The Finnish Environment Institute (SYKE), hydropower companies and regional Centres for Economic Development, Transport and the Environment (ELY centres; they have since been replaced by Economic Development Centers, but were still active at the end of 2023, the cutoff date for data in CAMELS-FI, which is why we use the old organization name) maintain hundreds of hydrological gauges with long time series in Finland. The data are shared openly through SYKE's Hydrology API (Hydrologiarajapinta, 2026; https://rajapinnat.ymparisto.fi/api/Hydrologiarajapinta/1.0, last access: 13 May 2026).

The catchments included in CAMELS-FI were selected according to the following criteria:

The gauge must have had observations in 1995 or more recently. This is to ensure that most of the gauges have concurrent observations.

The catchments for the selected gauges were calculated with the Python bindings of WhiteboxTools (WB tools) (2.3.6), Geopandas (1.0.1) and Rasterio (1.4.3) using the Elevation model 10 m (DEM) of the National land survey of Finland (NLS) (den Bossche et al., 2024; Gillies et al., 2024; Lindsay, 2014; Elevation model 10 m, 2019). To ensure good quality catchment delineation, SYKE's river channel and level 3 basin division data were used as guiding features (Valuma-aluejako, 2023; Uomaverkosto, 2024). Due to the DEM only covering Finland, channel sections crossing the national border were removed, and in case of some minor bifurcations, the channel network was modified slightly. The known channels were virtually excavated one meter deeper than their surroundings, and the level 3 basin divisions were transformed into “walls”, except where they intersect with channels. Subsequently, the DEM was then breached and depressions filled with the “breach depression least cost” WB tool. Eight directional flow pointers were computed, streams were identified as places where at least 40 000 cells flow into that cell, and gauges were snapped to those streams. Some gauges had to be moved slightly so that they snapped to the correct location. After that, catchment boundaries were calculated for each gauge. All catchment boundaries were inspected individually by visualizing them alongside other catchments and all the other datasets used in catchment delineation, followed by a visual check for inconsistencies. The inspection always included both an overview of the whole catchment and a detailed, section-by-section examination of the entire border. Where inconsistencies were identified, additional virtual walls were added and the delineation procedure was repeated until a satisfactory result was achieved. Parts of the catchments crossing international borders were added manually by selecting and merging SYKE's level 4 basin divisions. The resulting catchments cover 73 % of Finland's total land area, and vary in size from 7 to 50 672 km2 (median 762 km2).

Diverse metadata attributes of the gauge and catchment are presented to support use (Table 1). Gauge location, gauge name and basin name were downloaded from SYKE's hydrology API. Nestedness, meaning the number of larger catchments in CAMELS-FI that the catchment is part of, the catchment area and the percentage of catchment outside of Finland were calculated for all catchments. The maximum nestedness is 13, and there are 77 catchments with nestedness of zero from 55 basins. Additionally, the hydrologic region of the catchment is also provided (Lapland, Lakes or Coast, after Korhonen and Kuusisto, 2010, Fig. 1b).

Daily volumetric discharge (Q, m3s-1) for 1961 to 2023 was downloaded from SYKE's hydrology API, and specific discharge (q, mmd-1) was calculated based on catchment areas (Table 2). When it is not necessary to separate the two meanings, we use the term discharge without other specifiers. Volumetric discharge is calculated from water level using daily average water level and a rating curve, except for hydropower plants, for which the discharge values are provided directly by the operators. Rating curves are based on direct discharge measurements with mechanical current meters before 1990s and mainly with acoustic doppler current profilers (ADCP) after that.

Water levels have been measured continuously since the 1960s. Initially, observations were made using automatic limnographs equipped with wells connected to the main river channel or lake via pipes. In the 1960s, daily values were read manually from the limnograph each morning at 08:00 LT. From the early 1970s onward, daily mean water levels were calculated from the limnograph records and stored in a database. Measurement processes became digitalized from 1984 onwards up to the early 2000s, when automated systems using pressure sensors were introduced. During this period, daily average water levels were derived from hourly measurements.

The length of the provided hydrological observation series varies from 5 to 63 years, with a median of 45 years and three quarters of the gauges have had observations for 30 years or more (Fig. 2). Each discharge observation has also a corresponding quality flag, with most common flags being normal observations (n=4669213, 91.4 %) and ice correction related discharge reductions (n=393023, 7.7 %), while the rest indicate varying types of quality issues or corrections (n=44226, 0.9 %). It should be noted that for virtual gauges, the quality flags of all contributing gauges are provided. For a full list of quality flag classes see Appendix B. Textual remarks related to small amount of the flags (n=13282, 0.26 %), mostly from class “other” are also provided. The original remarks were in Finnish and were translated manually to English by the authors.

