Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high-resolution re-simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic Systems Model (RASM) and conducted coupled land-atmosphere modeling for Alaska and Yukon River Basin at 4-km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and re-optimization of CTSM. A multi-decadal climate dataset (1990-2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational datasets, this dataset well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation-to-precipitation ratios, and streamflow. The simulated precipitation shows wet bias during the spring season and simulated air temperatures exhibit dampened seasonality with warm biases in winter and cold biases in summer. We used transfer entropy to investigate the discrepancy in connectivity of hydrologic fluxes between the offline CTSM and coupled models, which contributed to their discrepancy in streamflow simulations.

A document by Nicholas D. Lybarger. Click on the document to view its contents.

Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model vesion 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive dataset, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.

Climate change is leading to river ice thinning and shorter ice cover durations, posing significant risks to travel safety and ecosystem health. Due to limited in-situ observations in Alaska, models and remote sensing are employed to understand changing river conditions. This study conducts a comparative evaluation of statistical, machine learning, and remote sensing techniques to assess river ice presence and thickness across Alaska and the Yukon River basin. Sentinel-1 synthetic aperture radar data, climate model outputs, and in-situ river ice observations throughout Alaska are used to evaluate the regional applications of these techniques for determining river ice phenology and thickness. Our analysis reveals that ice presence can be accurately identified using Sentinel-1 images and climate data processed through machine learning models, achieving high accuracy across Alaska. Predicting ice break-up and freeze-up with these methods also yields high accuracy, with a root mean square error (RMSE) of 5.3 and 15.0 days, respectively, for machine learning at out-of-sample locations. Statistical, machine learning, and remote sensing techniques each demonstrated similar performance in determining ice thickness, with RMSEs ranging from 18 to 23 cm for out-of-sample years or locations. However, an ensemble of these methods significantly reduced the RMSE to 13 cm. Using the best-performing models, we generated high-resolution estimates of river ice phenology and thickness for major rivers in Alaska. The ensemble river ice thickness methods and the machine learning ice presence model show promise for widespread application in diverse regions, facilitating environmental monitoring and enhancing river ice safety.

The Arctic hydrological system is an interconnected system that is experiencing rapid change. It is comprised of permafrost, snow, glacier, frozen soils, and inland river systems. Permafrost degradation, trends towards earlier snow melt, a lengthening snow-free season, soil ice melt, and warming frozen soils all challenge hydrologic simulation under climate change in the Arctic. In this study, we provide an improved representation of the hydrologic cycle across a regional Arctic domain using a generalizable optimization methodology and workflow for the community. We applied the Community Terrestrial Systems Model (CTSM) across the US state of Alaska and the Yukon River Basin at 4-km spatial resolution. We highlight several potentially useful high-resolution CTSM configuration changes. Additionally, we performed a multi-objective optimization using snow and river flow metrics within an adaptive surrogate-based model optimization scheme. Four representative river basins across our study domain were selected for optimization based on observed streamflow and snow water equivalent observations at ten SNOTEL sites. Fourteen sensitive parameters were identified for optimization with half of them not directly related to hydrology or snow processes. Across fifteen out-of-sample river basins, thirteen had improved flow simulations after optimization and the median Kling-Gupta Efficiency of daily flow increased from 0.40 to 0.63. In addition, we adapted the Shapley Decomposition to disentangle each parameter’s contribution to streamflow performance changes, with the seven non-hydrological parameters providing a non-negligible contribution to performance gains. The snow simulation had limited improvement, likely because snow simulation is influenced more by meteorological forcing than model parameter choices.

Alaska and the Yukon are a challenging area to develop observationally based spatial estimates of meteorology. Complex topography, frozen precipitation undercatch, and extremely sparse observations all limit our capability to accurately estimate historical conditions. In this environment it is useful to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit recently-developed ensemble Climatologically Aided Interpolation (eCAI) systems to produce daily historical observations of precipitation and temperature across Alaska and the Yukon territory at a 2 km grid spacing for the time period 1980-2013. We extend the previous eCAI method to include an ensemble correction methodology to address precipitation gauge undercatch and wetting loss, which is of high importance for this region. Leave-one-out cross-validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble has skillful reliability compared to climatology and significant discrimination of events across different precipitation thresholds. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 show large inter-product differences, particularly in precipitation across the complex terrain of SE and northern Alaska. Finally, long-term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation.