Connecticut River Basin Research Articles

River Basin Export Reduction Optimization Support Tool; a tool to screen options for reducing nutrient loads while minimizing cost.

Excess loading of nitrogen and phosphorus to river networks causes environmental harm, but reducing loads from large river basins is difficult and expensive. We develop a new tool, the River Basin Export Reduction Optimization Support Tool (RBEROST) to identify least-cost combinations of management practices that will reduce nutrient loading to target levels in downstream and mid-network waterbodies. We demonstrate the utility of the tool in a case study in the Upper Connecticut River basin in New England, USA. The total project cost of optimized lowest-cost plans ranged from $18.0 million to $41.0 million per year over 15 years depending on user specifications. Plans include both point source and non-point source management practices, and most costs are associated with urban stormwater practices. Adding a 2% margin of safety to loading targets improved estimated probability of success from 37.5% to 99%. The large spatial scale of RBEROST, and the consideration of both point and non-point source contributions of nutrients, makes it well suited as an initial screening tool in watershed planning.

Responsibilities to Decolonize Environmental Education: A Co-Learning Journey for Graduate Students and Instructors

We share our collective stories as instructors and graduate students with an interest in decolonial education on how we learned together in a course on Indigenous knowledge systems (IKS). The course occurred in the environmental studies department at a predominantly White graduate school in the Connecticut river basin in the area now known as the USA. The topic of IKS is steadily gaining interest in the environmental education (EE) field, as evidenced by an increase (albeit small) in the number of publications in peer-reviewed journals. At the same time, decolonial educators are looking for ways to teach IKS in an ethical and respectful manner. Our goal for this paper was to share how we grappled with questions around ethics and cultural appropriation. For instance, as decolonial educators who are not Indigenous to communities where we work and reside, can we facilitate lessons on IKS? If so, how can we do it in a manner that honors IKS and knowledge holders, is ethical, respectful and not appropriating? We learned that applying decolonization factors was crucial. Specifically, our work revealed four key decolonization factors: centering programs in Indigenous philosophies of education, privileging Indigenous voices and engaging Elders as experts, promoting Etuptmumk/two-eyed seeing, and employing Indigenous ways of teaching and learning. This paper makes contributions to the environmental education field, particularly decolonial educators who are seeking respectful and ethical ways to engage with Indigenous knowledge systems.

Modeling the Influence of Public Risk Perceptions on the Adoption of Green Stormwater Infrastructure: An Application of Bayesian Belief Networks Versus Logistic Regressions on a Statewide Survey of Households in Vermont

There is growing environmental psychology and behavior literature with mixed empirical evidence about the influence of public risk perceptions on the adoption of environmentally friendly “green behaviors”. Adoption of stormwater green infrastructure on residential properties, while costlier in the short term compared to conventional greywater infrastructure, plays an important role in the reduction of nutrient loading from non-point sources into freshwater rivers and lakes. In this study, we use Bayesian Belief Networks (BBNs) to analyze a 2015 survey dataset (sample size = 472 respondents) about the adoption of green infrastructure (GSI) in Vermont’s residential areas, most of which are located in either the Lake Champlain Basin or Connecticut River Basin. Eight categories of GSI were investigated: roof diversion, permeable pavement, infiltration trenches, green roofs, rain gardens, constructed wetlands, tree boxes, and others. Using both unsupervised and supervised machine learning algorithms, we used Bayesian Belief Networks to quantify the influence of public risk perceptions on GSI adoption while accounting for a range of demographic and spatial variables. We also compare the effectiveness of the Bayesian Belief Network approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSI). The results show that influencing factors for current adoption differ by the type of GSI. Increased perception of risk from stormwater issues is associated with the adoption of rain gardens and infiltration trenches. Runoff issues are more likely to be considered the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered the residents’ responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), the BBN approach produces more accurate predictions compared to logistic regression. The results provide insights for further research on how to encourage residents to take measures for mitigating stormwater issues and stormwater management.

