Hydrological systems show large variability across the globe and the response to precipitation is very different; often even neighboring rivers differ in streamflow and soil moisture. This can be due to various physical properties of the landscape, different area of upstream catchment or human alteration from water management.
The water balance
The simple water balance equation:
Q = P – ET – ΔS
(where: Q = Water discharge; P = Precipitation; ET = EvapoTranspiration and S = Storage) is puzzling hydrologists all over the world, as discharge can be difficult to monitor due to fluctuations in the river channel, precipitation is often heterogeneous for an upstream catchment area, evapotranspiration from water surfaces or vegetation is impossible to measure and the storage is normally unknown, especially underground.
To gain understanding of dominant water processes and emergent patterns in a changing world, we combine different types of spatial data and measurements (such as databases of physiography, water management, meteorological grids, occasional field sampling, continuous field observations, satellite, radar, etc.) with the numerical modelling of hydrological processes (as far as we understand them). At present, we mainly use thefor numerical modelling to gain understanding of hydrological processes across the world.
Some Research and Development questions
How can new data contribute to the improvement of process understanding and model accuracy?
Open data and global datasets are currently evolving fast, through new techniques of earth observations, citizen’s observatories and public portals. The new data may be used for finding relations between physiography and water fluxes, model-parameter constrains and evaluation of model assumptions.
How do dominant hydrological features in one region compare to those of another?
The reasons behind similarities and discrepancies in catchment and river behavior are analyzed in the multi-basin model by comparing fluxes and characteristics between different catchments. Hypothesis on drivers can easily be explored in the multi-basin model system, which covers large samples of data from many rivers.
What are the effects of change in society and environment on water resources?
There are complex relationships and interactions between humans and the environment. Short term predictions can be made based on models combined with recent observations while long term predictions are made using estimated changes in scenario modelling (i.e. remedial measures, climate changes).
Our core publications in this Scientific focus
Andersson J.C.M., Arheimer B., Traoré F., Gustafsson D., Ali A. 2017. Process refinements improve a hydrological model concept applied to the Niger River basin. Hydrological Processes pp.1-15.
Arheimer, B., Dahné, J., Donnelly, C., Lindström, G. and Strömqvist, J. 2012. Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea basin – influence of input-data quality and scale. Hydrology research 43(4):315-329.
Donnelly, C, Andersson, J.C.M. and Arheimer, B., 2016. Using flow signatures and catchment similarities to evaluate a multi-basin model (E-HYPE) across Europe. Hydr. Sciences Journal 61(2):255-273,
Hundecha, Y., Arheimer, B., Donnelly, C., Pechlivanidis, I. 2016. A regional parameter estimation scheme for a pan-European multi-basin model. Journal of Hydrology: Regional Studies, Volume 6, June 2016, Pages 90-111.
Kuentz, A., Arheimer, B., Hundecha, Y., and Wagener, T. 2017. Understanding hydrologic variability across Europe through catchment classification, Hydrol. Earth Syst. Sci., 21, 2863-2879,.
Pechlivanidis, I. G. and Arheimer, B. 2015. Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case, Hydrol. Earth Syst. Sci., 19, 4559-4579,.
Strömqvist, J., Arheimer, B., Dahné, J., Donnelly, C. and Lindström, G. 2012. Water and nutrient predictions in ungauged basins – Set-up and evaluation of a model at the national scale. Hydrological Sciences Journal 57(2):229-247.