Search Results (2)

Lakes, either artificial or natural, are greatly important as a component in their catchments’ hydrology, but also as ecosystem service providers. However, due to climate change, they have begun to face numerous problems with their water quality and quantity. Furthermore, general circulation models (GCMs) show future climate change with a reduction in rainfall and increase in temperature. The aim of the current study is to present an application where GCMs and state-of-the-art hydrological modelling system MIKE SHE/MIKE 11 are combined for assessing the response of a Greek lake in terms of its water balance and water level under climate change. Four general circulation models (GCMs; GFDL-CM3, MIROC-ESM-CHEM, MIROC-ESM, IPSL-CM5A-LR) for the extreme climate change scenario of RCP8.5 were used in the basin of Lake Zazari in Greece as a case study. Results showed that, by keeping the irrigated demands (the main water user) unchanged in the future, the lake exhibited a lower water level for all GCMs, fluctuating from −0.70 to −1.8 m for the mean (min) water level and from −0.30 to −1.20 m for the mean (max) water level. Instead of the above and by preserving the amount of withdraw water n from the lake at a certain percentage of inflows, the irrigated area should be reduced from 54.1% to 64.05% depending on the circulation model. Full article

The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale. Full article