Modeling spatially non-stationary land use/cover change in the lower Connecticut River Basin by combining geographically weighted logistic regression and the CA-Markov model

ABSTRACTSimulating land use/cover change (LUCC) and determining its transition rules have been a focus of research for several decades. Previous studies used ordinary logistic regression (OLR) to determine transition rules in cellular automata (CA) modeling of LUCC, which often neglected the spatially non-stationary relationships between driving factors and land use/cover categories. We use an integrated geographically weighted logistic regression (GWLR) CA-Markov method to simulate LUCC from 2001–2011 over 29 towns in the Connecticut River Basin. Results are compared with those obtained from the OLR-CA-Markov method, and the sensitivity of LUCC simulated by the GWLR-CA-Markov method to the spatial non-stationarity-based suitability map is investigated. Analysis of residuals indicates better goodness of fit in model calibration for geographically weighted regression (GWR) than OLR. Coefficients of driving factors indicate that GWLR outperforms OLR in depicting the local suitability of land use/cover categories. Kappa statistics of the simulated maps indicate high agreement with observed land use/cover for both OLR-CA-Markov and GWLR-CA-Markov methods. Similarity in simulation accuracy between the methods suggests that the sensitivity of simulated LUCC to suitability inputs is low with respect to spatial non-stationarity. Therefore, this study provides critical insight on the role of spatial non-stationarity throughout the process of LUCC simulation.

Quantifying the impacts of climate change and land use/cover change on runoff in the lowerConnecticut River Basin

AbstractClimate change and land use and cover change (LUCC) have had great impacts on watershed hydrological processes. Although previous studies have focused on quantitative assessment of the impacts of climate change and human activities on decreasing run‐off change, few studies have examined regions that have significant increasing run‐off due to both climate variability and land cover change. We show that annual run‐off had a significant increasing trend from 1956 to 2014 in the U.S. lower Connecticut River Basin. Abrupt change point years of annual run‐off for four subbasins are detected by nonparametric Mann–Kendall–Sneyers test and reconfirmed by the double mass curve. We then divide the study period into 2 subperiods at the abrupt change point year in the early 1970s for each subbasin. The Choudhury–Yang equation based on Budyko hypothesis was used to calculate precipitation and potential evapotranspiration, and landscape elasticities of run‐off. The results show that the difference in mean annual run‐off between 2 subperiods for each subbasin ranged from 102 to 165.6 mm. Climate variations were the primary drivers of increasing run‐off in this region. Quantitative contributions of precipitation and potential evapotranspiration in all subbasins are 106.5% and −3.6% on average, respectively. However, LUCC contributed both positively and negatively to run‐off: −18.6%, −13.3%, and 10.1% and 9.9% for 4 subbasins. This may be attributed to historical LUCC occurring after the abrupt change point in each subbasin. Our results provide critical insight on the hydrological dynamics of north‐east tidal river systems to communities and policymakers engaged in water resources management in this region.

Geographic variation in host fish use and larval metamorphosis for the endangered dwarf wedgemussel

Abstract Host fishes play a crucial role in survival and dispersal of freshwater mussels (Unionoida), particularly rare unionids at conservation risk. Intraspecific variation in host use is not well understood for many mussels, including the endangered dwarf wedgemussel (Alasmidonta heterodon) in the USA. Host suitability of 33 fish species for dwarf wedgemussel glochidia (larvae) from the Delaware and Connecticut river basins was tested in laboratory experiments over 9 years. Relative suitability of three different populations of a single host fish, the tessellated darter (Etheostoma olmstedi), from locations in the Connecticut, Delaware, and Susquehanna river basins, was also tested. Connecticut River basin A. heterodon metamorphosed into juvenile mussels on tessellated darter, slimy sculpin (Cottus cognatus), and Atlantic salmon (Salmo salar) parr. Delaware River basin mussels metamorphosed using these three species, as well as brown trout (Salmo trutta), banded killifish (Fundulus diaphanus), mottled sculpin (Cottus bairdii), striped bass (Morone saxatilis), and shield darter (Percina peltata). Atlantic salmon, striped bass, and sculpins were highly effective hosts, frequently generating 5+ juveniles per fish (JPF) and metamorphosis success (MS; proportion of attaching larvae that successfully metamorphose) ≥ 0.4, and producing juveniles in repeated trials. In experiments on tessellated darters, mean JPF and MS values decreased as isolation between the mussel source (Connecticut River) and each fish source increased; mean JPF = 10.45, 6.85, 4.14, and mean MS = 0.50, 0.41, and 0.34 in Connecticut, Delaware, and Susquehanna river darters, respectively. Host suitability of individual darters was highly variable (JPF = 2–11; MS = 0.20–1.0). The results show that mussel–host fish compatibility in A. heterodon differs among Atlantic coastal rivers, and suggest that hosts including anadromous Atlantic salmon and striped bass may help sustain A. heterodon in parts of its range. Continued examination of host use variation, migratory host roles, and mussel–fish interactions in the wild is critical in conservation of A. heterodon and other vulnerable mussel species.

