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.

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

AI-based model emulators have emerged as a pragmatic strategy for calibrating Earth System models or their components (e.g., land, atmosphere, ocean), circumventing the previously insurmountable hurdle of the process-heavy models’ computational expense. Such emulators require large, spatially diverse datasets for training, however, which – in the land/hydrology context – contrasts with parameter estimation approaches that have traditionally emphasized optimizing model performance for individual basins, followed by similarity-based transfer schemes for parameter regionalization. Compared to calibrating basins individually, direct land/hydrology process model calibration approaches typically perform worse when trained jointly to large collections of basins. Building on insights from large-sample deep learning hydrologic modeling, this study introduces a Large-Sample Emulator (LSE) approach that unifies and streamlines process model parameter calibration and regionalization. Tested across 627 basins in the continental United States using the Community Terrestrial Systems Model (CTSM), the LSE approach consistently improves runoff predictions in all basins, outperforming the Single-Site Emulator (SSE) in both single-objective and multi-objective calibration tasks. Moreover, LSE-based regionalization in unseen basins, evaluated through spatial cross-validation, achieves better results than the default parameters in most cases. This LSE framework offers a promising strategy for effective large-domain process-based model calibration and regionalization.

Despite the proliferation of computer-based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor re-used. Given the commonalities between existing process-based hydrological models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model-agnostic preprocessing of specific datasets from the model-specific requirements that models impose on their input files. We use this workflow to create large-domain (global, continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling’‘ (CWARHM; pronounced “swarm”).

Most hydropower utilities rely on flow forecasts to manage the water resources of their reservoir systems and to help marketers and schedulers make efficient use of power generating resources. Flow forecast providers and dam operators typically assess the value of flow forecasts by assessing the skill of the forecasts in a verification exercise. Although there are many flow forecasting approaches available—from physics-based approaches associated with statistical pre and post processors and data assimilation, to emerging machine-learning based approaches—there is little consensus on how to choose the best forecast product. Nor are there established methods for translating forecast skill—a summary statistic amalgamating multiple types of errors —to forecast value (benefits or avoided cost) as perceived by a marketer or scheduler. In this work we develop such an approach by combining a water resources management model with a power grid model. Flow forecasts are developed at 85 locations for a varying range of skills, from perfect, to persistent and in-between. Using reservoir and power grid simulations over the Western U.S., we propagate flow forecasts through the power grid model, mapping flow forecast skill to regional hydropower revenues, production costs and carbon emissions. We develop a deeper understanding of the influence of regional and seasonal differences in markets and hydrologic dynamics on forecast value. We discuss future research directions to integrate hydrologic forecasts into decision-making at the utility and wider system scale.

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.