functions with unknown expansion coefficients to disentangle the structural, correlative and total contribution of input factors. We contrast the default HDMR methodology with cubic B‐splines and sequential coefficient estimation against its successor, HDMRext of Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0), which uses polynomial component functions with an extended orthonormalized basis. Benchmark experiments confirm that HDMR and HDMRext parse out the structural and correlative contributions of input factors to the model output and infer an optimal experimental design with parameter correlation. Our last study applies HDMRext to probabilistic SA of a watershed model. The multivariate posterior parameter distribution supports model emulation and yields sensitivity indices that pertain to measured discharge data.

Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling

Ensemble approaches can be a quick way to improve hydrological prediction accuracy by considering multiple plausible modeling representations of a system. Previous studies showed that the use of multiple types of models could increase prediction accuracy, but model selection subjectivity and parameter equifinality issues remain unsolved. This study proposes to consider multiple parameter sets identified in the model calibration process and information on their performance for improved accuracy of ensemble hydrological prediction. In this study, we investigate whether multi-parameter ensemble (MP) compared to multi-model ensemble (MM) approaches can improve the accuracy of hydrology and water quality prediction. A variety of ensemble methods, including simple averaging, median filtering, weighted averaging, Bayesian model averaging (BMA), and their variants, were tested in watershed modeling implemented to predict daily water discharge and total phosphorus (TP) loads of a study watershed. The parameter spaces of two watershed loading models, Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-FORTRAN (HSPF), were sampled using a multi-objective heuristic optimization algorithm, namely A Multi-Algorithm Genetically Adaptive Multiobjective (AMALGAM). The modeling experiment showed that the MP approach more effectively improved modeling accuracy statistics compared to the MM of both models as well as single-model and single-parameter approaches. The ensemble approaches did not always improve modeling accuracy, and their efficiency depended on ways to determine weights for ensemble members. The BMA method outperformed the other weighting schemes tested in this study, and its performance was responsive to the number and diversity (or variance) of ensemble member candidates and the computational cost to create the candidates. The findings demonstrate how the information of parameter sets sampled in model calibration and their performance statistics could help improve hydrological prediction accuracy.

The Effects Atmospheric Changes Have on Runoff

Over the last century, the earth has seen unprecedented atmospheric concentrations contaminate our ecosystems due to human activity. Predictions state the introduction of carbon dioxide, methane, nitrous oxide, and chlorofluorocarbons (CFCs), will increase temperatures, and change the amount and location of precipitation causing more runoff. This could potentially result in disturbance events such as floods, to be more frequent and severe. This study aims to perform an assessment of the effects of a range of hypothetical climate changes on runoff in the North-east Pond River watershed, located in Newfoundland.
 To carry this out a watershed runoff model simulates runoff in the basin for current climatic conditions and for hypothetical climatic conditions that represent a range of possible climate changes (Bobba et al., 1997). The hypothetical changes in climate will showcase the effects of a 2oC increase in temperature on the total annual precipitation. This will then be compared to flood forecasting models to analyze how runoff will be affected by various climatic conditions, inducing unusual flooding events (Wijayarathne & Coulibaly, 2020). Previous studies have indicated the runoff sensitivity in watersheds to changes in temperatures which raises concerns as to the adverse effects this may cause in limiting water resources in the semi-arid regions in parts of Canada and the U.S. Thus, there is a need to increase the understanding of the sensitivity of water resources in Canadian watersheds to climate variability and climate change as effects of this magnitude on the North-east Pond River could have significant environmental implications.

Toward the estimation of the transfer coefficient in karst systems: Using baseflow recession coefficient under matrix-restrained flow regime

Using the reservoir model theory, this study developed an equation for the relationship between the transfer and recession coefficients under a matrix-restrained flow regime. Assuming Darcy's law, the proposed equation was developed based on conventional parameters of karst systems, such as recession coefficient, specific yield, and catchment area. Firstly, the equation was evaluated through forward and inverse modeling of an idealized karst system with MODFLOW-2005 Conduit Flow Process (CFP) and KarstMod, respectively. Then, calibration of a KarstMod and a CFP model of the Baget karstic watershed in France was carried out to check the efficiency of the developed equation.Results of the first step showed that the optimal value of the transfer coefficient based on KarstMod for the linear transfer flow is very close to the baseflow recession coefficient values. Therefore, the transfer coefficient in KarstMod may be considered as the recession coefficient from the matrix to the conduit compartment. Moreover, substituting these two coefficients in the developed equation caused less than 10% errors in estimating the transfer coefficients assigned to the idealized models in CFP. In addition, adding the recession coefficient as prior information in the calibration process affects the recession coefficient of the simulated time series and provides a reliable evaluation of the internal dynamics of the system. Therefore, the results of this research show that the recession coefficients can be used as an initial estimate of the transfer coefficient in the baseflow condition. This achievement can significantly assist the discrete-continuum model calibration and further support the water balance calculations based on the recession data.