A framework to improve hyper-resolution hydrological simulation in snow-affected regions

Snow processes in mid- and north-latitude basins and their interaction with runoff generation at hyperresolution (<1km and <hourly) pose challenges in current state-of-the-art distributed hydrological models. These models run typically at macro to moderate scales (>5km), representing land surface processes based on simplified couplings of snow thermal physics and the water cycle in the soil-vegetation-atmosphere (SVA) layers. This paper evaluates a new hydrological model capable of simulating river flows for a range of basin scales (100km2 to >10,000km2), and a particular focus on mid- and north-latitude regions. The new model combines the runoff generation and fully distributed routing framework of the Coupled Routing and Excess STorage (CREST) model with a new land surface process model that strictly couples water and energy balances at the SVA layer, imposing closed energy balance solutions. The model is vectorized and parallelized to achieve long-term (>30years) high-resolution (30m to 500m and subhourly) simulations of large river basins utilizing high-performance computing. The model is tested in the Connecticut River basin (20,000km2), where flooding is frequently associated with interactions of snowmelt triggered by rainfall events. Model simulations of distributed evapotranspiration (ET) and snow water equivalence (SWE) at daily time step are shown to match accurately ET estimates from MODIS (average NSCE and bias are 0.77 and 6.79%) and SWE estimates from SNODAS (average correlation and normalized root mean square error are 0.94 and of 19%); the modeled daily river flow simulations exhibit an NSCE of 0.58 against USGS streamflow observations.

Quantifying relative uncertainties in the detection and attribution of human-induced climate change on winter streamflow

In spite of recent popularity for investigating human-induced climate change in regional areas, understanding the contributors to the relative uncertainties in the process remains unclear. To remedy this, this study presents a statistical framework to quantify relative uncertainties in a detection and attribution study. Primary uncertainty contributors are categorized into three types: climate data, hydrologic, and detection uncertainties. While an ensemble of climate models is used to define climate data uncertainty, hydrologic uncertainty is defined using a Bayesian approach. Before relative uncertainties in the detection and attribution study are quantified, an optimal fingerprint-based detection and attribution analysis is employed to investigate changes in winter streamflow in the Connecticut River Basin, which is located in the Eastern United States. Results indicate that winter streamflow over a period of 64years (1950–2013) lies outside the range expected from natural variability of climate alone with a 90% confidence interval in the climate models. Investigation of relative uncertainties shows that the uncertainty linked to the climate data is greater than the uncertainty induced by hydrologic modeling. Detection uncertainty, defined as the uncertainty related to time evolution of the anthropogenic climate change in the historical data (signal) above the natural internal climate variability (noise), shows that uncertainties in natural internal climate variability (piControl) scenarios may be the source of the significant degree of uncertainty in the regional Detection and Attribution study.

Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin

AbstractFrequency analysis of streamflow provides an essential ingredient in our understanding of hydrologic events and provides needed guidance in the design and management of water resources infrastructure. However, traditional hydrologic approaches often fail to include important external effects that can result in unpredictable or unforeseen changes in streamflow. Moreover, previous studies investigating multiple characteristics of streamflow do not address a nonstationary approach. This study explores nonstationary frequency analysis of bivariate characteristics, including occurrence and severity, of annual low flow in the Connecticut River Basin, United States. To investigate bivariate low flow frequency, copulas and their marginal distributions are constructed by using stationary and nonstationary approaches. Our study results indicate that streamflow used in this study demonstrate significant nonstationarity. Over time, the occurrence and severity of low flows are shown to be lower with the same probability based on the results of nonstationary copulas. Bivariate low flow frequencies in the years 1970, 2000, and 2030, and their joint return periods are estimated under the nonstationary copulas. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Using high‐resolution satellite precipitation for flood frequency analysis: case study over the Connecticut River Basin

This study evaluates the feasibility of using satellite precipitation datasets in flood frequency analysis based on the accuracy of different return period flows derived using a hydrological model driven with satellite and ground‐based reference rainfall fields over the Connecticut River Basin. Four quasi‐global satellite products (TRMM‐3B42V7, TRMM‐3B42RT, CMORPH, and PERSIANN) at 3‐h/0.25° resolution and the National Weather Service (Stage IV) gauge‐adjusted radar rainfall dataset (representing the reference rainfall) are integrated in this study, with the Coupled Routing and Excess Storage distributed hydrological model to simulate annual peak flows during warm season (May–November) months. The log‐Pearson type III frequency distribution applied to an 11‐year record of annual peak flow data is used to derive different return period flows. Evaluation against the Stage IV‐driven simulations shows that the TRMM‐3B42V7 product has the highest correlation and lowest bias in terms of the derived annual maxima flows compared to the other satellite products. In terms of the different return period flood frequency curves, the various satellite product‐based results well‐represent the variability across the different basins depicted in the reference precipitation‐driven simulations. With the increasing record length of high‐resolution satellite products, results from this paper can motivate future studies over basins lacking adequate ground‐based records to support flood frequency analyses.

Integrating Remote Sensing Data on Evapotranspiration and Leaf Area Index with Hydrological Modeling: Impacts on Model Performance and Future Predictions

Abstract Using the Connecticut River basin as an example, this study assesses the extent to which remote sensing data can help improve hydrological modeling and how it may influence projected future hydrological trends. The dynamic leaf area index (LAI) derived from satellite remote sensing was incorporated into the Variable Infiltration Capacity model (VIC) to enable an interannually varying seasonal cycle of vegetation (VICVEG); the evapotranspiration (ET) data based on remote sensing were combined with ET from a default VIC simulation to develop a simple bias-correction algorithm, and the simulation was then repeated with the bias-corrected ET replacing the simulated ET in the model (VICET). VICET performs significantly better in simulating the temporal variability of river discharge at daily, biweekly, monthly, and seasonal time scales, while VICVEG better captures the interannual variability of discharge, particularly in the winter and spring, and shows slight improvements to soil moisture estimates. The methodology of incorporating ET data into VIC as a bias-correction tool also influences the modeled future hydrological trends. Compared to the default VIC, VICET portrays a future characterized by greater drought risk and a stronger decreasing trend of minimum river flows. Integrating remote sensing data with hydrological modeling helps characterize the range of model-related uncertainties and more accurately reconstruct historic river flow estimates, leading to a better understanding and prediction of hydrological response to future climate changes.

