At the global scale, there are two categories of hydrological models: Land Surface Models (LSMs) and Global Hydrological Models (GHMs). The LSMs focus on describing the vertical exchange of heat and water by solving the surface energy and water balance. These were originally developed by the atmospheric modelling community to simulate fluxes from the land to the atmosphere because of the crucial linkages between the land surface and climate. The skill of LSMs might be limited in accurately simulating the terrestrial water storage changes and its compartments. In contrast, GHMs focus on solving the water balance equation and simulating catchment outlet streamflow. One of the primary differences between LSMs and GHMs is the more physical basis of LSMs, including water and energy balances, compared to the more empirical water budget approaches included in most GHMs. Additionally, GHMs are increasingly modelling human interventions, including water use and water resources infrastructure, which most LSMs might skip it. The performance of these GHMs and LSMs varies because of the different physical representations of land-surface processes, differences in model structure and physics, parameterisation, and atmospheric forcing inputs (Mehrnegar et al., 2020).
In our team, we have experiences with working with various large-scale hydrological models such the WaterGAP Hydrological Model, NOAH-MP, LISFLOOD, W3RA and W3. We also develop frameworks to regionalise these models such as Calibration (Mostafaei et al., 2018), Data Assimilation (Mehrnegar et al., 2021), as well as simultaneous Calibration and Data Assimilation C/DA (Schumacher et al., 2016 and 2018).
Currently the W3RA and W3 models are being adopted for Multi-Sensor DA activities of the DANSk-LSM and MuSe-BDA. We run these models on 10 km and 5 m resolution globally and we are working towards localising their parameters for 1km resolution, continentally.
Figure 1: Continental and global scale hydrological modelling. Our group produces 5km and 10km resolution water storage simulations based on various forcing input data.
WaterGAP simulations of groundwater within the Murray Darling River Basin:
Original model run
After merging with GRACE data (see Schumacher et al., 2018)
Related Publications:
Mehrnegar, N., Jones, O., Singer, M.B., Schumacher, M., Jagdhuber T., Scanlon, B.R., Rateb, A. Forootan, E., 2021. Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data intoW3RA https://doi.org/10.1016/j.scitotenv.2020.143579
Mehrnegar, N., Jones, O., Singer, M.B., Schumacher, M., Bates, P., Forootan, E., 2020. Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA). Adv. Water Resour. 138, 103,528 – https://doi.org/ 10.1016/j.advwatres.2020.103528
Mostafaie, A., Forootan, E., Safari, A., Schumacher, M., 2018. Comparing multi- objective optimization techniques to calibrate a conceptual hydrological model using in situ runoffand daily grace data. Comput. Geosci. 22 (3), 789–814. – https://doi.org/10.1007/s10596-018-9726-8
Schumacher, M., Forootan, E., van Dijk, A.I.J.M., Müller Schmied, H., Crosbie, R.S., Kusche, J., Döll, P., 2018. Improving drought simulations within the Murray- Darling Basin by combined calibration/assimilation of GRACE data into the water- GAP Global Hydrology Model. Remote Sens. Environ. 204, 212–228. – https://doi. org/10.1016/j.rse.2017.10.029.
Schumacher, M., Kusche, J., Döll, P., 2016. A systematic impact assessment of grace error correlation on data assimilation in hydrological models. J. Geod. 90 (6), 537–559. – https://doi.org/10.1007/s00190-016-0892-y