The 17 catchments that are part of SYKE's hydrological reference network and the global ROBIN (Reference Observatory of Basins for International hydrological climate change detection) initiative are indicated in metadata attributes (Turner et al., 2025). SYKE has put extra effort into ensuring that these gauges have very high-quality observations, and they are largely unaffected by human disturbances.

Supplementary daily discharge values are also provided for a few locations where water is pumped from one catchment to another. For these artificial bifurcation gauges, the only static attributes provided are the gauge metadata, and no catchment is provided. The locations are: Päijänne Water Tunnel (1092 and 1093); Paimio River to Aura River (1115); Lokka reservoir to Luiro River (1352) and Saimaa Canal to Rakkola River (3683). Overall, the effects for both source and recipient catchments are usually relatively minor, although the effect might be noticeable during low discharge. These artificial bifurcations are completely separated from the rest of the data and are not counted as catchments in CAMELS-FI.

Daily meteorological attributes from 1961 to 2023 were derived for the catchments mainly from the Finnish Meteorological Institute's (FMI) 1km×1km gridded daily climatology of Finland (Aalto et al., 2016). The product has been created by applying Kriging with external predictors of lake and sea cover, elevation and relative altitude to FMI's observation network. It should be noted that minimum temperature near ground is missing from the original dataset from 16 October 2022 onwards, which is why it is also missing from CAMELS-FI. Potential evapotranspiration (PET) was only available for the months from April to September from 1981 onwards from FMI (Pirinen et al., 2022). Thus, we also include snow evaporation from ERA5 (Hersbach et al., 2020) and PET from Singer et al. (2021). Both FMI and Singer et al. approximate PET of well-watered grassland, Singer et al. with FAO Penman–Monteith equation (Allen, 1998), while FMI uses slightly modified version (Allen et al., 2006). In addition to providing the aforementioned data, we combined the data into one convenient PET attribute, which was also used for calculating climatic signatures (see Sect. 4.2). This was done by using snow evaporation when the snow depth of the catchment was over 1 cm, and filling the snow-free days with FMI PET if it was available; the remainder was completed by using Singer et al. (2021). We recommend using this combined PET, unless the end user has a specific reason to do otherwise, because it doesn't have temporal gaps, takes snow period into account in a reasonable way and provides the best available data for any month.

FMI does not include snow water equivalent (SWE) in their gridded product, only snow depth. However, as snowmelt typically causes most major high discharge events in Finland, we felt that including it was necessary. The SWE product in ERA5-Land has been benchmarked to perform overall better than other available products, so we chose it (Mudryk et al., 2025; Muñoz-Sabater et al., 2021). However, as there are many SWE products that have been benchmarked relatively similarly in non-mountainous regions, and there are some differences between the products, we decided to also include ESA's CCI3-1, which is available from the beginning of 1979, usually from October to May (Luojus et al., 2024). It is provided typically for every other day and has occasional gaps of two or three days, so gaps of less than three days were interpolated linearly to obtain daily values. See Sect. 6.3 for a brief comparison between the different snow products.

The catchments' daily values were extracted by calculating the areal average of the grid for each catchment, considering all pixels that touch the catchment to avoid problems with the smallest catchments for coarser resolution products. ERA5 and ERA5-Land offer hourly data, which were aggregated to daily values. The portions of catchments beyond Finnish borders were not included in the catchment borders used for calculations, as is the case for all static catchment attributes as well.

Hydrologic signatures were calculated from the daily specific discharge and precipitation data described in Sect. 3. Definition of the water year for Finland was obtained from Irannezhad et al. (2024), meaning that the water year starts on 1 September and ends on 31 August. Hydrologic signatures were calculated with available observations for the water years of the climatological standard period 1991–2020, except for a few gauges with less than five years of observations during the period, for which all available data were used. All those gauges had started measurements after 2015, excluding gauge 1353, which had a gap in observations between 1989 and 2020. Hydrologic signatures were calculated using code from https://github.com/naddor/camels (last access: 20 March 2025).

Climatic signatures of the catchments were calculated for water years 1991–2020 from daily precipitation, PET, and mean temperature, using code from (Addor, 2020; https://github.com/naddor/camels, last access: 20 March 2025). Additionally, mean temperature was calculated for all the catchments for the same period. We chose to use the combined PET that was described in Sect. 3.2 since it gave realistic yearly mean PET values for the full year.

Topography descriptors were derived from the NLS's DEM, without hydrological preprocessing (Elevation model 10 m, 2019). For gauge elevation, the pixel touching the gauge location was used. Other elevation measures were derived by using all the pixels of the DEM that touch the catchment area. Please note that gauge elevation may differ significantly from the lowest elevation of the catchment due to deep open-pit mines inside the catchment, which is why both are provided.

Land cover can change significantly on a decadal scale. Thus, instead of providing one value for each land cover class, we follow the example of CAMELS-CH (Höge et al., 2023), and provide values for all four CORINE Land Cover (CLC) products made for Finland, from the years 2000, 2006, 2012 and 2018 (Corine maanpeite 2000, 2002; Corine maanpeite 2006, 2008; Corine maanpeite 2012, 2014; Corine maanpeite 2018, 2020). Linearly interpolated yearly values for 2000–2018 are also provided per catchment. Unfortunately, we were unable to find earlier harmonized land cover time series datasets, so our land cover time series covers only the most recent decades. CLCs are produced nationally with harmonized criteria set by the European Environment Agency. We used the Finnish versions of CLCs, which have higher resolution and a smaller minimum feature area than the Europe-wide products. They have been created using existing datasets at SYKE combined with automated satellite image interpretation. We created a similar classification to CAMELS-CH and CAMELS-SE, which also used CLC as their land cover data. Each CLC edition has seen some small changes to the classes present, but in our opinion, these are largely inconsequential for hydrological tasks. Exact CLC classes used for each year are presented in Appendix A1.

Soil class areal portions were calculated for each class based on the Superficial deposits of Finland 1:200000 (2018) soil classification map by the Geological Survey of Finland (GTK). It should be noted that the areal percentages of soil classes do not total 100, because water and built areas were excluded. These data are different from the soil information provided by most other CAMELS, since GTK classifies soils based on the dominant grain size, instead of providing weight portions of different grain sizes. However, Sweden has a similar soil classification system, and we decided to follow the soil classes present in CAMELS-SE (Teutschbein, 2024). It should be noted, that quaternary sediment deposits form mostly only a thin or, in some locations, a non-existent layer above bedrock, which is why bedrock is considered as a “soil” class. Descriptions of the exact soil classes used are presented in Appendix A2. In terms of groundwater, the most important landforms in Finland are eskers and terminal moraines (Fin. salpausselkä), which form the most important aquifers in Finland due to their high sand and gravel content (Katko et al., 2006). These landforms have a thin and long shape, meaning that they are poorly represented by coarse resolution datasets, which is why Global or Europe-wide soil datasets were considered unsuitable for Finland. Soil depth was averaged from the Superficial deposit thickness 1:1000000 product from GTK (Superficial deposit thickness 1:1000000, 2018).

Rock type areal classes were calculated for each class based on the Bedrock of Finland 1:1000000 (Geological Survey of Finland, 2016) rock classification map by GTK. In Finland the bedrock is mostly crystalline and conducts water very poorly outside of fractures (Karro and Lahermo, 1999). Thus, the type of rock beneath the soil has only little influence on the discharge on the surface. Due to this, no national hydrolithological data exists for Finland, and hydrolithological attributes present in many previous CAMELS cannot be provided. Crack density could also be useful information for hydrology, but no good quality national dataset exists for Finland.

CAMELS-FI contains catchments with vastly different levels of human influence, and multiple different attributes are provided to help dataset users decide which catchments suit their needs. Catchments are divided into three regulation classes based on regulation information from SYKE's Lake API (Järvirajapinta, 2025; https://rajapinnat.ymparisto.fi/api/jarvirajapinta/1.0, last access: 23 December 2025) and manual assessment of discharge plots. The classes are: No active regulation (n=131), Minor active regulation (n=26) and Major active regulation (n=163). Not actively regulated catchments may still have dams, but the dams only dampen natural variations slightly. They also include some catchments with a regulation permit that is either not used or its effects are nearly inconsequential for the discharge at the gauge, such as artificial permanent lowering of lake level before 1961. Minor active regulation includes catchments which have regulation that influences the discharge, but the end result is still relatively close to natural. Major active regulation includes the rest of the catchments where the discharge is strongly controlled, most commonly by a hydropower station with a reservoir. Gauge 3536 changed from majorly to minorly regulated in 2013, and was classified as minorly regulated. Reservoir capacity (difference between maximum and minimum) and count, as well as the count of other regulation types, are provided. We define a lake as a reservoir if the regulation permit has a known capacity; otherwise, it is deemed to be other regulation. Dam count is also provided based on NLS's Topographic Database (National Land Survey of Finland, 2024). It should be noted that the authors noticed some dams missing from the topographic database, so the given counts must be considered as approximate. The population count of each catchment, based on Statistics Finland's 1km×1km grid for 2023, is also included (Statistics Finland, 2024).

