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Using a simplified two-stage ice sheet model, we explore the potential of statistical data assimilation methods to improve predictions of glacier melt, which has significant implications for reducing uncertainty in projections of sea level rise. Through twin experiments utilizing artificial data, we find that the ensemble Kalman filter improves simulations of glacier evolution initialized with incorrect initial conditions and parameters, providing us with better predictions of future glacier melt. We explore the number of observations necessary to produce an accurate model run. We also explore optimal observation assimilation schemes, and determine that deviations from the true glacier response that stem from having few data points in the pre-satellite era can be corrected with modern observation data. Our results show that statistical data assimilation methods have great potential to improve complex glacier models using real-world observations. 

Abstract New York City (NYC) faces many challenges in the coming decades due to climate change and its interactions with social vulnerabilities and uneven urban development patterns and processes. This New York City Panel on Climate Change (NPCC) report contributes to the Panel's mandate to advise the city on climate change and provide timely climate risk information that can inform flexible and equitable adaptation pathways that enhance resilience to climate change. This report presents up‐to‐date scientific information as well as updated sea level rise projections of record. We also present a new methodology related to climate extremes and describe new methods for developing the next generation of climate projections for the New York metropolitan region. Future work by the Panel should compare the temperature and precipitation projections presented in this report with a subset of models to determine the potential impact and relevance of the “hot model” problem. NPCC4 expects to establish new projections‐of‐record for precipitation and temperature in 2024 based on this comparison and additional analysis. Nevertheless, the temperature and precipitation projections presented in this report may be useful for NYC stakeholders in the interim as they rely on the newest generation of global climate models. 

Abstract Groundwater aquifers are an essential source of drinking water, and must be protected against contamination. Phosphogypsum stacks originating from the processing of phosphate rock contain small amounts of radionuclides, such as²²⁶Ra. In September 2016, a sinkhole located beneath a phosphogypsum stack collapsed under central Florida’s carbonate karst terrain, where the aquifer is mostly confined, raising concern over water quality in the regions nearby. Monitoring and modeling the transport of the contaminated plume is vital to ensure drinking water criteria are met and to improve decision making regarding treatment. To achieve this, a geochemical modeling using PHREEQC software was employed to investigate the trajectory of the plume based on hydraulic and hydrologic conditions. Adsorption was simulated as a removal mechanism that could further reduce the intensity of the plume. The aquifer’s response to the release of contaminated water from the collapsed stack was quantified by simulating a number of scenarios, including variable radionuclide leakage quantities. Results suggest that it may take between 11–17 years and between 5.2 to 8.3 km from the sinkhole leak to reduce radionuclide concentrations to previous levels. Coupling the adsorption effect by minerals in Floridan aquifer (e.g. ferrihydrite, carbonate) can reduce radionuclide migration time to 9–16 years and distances between 4.3 to 7.8 km from the sinkhole leak. It can also reduce the distance needed to lower radionuclide concentrations, though not significantly. Additionally, due to the complexities of soil chemistry, the importance of groundwater remediation is emphasized. 

Abstract The flow duration curve (FDC) is a hydrologically meaningful representation of the statistical distribution of daily streamflows. The complexity of processes contributing to the FDC introduces challenges for the direct exploration of physical controls on FDC. In this paper, the controls of climate and catchment characteristics on FDC are explored using a stochastic framework that enables construction of the FDC from three components of streamflow: fast and slow flow (during wet days) and slow flow during dry days. The FDC during wet days (FDC_w) is computed as the statistical sum of the fast flow duration curve (FFDC) and the slow flow duration curve (SFDC_w), considering their dependency. FDC is modeled as the mixture distribution of FDC_wand the slow flow duration curve during dry days (SFDC_d), by considering the fraction of wet days (δ) for perennial streams and bothδand the fraction of days of zero streamflow for ephemeral streams. The Kappa distribution is employed to fit the FFDC, SFDC_w, and SFDC_dfor 300 catchments from Model Parameter Estimation Experiment (MOPEX) across the United States. Results show that the 0–20th percentile of FDC is controlled by FFDC and SFDC_w, the 90–100th percentile of FDC is controlled by SFDC_d, and the 20–90th percentile of FDC is controlled by three components. The relationships between estimated Kappa distribution parameters and climate and catchment characteristics reveal that the aridity index, the coefficient of variation of daily precipitation, timing of precipitation, time interval between storms, snow, topographic slope, and slope of recession slope curve are dominant controlling factors.