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School of Geography

Lex Comber Prof Lex Comber

Contact details

Room 10.118, Irene Manton Building
School of Geography
University of Leeds
University Road
Leeds LS2 9JT   UK

Email:
a.comber

Telephone:
+44 113 343 9225 (internal: 39225)

Student hours:
My office hours are really varied this year. Please email me for an appointment.

Work in progress

NERC / Newton Fund: Modelling and Managing Critical Zone Relationships between Soil, Water and Ecosystem Processes Across the Loess Plateau (CI with Rothamsted)

This project on soil loss and erosion in the Loess Plateau of China in the upper and middle reaches of China's Yellow River develops multi-scale approaches to link collected environmental, biological and agronomic data collected by experiment, remote sensing data and modelling approaches.

The Leeds work focusses on a number of areas

1. Land Use classification and change analysis over the whole Loess Plateau.

This will generate maps of land use for each year over the period of analysis (e.g. 2000 to 2015). This will extend a recently developed method for developing time series analyses of MODIS Enhanced Vegetation Index (EVI) data (Tsutsumida et al., 2016; Tsutsumida & Comber, 2015), will generate spatially distributed measures of error and uncertainty for each class and allow land use change to be determined. MODIS has a 250m pixel size and the methodology will allow sub-pixel proportions of individual land use classes to be determined. That is each pixel will not be allocated to one class.

2. Inferring run off, erosion and land degradation at the sub-catchment level land.

The time series land use data will be extracted for subsets of the study area (e.g. sub-catchment, watershed or ecological zone) that the project has a focus on. In these, the locations of specific land use changes associated with degradation related processes will be identified. Specific land use changes are associated with changes in runoff, sediment loss and erosion over specific time frames. For example, areas that have undergone land use conversions from Cropland / Arable to Trees. The specific land use changes will be informed by the nature and location of the land use processes, measurements being examined by WP1 and WP2. The changes in runoff etc., related to land use change from the analysis of the MODIS remote sensing data will be compared to the changes suggested by the models being extensively developed in WP1 and WP2 and used to calibrate the sub-catchment scale models.

3. Large scale modelling of run off, erosion and land degradation.

The calibrated sub-catchment remote sensing models will allow a whole study area model to be developed from remote sensing data. The calibration processes in (2) above will ensure that the remote sensing land use change models reliably predict runoff in the study area sub-sets (sub-catchments etc.). A large scale model, covering the entire study area, will then be developed that infers runoff, etc., in relation to land use change across the whole study area including in previously un-sampled locations. These locations areas will be validated in order to validate the changes in runoff etc., suggested by the model.

Start: January 2016 Duration: 3 Years

 

SARIC (BBSRC / NERC / ESRC): Real-time predictions of pesticide run-off risk which: multi-scale visualisations of water quality risks and costs (PI with Cranfield University, Rothamsted Research, University of Reading (Agrimetrics), Bangor University)

This Research Translation Project develops a proof of concept to tests the value of real-time predictions of agro-chemical run-off risk at two scales of decision making: field scale for on farm decisions about agro-chemical applications risk and catchment scale for water company groundwater abstraction decisions.

Agro-chemicals (fertilisers, pesticides, herbicides, etc) are less effective if they are washed away soon after they are applied. They can also negatively affect ground water quality and the environment. The farmer may have to re-apply the agro-chemical and water companies may have treat groundwater to meet drinking water quality standards, and in some cases change water abstraction locations. For both farmers and water companies additional costs are incurred.

This project develops proofs of concept for 2 web-mapping tools to model the risk associated with agro-chemical applications: a catchment-scale tool to support water company decision making and a field-scale tool to support farmer decision making. Both tools combine live, real-time data from the Met Office on rainfall type and probability with landscape models of underlying soil, landform, drainage, land use etc. in order to model agro-chemical runoff risk. User-groups will feedback their experiences about the operational use and functionality of the tools to provide information for the modelling and programming teams to adjust the background engine and front-end functionality.

The project outputs will include the specification of for national decision tools, targeted at farmers and water companies, to quantify the risks associated with a full set of common agro-chemical applications designed be accessed using desktop PCs and smartphones.

 

Start: January 2017 Duration: 19 months