Understanding and projections of space-time variability of summer hydroclimate and ecology in the United States Prairie Pothole Region
Bibliographic record
Abstract
Within the northern Great Plains of the United States is the southeast Prairie Pothole Region (SEPPR), an important habitat for waterfowl, pheasants, deer, and many unique species that cannot be found anywhere else in the regional landscape. There are millions of wetlands in this region that provide habitat for these species as well as floodwater storage. The region is highly sensitive to variations in climate, and it is projected to face changes in temperature and precipitation in the future. Some of these changes will undoubtedly take place during the summer months. Summer precipitation in the SEPPR is a significant portion of the yearly rainfall, and it provides hydrologic inflows which help to sustain the ecosystem after spring snowmelt. Thus, understanding, modeling, and projecting the summer hydroclimatology and ecology is crucial for resource managers of the SEPPR in managing the ecosystem efficiently. They must do so despite a current lack of climate information to inform their decisions. Their work can be made easier by expanding on available summer climate and climate variability information and providing unique tools that provide predictions with appropriate lead times for their decision timelines.Motivated by this broader need, four research questions emerge, and their answers shaped this dissertation. They are (i) what large-scale mechanisms and teleconnections influence summer precipitation variability, (ii) what are the dominant sources and pathways of moisture for the region's summer precipitation and extreme precipitation events, (iii) how well does a predictive model for pond counts perform using large-scale climate variables as predictors, and (iv) can wetland vegetation be adequately modeled using a point-based, physical numerical model coupled with climate information?Investigative efforts at answering these questions led to four key contributions. (1) Understanding the space-time variability of summer hydroclimatology and potential mechanisms is an important first step as this is strongly related to the summer ecology. We establish teleconnections and potential mechanisms driving the SEPPR summer precipitation variability through multivariate analysis of large-scale climate variables and regional rainfall. Interannual variability of SEPPR summer hydroclimatology was found to be strongly connected to northern Pacific, equatorial Pacific, and Atlantic sea surface temperatures (SSTs), 500 mb heights over the western U.S., and the Palmer Drought Severity Index (PDSI) over the SEPPR. The SST teleconnection in the equatorial Pacific Ocean resembles La Niña, and a large-scale low-pressure region over the northwest U.S. observed in the 500 mb and 850 mb heights and their associated winds - a pattern similar to the Great Plains Low Level Jet (GPLLJ) - is a potential mechanism of the precipitation variability. These teleconnections may persist from preceding winter and spring seasons offering potential for long lead forecast of summer hydrology and consequently ecology. (2) Using the Lagrangian parcel-tracking model HYSPLIT on all historical rainy days of record, moisture sources and pathways of summer rainfall were investigated. Analysis of back trajectories generated by HYSPLIT shows that land is the primary source of moisture for both normal and extreme summer rainfall events indicating moisture recycling plays an important role in precipitation generation. Secondary sources are the Gulf of Mexico (GoM) and the Pacific Ocean with the former having the larger impact. The GPLLJ is the most prominent pathway impacting both the land- and GoM-sourced events. Furthermore, land-sourced events show a connection to the El Niño Southern Oscillation (ENSO), soil moisture over the western U.S., and low-pressure systems over the SEPPR. Events sourced by the GoM share the connection to soil moisture over the western U.S., but also show connections to SSTs in the north Pacific and Atlantic Oceans and the GoM, soil moisture in northern Mexico, and a trough of 850 mb heights extending south to the SEPPR from Canada. These results provide valuable insights into sources and pathways for SEPPR summer rainfall and could help improve SEPPR summer rainfall predictions. Importantly, contributions (1) and (2) both convey a distinct coupling between land, atmosphere, and the ocean. (3) We provide SEPPR resource managers with a predictive tool by employing an underutilized statistical forecasting technique - multivariate Canonical Correlation Analysis - to develop multisite forecasting models for spring and summer SEPPR pond counts. These models predict spring (May) and summer (July) pond counts for each region of the United States (U.S.) Fish and Wildlife Service’s pond and waterfowl surveys. The suite of predictors, developed using knowledge gained from our first contribution, are of antecedent, large-scale climate variables and indices. The models issue forecasts at the start of all months preceding their forecast dates beginning on March 1st. The models exhibited very good skill, and performance increased as lead time decreased, even at long lead times. These skillful forecasts will be of immense help in sustainable management of the ecology in the region. (4) In the final contribution, we once again provide SEPPR resource managers with a novel, predictive tool capable of simulating multiple vegetation types native to the SEPPR. This integrated climate-ecological modeling framework (ICEMF) couples a stochastic weather generator that can be conditioned on climate forecasts along with SEPPR climate, soil, and vegetation information in an ecological model, DayCent, to simulate ensembles of vegetation attributes in the SEPPR - e.g. primary production, carbon and nitrogen fluxes, etc. The integrated framework can generate vegetation ensembles at seasonal or multi-year time scales using seasonal probabilistic climate forecasts and projections under climate change. Using probability forecasts, we demonstrate one possible utility of the ICEMF. Our simulations of seasonal vegetation NPP generally improved upon simulations using climatological forecasts.The combination of new insights into the space-time hydroclimate variability, moisture sources and pathways of summer moisture, a multi-site forecasting model for ponds that supports SEPPR ecology, and the ICEMF makes a significant contribution to the broader community. These can be applied to model other ecological systems in the world, enabled to study impacts of climate change, and help with efficient and sustainable management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".