The quality of discharge values varies both in time and between the gauges. Discharge measurements from (hydropower) dams have generally had high quality from the 1960s, as their geometry and discharge properties are well known and don't change. For other locations, the discharge values are based on more uncertain rating curves for the earlier decades. Rating curves started to be updated regularly every three years only in the 1990s for gauges maintained by SYKE. This may cause additional inaccuracies in the earlier decades due to changes in the morphology of the river channel. Actual discharge measurements that form the basis for the rating curve also improved substantially around the same time in large rivers due to the adoption of ADCPs (Korhonen, 2007). There is significant variation between different ELY centres in the quality of rating curves. Some are very close to SYKE, others do not update their rating curves as regularly.

The other component of quality affecting the discharge values is the accuracy of water level measurements. The water level accuracy of automatic limnigraphs that were used until 1990s was 0.5–2 cm, while the pressure sensors that have replaced them since have an accuracy of 0.1–1 cm. The effect of this on the discharge varies by gauge. Locations where small increase in water level corresponds to large increases in discharge, such as outlets of lakes, are most affected by this type of uncertainty. The water level measurements are validated monthly with manual measurements (Korhonen, 2007).

Three main quality checks are applied for non-hydropower gauges by SYKE. Firstly, water level observations outside of predetermined low and high thresholds are removed. The daily value is calculated if at least 60 % of the instantaneous values exist, and a day with any missing instantaneous values gets quality flag 108, missing instantaneous values. Finally, the daily discharge values are inspected manually once a year, and erroneous values are removed.

The wintertime raw discharge values are influenced by ice cover almost every winter for all of the gauges that are not part of a dam. The effects of ice are especially significant during ice jams. The wintertime discharge values are corrected (reducted) afterwards by an expert at the SYKE for 61 gauges in CAMELS-FI. Some of the gauges only receive occasional corrections for ice jam occurrences. Only one person has been appointed to handle the task at any time. When the person changes, new one is trained for the task, but the task is so manual that each person leaves their mark on the corrections. A computer-aided system has been in use since 1997, and the current system since 2008; previously the correction was done using a semi-logarithmic graph. Even the corrected values are inevitably less accurate than open channel values, with errors of 5 %–20 % common (Korhonen, 2007). Gauge metadata attributes include information of whether ice corrections are made for the gauge and discharge quality flags indicate periods that have been corrected (flag 3, reduction, see Appendix B).

The effect of timeseries length of discharge observations to the stability of hydrologic signatures was investigated, because previous studies have shown that hydrological conditions between consecutive years can exhibit temporal autocorrelation (Lun et al., 2020; O'Connell et al., 2023). Thus, short time series may not represent long-term average conditions. Hydrologic signatures of each gauge with short (5–9 years, n=36) and medium (10–19 years, n=47) timeseries were compared to the group of five closest long timeseries (>20 years) gauges from the same hydrological region and regulation level. More specifically, the standard deviation (SD) of the signatures of the long timeseries group was calculated, and the change to the standard deviation was calculated when adding the shorter time series gauge to the group. This was repeated for all gauges with short or median timeseries and Welch's t-test (one-sided, α=0.05; Welch, 1947) was applied to see if there is significant overall difference between the SDs of the two groups. The results show that for mean discharge, runoff ratio, stream elasticity and mean half flow date, the shorter timeseries deviated significantly more from the long timeseries than the long timeseries deviated from each other, on average (Table 4). The effect on the same signatures was smaller, but still significant for gauges with medium length time series (Appendix E). Therefore, analysis of long-term conditions sensitive to these four signatures should preferably focus to timeseries of over 20 years. Conversely, five to ten years seems to be long enough for giving relatively stable estimates of the other hydrological signatures.

