DRiSL: The Drought Risk finance Science Laboratory

DRiSL：干旱风险金融科学实验室

• 批准号: NE/R014272/1
• 负责人: Martin Todd
• 金额: $ 47.07万
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR014272%2F1
• 关键词: DRiSL; Drought; Risk; finance; Science

Whether on television, newspapers, the internet or first-hand we have all seen the damage that floods, droughts and other weather hazards can have on people's lives and their livelihoods. It is a sad fact that such hazard events disproportionately impact developing countries and poor people. However it is also increasingly evident that acting before a disaster occurs can save lives. For example, frontline humanitarian organisations and government agencies can themselves prepare by getting supplies and staff in readiness. More importantly, agencies can directly help the population prepare so that the impacts of a hazard are actually much reduced. Such actions depend on the lead time of a forecast but can range for example from distributing money, drought-resistant seeds, animal fodder to communities to ensuring evacuation procedures are followed. Acting before an event means they can also do this at a lower cost than the traditional 'late' post disaster humanitarian response. As a result there is growing momentum within the humanitarian system to move beyond the current 'begging bowl' funding model of post-disaster appeals, towards obtaining and distributing humanitarian funds before a disaster occurs. This change can enable humanitarians to mobilise more collaboratively, more predictably, and importantly in anticipation of crises. For this to occur requires trustworthy forecasts of hazards like storms, floods and droughts, and credible information on the condition of the people and systems exposed to them. Forecast based financing and Disaster Risk financing initiatives, utilise information to anticipate potential disasters and set pre-agreed triggers for the release of disaster prevention finance. The advantage of this approach is that it is data-driven and objective. It thereby circumvents long debates around potentially conflicting early warning signs which tend to paralyse humanitarian action. It puts in place a robust predictable process to release funding or initiate action before a disaster occurs. Humanitarian agencies working on developing these systems face a problem, however. They are not scientists nor social scientists; but they need to use information from both realms of research to trigger the systems and have confidence in this information.They also must be accountable to the people that the system looks to support and the donors that finance it. The START Network Drought financing facility (DFF) and the Weithungerhilfe (WHH) Madagascar Forecast based financing project are both at this juncture of selection and development of scientific data to apply to these initiatives. The DFF having begun the design with a Global Parametric model and have a prototype model that requires testing and evaluation, whereas the WHH Madagascar Forecast based financing project is starting out from the beginning. However, currently no process, independent honest broker, or method to provide an independent review of the scientific (science and social science) credibility of these systems exists in an operational context. This is a stumbling point in the adoption of these ground breaking initiatives by other organisations.This project looks to meet the needs of humanitarian agencies. In particular it will provide "scientific due diligence" to the forecast and action components of these proactive schemes and hence ensure that the information going into them is as trustworthy as possible. It will assess a suit of global drought models in regard to their uncertainty and ability to depict emerging food security crisis. Global data products will be explored alongside data on the ground of drought and food security events in the three test sites associated of Pakistan, Zimbabwe and Madagascar. It will help the humanitarian practitioners understand the limitations of the science for decision making and the fundamental risk of acting proactively when acting with forecast and monitoring information.

