Estimate of Long-Term Water Availability for a Reservoir in Texas Using a Markov Chain Monte Carlo Method with Paleo Drought and Trend Consideration
Bibliographic record
Abstract
How much water supply can come from a reservoir in Texas in 2030, 2040, 2050, 2060, 2070, and 2080? This is a key question that must be answered in the water planning process in Texas. Current regional water planning rules require regional water planning groups (RWPGs) to use reservoir firm yield, which is the maximum annual diversion from a reservoir at 100% reliability during a repeat of the drought-of-record. Firm yield is traditionally simulated using the Texas Commission on Environmental Quality (TCEQ) water availability model (WAM) Run 3 as an estimate of future water availability, while also considering the effect of future sedimentation within a reservoir. However, most of the TCEQ WAMs use hydrologic input extending from the 1940s to the 1990s, with only a few basins having records extended to recent years. Therefore, there is a need for longer simulations that can cover a range of possible hydrologic scenarios. In this study, we analyze long-term firm yield for Lake Meredith located in the Canadian River Basin. Using a reconstructed Palmer Drought Severity Index (PDSI) data set (extending from 1400 to 2003), we assessed critical drought periods and selected a 14-year drought (with a 600-year recurrence interval) as a potential new benchmark drought for the Lake Meredith watershed. We applied Markov chain Monte Carlo (MCMC) methods to generate a synthetic hydrologic data set (10,000 series) for the future period (from 2019 through 2080) using lag-1 annual drought transitional probabilities generated from, and incorporating the long-term trend in, the naturalized flows upstream of Lake Meredith for the period 1940 through 2018. Net annual reservoir evaporation that corresponds to the annual naturalized flow, projected reservoir capacity, and volume-area rating curves for each planning decade are input to the TCEQ’s Canadian River Basin WAM to simulate 10,000 series of future reservoir firm yield for each decade. Based on the 14-year benchmark drought-of-record, and a 90% exceedance probability, we find that future water availability from Lake Meredith decreases over the planning decades, with about 15 million cubic meters per year in 2030 reducing to about 0.9 million cubic meters per year in 2080. These results indicate an increase in drought risk exposure for the Lake Meredith watershed, given the occurrence of a drought worse than the observed 2010s drought-of-record. The findings could inform a water supply risk tolerance assessment by local water user groups and water planners in the Canadian River Basin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".