An integrated water resources, climate and land use changes model for Shannon River catchment

Abstract

The main goal of this research is to create an innovative and integrated environmental modelling framework and tool by applying Machine Learning Techniques to a Geographic Information system (GIS). This will allow the scientific community, modellers, planners and decision makers to study the impact of climate and land use changes on water resources on the large catchment scale. The main simulation steps for this tool arc: Climate change modelling and projection. -- Land use scenarios modelling and projection. -- Hydrological system simplitying algorithm and long-term variation analysis for historical data such as water level and streamflow. -- Future dynamical spatially distributed water balance projection. -- Using Machine Learning Techniques to model and predict water level and streamflow regime. The developed tool uses as test and validation case the Shannon River catchment in Ireland. In this research, climate change projections for the Shannon River arc simulated and presented using a Geographical Spatially Distributed Water Balance Model (GEO-CWB) for several climate variables (temperature, precipitation, wind speed, solar radiation and relative humidity) from multi-GCM ensembles for three future time intervals (2020, 2050, and 2080) using a range of different Representative Concentration Pathways (RCPs). This research presents the innovative use of GIS as the climate change downscaling environment, which is unprecedented in literature.