Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Modelling the Potential Distribution of <i>Vitellaria paradoxa</i>subsp. <i>nilotica</i>(C.F. Gaertn) across the Kidepo Landscape of Uganda in the Face of Climate Change

Climate change and human activities are increasingly linked with the extinction of species globally. In semi-arid regions, these pressures threaten the natural distribution and ecology of species. The threat that the shea butter tree (Vitellaria paradoxa subsp. nilotica) faces from human activity is well researched yet the sensitivity of its distribution to climate change remains barely known. We set out to assess the potential distribution of Vitellaria under different climate change scenarios using a MaxEnt. A current distribution model was first developed using only biophysical variables of soil type, temperature, precipitation, land use type, and elevation. This model was then projected onto two global warming scenarios (RCP 4.5 & RCP 8.5) for 2050 and 2070 using multi-model averages (BCC-CSM, CSM4, and MIROC5) derived from three general circulation models. Reductions are seen in distribution area across the landscape with soil type being the most important variable. These results draw useful implications for conservation of Vitellaria in that they show how it is vulnerable is to a changing climate as its natural range is mostly reduced. Since climate change is important in the distribution of the shea butter tree, the areas with highest suitability in this study can be used in establishing the Shea butter tree sustainable use zones/area within the Kidepo Critical Landscape (KCL), Uganda.

GEOEssential–mainstreaming workflows from data sources to environment policy indicators with essential variables

When defining indicators on the environment,the use of existing initiatives should be a priority rather than redefining indicators each time.From an Information,Communication and Technology perspective,data interoperability and standardization are critical to improve data access and exchange as promoted by the Group on Earth Observations.GEOEssential is following an end-user driven approach by defining Essential Variables(EVs),as an intermediate value between environmental policy indicators and their appropriate data sources.From international to local scales,environmental policies and indicators are increasingly percolating down from the global to the local agendas.The scientific business processes for the generation of EVs and related indicators can be formalized in workflows specifying the necessary logical steps.To this aim,GEOEssential is developing a Virtual Laboratory the main objective of which is to instantiate conceptual workflows,which are stored in a dedicated knowledge base,generating executable workflows.To interpret and present the relevant outputs/results carried out by the different thematic workflows considered in GEOEssential(i.e.biodiversity,ecosystems,extractives,night light,and food-water-energy nexus),a Dashboard is built as a visual front-end.This is a valuable instrument to track progresses towards environmental policies.