Browsing by Author "Stachelek, Joseph"
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- From concept to practice to policy: modeling coupled natural and human systems in lake catchmentsCobourn, Kelly M.; Carey, Cayelan C.; Boyle, Kevin J.; Duffy, Christopher J.; Dugan, Hilary A.; Farrell, Kaitlin J.; Fitchett, Leah Lynn; Hanson, Paul C.; Hart, Julia A.; Henson, Virginia Reilly; Hetherington, Amy L.; Kemanian, Armen R.; Rudstam, Lars G.; Shu, Lele; Soranno, Patricia A.; Sorice, Michael G.; Stachelek, Joseph; Ward, Nicole K.; Weathers, Kathleen C.; Weng, Weizhe; Zhang, Yu (Ecological Society of America, 2018-05-03)Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well‐developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual–empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi‐disciplinary tool that captures diverse processes that operate at multiple scales, including land‐management decision‐making, hydrologic‐solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross‐disciplinary dialogue to move CNHS lake‐catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision‐making and policy.
- Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscalesStachelek, Joseph; Weng, W.; Carey, Cayelan C.; Kemanian, Armen R.; Cobourn, Kelly M.; Wagner, Tyler K.; Weathers, Kathleen C.; Soranno, Patricia A. (2020-12)Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake's watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.
- Integrating fast and slow processes is essential for simulating human-freshwater interactionsWard, Nicole K.; Fitchett, Leah Lynn; Hart, Julia A.; Shu, Lele; Stachelek, Joseph; Weng, Weizhe; Zhang, Yu; Dugan, Hilary A.; Hetherington, Amy L.; Boyle, Kevin J.; Carey, Cayelan C.; Cobourn, Kelly M.; Hanson, Paul C.; Kemanian, Armen R.; Sorice, Michael G.; Weathers, Kathleen C. (Springer, 2019-10-01)Integrated modeling is a critical tool to evaluate the behavior of coupled human–freshwater systems. However, models that do not consider both fast and slow processes may not accurately reflect the feedbacks that define complex systems. We evaluated current coupled human–freshwater system modeling approaches in the literature with a focus on categorizing feedback loops as including economic and/or socio-cultural processes and identifying the simulation of fast and slow processes in human and biophysical systems. Fast human and fast biophysical processes are well represented in the literature, but very few studies incorporate slow human and slow biophysical system processes. Challenges in simulating coupled human–freshwater systems can be overcome by quantifying various monetary and non-monetary ecosystem values and by using data aggregation techniques. Studies that incorporate both fast and slow processes have the potential to improve complex system understanding and inform more sustainable decision-making that targets effective leverage points for system change.
- Predicting lake surface water phosphorus dynamics using process-guided machine learningHanson, Paul C.; Stillman, Aviah B.; Jia, Xiaowei; Karpatne, Anuj; Dugan, Hilary A.; Carey, Cayelan C.; Stachelek, Joseph; Ward, Nicole K.; Zhang, Yu; Read, Jordan S.; Kumar, Vipin (2020-08-15)Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 mu g P L-1, 22.7 mu g P L-1, and 20.7 mu g P L-1, respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 mu g P L-1, 10.9 mu g P L-1, and 10.7 mu g P L-1. Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 mu g P L-1 and 28.0 mu g P L-1, respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.