无论是在电视、报纸、互联网上还是在第一手资料中，我们都看到了洪水、干旱和其他天气灾害对人们生活和生计造成的破坏。令人悲哀的是，这种灾害事件对发展中国家和穷人的影响尤为严重。然而，越来越明显的是，在灾害发生之前采取行动可以挽救生命。例如，前线人道主义组织和政府机构可以自己准备物资和工作人员。更重要的是，各机构可以直接帮助民众做好准备，从而实际上大大减少灾害的影响。这些行动取决于预报的准备时间，但范围可以从向社区分发资金、抗旱种子、动物饲料到确保遵循疏散程序。在事件发生前采取行动意味着他们也可以以比传统的“后期”灾后人道主义反应更低的成本采取行动。因此，人道主义系统内的势头越来越大，要超越目前灾后呼吁的“乞讨碗”供资模式，争取在灾害发生前获得和分发人道主义资金。这一变化可以使人道主义者在预测危机时更加协作，更可预测，更重要的是。要做到这一点，就需要对风暴、洪水和干旱等灾害进行可靠的预测，并提供关于遭受灾害的人和系统状况的可靠信息。基于预测的融资和灾害风险融资计划，利用信息预测潜在的灾害，并设定预先商定的发放防灾资金的触发点。这种方法的优点是数据驱动和客观。因此，它避免了围绕可能相互冲突的预警信号进行的长时间辩论，而这些信号往往会使人道主义行动陷于瘫痪。它建立了一个强有力的可预测程序，以便在灾害发生前发放资金或采取行动。然而，致力于开发这些系统的人道主义机构面临着一个问题。他们不是科学家，也不是社会科学家;但他们需要利用来自两个研究领域的信息来启动系统，并对这些信息有信心。他们还必须对系统希望支持的人和资助它的捐助者负责。START网络干旱融资基金（DFF）和Weithungerhilfe（WHH）马达加斯加预测为基础的融资项目都在选择和发展的科学数据，以适用于这些举措的这一时刻。DFF已经开始设计全球参数模型，并有一个原型模型，需要测试和评估，而WHH马达加斯加预测为基础的融资项目正在从一开始就开始。然而，目前没有任何程序、独立的诚实经纪人或方法来对这些系统的科学（科学和社会科学）可信度进行独立审查。这是其他组织采用这些开创性举措的一个绊脚石。该项目旨在满足人道主义机构的需求。特别是，它将为这些主动计划的预测和行动部分提供“科学的尽职调查”，从而确保进入这些计划的信息尽可能可靠。它将评估一套全球干旱模型的不确定性和描述新出现的粮食安全危机的能力。全球数据产品将与巴基斯坦、津巴布韦和马达加斯加三个相关试验场的干旱和粮食安全事件的实地数据一起加以探讨。它将帮助人道主义工作者了解决策科学的局限性，以及在利用预测和监测信息采取行动时采取主动行动的基本风险。

项目成果

The El Niño event of 2015–16: Climate anomalies and their impact on groundwater resources in East and Southern Africa

2015-16年厄尔尼诺事件：气候异常及其对东部和南部非洲地下水资源的影响

• DOI: 10.5194/hess-2018-516
• 发表时间: 2018
• 期刊: Journal of Geophysical Research
• 作者: S. Kolusu;M. Shamsudduha;M. Todd;R. Taylor;D. Seddon;J. Kashaigili;G. Ebrahim;M. Cuthbert;J. Sorensen;K. Villholth;A. MacDonald;D. MacLeod
• 通讯作者: D. MacLeod

Advancing operational flood forecasting, early warning and risk management with new emerging science: Gaps, opportunities and barriers in Kenya

• DOI: 10.1111/jfr3.12884
• 发表时间: 2023-03
• 期刊: Journal of Flood Risk Management
• 影响因子: 4.1
• 作者: Augustine Kiptum;E. Mwangi;George Otieno;A. Njogu;M. Kilavi;Zacharia Mwai;D. MacLeod;J. Neal;Laurence Hawker;T. O'Shea;Halima Saado;E. Visman;Bernard Majani;M. Todd

Direct and indirect seasonal rainfall forecasts for East Africa using global dynamical models

使用全球动态模型对东非进行直接和间接季节性降雨预测

• DOI: 10.1002/joc.6260
• 发表时间: 2019
• 期刊: International Journal of Climatology
• 作者: Colman A

Evaluation and validation of TAMSAT -ALERT soil moisture and WRSI for use in drought anticipatory action

TAMSAT -ALERT 土壤湿度和 WRSI 用于干旱预测行动的评估和验证

• DOI: 10.1002/met.1959
• 发表时间: 2020
• 期刊: Meteorological Applications
• 影响因子: 2.7
• 作者: Boult V

TAMSAT-ALERT v1: A new framework for agricultural decision support

TAMSAT-ALERT v1：农业决策支持的新框架

• DOI: 10.5194/gmd-2017-316
• 发表时间: 2018
• 作者: Asfaw D