To assess climate change impact on the hydrology of Izmit Bay, a coupled model chain using the results of four combinations of Global Climate Models (GCMs) and Regional Climate Models (RCMs) and consisting two hydrological models (mGROWA and PROMET) and one hydrodynamic model (MIKE 3HD) was established. Climate model data of the 4 GCM-RCM combinations were applied to both hydrological models. The resulting 8 streamflow data of the hydrological models were then applied to the MIKE 3HD to assess possible hydrodynamic situations in Izmit Bay. Related model results indicate a range of possible future streamflow regimes suitable for the analysis of climate change impact on Izmit Bay. In order to evaluate the effects of the hydrological changes only on the bay, the bay was considered as closed in terms of hydrodynamics. There is a clear indication that the climate change induced impacts on streamflow may influence the sea level in the Bay to a minor extent. However, climate change induced water exchange processes in the Bay may have a much bigger influence. Hence, it is suggested that further simulations should be run once the hydrologic regime of the Marmara Sea has been assessed in a broader macro-scale study.
The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, spatially distributed information on snow is potentially available, but often lacks the required spatial or temporal requirements for hydrological applications. For the assurance of a high spatial and temporal resolution, however, it is often necessary to combine several methods like Earth Observation (EO), modelling and in situ approaches. Such a combination was targeted within the business applications demonstration project SnowSense (2015–2018), co-funded by the European Space Agency (ESA), where we designed, developed and demonstrated an operational snow hydrological service. During the run-time of the project, the entire service was demonstrated for the island of Newfoundland, Canada. The SnowSense service, developed during the demonstration project, is based on three pillars, including (i) newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS); (ii) EO snow cover products on the snow cover extent and on information whether the snow is dry or wet; and (iii) an integrated physically based hydrological model. The key element of the service is the novel GNSS based in situ sensor, using two static low-cost antennas with one being mounted on the ground and the other one above the snow cover. This sensor setup enables retrieving the snow parameters SWE and liquid water content (LWC) in the snowpack in parallel, using GNSS carrier phase measurements and signal strength information. With the combined approach of the SnowSense service, it is possible to provide spatially distributed SWE to assess run-off and to provide relevant information for hydropower plant management in a high spatial and temporal resolution. This is particularly needed for so far non, or only sparsely equipped catchments in remote areas. We present the results and validation of (i) the GNSS in situ sensor setup for SWE and LWC measurements at the well-equipped study site Forêt Montmorency near Quebec, Canada and (ii) the entire combined in situ, EO and modelling SnowSense service resulting in assimilated SWE maps and run-off information for two different large catchments in Newfoundland, Canada.
An exact parameterization of vegetation is important for correct hydrological modeling since transpiration is a key variable in the water cycle. A data assimilation strategy, using spatially and temporally distributed vegetation parameters derived from Landsat satellite images to improve calculations of the hydrological model PROMET, is established and implemented by VISTA within the frame of the EU FP7 project CLIMB (Climate Induced Changes on the Hydrology of Mediterranean Basins) in the catchment of the river Noce (Italy). Also, a comparison of modeled snow cover and snow cover maps derived from satellite imagery shows advantages and disadvantages of assimilating snow cover maps from remote sensing in a hydrological model.
