26 research outputs found
Urban Water Conservation and Efficiency Potential in California
Improving urban water-use efficiency is a key solution to California's short-term and longterm water challenges: from drought to unsustainable groundwater use to growing tensions over limited supplies. Reducing unnecessary water withdrawals leaves more water in reservoirs and aquifers for future use and has tangible benefits to fish and other wildlife in our rivers and estuaries. In addition, improving water-use efficiency and reducing waste can save energy, lower water and wastewater treatment costs, and eliminate the need for costly new infrastructure
Incentive-Based Instruments for Water Management
This report provides a synthesis review of a set of incentive-based instruments that have been employed to varying degrees around the world. It is part of an effort by The Rockefeller Foundation to improve understanding of both the potential of these instruments and their limitations. The report is divided into five sections. Section 1 provides an introduction to the synthesis review. Section 2 describes the research methodology. Section 3 provides background on policy instruments and detail on three incentive-based instruments -- water trading, payment for ecosystem services, and water quality trading -- describing the application of each, including their environmental, economic, and social performances, and the conditions needed for their implementation. Section 4 highlights the role of the private sector in implementing these instruments, and Section 5 provides a summary and conclusions
Policy Options for Water Management in the Verde Valley, Arizona (Executive Summary)
The water of the Verde Valley, both in the ground and flowing at the surface, is a natural resource that is critical to the regional economy, environmental sustainability, and quality of life -- but the Verde River faces unprecedented threats from over-allocation, development, and lack of cohesive water management. This report presents the results of three related initiatives designed to examine possible futures for the Verde and provides information for stakeholders and decision makers regarding the Verde Valley's water resources, its economic value, and possible tools for sustainable water management.Our analysis included modeling the effects of growth on river flows and on the regional economy. Population growth and development in the basin, if not mitigated, are likely to cause further decrease in the summer base flow in the Verde River. Decreases in the Verde River's flow have already been observed, and further reductions could have harmful side effects on the region's economy and could lead to federal intervention in local water management to maintain habitat for endangered species
Assessing Socio-Economic Vulnerability to Climate Change in California
Assessing the Local Impacts of Climate Chang
mheberger/delineator: 1.1
Version 1.1 fixes a bug that occurred rarely where the script created a shapely MultiPolygon instead of the expected Polygon
Amélioration de l'observation du cycle de l'eau à l'échelle globale grâce à la télédétection par satellite et à la modélisation par réseaux de neurones
Satellite remote sensing is commonly used to observe the hydrologic cycle at spatial scales ranging from river basins to the globe. Yet, it remains difficult to obtain a balanced water budget using remote sensing data, which highlights the errors and uncertainties in earth observation (EO) data. This research aimed to improve estimates of precipitation, evapotranspiration, runoff, and total water storage change at the global scale using a combination of analytical methods (optimal interpolation, OI) and statistical modeling methods including neural networks (NN). Models were trained on a set of 1,358~river basins and validated them on an independent set of 340~basins and in-situ observations of precipitation, evapotranspiration, and river discharge. The models are extended to make pixel-scale predictions in 0.5° grid cells for near-global coverage. Calibrated datasets result in lower water budget residuals in validation basins: the mean and standard deviation of the imbalance is 11±44 mm/mo when calculated with uncorrected EO data and 0.03±24 mm/mo after calibration by the NN models. The results allow us to make more accurate estimates of missing water cycle components, for example to estimate evapotranspiration in un-instrumented areas, or to predict discharge in ungaged basins. The results can also indicate to data producers where their products seem incoherent with other datasets and where enhanced calibration could lead to improvements. Finally, this research demonstrates the use of neural networks and machine learning for the integration of satellite data and for the study of the water cycle.La télédétection par satellite est couramment utilisée pour observer le cycle hydrologique à des échelles spatiales allant des bassins fluviaux au globe terrestre. Pourtant, il reste difficile d'obtenir un bilan hydrique équilibré en utilisant des données