A dynamic dendritic connectivity assessment tool for the planning and design of barrier mitigation strategies in river networks

ContextIncreasing fragmentation of rivers caused by barriers continues to impact watersheds, especially disruption of fish migration patterns and loss of access to spawning and nursery habitats. Infrastructure expansion and ageing installations exacerbate the problem, reducing effectiveness of management in addressing barriers. Reduction in watershed connectivity requires effective tools capable of guiding river managers in remediation actions.ObjectivesOur objective was to develop a watershed dynamic connectivity assessment tool (D-CAT), a customizable geospatial tool capable of river analyses at varying watershed scales. The tool uses proven watershed connectivity modelling combined with an effective evaluation approach to provide measurable change model outcomes.MethodsThree different barrier encounter scenarios were examined using the D-CAT tool to study watershed connectivity. The tool was tested on a watershed area of the River Derwent, a major tributary of the River Trent system in the United Kingdom.ResultsFor each barrier scenario tested, the D-CAT tool indicated which barriers could be removed or modified to provide the greatest watershed connectivity improvement. The tool provided a prioritized list of barriers for each scenario under varying choices for the number of barriers to remove, demonstrating the utility of the D-CAT tool to planning and design.ConclusionsThe D-CAT tool is built to handle geographical data and variable user input, allowing in-depth watershed connectivity analysis. The tool forecasts the cumulative effects of alternative change scenarios for watershed connectivity, providing prioritization and optimization of removal strategies. The D-CAT tool offers significant support for landscape management towards better barrier removal/remediation decisions.

Periphyton Phosphorus Uptake in Response to Dynamic Concentrations in Streams: Assimilation and Changes to Intracellular Speciation.

Effective modeling and management of phosphorus (P) losses from landscapes to receiving waterbodies requires an adequate understanding of P retention and remobilization along the terrestrial-aquatic continuum. Within aquatic ecosystems, the stream periphyton can transiently store bioavailable P through uptake and incorporation into biomass during subscouring and baseflow conditions. However, the capacity of stream periphyton to respond to dynamic P concentrations, which are ubiquitous in streams, is largely unknown. Our study used artificial streams to impose short periods (48 h) of high SRP concentration on stream periphyton acclimated to P scarcity. We examined periphyton P content and speciation through nuclear magnetic resonance spectroscopy to elucidate the intracellular storage and transformation of P taken up across a gradient of transiently elevated SRP availabilities. Our study demonstrates that the stream periphyton not only takes up significant quantities of P following a 48-h high P pulse but also sustains supplemental growth over extended periods of time (10 days), following the reestablishment of P scarcity by efficiently assimilating P stored as polyphosphates into functional biomass (i.e., phospho-monoesters and phospho-diesters). Although P uptake and intracellular storage approached an upper limit across the experimentally imposed SRP pulse gradient, our findings demonstrate the previously underappreciated extent to which the periphyton can modulate the timing and magnitude of P delivery from streams. Further elucidating these intricacies in the transient storage potential of periphyton highlights opportunities to enhance the predictive capacity of watershed nutrient models and potentially improve watershed P management.

High Dissolved Carbon Concentration in Arid Rocky Mountain Streams.

Warming in mountains is known to intensify aridity and threaten water availability globally. Its impacts on water quality, however, have remained poorly understood. Here we collate long-term (multi-year to decadal mean), baseline stream concentrations and fluxes of dissolved organic and inorganic carbon, two essential indicators of water quality and soil carbon response to warming, across more than 100 streams in the United States Rocky Mountains. Results show a universal pattern of higher mean concentrations in more arid mountain streams with lower mean discharge, a long-term climate measure. A watershed reactor model revealed less lateral export of dissolved carbon (via less water flow) out of the watersheds in more arid sites, leading to more accumulation and higher concentrations. Lower concentrations typically occur in cold, steep, and compact mountains with higher snow fraction and lower vegetation cover, which generally have higher discharge and carbon fluxes. Inferring from a space-for-time perspective, the results indicate that as warming intensifies, lateral fluxes of dissolved carbon will decrease but concentrations will increase in these mountain streams. This indicates deteriorating water quality and potentially elevated CO2 emission directly from the land (instead of streams) in the Rockies and other mountain areas in the future climate.

Updating SWAT+ to Clarify Understanding of In‐Stream Phosphorus Retention and Remobilization: SWAT+P.R&R

AbstractEfforts to reduce riverine phosphorus (P) loads have not been as fruitful as expected or hoped. One reason for the failure of these efforts appears to be that models used for watershed P management have understated and misrepresented the role of in‐stream processes in shaping watershed P export. Here, we update the latest release of the Soil and Water Assessment Tool (SWAT+), a widely used watershed management model, to better represent in‐stream P retention and remobilization (SWAT+P.R&R). We add new streambed pools where P is stored and tracked, and we incorporate three new processes driving in‐stream P dynamics: (a) deposition and resuspension of sediment‐associated P, (b) diffusion of dissolved P between the water column and streambed, and (c) adsorption and desorption of mineral P. The objective of this modeling work is to provide a diagnostic tool that enables researchers to challenge existing assumptions regarding how watersheds store, transform, and transport P. Here, in a first diagnostic analysis, SWAT+P.R&R helps reconcile in‐stream P retention theory (that P is retained at low flows and remobilized at high flows) and a discordant data set in our validation watershed. SWAT+P.R&R results (a) clarify that the theorized relationship between P retention and flow is only valid (for this point‐source affected testbed, at least) at the temporal scale of a single rising‐or‐falling hydrograph limb and (b) illustrate that hysteresis obscures the relationship at longer temporal scales. Future work using SWAT+P.R&R could further challenge assumptions regarding timescales of in‐stream P legacies and sources of P load variability.