Calculating Sustainability Indices of Water and Basin to Maintain Sustainability Development (Case Study: Gamasiab Basin Watershed)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
1-Introduction Loucks (2000) and Margerum (1999) defined the concept of sustainability paradigm for multi-purpose projects to obtain the consent of stakeholders in decision-making process. Accordingly, Chavez and Alipaz (2007) claimed that sustainability of water resources directly depends on the political, life, environmental and hydrological situations, although there are little to integrate them in the same manner. Sullivan and Meigh, (2005) stated that there have been a desire to measure and describe different aspects of indicators of sustainable development of water resources. Kondratyev et al, (2002) and Ioris et al, (2008) believe that the current restrictions on the stability of most aspects are in biophysical methods. Most of mining socio-economic factors are caused by environmental driving forces. Wang and Innes, (2005) used confirmed auditing systems approach of sustainable forest management accompanied by the evaluation of regional sustainable development to test land sustainability and use of water resources in river basins Mine, Foujin in the China . The results show that the basin has little power for sustainable development. 2-Materials and Methods The water sustainability indicators: these indicators include the quantity of water resources and their availability such as: Dry Season Flow by River Basin This indicator was developed by the World Resources Institute (WRI) as a part of the Pilot Analysis of Global Ecosystems (PAGE) (WRI, 2000) for the description of water conditions on a river basin level. It considers the temporal variability of water availability that is essential for some places like the regions with rainy and dry seasons. Watersheds with a dry season are the places where less than 2% of the surface runoff is available in the 4 driest months of the year. This indicator is calculated by dividing the volume of runoff during the dry season, i.e. during the four consecutive months with the lowest cumulative runoff, by the population. Water availability index (WAI) Meigh et al. (1999) calculated GWAVA (Global Water AVailability Assessment) model the temporal variability of water availability. The index includes surface water as well as groundwater resources comparing the total amount to the demands of all sectors, i.e. domestic, industrial and agricultural demands. The month with the maximum deficit or minimum surplus respectively is decisive. The index is normalized in the range of –1 to +1. When the index is zero, availability and demands are equal. Vulnerability of Water Systems Gleick (1990) developed this index for watersheds in the United States as part of an assessment of the potential impacts of climate change for water resources and water systems. Watershed Sustainability Index A sustainable and integrated water management need the engagement of all stakeholders. Such water management has demonstrated to be capable of integrating all issues of water resources management (loucks, 2000). Water sustainability indices, namely Water Poverty Index (WPI) are presented by Sullivan (2002), Canadian Water Sustainability Index (CWSI) by the Policy Research Initiative (Policy Research Initiative, 2007) and Watershed Sustainability Index (WSI) by Chaves and Alipaz (2007). All these three indices have the same goal to provide information on current conditions of water resources, provide inputs to decision makers and prioritize water-related issues. 3-Results and Discussion In this study, the combination of indicators, which are directly from output of watershed simulation models, are used. The computation of indicators has been done in excel making three dimensional matrix of scenario- alternative and indicators. Solving these matrix has been done by multi criteria decision making (MCDM). Compromise programing is one of these methods that is used in this study. 4-Conclusion Studying in the Gamasiab watershed, simulated by WEAP model and the compromise programing, showed that in the scenario only Jamishan dam is the option to decrease the losses and increase water efficiency alternative. In the other hand, the decrease losses alternative is the best one in the scenario three dams.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it