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Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.

Considerable efforts are underway to restore watersheds and estuaries downstream impacted by urban development; however, climate change (CC) may be undermining them. Current methods are limited in their ability to predict hydrology and water quality with CC and assess its effect on the efficiency of stormwater control measures (SCMs). We developed a method using downscaled global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to project precipitation and temperatures; these were used to force a Storm Water Management Model (SWMM). Three scenarios, a historical and two Representative Concentration Pathways (RCP 4.5 and 8.5) with five GCMs, were used to produce ensemble results. All GCMs in both RCP scenarios projected increases in precipitation and temperature compared to historical conditions. Both RCPs exhibited their largest increases in precipitation, streamflow, total suspended solids (TSS), total nitrogen (TN), and total phosphorous (TP) loads in the winter, summer exhibited the largest increase in temperature. Median loads of TSS, TN, and TP increased 3.1%, 2.5%, and 9.9%, respectively, for RCP 4.5, and increased 3.8%, 3.1%, and 10.4%, respectively, for RCP 8.5. Median reductions in TSS, TN, and TP SCM efficiency for RCP 4.5 were projected to be 6%, 7%, and 11%, respectively; and 11%, 12%, and 17% for RCP 8.5, respectively. Thus, it is likely that additional efforts will be needed to meet water quality goals in the future. Methods such as these can help create climate resilient watershed improvement strategies and guide urban stormwater planning against likely future changes as a result of CC.

A challenge for climate impact studies is the selection of a limited number of climate model projections among the dozens that are typically available. Here, we examine the impacts of methods for climate model selection on projections of runoff change for five different watersheds across the conterminous USA. The results from an ensemble of 29 global climate models and 29 corresponding hydrological model simulations are compared with the results that would have been obtained by applying six different selection methods to the climate model data and using only the selected models to drive the hydrological model. We evaluate each selection method based on whether the runoff projections produced by the method meet the method’s objective and on whether the results are sensitive to the number of models chosen. The Katsavounidis–Kuo–Zhang (KKZ) method, which is intended to maximize the spread in the projected climate change, was the only method that met both criteria for suitability. Although the KKZ method generally performed well, the results from both it and the other methods varied somewhat unpredictably based on region and number of models chosen. This study shows that the methods and models used in similar top–down studies should be carefully chosen and that the results obtained should be interpreted with caution.

Most present forecast systems for estuaries predict conditions for only a few days into the future. However, there are many reasons to expect that skillful estuarine forecasts are possible for longer time periods, including increasingly skillful extended atmospheric forecasts, the potential for lasting impacts of atmospheric forcing on estuarine conditions, and the predictability of tidal cycles. In this study, we test whether skillful estuarine forecasts are possible for up to 35 days into the future by combining an estuarine model of Chesapeake Bay with 35‐day atmospheric forecasts from an operational weather model. When compared with both a hindcast simulation from the same estuarine model and with observations, the estuarine forecasts for surface water temperature are skillful up to about 2 weeks into the future, and the forecasts for bottom temperature, surface and bottom salinity, and density stratification are skillful for all or the majority of the forecast period. Bottom oxygen forecasts are skillful when compared to the model hindcast, but not when compared with observations. We also find that skill for all variables in the estuary can be improved by taking the mean of multiple estuarine forecasts driven by an ensemble of atmospheric forecasts. Finally, we examine the forecasts in detail using two case studies of extreme events, and we discuss opportunities for improving the forecast skill.

Nutrient export from agricultural landscapes is a water quality concern and the cause of mitigation activities worldwide. Climate change impacts hydrology and nutrient cycling by changing soil moisture, stoichiometric nutrient ratios, and soil temperature, potentially complicating mitigation measures. This research quantifies the impact of climate change and climate anomalies on hydrology, nutrient cycling, and greenhouse gas emissions in an agricultural catchment of the Chesapeake Bay watershed. We force a calibrated model with seven downscaled and bias-corrected regional climate models and derived climate anomalies to assess their impact on hydrology and the export of nitrate (NO3-), phosphorus (P), and sediment, and emissions of nitrous oxide (N2O) and di-nitrogen (N-2). Modelaverage (+/- standard deviation) results indicate that climate change, through an increase in precipitation and temperature, will result in substantial increases in winter/spring flow (10.6 +/- 12.3%), NO3-(17.3 +/- 6.4%), dissolved P (32.3 +/- 18.4%), total P (24.8 +/- 16.9%), and sediment (25.2 +/- 16.6%) export, and a slight increases in N2O (0.3 +/- 4.8%) and N-2 (0.2 +/- 11.8%) emissions. Conversely, decreases in summer flow (-29.1 +/- 24.6%) and the export of dissolved P (-15.5 +/- 26.4%), total P (-16.3 +/- 20.7%), sediment (-20.7 +/- 18.3%), and NO3-(-29.1 +/- 27.8%) are driven by greater evapotranspiration from increasing summer temperatures. Decreases in N2O (-26.9 +/- 15.7%) and N-2 (-36.6 +/- 22.9%) are predicted in the summer and driven by drier soils. While the changes in flow are related directly to changes in precipitation and temperature, the changes in nutrient and sediment export are, to some extent, driven by changes in agricultural management that climate change induces, such as earlier spring tillage and altered nutrient application timing and by alterations to nutrient cycling in the soil. (C) 2018 Elsevier B.V. All rights reserved. National Science FoundationNational Science Foundation (NSF) [1360415, 1343802]; USDAUnited States Department of Agriculture (USDA) [2012-67019-19434] We would like to acknowledge high-performance computing support from Yellowstone (http://n2t.net/ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, support from the National Science Foundation under award numbers 1360415 and 1343802, and funding support from the USDA under project number 2012-67019-19434. All data, methods, and code used in this manuscript are available upon request. Public domain – authored by a U.S. government employee