Hydrological changes in the U.S. Northeast using the Connecticut River Basin as a case study: Part 2. Projections of the future

The focus of this study is on whether the recent warming-induced hydrologic changes in the U.S. Northeast will continue in the future (2046–2065) and how future changes of precipitation characteristics may influence other hydrological processes in the Connecticut River Basin (CRB). Our previous study (Parr and Wang 2014) examines the impact of climate changes during 1950–2011 on hydrological processes in the Northeast using the CRB as a case study. Our results showed a clear increase of precipitation intensity and suggested that the basin is entering a wetter regime more subject to meteorological flood conditions than to drought conditions. For this future analysis, three North American Regional Climate Change Assessment Program (NARCCAP) models are used to derive the meteorological forcing for the Variable Infiltration Capacity (VIC) hydrological model, using both present day and the future projected A1B scenario climate. Our future projections indicate wetter winters including significantly greater precipitation, runoff, and soil moisture, decreases to spring runoff, and enhanced ET for all four seasons. We also find a shift toward earlier and faster snow melting and an earlier date of peak discharge. Future precipitation extremes show a decreased amount compared to the early 21st Century, but increased when compared to our entire historic period or the late 20th Century, as well as a consistently increasing mean intensity throughout the past and future. Analyses of extreme hydrologic events reveals changing characteristics of flooding involving increasing duration but decreasing frequency of flood events as well as a reduction of drought risk.

Decision Support System for Water and Environmental Resources in the Connecticut River Basin

AbstractThis paper describes the development and application of a reservoir management decision support system for evaluating floodplain benefits and socioeconomic trade-offs of reservoir management alternatives in the Connecticut River watershed. The decision support system is composed of a reservoir system simulation model, an ecological model, and two river hydraulics models. The reservoir model simulated current operations at 73 reservoirs and flows at locations of interest in the Connecticut River watershed. Regulated flows from the reservoir model were compared with unregulated flows, both statistically and spatially, for a suite of environmental flow metrics based on inundation patterns related to floodplain vegetation communities. Analyses demonstrate use of the decision support system and show how its use illuminates (1) trends in existing hydrologic alteration for the Connecticut River mainstem and one of its tributaries, the Farmington River, and (2) management scenarios that might have ecologi...

Evaluating Multi-Scale Flow Predictions for the Connecticut River Basin

This case study evaluates a computationally efficient distributed hydrological model, named Coupled Routing and Excess Storage (CREST), for flood modeling of basins in the Connecticut River Basin (CRB). Simulation of discharges is performed by forcing CREST with a long record (eight years) of high resolution radar-rainfall data and potential evapotranspiration maps derived from the North American Regional Reanalysis. The model performance is evaluated against observed streamflows obtained from United States Geological Survey gauging stations at outlet and interior points of various CRB sub-basins. CREST parameters were calibrated based on a three year record (2005-2007) and validated for the remaining data period (2003-2004 and 2008-2009). The model performance evaluation is based on different metrics, including the Nash-Sutchliffe Coefficient of Efficiency (NSCE), Mean Relative Error (MRE), Root Mean Square Error (RMSE), and Pearson Correlation Coefficient (PCC). The analysis shows that CREST slightly underestimated the peak flows, but exhibited a generally good capability in simulating the stream flow variability for the CRB basins. Specifically, NSCE, MRE, RMSE, and PCC values of hourly flow simulations varied from 0.31 to 0.58, -0.06 to 0.13, 61 to 121 (mm) and 0.60 to 0.83, respectively. At daily time scale the performance metrics exhibited improved values indicating that CREST has sufficient accuracy for long term multi-scale hydrologic simulations.

Quantifying flooding regime in floodplain forests to guide river restoration

Determining the flooding regime needed to support distinctive floodplain forests is essential for effective river conservation under the ubiquitous human alteration of river flows characteristic of the Anthropocene Era. At over 100 sites throughout the Connecticut River basin, the largest river system in New England, we characterized species composition, valley and channel morphology, and hydrologic regime to define conditions promoting distinct floodplain forest assemblages. Species assemblages were dominated by floodplain-associated trees on surfaces experiencing flood durations between 4.5 and 91 days/year, which were generally well below the stage of the two-year recurrence interval flood, a widely-used benchmark for floodplain restoration. These tree species rarely occurred on surfaces that flooded less than 1 day/year. By contrast abundance of most woody invasive species decreased with flooding. Such flood-prone surfaces were jointly determined by characteristics of the hydrograph (high discharges of long duration) and topography (low gradient and reduced valley constraint), resulting in increased availability of floodplain habitat with increasing watershed area and/or decreasing stream gradient. Downstream mainstem reaches provided the most floodplain habitat, largely associated with low-energy features such as back swamps and point bars, and were dominated by silver maple (Acer saccharinum). However, we were able to identify a number of suitable sites in the upper part of the basin and in large tributaries, often associated with in-channel islands and bars and frequently dominated by sycamore (Platanus occidentalis) and flood disturbance-dependent species. Our results imply that restoring flows by modifying dam operations to benefit floodplain forests on existing surfaces need not conflict with flood protection in some regional settings. These results underscore the need to understand how flow, geomorphology, and species traits interact to produce characteristic patterns of floodplain vegetation, and that these interactions should form the basis of effective river restoration and conservation.

