Catchment Risk Assessments using Multi-Scale Data (CARISMA)

CARISMA项目(使用多尺度数据进行流域风险评估)旨在通过整合来自社区领导的监测(水文学“公民科学”)的独立信息来源和遥感领域的最新发展，解决集水区水平衡评估中数据稀缺的问题。该项目专注于撒哈拉以南非洲的埃塞俄比亚和坦桑尼亚的两个研究地区，这两个地区的此类问题特别严重，但旨在开发一种更普遍适用的方法。许多集水区或河流流域由于过度开采、土地利用的变化或气候变化和变化而遭受水资源压力。水压力可能被认为是整个集水区的长期趋势，但更多的时候被视为季节性或多年性干旱等间歇性事件，可能在空间上位于某些河流支流或子集水区。因此，如果在正确的地点和正确的时间没有足够的数据，就很难确定水压力的根本原因，以及特定的用水群体如何为潜在的解决办法做出贡献。我们最近的研究表明，社区领导的监测是可行的，可以提供可信的关键水文信息，以增进对地表水和地下水资源的了解，而且多级治理办法是处理水管理政策的可行办法。我们的工作表明，数据共享平台(如“环境虚拟观测站”或“决策剧院”)有潜力帮助整合和呈现信息，并以各种方式支持各级决策，但它们的设计需要以用户为导向，以促进它们的采用。这些新出现的模式为更好的环境管理提供了新的机会，但需要参与性地发展开放和透明的系统，以便综合多种信息来源，以取得成功的成果。该项目旨在通过与埃塞俄比亚(阿巴伊河流域管理局)和坦桑尼亚(鲁菲吉河流域管理局)两个流域的合作伙伴和利益攸关方共同开发一个原型数据集成和展示平台，在适当的空间尺度上量化流域水平衡的主要水文指标，以便在水资源紧张的流域制定可持续的水资源管理政策和做法，从而在我们以前的研究基础上再接再厉。将使用参与式方法确定与代表社区、生态系统、企业和治理利益的利益攸关方群体相关的指标。拟议平台利用可公开获得的遥感数据对关键的水文组成部分进行空间评估，特别是降雨和蒸散，并利用社区主导的低成本地面变量监测(包括降雨量、河流水位和流量以及地下水位)，以补充现有的正式监测网络。将使用标准化的水核算模型框架对每个组成部分的不确定性如何有助于全面了解水平衡进行评估。这将有助于更好地量化集水区缺水的贡献者，为多方利益攸关方决策提供信息。了解减少来自不同数据源的不确定性将为指导适当设计监测网络提供基础，并为支持基于风险的水资源管理方法提供证据。原型数据平台的项目输出将为我们的合作伙伴世界自然基金会英国分会的战略迈出关键一步，该战略旨在建立开发和共享更好的水文数据的通用能力，以支持其全球活动，特别是通过其水管理计划。世界自然基金会坦桑尼亚分会对该项目的密切参与将为实现这一目标迈出切实的第一步。

The CARISMA project (Catchment Risk Assessments using Multi-Scale Data) aims to address issues of data scarcity in catchment water balance assessments through integrating independent sources of information from community-led monitoring (hydrological 'citizen science') and recent developments in remote sensing. The project focusses on two study areas in sub-Saharan Africa, in Ethiopia and Tanzania, where such problems are particularly acute, but aims to develop a more generally applicable methodology.Many catchments or river basins undergo water stress due to a combination of over-abstraction, changes in land-use, or climatic variability and changes. Water stresses may be felt as long-term trends over whole catchment areas, but more often are seen as episodic events such as seasonal or multi-annual drought, and may be spatially located in certain river tributaries or sub-catchments. It is then difficult to identify the underlying causes of water stress, and how particular groups of water users could contribute to potential solutions, if insufficient data are available in the right place and at the right time. Our recent research has demonstrated the viability of community-led monitoring to provide credible key hydrological information to improve understanding of surface and groundwater resources, and that multi-level governance approaches are a feasible way of addressing water management policy. Our work has shown that data-sharing platforms (such as 'Environmental Virtual Observatories' or 'Decision Theatres') have potential to help integrate and present information is ways that support decision making at all levels, but their design needs to be user-driven to facilitate their adoption. These emerging paradigms open up new opportunities for better environmental management, but require participatory development of open and transparent systems for integration of multiple sources of information to provide successful outcomes. This project aims to build on our previous research by co-developing with partners and stakeholders in two catchment in Ethiopia (Abay River Basin Authority) and Tanzania (Rufiji River Basin Authority) a prototype data integration and presentation platform that will quantify key hydrological indicators of catchment water balances at spatial scales appropriate to developing sustainable water management policies and practices in water-stressed catchments. Indicators relevant to stakeholder groups representing community, ecosystem, business, and governance interests will be identified using a participatory approach. The proposed platform uses publicly available remote sensing data for spatial assessments of key hydrological components, particularly rainfall and evapotranspiration, and community-led low-cost monitoring of ground-based variables (including rainfall, river levels and flows, and groundwater levels) to complement available formal monitoring networks. Evaluation of how uncertainty in each component can contribute to overall understanding of water balances will be assessed using a standardised water accounting modelling framework. This can then support better quantification of contributors to catchment water scarcity, to inform multi-stakeholder decision making. Understanding of uncertainty reduction from the different data sources will provide the basis for guidance on appropriate design of monitoring networks, and evidence to support a risk-based approach to water management.The project output of a prototype data platform will provide a key step towards our partner WWF-UK's strategy of working towards a generic capability for developing and sharing better hydrological data to underpin their global activities, particularly through their Water Stewardship Programme. Close involvement of WWF-Tanzania in this project will provide a tangible first step towards this goal.