Near-real-time adjusted reanalysis forcing data for hydrology

Abstract

Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of highquality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data-ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation

“…For meteorological data, global precipitation from re-analysis products are well known to contribute a lot to the output uncertainty in traditional global modelling (e.g. Döll and Fiedler, 2008;Biemans et al, 2009), and this was still the case when applying catchment modelling; although the precipitation grid was bias adjusted against observations (Berg et al, 2018) and further adjusted with elevation during calibration, the density of stations at the global scale was not sufficient for the resolution of the catchments. New highresolution products from the meteorological community have the potential to become a game changer in global hydrological modelling.…”

“…At the global scale, a seamless dataset for several decades is necessary for consistent model forcing, to also cover hydrological features with large storage volumes. For WWH version 1.3 precipitation and temperature were achieved from the Hydrological Global Forcing Data (HydroGFD; Berg et al, 2018), which is an in-house product of SMHI that combines different climatological data products across the globe. This global dataset spans a long climatological period up to near-real time and forecasts (from 1961 to 6 months ahead).…”

“…WWH is developed incrementally, and the current version 1.3 was based on previous versions, where version 1.0 only included the most basic functions to run a HYPE model and was forced by MSWEP (Beck et al, 2017) and CRU (Harris and Jones, 2014). Version 1.2 included distributed geophysical and hydrographical features, and finally, version 1.3 (described below) included estimated parameter values and was forced by the Hydro-GFD meteorological dataset, which also provides operational forecasts at a 50 km grid (Berg et al, 2018). Gridded forcing data were linked to catchments using the grid point nearest to the catchment centroid.…”

“…Catchment modellers tend to study differences and similarities in flow signatures as well as in catchment characteristics to improve understanding of hydrological processes (e.g. Sawicz et al, 2014;Berghuijs et al, 2014;Pechlivanidis and Arheimer, 2015;Rice et al, 2015). In large-sample hydrology it is not possible to examine each hydrograph individually using inspection.…”

Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions

“…There are also nine forecast regions with daily local reservoir inflow time series covering the period 1981-2015 available from the independent regulator company Vattenregleringsföretaget (VRF) AB. Historical time-series of daily precipitation and air temperature values from the HydroGFD dataset [47] for the period 1981-2015 were used to force the model. Satellite-based snow data representing fractional snow cover (FSC) and snow water equivalent (SWE) were obtained from a service provided by the EU FP7 CryoLand project [48].…”