Bringing Water into an Integrated Assessment Framework
Bibliographic record
Abstract
We developed a modeling capability to understand how water is allocated within a river basin and examined present and future water allocations among agriculture, energy production, other human requirements, and ecological needs. Water is an essential natural resource needed for food and fiber production, household and industrial uses, energy production, transportation, tourism and recreation, and the functioning of natural ecosystems. Anthropogenic climate change and population growth are anticipated to impose unprecedented pressure on water resources during this century. Pacific Northwest National Laboratory (PNNL) researchers have pioneered the development of integrated assessment (IA) models for the analysis of energy and economic systems under conditions of climate change. This Laboratory Directed Research and Development (LDRD) effort led to the development of a modeling capability to evaluate current and future water allocations between human requirements and ecosystem services. The Water Prototype Model (WPM) was built in STELLA®, a computer modeling package with a powerful interface that enables users to construct dynamic models to simulate and integrate many processes (biological, hydrological, economics, sociological). A 150,404-km2 basin in the United States (U.S.) Pacific Northwest region served as the platform for the development of the WPM. About 60% of the study basin is in the state of Washington with the rest in Oregon. The Columbia River runs through the basin for 874 km, starting at the international border with Canada and ending (for the purpose of the simulation) at The Dalles dam. Water enters the basin through precipitation and from streamflows originating from the Columbia River at the international border with Canada, the Spokane River, and the Snake River. Water leaves the basin through evapotranspiration, consumptive uses (irrigation, livestock, domestic, commercial, mining, industrial, and off-stream power generation), and streamflow through The Dalles dam. Water also enters the Columbia River via runoff from land. The model runs on a monthly timescale to account for the impact of seasonal variations of climate, streamflows, and water uses. Data for the model prototype were obtained from national databases and ecosystem model results. The WPM can be run from three sources: 1) directly from STELLA, 2) with the isee Player®, or 3) the web version of WPM constructed with NetSim® software. When running any of these three versions, the user is presented a screen with a series of buttons, graphs, and a table. Two of the buttons provide the user with background and instructions on how to run the model. Currently, there are five types of scenarios that can be manipulated alone or in combination using the Sliding Input Devices: 1) interannual variability (e.g., El Niño), 2) climate change, 3) salmon policy, 4) future population, and 5) biodiesel production. Overall, the WPM captured the effects of streamflow conditions on hydropower production. Under La Niña conditions, more hydropower is available during all months of the year, with a substantially higher availability during spring and summer. Under El Niño conditions, hydropower would be reduced, with a total decline of 15% from normal weather conditions over the year. A policy of flow augmentation to facilitate the spring migration of smolts to the ocean would also reduce hydropower supply. Modeled hydropower generation was 23% greater than the 81 TWh reported in the 1995 U.S. Geological Survey (USGS) database. The modeling capability presented here contains the essential features to conduct basin-scale analyses of water allocation under current and future climates. Due to its underlying data structure iv and conceptual foundation, the WPM should be appropriate to conduct IA modeling at national and global scales.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".