Challenges and opportunities in large-scale river routing: development and application of a hyper-resolution river routing model to assess anthropogenic impacts on freshwater ecosystems
Bibliographic record
Abstract
Despite significant recent advancements, global hydrological models and their input databases still show limited capabilities in supporting many spatially detailed research questions and integrated assessments, such as required in freshwater ecology or applied water resources management. In this body of research, I analyze the reasons for the lack of modeling support, identify shortcomings and challenges of current models, and design a next-generation eco-hydrological river routing model, termed HydroROUT. Based on the global hydrographic data repository of HydroSHEDS, HydroROUT is the first global hyper-resolution river routing model and includes a nested, multi-scale model approach; advanced implementation of connectivity; and a novel implementation of object-oriented vector data structures in a graph-theoretical framework. I subsequently explore the applicability of the model for different settings and research applications by designing and conducting four case studies related to assessing human impacts on freshwater system integrity. These case studies include both data-rich and data-limited areas, with spatial scales ranging from small headwater streams to large rivers, involving regional to global extents. I apply HydroROUT in two distinct research domains. First, I assess its capacity to model global spatio-temporal patterns of dam impacts. A set of new indicators, including the River Connectivity Index (RCI) and the River Regulation Index (RRI) were developed as part of two case studies, providing previously overlooked insights into intra-basin variability of dam impacts. Second, I apply HydroROUT to assess its capability for water quality modelling, by estimating in-river concentrations and risk of pollutants in freshwater systems using mass balance approaches at large scales. For this purpose, HydroROUT was adapted to function as a chemical fate model, capable of providing first-order risk assessments from point- and diffuse chemical sources. The results of two case studies show significant risk from pharmaceuticals in river reaches of the Saint Lawrence River Basin, Canada, and in extended areas of continental China. These large-scale outcomes of the HydroROUT modelling approach are at a previously unachieved spatial resolution of 500 m and can thus support local planning and decision-making in many of the world's large river basins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".