IndicatoRs to Impacts for drought Surveillance and management (IRIS)

干旱是一种复杂、演变缓慢且代价高昂的自然灾害。检测它们的发生和跟踪它们的发展可能很困难，因为它们在水循环中传播。尽管英国通常是一个潮湿的国家，但最近在2012年和2018年发生的干旱对供水、农业和环境产生了重大影响。对未来气候变化的预测表明，在未来几十年里，这些变化将变得更加频繁和严重。因此，我们迫切需要对干旱有更强的适应能力和更好的准备，无论是现在还是在一个迅速变暖的世界中。干旱监测和预警是有效干旱管理的重要组成部分，但由于干旱定义方面的挑战、在干旱影响非常严重之前确定干旱影响的困难以及使用干旱监测和预警信息的广泛决策者的不同需求，这一点变得更加复杂。在英国，干旱研究在过去十年中取得了长足的进步，这要归功于由英国生态与水文学中心(UKCEH)领导的GB 1250万NERC干旱和水资源稀缺计划(DWSP)，以及英国生态与水文学中心大力参与的其他相关国际研究项目。这导致在了解干旱和开发干旱监测工具方面取得了重大进展。这包括提供实时水文气象数据和干旱指标的英国水资源门户网站。然而，这样的干旱监测工具通常缺乏关于干旱影响的信息--尽管这是决策者采取行动所需的最重要的证据，正如我们与来自供水、农业、卫生、能源和环境等部门的关键利益攸关方所做的工作所强调的那样。一段时间以来，了解MEW系统中常用的干旱指标(即描述物理干旱危害的指标，例如降雨或河流流量)与地面上看到的干旱影响之间的联系一直是干旱科学家的重点。然而，由于收集和记录干旱影响的挑战，分析通常是在大空间尺度上进行的(例如，对整个威尔士而言)，这与倾向于在从田野到集水区的更多局部尺度上管理水的决策者无关。在IRIS项目中，我们建议通过使用新的高分辨率干旱指标和干旱影响数据集来以高空间分辨率预测干旱影响，来解决这个空间尺度问题。我们将通过三个综合工作包(WPS)来实现这一点：WP1将专注于从多个来源收集干旱指标和干旱影响的数据，包括以前所未有的高分辨率为英国提供的新作物产量数据。我们还将使用近年来获得的高分辨率遥感数据，如哨兵2号，以得出干旱影响的替代指标(例如，植被指数和野火)。然后，WP2将使用WP1中收集的数据来确定干旱指标和干旱影响之间的关系，并利用统计和机器学习方法建立定量描述这些关系的“影响函数”。然后，这些关系可用于使用近乎实时的现成指标来预测潜在的干旱影响。在WP3中，我们将与主要利益攸关方合作，制定案例研究，评估影响预测是否有足够的技能用于管理干旱和减轻影响。该项目的结果和成果有可能通过未来的筹资机会扩大到全国范围的干旱影响预报系统。基于影响的干旱预测能力将彻底改变英国管理和缓解干旱的方式，并将有巨大的潜力转移到其他国家和环境。

Droughts are complex, slow-evolving and costly natural hazards. Detecting their onset and tracking their development can be hard, as they spread through the water cycle. Although the UK is stereotypically a wet country, recent droughts in 2012 and 2018 had significant impacts on water supplies, agriculture and the environment. Projected changes to future climate suggests that they will become more frequent and severe in the coming decades. We therefore need to urgently be more resilient to and better prepared for droughts, both now and in a rapidly warming world. Drought Monitoring and Early Warning (MEW) is an important part of effective drought management, but this is complicated by the challenges in defining drought, the difficulties in identifying drought impacts before they are very severe, and the diverse needs of the wide range of decision makers that use drought MEW information. In the UK, drought research has advanced substantially over the past decade thanks to the £12.5m NERC Drought and Water Scarcity Programme (DWSP) led by the UK Centre for Ecology & Hydrology (UKCEH), as well as other allied international research projects in which UKCEH was heavily involved. This has led to significant progress in the understanding of droughts and the development of drought MEW tools. This includes the UK Water Resources Portal, which provides real-time hydro-meteorological data and drought indicators. However, drought monitoring tools like this typically lack information on drought impacts - despite the fact that this is the single most important piece of evidence required by decision makers to take actions, as has been highlighted in work we have done with key stakeholders from sectors including water supply, agriculture, health, energy and the environment. Understanding the link between drought indicators commonly used in MEW systems (i.e. that describe the physical drought hazard, e.g. in terms of rainfall or river flows) with the drought impacts seen on the ground has been the focus of drought scientists for some time. However, due to the challenges of collecting and recording drought impacts, the analysis has generally been carried out at large spatial scales (e.g. for Wales as a whole), which are not relevant to decision makers who tend to manage water at more local scales, from field to catchment scale. In the IRIS project, we propose to address this issue of spatial scale by using new high resolution drought indicators and drought impact datasets to predict drought impacts at a high spatial resolution. We will do this across three integrated Work Packages (WPs): WP1 will focus on gathering data from multiple sources of both drought indicators and drought impacts, including new crop yield data at an unprecedentedly high resolution for the UK. We will also use high resolution remote sensing data which have become available in recent years such as Sentinel-2 to derive proxies for drought impacts (e.g., vegetation indices and wildfires). WP2 will then use the data gathered in WP1 to identify the relationships between drought indicators and drought impacts, and build 'impact functions' that describe these relationships quantitatively, using statistical and machine learning approaches. These relationships can then be used to forecast potential drought impacts using indicators that are readily available in near-real-time. In WP3 we will work with key stakeholders to develop case studies and assess whether the impact forecasts have sufficient skill to be used to manage droughts and mitigate impacts. The findings and outcomes of this project have the potential to be scaled up into a nation-wide drought impact forecasting system through future funding opportunities. An impact-based drought forecasting capability would revolutionise the way droughts are managed and mitigated in the UK, and would have huge potential for transferring to other countries and environments.