Hydrological changes in the U.S. Northeast using the Connecticut River Basin as a case study: Part 1. Modeling and analysis of the past

Increase of precipitation intensity is the most definite and detectable hydrological consequence of a warmer climate. Among all U.S. regions, the Northeast has witnessed the strongest increase of extreme precipitation in the past five decades. This study examines the impact of climate changes during 1950–2011 on hydrological processes in the Northeast using the Connecticut River Basin as a case study. In addition to precipitation and river discharge data from observations, the Variable Infiltration Capacity (VIC) hydrological model is used to derive hydrological variables for which long-term observational data are not readily available. Our results show a clear increase of precipitation intensity, with substantial increase in both the number of days with greater than 10mm precipitation and the simple daily intensity index. From 1950 to 2011, extreme precipitation amount (which is the total amount of precipitation from the upper 1% of daily precipitation) increased substantially, by 240% relative to the 1950 level. The weight of extreme precipitation as a fraction of total precipitation also increased, from about 10.6% in the 1950s to 30.4% in the 2000s. Despite the increase of precipitation extremes, the consecutive dry days experienced a slight decrease. Mean trend analysis shows indications of increasing precipitation amount, increasing discharge, increasing runoff ratios, increasing soil moisture, and a negligible evapotranspiration trend. Our simulations suggest that the basin is entering a wetter regime more subject to meteorological flood conditions than to drought conditions. A companion paper will investigate how these trends may persist or differ in the mid-21st century under continued warming.

A semiparametric multivariate, multisite weather generator with low‐frequency variability for use in climate risk assessments

A multivariate, multisite daily weather generator is presented for use in decision‐centric vulnerability assessments under climate change. The tool is envisioned to be useful for a wide range of socioeconomic and biophysical systems sensitive to different aspects of climate variability and change. The proposed stochastic model has several components, including (1) a wavelet decomposition coupled to an autoregressive model to account for structured, low‐frequency climate oscillations, (2) a Markov chain and k‐nearest‐neighbor (KNN) resampling scheme to simulate spatially distributed, multivariate weather variables over a region, and (3) a quantile mapping procedure to enforce long‐term distributional shifts in weather variables that result from prescribed climate changes. The Markov chain is used to better represent wet and dry spell statistics, while the KNN bootstrap resampler preserves the covariance structure between the weather variables and across space. The wavelet‐based autoregressive model is applied to annual climate over the region and used to modulate the Markov chain and KNN resampling, embedding appropriate low‐frequency structure within the daily weather generation process. Parameters can be altered in any of the components of the proposed model to enable the generation of realistic time series of climate variables that exhibit changes to both lower‐order and higher‐order statistics at long‐term (interannual), mid‐term (seasonal), and short‐term (daily) timescales. The tool can be coupled with impact models in a bottom‐up risk assessment to efficiently and exhaustively explore the potential climate changes under which a system is most vulnerable. An application of the weather generator is presented for the Connecticut River basin to demonstrate the tool's ability to generate a wide range of possible climate sequences over an extensive spatial domain.

