A process-based multisite multivariate stochastic weather generator for water resources management

When solving practical water resources management problems, situations when the amount of data is insufficient both terms of length and representativeness occur. Even the most extended observed series are short for reliable estimation and assessment long-term variability and extremes. A combination of stochastic weather generators, allowing the generation of arbitrarily long synthetic series of precipitation and air temperatures, and rainfall-runoff models, which transform them into an equally long synthetic series of river discharges, represent a feasible solution. This study aimed to develop a robust, practically oriented stochastic weather generator for rainfall-runoff modelling allowing the simulation of synthetic daily precipitation and air temperature series at multiple stations in catchments, considering seasonality, temporal dependency and spatial correlation. The design was based partly on the established methodological practices. In addition, it implements innovative components of temporal ad spatial multiscale disaggregation based process-oriented analysis of the processes involved and allows to consider the possibility of climate change. The ability of the weather generator to correctly reproduce characteristics of the observed rainfall and air temperature records was evaluated by both (temporal and spatial) statistical and hydrological process characteristics at each and across all stations.

Several case study results proved that the proposed concept of the generator is robust and practically applicable. It allows to reliably generate synthetic series of precipitation totals and air temperatures at individual stations in catchments and around simultaneously. Using a combined stochastic-deterministic rainfall-runoff modelling approach provides an infinite number of combinations of flow, including the extreme and the unobserved ones. It can be used to estimate extreme flood characteristics, determine hydrologic metrics of ecological flows, and detect changes in flow variability caused by land use and climate change.

Acknowledgements: This work was supported by the Slovak Research and Development Agency under Contract No. APVV-19-0340 and the VEGA Grant Agency No. 1/0632/19.