Modelling and managing critical zone relationships between soil, water and ecosystem processes across the Loess Plateau

黄土高原土壤、水和生态系统过程之间关键区域关系的建模和管理

• 批准号: NE/N007433/1
• 负责人: Lianhai Wu
• 金额: $ 51.22万
• 依托单位: Rothamsted Research
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FN007433%2F1
• 关键词: Modelling; managing; critical; zone; relationships

The Loess Plateau of China covers an area 2.5x the size of the UK (some 640,000 square km) in the upper and middle reaches of China's Yellow River and is renowned for having the most severe soil erosion in the world; deforestation, over-grazing and poor agricultural practice have resulted in degenerated ecosystems, desertification and unproductive agriculture in the region. To control severe soil erosion on the Loess Plateau, the Chinese government imposed a series of policies for fragile ecosystems, such as the 1999 state-funded "Grain-for-Green" project, which has resulted in significant land use changes. Related programmes have produced beneficial effects on soil erosion and water cycles. However, the impact of these changes in soil and water processes on related ecosystem services is unknown and demands further study. The proposed research will focus on three spatial scales: slope, watershed, and region. It uses a combination of A) experiments to collect environmental, biological and agronomic data; B) remote sensing data and C) modelling approaches. A) Data collection: Four experimental stations located in four main topographical regions of the Plateau are chosen as case studies: 1) Ansai Comprehensive Experimental Station of Soil and Water Conservation; 2) Changwu Agro-ecology Experiment Station; 3) Guyuan Ecological Station; 4) Shenmu Erosion and Environment Station. At each station, treatments of different vegetative covers, slopes, and the practices of soil and water conservation at the plot scale were set up in the 1980s and data collections include: soil water, canopy size, runoff, soil losses and meteorological records. Most of the Chinese members of this project have been involved in prior studies at the stations.At the slope scale, additional environmental, biological and agronomic data will be monitored in a sub-set of the plots. At watershed scale, four watersheds where the stations are located will be monitored. The spatial distribution of the following variables will be measured: precipitation, soil properties, vegetative types, canopy size, runoff and soil loss.B) Remote sensing data collection: At the regional scale, remote sensing combined with ground-truthing data will be used to investigate the spatial variability of vegetation type, land cover, productivity, the components of water balance, soil losses, soil type, etc.C) Modelling approaches: a cascade approach will be used to build an improved model framework applied to different spatial scales. Mechanistic soil-water-plant models will be applied to the slope scale. Their outputs will then be used as inputs for models at watershed level. Spatial empirical/statistical models will be used at the regional level. Observed and collected data from A) and B) will be used to further develop, calibrate and validate our models.Model simulations at the slope level will be used to reveal the dynamic mechanisms in soil and water in different regions and analyse the effects of vegetation type, soil type, slope degree, climatic factors and management practice. Watershed models will estimate soil and water carrying capacity for different vegetation types, predict the effect of land use/cover changes on soil losses, water cycle and ecosystem services and evaluate management scenarios in the practices of soil and water conservation, vegetative changes, and ecosystem services. The soil and water carrying capacity for different vegetation types and the optimal ecosystem services will be addressed at the regional scale. Outreach workshops and demonstrations will disseminate knowledge to farmers and policy makers.The proposed research will elucidate the coupled relationships between soil and water processes and agro-ecosystem services at various scales, and evaluate the effects of vegetation cover and changes in land use on water cycle, soil erosion, and ecosystem services across the Loess Plateau.

中国的黄土高原覆盖了中国黄河上游和中游英国大小（约640,000平方公里）的2.5倍面积，并以世界上最严重的土壤侵蚀而闻名；森林砍伐，过度放牧和不良的农业实践导致该地区退化的生态系统，荒漠化和非生产性农业。为了控制黄土高原上的严重土壤侵蚀，中国政府对脆弱的生态系统实施了一系列政策，例如1999年由国家资助的“谷物绿色”项目，这导致了重大的土地使用变化。相关计划对土壤侵蚀和水周期产生了有益的影响。但是，这些变化对土壤和水过程对相关生态系统服务的影响尚不清楚，需要进一步研究。拟议的研究将重点放在三个空间尺度上：坡度，流域和地区。它结合了A）实验来收集环境，生物学和农艺数据； b）遥感数据和c）建模方法。 a）数据收集：选择位于高原四个主要地形区域的四个实验站作为案例研究：1）ANSAI综合土壤和节水的实验站； 2）Changwu农业生态实验站； 3）鸟类生态站； 4）申木侵蚀和环境站。在每个站点，在1980年代建立了不同营养覆盖物，坡度和土壤和水保护的实践，数据收集包括：土壤水，冠层大小，径流，径流，土壤损失和气象记录。该项目的大多数中国成员都参与了站点的先前研究。在坡度量表中，将在该地块的子集中监视其他环境，生物学和农艺数据。在流域量表上，将监视四个分水岭。 The spatial distribution of the following variables will be measured: precipitation, soil properties, vegetative types, canopy size, runoff and soil loss.B) Remote sensing data collection: At the regional scale, remote sensing combined with ground-truthing data will be used to investigate the spatial variability of vegetation type, land cover, productivity, the components of water balance, soil losses, soil type, etc.C) Modelling approaches: a cascade approach will be used to build改进的模型框架应用于不同的空间尺度。机械土壤植物模型将应用于坡度尺度。然后，它们的输出将用作流域级别模型的输入。空间经验/统计模型将在区域级别使用。从a）和b）观察到的数据将用于进一步开发，校准和验证我们的模型。坡度的模拟将用于揭示不同地区土壤和水中的动态机制，并分析植被类型，土壤类型，坡度，坡度，气候因素和管理实践的影响。分水岭模型将估算不同植被类型的土壤和水的承载能力，预测土地使用/覆盖对土壤损失，水循环和生态系统服务的影响，并评估土壤和节水，营养变化以及生态系统服务实践中的管理方案。各种植被类型的土壤和水承载能力以及最佳生态系统服务将在区域范围内解决。推广讲习班和示威活动将向农民和政策制定者传播知识。拟议的研究将阐明各种规模的土壤与水过程与农业生态系统服务之间的耦合关系，并评估植被覆盖的影响以及土地使用对水周期，水土侵蚀，土壤侵蚀和生态系统服务的影响。

项目成果

Spatial prediction with categorical response variables

使用分类响应变量进行空间预测

• 发表时间: 2018
• 作者: Charlton M

The Importance of Scale and the MAUP for Robust Ecosystem Service Evaluations and Landscape Decisions

• DOI: 10.3390/land11030399
• 发表时间: 2022-03
• 期刊: Land
• 影响因子: 3.9
• 作者: A. Comber;P. Harris

Sociatal Geo-Innovation, 20th AGILE Conference Proceedings

社会地理创新，第 20 届 AGILE 会议论文集

• 发表时间: 2017
• 作者: Comber A

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

• DOI: 10.1111/tgis.12559
• 发表时间: 2019-10-01
• 期刊: TRANSACTIONS IN GIS
• 影响因子: 2.4
• 作者: Comber, Alexis;Wulder, Michael
• 通讯作者: Wulder, Michael

gwverse: A Template for a New Generic Geographically Weighted R Package

• DOI: 10.1111/gean.12337
• 发表时间: 2021-09
• 期刊: Geographical Analysis
• 影响因子: 3.6
• 作者: A. Comber;M. Callaghan;P. Harris;Binbin Lu;N. Malleson;C. Brunsdon