Modelling and managing critical zone relationships between soil, water and ecosystem processes across the Loess Plateau
Lead Research Organisation:
University of Leeds
Department Name: Sch of Geography
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Planned Impact
The primary impact will be the coupled benefits of environmental sustainability and the promotion of economic development and social welfare in the Loess region. This will be achieved through better understanding the relationships between the soil and water processes and agro-ecosystem services, including grain production, net primary production, carbon sequestration, water retention and soil erosion control. This research will provide guidance on practices and managements for sustainable ecosystem services in the fragile region, which will secure food production and save water resources in the arid Loess Plateau. Farmers in the area will receive guidance, enabling them to make informed decisions to adopt optimal agricultural and water management practises, thus increasing income by maintaining or increasing agricultural production. Likewise, environmental agencies and other organisations will have the tools to enable more accurate estimation of water balance, enabling stable management of ecosystems services.
Organisations
People |
ORCID iD |
| Alexis Comber (Principal Investigator) |
Publications
Ren Y
(2019)
Spatially explicit simulation of land use/land cover changes: Current coverage and future prospects
in Earth-Science Reviews
Comber A
(2018)
Distance metric choice can both reduce and induce collinearity in geographically weighted regression
in Environment and Planning B: Urban Analytics and City Science
Tsutsumida N
(2019)
Investigating spatial error structures in continuous raster data
in International Journal of Applied Earth Observation and Geoinformation
Comber A
(2018)
Geographically weighted elastic net logistic regression
in Journal of Geographical Systems
Comber A
(2018)
Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization
in Journal of Spatial Information Science
Luo Y
(2019)
When multi-functional landscape meets Critical Zone science: advancing multi-disciplinary research for sustainable human well-being.
in National science review
Hu J
(2017)
Quantifying the effect of ecological restoration on runoff and sediment yields A meta-analysis for the Loess Plateau of China
in Progress in Physical Geography: Earth and Environment
Xu R
(2018)
A Modified Change Vector Approach for Quantifying Land Cover Change
in Remote Sensing
Li T
(2017)
Gauging policy-driven large-scale vegetation restoration programmes under a changing environment: Their effectiveness and socio-economic relationships.
in The Science of the total environment
| Description | This research has developed methods and frameworks for sustainable land management, particularly in the context of competing pressures on land use (food, biodiversity, energy, flood defence etc). The nature of these findings are shown in the associated publications. We are in the process now of extending this work to develop decision support tools for end users (from field to policy). |
| Exploitation Route | Successful science often goes in directions that are unanticipated by research proposals. It is shame funding does not recognise this. |
| Sectors | Agriculture, Food and Drink,Energy,Transport |
| Title | Research methods for the use of Geographically weighted models for environmental research |
| Description | GWR in R workshop Lex Comber1 and Paul Harris2 1 School of Geography, University of Leeds, Leeds, LS2 9JT, UK Email: a.comber@leeds.ac.uk 2 Rothamsted Research, North Wyke, EX20 2SB, UK Email: paul.harris@rothamsted.ac.uk This workshop will describe how to apply Geographically Weighted Regression (GWR) as described in Brunsdon et al (1996) and Fotheringham et al (2002) to spatial data, using R and RStudio, the free open source statistical software. It will use the sub-catchment soils data for the Liudaogou watershed, as described in Wang et al (2009). The workshop aims are to get you using R / RStudio and this document describes the preparations you need to make before coming to the workshop. For the workshop you will need: 1) A computer with R and RStudio installed: R can be downloaded from here: https://cran.r-project.org and then you should download RStudio from here https://www.rstudio.com/products/rstudio/download2/. RStudio has R as its engine and has a nicer interface than R 2) To have gone through and run the code in the Owen guide (up to page 28) https://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf 3) You will need to have an internet connection - this is really important! 4) If you can, please bring your own data to explore using GWR. This should be a spatial dataset of some kind: a CSV or EXCEL file with latitude and longitude or a shapefile from ArcGIS are probably the most common formats. We can provide data if you do not have any. References Brunsdon, C.F., Fotheringham, A.S. and Charlton M. (1996). Geographically Weighted Regression - A Method for Exploring Spatial Non-Stationarity, Geographic Analysis, 28: 281-298. Fotheringham, A. S., C. Brunsdon, and M. Charlton. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: Wiley Wang, Y., Zhang, X. and Huang, C., 2009. Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China. Geoderma, 150(1), pp.141-149. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Impact | We trained ~50 workshop attendees, all post grads in the use of advanced geocomputational methods, making all the materials available online to them and others |
| URL | https://github.com/lexcomber/CAS_GW_Training |