Towards a virtual observatory for ecosystem services and poverty alleviation

对于秘鲁亚马逊地区帕卡亚-萨米拉国家保护区的土著社区来说，海龟养殖是一种成功的生存策略。这种做法保持了自然动物种群的数量，并提供了必要的食物和收入来源。然而，这项活动的成功与否在很大程度上取决于不稳定的河流水位的信息，在关键时刻，河流可能会淹没筑巢的海滩。在厄瓜多尔亚马逊地区的亚苏尼国家公园，丛林狩猎区受到了森林砍伐的威胁。与此同时，在厄瓜多尔、秘鲁和玻利维亚的安第斯山脉源头，灌溉水的可用性和质量在很大程度上取决于山地湿地的管理，并受到全球气候变化的潜在威胁。在许多情况下，当地社区的生计依赖于关键的生态系统服务，这些都是突出的例子。然而，只有利用气候、水文和生态数据的先进整合，才能对这些服务进行可持续管理。由于种种原因，目前整合此类数据的方法在很大程度上失败了。在帕卡亚萨米拉，可获得的当地数据很少，导致模型预测的不确定性很大。在安第斯高地，当地的政治家和管理者在解释模型模拟和设计适当的土地管理方案方面存在困难。最后，两种系统都可以从本地专家知识的结合中获益，以减少模型的不确定性。最近，已经开发了许多用于数据集成和用户交互的方法。它们被称为“虚拟天文台”（VO）。VO的最终目标是数据、模型和用户的完美融合。在世界范围内，正在进行许多协调一致的活动，以使这种整合成为现实。然而，对这些发展如何有益于环境服务管理和减轻贫穷的问题却很少注意。本计划将利用现有的虚拟天文台工具，为上述个案研究设计及实施一个环境预测系统。下一步，我们将开发、实施和评估工具，以提高这些系统在减贫具体条件下的价值，即：(1)改进模拟的交流。这一行动将特别侧重于模拟结果的可视化及其不确定性；(2)评估收集数据的价值。在数据稀疏和资源受限的环境下，优化设计新的数据收集策略至关重要。在这里，我们将开发方法来模拟不同数据对模型预测的价值；(3)整合本地管理人员在建模系统方面的知识和实践。该模块处理用户界面的开发，以评估模型，识别模型故障和拒绝模型。在很大程度上依赖于公共领域软件、开放标准和现有的VO工作，我们将开发一个跨学科、成本效益高、高度定制的环境数据分析和模拟平台。这个平台将立即提供给选定的个案研究，从而能够采取直接的减轻贫穷行动，使大约15000名当地居民受益。与当地利益相关者的密切合作和现有计划的整合确保了平台的快速采用。例如，将被整合的项目合作伙伴CONDESAN的InfoAndina网站拥有1600多名注册用户。同时，该项目将在模型仿真、交流和改进方面产生新的科学见解。PI在全球虚拟天文台社区的参与将确保研究结果将最佳地有利于该领域正在进行的研究。

For the indigenous communities of the Pacaya-Samira National Reserve in the Peruvian Amazon, turtle farming is a successful survival strategy. The practice keeps the natural animal population numbers up and provides a necessary source of food and income. However, the success of the activity is highly dependent on information about the erratic river level, which may flood nesting beaches at crucial times. In the Yasuni national park in the Ecuadorian Amazon, bush meat hunting regions are threatened by encroaching deforestation. At the same time in the Andean headwaters of Ecuador, Peru and Bolivia, the availability and quality of irrigation water depends strongly on mountain wetland management, and is potentially threatened by global climate change. These are striking examples of many situations where the livelihoods of local communities depend on crucial ecosystem services. However, a sustainable management of these services is only possible using an advanced integration of climatic, hydrological, and ecological data. Current approaches to integrate such data have largely failed for a variety of reasons. In Pacaya Samira, little local data are available resulting in very large uncertainties in the model predictions. In the Andean highlands, local politicians and managers have difficulties interpreting model simulations and design proper land management schemes. Finally, both systems can benefit strongly from the incorporation of local expert knowledge to reduce model uncertainties. Recently, many methodologies for data integration and user interaction have been developed. They are known under the common umbrella of a 'virtual observatory' (VO). The ultimate goal of a VO is a perfect integration of data, models and users. Worldwide, many coordinated activities are ongoing to make this integration a reality. However, far less attention has been paid to the question of how these developments can benefit environmental services management and poverty alleviation. This project will design and implement an environmental prediction system for the above mentioned case studies, using existing virtual observatory tools. In a next step, we will develop, implement and evaluate tools to improve the value of these systems in the specific conditions of poverty alleviation, i.e., (1) Improved communication of simulations. This action will particularly focus on the visualisation of modelling results and their uncertainties; (2) Assessing the value of collected data. In a data sparse and resources constrained environment, an optimal design of new data collection strategies is essential. Here we will develop methods to simulate the value of different data on the model predictions; (3) Integrating local managers' knowledge and practice in modelling systems. This module deals with the development of a user interface to evaluate models, identify model failures and reject models. Heavily relying on public domain software, open standards and existing VO efforts, we will develop a platform for interdisciplinary, cost-efficient and highly tailored environmental data analysis and simulation. This platform will be available immediately for the selected case studies, thus enabling direct poverty alleviation action benefiting an estimated 15000 local inhabitants. Close collaboration with local stakeholders and integration in existing initiatives ensures a quick adoption of the platform. For instance, the InfoAndina website of project partner CONDESAN which will be integrated, has more than 1600 registered users. At the same time, the project will generate novel scientific insights in model simulation, communication and improvement in a developing context. The involvement of the PI in the global Virtual Observatory community will ensure that the research results will optimally benefit ongoing research in this area.