Mountains are unkind to gridded data. The processes that matter most in Alpine terrain — orographic rainfall, elevation-dependent temperature, the sharp contrasts between a valley floor and a ridge a few kilometres away — live at scales that most continental products simply cannot see. A 10-km cell averages away exactly the gradients a hydrologist needs.
So we built something finer. Together with colleagues from the University of Trento, the C3A centre, Fondazione Edmund Mach, the Po River Basin District Authority, and led by Hossein Salehi, we have produced a 1-km, daily precipitation and temperature dataset covering the entire Po River District over the full 1991–2020 climatological reference period. To our knowledge, no publicly available product previously combined all three of those properties — kilometre resolution, daily steps, and a continuous 30-year record — for this basin.
The dataset is now openly available on Zenodo under a CC BY 4.0 licence.
Why the Po District is a hard test
The Po River District is arguably one of Europe's most topographically demanding hydrological systems. It spans roughly 83,000 km² and stretches from sea level in the Adriatic delta to nearly 4,800 m in the Alpine headwaters. That relief produces strong spatial contrasts in both rainfall and temperature, and it governs snow accumulation, melt, runoff, and water availability across the region. Any interpolation method that ignores elevation is doomed from the start.
How it was made
The dataset rests on a harmonised, multi-source observational network: 1,583 precipitation stations and 1,555 temperature stations surviving quality control, drawn from the regional ARPA agencies and supplemented with the EEAR-Clim dataset. We deliberately extended a 20-km buffer beyond the district boundary and pulled in stations there too, so that interpolation near the edges — and across watershed divides — would not be starved of data.
Interpolation was carried out in the GEOframe new kriging framework:
- Precipitation uses Ordinary Kriging.
- Temperature uses Detrended Kriging, with elevation as the trend variable. A daily linear regression estimates the lapse rate for each day, removes the elevation signal before kriging, then restores it at every grid cell. The lapse rate is therefore allowed to breathe with the synoptic conditions rather than being fixed at a textbook value.
A nice methodological detail: at every daily time step, the empirical semivariogram is re-fitted against five candidate models (Exponential, Gaussian, Linear, Power, Spherical), with parameters optimised by Particle Swarm Optimisation, and the best-fitting model is selected for that day. The structure of the field is re-estimated daily rather than imposed once.
Does it work?
Leave-one-out cross-validation across the whole 30-year record says yes.
- Precipitation: mean KGE above 0.84 (All-Days) and 0.82 (Wet-Days), with mean absolute errors of 1.28 mm and 3.05 mm respectively. As expected from kriging's conditional bias and smoothing, wet-day skill drops a little and shifts slightly toward underestimation of peaks.
- Temperature: mean KGE of 0.88, correlation of 0.98, MAE of 1.14 °C, RMSE of 1.5 °C, and essentially no bias (+0.02 °C). The elevation detrending earns its keep.
The honest caveats are spatial. Errors concentrate in the high-relief northern sectors and along the southern boundary, and skill declines measurably with altitude — for precipitation, MAE rises by roughly 0.54 mm per 1000 m on all days and 0.87 mm per 1000 m on wet days. This is a station-density story more than a method story: the mountains are where the gauges thin out. We also note that no wind-undercatch correction was applied, so high-elevation snowfall is likely underestimated — something to keep in mind for Alpine applications.
Reassuringly, the annual diagnostics show gradual, continuous improvement over the three decades (precipitation KGE climbing from ~0.76–0.78 in the early 1990s to ~0.83–0.84 by the mid-2010s) with no abrupt step changes — evidence that progressive network densification, not methodological artefacts, drives the trend. The dataset is temporally coherent.
What the resolution buys you
Benchmarking against E-OBS (~10 km) and EMO (1 arcmin) makes the case for going fine. Two extremes are particularly telling:
- During Storm Alex (October 2020), the maximum three-day accumulation reaches 482 mm in the 1-km product over western Piedmont, against only 277 mm in E-OBS — a loss of roughly 43% of the peak signal at coarser resolution.
- For minimum annual temperature, the 1-km field reaches −11.1 °C where E-OBS reads only −4.7 °C and EMO −9.8 °C. Pixel-averaging quietly amputates the cold tail of high-altitude climate.
In short: kilometre-scale topographic forcing can only be represented faithfully when the grid is commensurate with the physiographic gradients doing the forcing.
Getting the data
The product is distributed as annual NetCDF files (CF conventions), one per variable per year — 60 files in total — readable with xarray, netCDF4, R's ncdf4/terra, or QGIS/ArcGIS. It is intended as spatially consistent meteorological forcing for hydrological and ecohydrological models, not as calibration ground truth.
- Dataset (Zenodo): https://doi.org/10.5281/zenodo.19207256 — CC BY 4.0
- Code (GEOframe Krigings): https://github.com/geoframecomponents/Krigings
- Method reference: Bancheri et al. (2018), Geoscientific Model Development but a new method was actually implemented on that basis, and will be submitted soon (e.g. Andreis et al., 2026)
It is a small piece of infrastructure, but the kind we keep needing: a clean, open, high-resolution climatic backdrop against which the hydrology of a complex basin can actually be modelled.
Reference
- Hossein Salehi, Daniele Andreis, Sohaib Baig, Gaia Roati, Marco Brian, Francesco Tornatore, Giuseppe Formetta, and Riccardo Rigon, A 30-Year 1-km Daily Precipitation and Air Temperature Dataset for the Po River District (Italy), submitted to ESSD.
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