de télédétection, ce qui met en évidence les erreurs et les incertitudes des données d'observation de la Terre. Cette recherche visait à améliorer les estimations des précipitations, de l'évapotranspiration, de l'écoulement, et du changement du stockage total de l'eau à l'échelle mondiale en utilisant une combinaison de méthodes analytiques (interpolation optimale, OI) et de méthodes de modélisation statistique, y compris les réseaux neuronaux (NN). Les modèles ont été entraînés sur un ensemble de 1 358 bassins fluviaux et validés sur un ensemble indépendant de 340 bassins et sur des observations in situ des précipitations, de l'évapotranspiration et du débit des cours d'eau. Les modèles sont étendus pour faire des prévisions à l'échelle du pixel dans des cellules de grille de 0,5° pour une couverture quasi mondiale. Les ensembles de données calibrées donnent des résidus de bilan hydrique plus faibles dans les bassins de validation : la moyenne et l'écart-type du déséquilibre sont de 11±44 mm/mo lorsqu'ils sont calculés avec des données non corrigées et de 0,03±24 mm/mo après calibrage par les modèles NN. Les résultats nous permettent de faire des estimations plus précises des composantes manquantes du cycle de l'eau, par exemple pour estimer l'évapotranspiration dans les zones non instrumentées, ou pour prédire le débit dans les bassins non jaugés. Les résultats peuvent également indiquer aux producteurs de données où leurs produits semblent incohérents par rapport à d'autres ensembles de données et où un étalonnage plus poussé pourrait apporter des améliorations. Enfin, cette recherche démontre l'utilisation des réseaux neuronaux et de l'intelligence artificielle pour l'intégration des données satellitaires et pour l'étude du cycle de l'eau
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agencies”). It does not necessarily represent the views of the funding agencies, their respective officers, agents and employees, or the State of California. The funding agencies, the State of California, and their respective officers, employees, agents, contractors, and subcontractors make no warrant, express or implied, and assume no responsibility or liability for the results of any actions taken or other information developed based on this paper; nor does any party represent that the uses of this information will not infringe upon privately owned rights. This paper is being made available for informational purposes only and has not been approved or disapproved by the funding agencies, nor have the funding agencies passed upon the accuracy, currency, completeness, or adequacy of the information in this paper. Users of this paper agree by their use to hold blameless each of the funding agencies for any liability associated with its use in any form. This work shall not be used to assess actual coastal hazards, insurance requirements or property values, and specifically shall not be used in lieu of Flood Insurance Studies and Flood Insurance Rate Maps issued by the Federal Emergency Management Agency (FEMA). FINAL PAPE
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The Impacts of Sea Level Rise on the San Francisco Bay
Over the past century, sea level has risen nearly eight inches along the California coast, and general circulation model scenarios suggest very substantial increases in sea level as a significant impact of climate change over the coming century. This study includes a detailed analysis of the current population, infrastructure, and property along the San Francisco Bay that are at risk from projected sea level rise if no actions are taken to protect the coast. The sea level rise scenario was developed by the State of California from medium to high greenhouse gas emissions scenarios from the Intergovernmental Panel on Climate Change but does not reflect the worst‐case sea level rise that could occur. If development continues in the areas at risk, all of these estimates will rise. No matter what policies are implemented in the future, sea level rise will inevitably change the character of the San Francisco Bay.
We estimate that a 1.0 meter (m) sea level rise will put 220,000 people at risk of a 100‐year flood event, given today’s population. With a 1.4 m increase in sea levels, the number of people at risk of a 100‐year flood event would rise to 270,000. Among those affected are large numbers of low‐ income people and communities of color, which are especially vulnerable. Critical infrastructure, such as roads, hospitals, schools, emergency facilities, wastewater treatment plants, power plants, and more will be at increased risk of inundation, as will vast areas of wetlands and other natural ecosystems. In addition, the cost of replacing property at risk of coastal flooding with a 1.0 m rise in sea levels is 62 billion (in year 2000 dollars). Continued development in vulnerable areas will put additional areas at risk and raise protection costs. A number of structural and non‐structural policies and actions, which are described qualitatively, could be implemented to reduce these risks