Convective-Scale Impacts of Deforestation on Amazonian Rainfall

森林砍伐对亚马逊降雨的对流规模影响

• 批准号: NE/V012681/1
• 负责人: David Schultz
• 金额: $ 69.9万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV012681%2F1
• 关键词: Convective; Scale; Impacts; Deforestation; Amazonian

The Amazon rainforest contains 40% of all remaining tropical rainforest in the world, but has seen rapid deforestation since the 1960s, and as much as 40% of the Brazilian Amazon could be deforested by 2050. Land-use change is an important man-made driver of climate change. We know that deforestation will generally make the atmosphere both warmer and drier, but how these changes will affect rainfall is more complex. Climate models mostly predict that deforestation will reduce rainfall, but the amount varies from 0 to 60% across different studies. Climate models use grid boxes of 10s to 100s km, which are much larger than a typical cloud. While cloud properties can be estimated from the conditions in the grid box, calculating the amount of rainfall is very uncertain, especially in the tropics. One solution is to run a model with much smaller grid boxes, but focusing on a small region, so that clouds and the detailed deforestation patterns found in the Amazon can be represented explicitly. These studies show that the surface patterns alter local weather patterns, increasing rainfall over the deforested patches, which contradicts climate models. However, because these studies focus on smaller regions, we do not know if these local effects are important for the water cycle of the entire Amazon.This project will combine both approaches, using the increased computing power now available to simulate, for the first time, the entire Amazon basin while also explicitly representing clouds. This is a crucial improvement, because past studies have shown that resolving clouds leads to a complete change in model behaviour, greatly improving how tropical rainfall is represented, including climate extremes like flooding and droughts which have the most impact on local populations. We will use these simulations to investigate how increasing deforestation will affect rainfall over the Amazon, and how these changes compare to those caused by global climate change driven by increasing carbon dioxide levels. The project is particularly exciting because it will provide a comprehensive understanding of how deforestation affects rainfall, simulating both changes in regional climate and the local weather patterns within it which directly affect people. Tropical rainfall is a key area of research in climate modelling, because although it is the most important climatic parameter to end users, it is also the most uncertain. For example rainfall drives a number of economic sectors such as agriculture and hydroelectric power, and while deforestation is used to clear land for agriculture, reductions in rainfall could reduce the yield per hectare, negating any economic gain from increasing the agricultural area. Patterns of deforestation can also affect where it rains, which could help planners identify ways to mitigate some of the negative effects on the remaining forest. This project will engage with stakeholders in the region through workshops to improve our physical understanding in a targeted way to address global challenges which have direct relevance to many people.

亚马逊雨林占世界上所有热带雨林的40%，但自20世纪60年代以来，森林砍伐迅速，到2050年，巴西亚马逊雨林的40%可能会被砍伐。土地使用变化是气候变化的一个重要人为驱动因素。我们知道，森林砍伐通常会使大气变得更加温暖和干燥，但这些变化如何影响降雨则更为复杂。气候模型大多预测森林砍伐会减少降雨量，但不同的研究中，这一数字从0到60%不等。气候模型使用10到100公里的网格框，比典型的云大得多。虽然云的性质可以从网格框中的条件估计出来，但计算降雨量是非常不确定的，特别是在热带地区。一种解决方案是运行一个具有更小网格框的模型，但专注于一个小区域，以便可以显式地表示亚马逊地区的云和详细的森林砍伐模式。这些研究表明，地表模式改变了当地的天气模式，增加了森林砍伐区的降雨量，这与气候模型相矛盾。然而，由于这些研究集中在较小的区域，我们不知道这些局部影响对整个亚马逊的水循环是否重要。这个项目将联合收割机结合两种方法，利用现在增加的计算能力，首次模拟整个亚马逊流域，同时也显式地表示云。这是一个至关重要的改进，因为过去的研究表明，解决云导致模型行为的完全改变，大大改善了热带降雨的表现方式，包括对当地人口影响最大的洪水和干旱等极端气候。我们将使用这些模拟来研究森林砍伐的增加将如何影响亚马逊地区的降雨量，以及这些变化如何与二氧化碳水平增加导致的全球气候变化所造成的变化进行比较。该项目特别令人兴奋，因为它将全面了解森林砍伐如何影响降雨，模拟区域气候变化和直接影响人类的当地天气模式。热带降雨量是气候建模研究的一个关键领域，因为尽管它是对最终用户最重要的气候参数，但也是最不确定的。例如，降雨推动了农业和水力发电等一些经济部门的发展，虽然砍伐森林是为了开垦土地用于农业，但降雨量的减少可能会降低每公顷的产量，从而抵消了增加农业面积所带来的任何经济收益。森林砍伐的模式也会影响降雨的地方，这可以帮助规划者找到减轻对剩余森林的一些负面影响的方法。该项目将通过研讨会与该地区的利益相关者接触，以有针对性的方式提高我们的物理理解，以应对与许多人直接相关的全球挑战。

项目成果

Contrasting impacts of forests on cloud cover based on satellite observations.

• DOI: 10.1038/s41467-022-28161-7
• 发表时间: 2022-02-03
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Xu R;Li Y;Teuling AJ;Zhao L;Spracklen DV;Garcia-Carreras L;Meier R;Chen L;Zheng Y;Lin H;Fu B
• 通讯作者: Fu B

Deforestation and changes in rainfall across the Amazon - reducing uncertainty using a continental scale convection permitting domain

亚马逊地区的森林砍伐和降雨量变化 - 使用大陆范围的对流允许域减少不确定性

• DOI: 10.5194/egusphere-egu23-6107
• 发表时间: 2023
• 作者: Bassett R

Detection of Land-Use Change and Rapid Recovery of Vegetation after Deforestation in the Congo Basin

刚果盆地森林砍伐后土地利用变化和植被快速恢复的检测

• DOI: 10.1175/ei-d-22-0020.1
• 发表时间: 2023
• 期刊: Earth Interactions
• 影响因子: 2
• 作者: Adams C

Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data

使用 Landsat-8 和 PlanetScope 卫星星座数据的时间序列评估亚马逊森林排污和灾后恢复的程度

• DOI: 10.3390/rs15123196
• 发表时间: 2023
• 期刊: Remote Sensing
• 影响因子: 5
• 作者: Ping D