The impact of future climate and land use/cover change on water resources in the Ndembera watershed and their mitigation and adaptation strategies
Bibliographic record
Abstract
Land use/cover and climate changes have a great influence on the hydrological processes in the watershed. The impacts of land use/cover and climate change are set to increase in the future due to the increased clearance of virgin forest lands for agriculture and the rise of global warming. The way in which the future climate will interact with the land use changes and affect the water balance in the watersheds requires more attention. This study was carried out in the Ndembera river watershed in Usangu basin, Tanzania, whereby the Soil and Water Assessment Tool was used to (i) assess the impact of near future (2010–2039) climate and 2013–2020 land use/cover change on the water balance and streamflow and (ii) evaluate the effectiveness of four land and water management practices as the mitigation and adaptation strategies for the impacts of climate and land use/cover changes. The 2020 land use/cover was predicted using Markov Chain and Cellular Automata models based on 2006 and 2013 land use/covers. The near-future climate scenario was generated from the Coupled Model Intercomparison Project 5 General Circulation Models. During the period from 2013 to 2020, the agricultural land and evergreen forests will increase by nearly 10 and 7%, respectively. Mixed forests will decrease by 12%. Such land use/cover changes will decrease the total water yield by nearly 13% while increasing evapotranspiration and surface runoff by approximately 8 and 18%, respectively. This moisture balance changes will be aggravated by warmer near-future mean annual temperatures (1.1 °C) and wetter conditions (3.4 mm/year) than in the baseline period (1980–2009). The warmer future climate will increase evapotranspiration and decrease water yield by approximately 35 and 8%, respectively. The management practices such as filter strips can reduce the annual evapotranspiration by 6%, and increase stream-flow by 38% in February. The future land use/cover changes will interact with the near-future warmer temperatures and reduce water availability in the Ndembera watershed. Land and water management practices have great potential to mitigate the impacts of future climate and land use/cover changes on water resource, thus increasing its availability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".