Rainwater harvesting (RWH) systems recycle runoff, increasing the sustainability of water supplies; they may also reduce runoff discharges, and thus help meet water quality objectives. RWH systems receive runoff and thus will likely be impacted by changes in rainfall induced by climate change (CC). In this paper, we assess CC impacts on RWH with respect to the reliability of water supply, defined as the proportion of demands that are met; and the reliability of runoff capture, defined as the amount stored and reused, but not spilled. Hypothetical RWH systems with varying storage, rooftop catchments, irrigated areas, and indoor water demand for 17 locations across the U.S. were simulated for historical (1971–1998) and future (2041–2068) periods using downscaled climate model data assuming future medium-high greenhouse gas emissions. The largest change in runoff capture reliability would occur in Chicago (−12.4%) and Los Angeles (+12.3%), respectively. The largest change in water supply reliability would occur in Miami (+22%) and Los Angeles (−17.9%), respectively. The effectiveness of RWH systems for runoff capture is likely to be reduced in the eastern, northwestern, and southeastern U.S. Conversely, for most locations in the western, southern, and central U.S., RWH systems are expected to become less effective for water supply purposes. The additional storage needed to compensate for these reductions in water supply and/or runoff capture benefits was estimated. The results of this study can be used to design more resilient RWH systems with respect to CC, and thus maximize the dual objectives of RWH.

Secular tidal trends are present in many tide gauge records, but their causes are often unclear. This study examines trends in tides over the last century in the Chesapeake and Delaware Bays. Statistical models show negative M2 amplitude trends at the mouths of both bays, while some upstream locations have insignificant or positive trends. To determine whether sea-level rise is responsible for these trends, we include a term for mean sea level in the statistical models and compare the results with predictions from numerical and analytical models. The observed and predicted sensitivities of M2 amplitude and phase to mean sea level are similar, although the numerical model amplitude is less sensitive to sea level. The sensitivity occurs as a result of strengthening and shifting of the amphidromic system in the Chesapeake Bay and decreasing frictional effects and increasing convergence in the Delaware Bay. After accounting for the effect of sea level, significant negative background M2 and S2 amplitude trends are present; these trends may be related to other factors such as dredging, tide gauge errors, or river discharge. Projected changes in tidal amplitudes due to sea-level rise over the twenty-first century are substantial in some areas, but depend significantly on modeling assumptions.

The response of salinity in the Delaware Estuary to climatic variations is determined using statistical models and long-term (1950-present) records of salinity from the U.S. Geological Survey and the Haskin Shellfish Research Laboratory. The statistical models include non-parametric terms and are robust against autocorrelated and heteroscedastic errors. After using the models to adjust for the influence of streamflow and seasonal effects on salinity, several locations in the estuary show significant upward trends in salinity. Insignificant trends are found at locations that are normally upstream of the salt front. The models indicate a positive correlation between rising sea levels and increasing residual salinity, with salinity rising from 2.5 to 4.4 per meter of sea-level rise. These results are consistent with results from 1D and dynamical models. Wind stress also appears to play some role in driving salinity variations, consistent with its effect on vertical mixing and Ekman transport between the estuary and the ocean. The results suggest that continued sea-level rise in the future will cause salinity to increase regardless of any change in streamflow.

Assessing climate change (CC) impacts on urban watersheds is difficult due to differences in model spatial and temporal scales, making prediction of hydrologic restoration a challenge. A methodology was developed using an autocalibration tool to calibrate a previously developed Storm Water Management Model (SWMM) of Difficult Run in Fairfax, Virginia. Calibration was assisted by use of multi-objective optimization. Results showed a good agreement between simulated and observed data. Simulations of CC for the 2041–2068 period were developed using dynamically downscaled North American Regional CC Assessment Program models. Washoff loads were used to simulate water quality, and a method was developed to estimate treatment performed in stormwater control measures (SCMs) to assess water quality impacts from CC. CC simulations indicated that annual runoff volume would increase by 6.5%, while total suspended solids, total nitrogen, and total phosphorus would increase by 7.6%, 7.1%, and 8.1%, respectively. The simulations also indicated that within season variability would increase by a larger percentage. Treatment practices (e.g., bioswale) that were intended to mitigate the negative effects of urban development will need to deal with additional runoff volumes and nutrient loads from CC to achieve the required water quality goals. Published version