A simple comparison on the agreement of precipitation, PET and specific discharge as well as different snow products is presented here to probe the general agreement, or lack thereof, of different data sources. PET is on average larger in the boreal zone than actual evapotranspiration (AET) (Peltoniemi et al., 2015). This prevents comparisons that would directly calculate water balance using different subsets of precipitation, specific discharge and AET, since CAMELS-FI does not include AET estimates. However, some general agreement indicators can still be used. For example, over long timescales precipitation should always exceed specific discharge and difference of them should not exceed PET, assuming no water flows through catchment borders and water storages remain unchanged. These “bounded water balance” indicators were calculated for all catchments in CAMELS-FI using p_mean, pet_mean and q_mean meteorologic and hydrologic indices (Table 3). The meteorological and discharge data are within expected bounds for most of the catchments in CAMELS-FI (Fig. 3d). However, specific discharge exceeds precipitation for gauges 1369, 3335 and 3728. The gauge 3335 is probably explained by the mountainous terrain of the catchment, since it is a known issue that rain gauges underestimate precipitation during snowfall in windy conditions (Saltikoff et al., 2015). The other two catchments are heavily regulated and have relatively short observation periods within the reference climatological period 1991–2020, and thus changes to water storages are likely causes. PET is smaller than the difference of precipitation and specific discharge for gauges 1366, 3155, 3324 and 3727. Discharge is anomalously low for 3727 and the discrepancy is much smaller for the other three catchments. The reservoir lake of 3727 is also directly upstream of the reservoir of 3728, so they are strongly coupled. The gauge 1366 is heavily regulated, has a short observation period and is downstream of 3728 and 3728. 3155 has a similarly short time series and is heavily regulated. For gauge 3324, the cause is likely a large kettle lake (Sääksjärvi) that has been assigned to its catchment by the catchment delineation process based on surface topography. However, this kettle lake does not have an outlet stream, and is only drained trough groundwater. Thus it is likely that the lake is also “leaking” water to neighbouring catchments. Following our goal of inclusivity, we decided to not remove these catchments from the data, as they can still be useful for many tasks, and all had plausible causes for the discrepancies.

The temporal consistency of combined PET was investigated for step changes, as it is a combination of different products. This was done by comparing the change in value when switching from one source product to another to the variation of those products four days before and after the change. In the autumn, the changes from one product to another are the same magnitude as the typical variation of the involved products and the typical variation is also low, less than 0.21 mmd-1 for all source products. In the spring, the overall variation in the source products is higher around the changes from one product to another than in autumn. There is also a clear step change when transitioning from snow evaporation to FMI's PET, mean change 1.8 mmd-1, which is approximately three times larger than typical variation of FMI's PET at that time of the year. However, while this change is too quick (one day), this sort of increase in potential evaporation when snow has melted is not completely unrealistic over slightly longer timescales, such as one week. Melting and evaporating snow has high albedo and prevents temperatures rising much above zero, while increasing the relative humidity of the air above. After the snow has melted, albedo lowers, temperature rises higher on sunny days, relative humidity lowers and plants start photosynthesizing (Betts et al., 2001). This leads to rapid increase in potential evaporation. It should be noted that actual evaporation can be considerably lower depending on land cover and water availability.

The FMI's snow depth and two SWE products, ERA5-Land and CCI3-1, were compared by counting the average number of snow days during water years 1991–2020. A day was considered a snow day if snow depth exceeded 1 cm, or SWE was over 1 mm. ERA5-land estimated on average 189.4 snow days, over three weeks more than CCI3-1 (161.9 dyr-1) or FMI's snow depth (163.6 dyr-1). This pattern holds generally in all parts of Finland. The similar averages of FMI's snow depth and CCI3-1 are probably explained by the fact that CCI3-1 combines ground station snow depth observations with microwave satellite observations (Takala et al., 2011). It should be noted that on the catchment level there is substantial variation between all products, which is probably due in part to the different grid sizes of the products and in part by methodological differences.

The values of the two SWE products were also compared by subtracting ERA5-Land from CCI3-1 (Fig. 6). ERA5-Land provides SWE values that are on average 11.3 mm higher than CCI3-1. However, the difference has exceeded 100 mm every year in the snowier parts of Finland. This is most likely caused by CCI3-1's known issue with severely underestimating SWE values higher than 150 mm due to limitations of microwave satellite measurements (Takala et al., 2011).

Based on these results, we recommend using FMI's snow depth if SWE is not needed, and swe from ERA5-Land, in case the user only needs one snow data source. However, we would be cautious to place too much trust in ERA5-Land, since it estimates too long snow periods.