The goal of this project is to identify statistical trends in observed and simulated maximum, minimum and base (mostly groundwater contribution during low flow months) flows in the Northeast Climate Science Center domain during the 20th and 21st century, assess the temporal (annual and seasonal) and spatial distribution of the trends, and evaluate the impact of warmer climates on the statistical properties of streamflows (mean and variance). A secondary goal is to determine what GCMs best represent the observed climatology of the region using statistical metrics.
Base and minimum flows are vital for fish ecosystem functioning and for riparian vegetation. Climate projections indicate summers will get warmer and drier in the NE CSC domain which will affect aquatic ecosystems. Larger streamflows peaks will affect existing infrastructure, e.g. bridges, dams, cities).
Keith Kuk-Hyun Ahn completed a non stationary analysis for streamflow in the connecticut river basin, developing regional flood frequency analysis using spatial proximity and basin characteristics in the northeastern United States, and developed drought forecasting methodology using climate indices. He also built the SWAT model for Deerfield basins.
Nirajan Dhakal's findings include the following:
- The high and low flow seasonality of catchments in the northeastern Unites States is likely to change under climate change because of anticipated alterations of precipitation as well as snow accumulation and melt. Information on this change is crucial for flood protection polices for example for regional flood frequency analysis. The objective of this study is to evaluate the change in seasonality of daily stream flows (both high and low) in northeastern Unites States.
- In areas where high flows are generated by more than one distinct hydrologic process (e.g., snowmelt- and rainfall generated peaks), peak discharge data should be considered to be drawn from subpopulations with different statistical characteristics. For an appropriate and effective hydrologic design it is necessary to consider these two characteristics of precipitation to estimate the probability distribution by introducing mixed distribution.
- Quezada-Hofflinger, Alvaro, Somos-Valenzuela Marcelo A., and Vallejos-Romero Arturo, 2017 Response Time to Flood Events using a Social Vulnerability Index (ReTSVI), Natural Hazards and Earth System Sciences Discussions, DOI 10.5194/nhess-2017-395
- Demaria, Eleonora M. C., Palmer Richard N., and Roundy Joshua K. 2016. Regional climate change projections of streamflow characteristics in the Northeast and Midwest U.S. Journal of Hydrology: Regional Studies, 5, 309-323
- Demaria, E.M.C., Roundy, J. K., Palmer, R.N., and Wi, S. 2016. The Effects of Climate Change on Seasonal Snowpack and the Hydrology of the Northeastern and Upper Midwest, U.S. , Journal of Climate,DOI 10.1175/JCLI-D-15-0632.1.
- Ahn, K.H., and Palmer, R.N., 2016. Regional Flood Frequency Analysis using Spatial Proximity and Basin Characteristics: Quantile Regression vs. Parameter Regression Technique, Journal of Hydrology, doi.10.1016/j.jhydrol.2016.06.047
- Ahn, K., Merwade, V., Ojha C.S.P., and Palmer Richard P. N., 2016 Quantifying relative uncertainties in the detection and attribution of human-induced climate change on winter streamflow Journal of Hydrology, 542:304 - 316, 10.1016/j.jhydrol.2016.09.015
- Ahn, K.H., and Palmer, R.N., 2016. Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin Hydrological Processes, 30(19)3518 - 3532 DOI 10.1002/hyp.10876
- Leslie DeCristofaro (UMass Amherst); Richard Palmer (PdD, PE, UMass Amherst). 2015. Evaluating the Performance of Multiple Alternative Operating Rules under Climate Change: A Case Study of New York City. World Environmental and Water Resources Congress 2015: pp. 2147-2156.
- Keith Kuk-Hyun Ahn, Richard N. Palmer. 2015. Trend and Variability in Observed Hydrological Extremes in the United States. Journal of Hydrologic Engineering. Journal of Hydrological Engineering.
- Guihan, R., Polebitski, A., Palmer, R., and Wood, A. 2014. The Value of Forecasts in Reservoir Operations Management, Proceedings of ASCE's 2014 World Environmental and Water Resources Congress, pp. 1041-1049.
- Whateley, S., Palmer, R.N., and Brown, C. 2014. Seasonal Hydroclimatic Forecasts as Innovations and the Challenges of Adoption by Water Managers, ASCE Journal of Water Resources Planning and Management, 10.1061/(ASCE)WR.1943-5452.0000466, 04014071.
- Rossi, N., DeCristofaro, L, Brown, C., Steinschneider, S. and Palmer, R. 2014. Potential Impacts of Changes in Climate on Turbidity in New York City’s Ashokan Reservoir, accepted for publication in ASCE Journal of Water Resources Planning and Management.
- Guihan, R., Polebitski, A., Palmer, R., and Brown, C. 2013. Integrating Climate Forecasts and Reforecast Products into Reservoir Operations Management, Proceedings of ASCE's 2013 World Environmental and Water Resources Congress, pp. 1574-1580.
Richard Palmer, PE, Ph.D., F ASCE, D.WRE, University of Massachusetts; David Ahlfeld, Ph.D., University of Massachusetts; Rachael Weiter, B.S., University of Massachusetts; Gordon Clark, B.S., University of Massachusetts. Developing a Screening Model for the Prioritization of Culvert Repair and Replacement. ASCE EWRI Conference, Austin, TX. May 2015.
- Finished a distributed model (WR-hydro) for the deerfield, this model include NARCCAP projections to assess potential high flow impacts, now we are preparing a publication for this study.
- We are in the phase of calibrating a hidrology model that covers the northeast of the US from Virginia to Maine in a 3 km resolution to quantify impact of climate change in the hydrology of the region.
- Forecasting informed Hierarchical Bayesian modeling for low-flow in the east U.S. (December, 2015)
- Related project: The NE CSC's NorEaST Stream Temperature Inventory and Web Portal project
- Related project: UMass Extension's River and Stream Continuity project
- Related project: North Atlantic LCC's Restoring Aquatic Connectivity and Increasing Resilience project