Seasonal controls on meteoric7Be in coarse-grained river channels

Cosmogenic 7Be is a natural tracer of short-term hydrological processes, but its distribution in upland fluvial environments over different temporal and spatial scales has not been well described. We measured 7Be in 450 sediment samples collected from perennial channels draining the middle of the Connecticut River Basin, an environment that is predominantly well-sorted sand. By sampling tributaries that have natural and managed fluctuations in discharge, we find that the 7Be activity in thalweg sediments is not necessarily limited by the supply of new or fine-grained sediment, but is controlled seasonally by atmospheric flux variations and the magnitude and frequency of bed mobilizing events. In late winter, 7Be concentrations in transitional bedload are lowest, typically 1 to 3 Bq kg−1 as 7Be is lost from watersheds via radioactive decay in the snowpack. In mid-summer, however, 7Be concentrations are at least twice as high because of increased convective storm activity which delivers high 7Be fluxes directly to the fluvial system. A mixed layer of sediment at least 8 cm thick is maintained for months in channels during persistent low rainfall and flow conditions, indicating that stationary sediments can be recharged with 7Be. However, bed mobilizing rain on snowmelt events in late Spring can ‘reset’ 7Be amounts and concentrations in the channel as previously buried ‘old’ sediment with low 7Be is mixed into the thalweg. We conclude that given proper temporal and spatial sampling, 7Be is a valuable tracer of seasonal-timescale mass transport and exchange in coarse-grained fluvial systems. Copyright © 2013 John Wiley & Sons, Ltd.

Simulations of Historical and Future Trends in Snowfall and Groundwater Recharge for Basins Draining to Long Island Sound

AbstractA regional watershed model was developed for watersheds contributing to Long Island Sound, including the Connecticut River basin. The study region covers approximately 40 900 km2, extending from a moderate coastal climate zone in the south to a mountainous northern New England climate zone dominated by snowmelt in the north. The input data indicate that precipitation and temperature have been increasing for the last 46 years (1961–2006) across the region. Minimum temperature has increased more than maximum temperature over the same period (1961–2006). The model simulation indicates that there was an upward trend in groundwater recharge across most of the modeled region. However, trends in increasing precipitation and groundwater recharge are not significant at the 0.05 level if the drought of 1961–67 is removed from the time series. The trend in simulated snowfall is not significant across much of the region, although there is a significant downward trend in southeast Connecticut and in central Massachusetts. To simulate future trends, two input datasets, one assuming high carbon emissions and one assuming low carbon emissions, were developed from GCM forecasts. Under both of the carbon emission scenarios, simulations indicate that historical trends will continue, with increases in groundwater recharge over much of the region and substantial snowfall decreases across Massachusetts, Connecticut, southern Vermont, and southern New Hampshire. The increases in groundwater recharge and decreases in snowfall are most pronounced for the high emission scenario.

Influences of North Atlantic climate variability on low-flows in the Connecticut River Basin

Summary Connections between summertime, ecologically relevant low-flow indicators and both winter and spring climate phenomena are explored for the Connecticut River Basin, with an emphasis on assessing forecast potential. Low-flow streamflow statistics deemed important for ecological health, including minimum 1-day mean flows, minimum 7-day mean flows, and monthly streamflow averages from June to September, are derived from 61 years of continuous, daily streamflow data at 15 United States Geological Survey streamflow gauging stations across the basin. Relationships between the ecological flow indicators with leading sea-surface temperature and sea-level pressure are investigated using correlation and composite analysis. Results suggest lagged relationships of up to 5 months between summer streamflow and the wintertime North Atlantic Oscillation, springtime east coast pressure trough, and springtime North Atlantic Tripole. These climate states have been linked to shifts between zonal and meridonal airflow as well as sea-surface temperature anomalies off the coast of the eastern US, both of which have implications for the movement of moisture systems over the study region. This study suggests that residual influences on airflow and sea-surface temperature persist into the summer following these earlier climate states, influencing low-flow hydrology in the region. As eco-hydrologic flow targets often conflict with other stakeholder objectives within a watershed, reservoir operators may utilize such lagged teleconnection patterns to predict annual low-flow characteristics in the region and help negotiate tradeoffs between traditional water management objectives and those emphasizing ecological conservation.