Information about the energy and water exchanges between the land surface and the lower atmosphere (i.e. land-atmosphere interactions) is necessary for example to improve our understanding of the effect of land-atmosphere interactions on the exacerbation of temperature and precipitation extremes. Observations of energy and water fluxes at the land surface usually rely on the eddy covariance method. There is a wide network of these measurements providing data over all continents but with large spatial gaps in Africa, Asia, South America and Oceania. Additionally, other problems are associated with these observational methods such as the energy and water balance non-closure. To improve the spatial coverage of land-atmosphere interactions data considering the energy and water balance closure, we explore the combination of remote sensing data and a physical-based model. The High resOlution Land Atmosphere Parameters from Space (HOLAPS) framework is a one dimensional modelling framework that solves the energy and water balance at the land surface using remote sensing data and reanalysis products as forcings. Preliminary results from the evaluation ofHOLAPS outputs over Europe at 5 km resolution show an improvement in the simulation of latent heat flux when using remote sensing data in comparison with results using only reanalysis data as forcing. Additionally, we see a moderate improvement in HOLAPS latent heat flux estimates against energy-balance corrected eddy covariance measurements in comparison with other products that solve the energy and water balance equations, such as the ERA5Land product. The new HOLAPS product is available at hourly resolution for the period 2001 to 2016 and these estimates can be useful for agriculture and forest management activities and to evaluate the representation of land-atmosphere feedbacks in weather and climate models.

To test this hypothesis, we developed a reservoir module in the mesoscale hydrological model (mHM, https://mhm-ufz.org). mHM is tested across seven model resolutions ranging from 1 km to 100 km. The experiment set is the GRanD database, wherein the scalability of the reservoir set is tested for the whole set (7320 reservoirs) and the scalability of reservoir inflow simulation is tested at the headwater reservoirs (approx. 1500 reservoirs). Preliminary results in 70+ headwater reservoirs show that SCC routing preserves the full reservoir set across all scales. In comparison, the classic D8 routing scheme loses 15%, 25% and 50% reservoirs at 0.125 degree, 0.25 degree and 0.50 degree model resolutions, respectively. This indicates the potential of SCC in regulating interscale discrepancies in reservoir states and fluxes, leading to virtually seamless model performance.

The dilemma for modellers using distributed HMs is to compromise in resolution  (i.e., runtime) or to compromise on the number of reservoirs to model. Based on the preliminary results, SCC is poised to solve this long-standing dilemma and complete the quest for scalable hydrological modeling with reservoirs. The findings of this study would contribute to the contemporary effort of hydrological modeling society towards improved global water balance closure, where a good representation of reservoirs and lakes is a crucial element.