Watershed Data Report

This report was created with the free Global Watersheds web app on . Creative Commons License BY 4.0.

Contents

  1. Political Boundaries
  2. Population
  3. Land Cover
  4. Hydrology
  5. GRACE Terrestrial Water Storage
  6. Irrigation
  7. Dams
  8. Longest River
  9. Topography
  10. Endangered Species
  11. References

This report is for the watershed with an outlet near 45°27'39"N, 16°23'59"E, or (45.461, 16.400), with a drainage area of around 9,500 km².

Political Boundaries

The watershed is located in three countries, as shown below in Table 1 below.

Table 1. Countries in the watershed.

Country Area (km²) Percent of watershed
Croatia 7,760 82%
Slovenia 1,010 11%
Bosnia and Herzegovina 741 8%

Data on political boundaries comes from Natural Earth, https://www.naturalearthdata.com.

Population

The watershed has an estimated population of 508,000 in the year 2020. Figure 1 shows how population has changed from 1990 to 2020. The population declined at an average rate of -0.8% per year over this time period.

Figure 1. Estimated watershed population from 1990 to 2020.

Population data comes from GlobPop, Global Gridded Population Estimates, created by researchers at Beijing Normal University (Liu 2024).

Human population growth can affect water quality via increased pollution from households, industry, and agriculture. Further, land use and land cover change associated with population growth can have a major impact on watersheds (see the next section of this report).

Land Cover

The most common land cover type in the watershed is tree cover, covering 6,070 km² in 2020. More detailed information about land cover and how it has changed is shown in Table 3 and Figure 2 below. Pairs of bars represent the area for each land cover type in the years 2000 and 2020.

Figure 2. Land cover in the watershed.

Table 3. Land cover in the watershed.

Land Cover Type Area in 2000, km² Area in 2020, km²   Change
Tree cover 6,150 6,070 -1%
Dense short vegetation 1,720 1,520 -11%
Built-up 711 1,030 44%
Wetland + tree cover 273 273 0%
Wetland + dense short vegetation 278 264 -5%
Cropland 244 213 -12%
Open surface water 41 44 5%

Land cover data comes from the GLAD: Global Land Cover and Land Use Change, 2000-2020 (Popatov et al. 2022). This dataset, created by researchers at the University of Maryland, is available online here. Classification is based on satellite imagery from Landsat and machine learning tools.

Land cover change can profoundly influence watershed hydrology and water quality. Urbanization and development can increase the impervious cover, causing more water to run off rather than infiltrate into the ground. This can decrease groundwater recharge and river baseflows, or the flows that occur during dry times. Deforestation and agricultural development are often accompanied by an increase in soil erosion and sediment loads. Other land use types are associated with water pollution. For example, agriculture can increase loads of pesticides and nutrients (nitrogen and phosphorus) from fertilizers. Urbanization and industrialization can cause contamination from a wide range of chemicals used in households and industry.

Hydrology

The average annual precipitation over the watershed is 1,187 mm/year. (Precipitation includes all forms of water, including snow and rain.) Some of this water leaves the watershed surface via evaporation and transpiration, or the loss of water from plants. Annual evapotranspiration is estimated at 661 mm/year. The basin climatology, or the monthly average precipitation and evapotranspiration, is shown in Figure 4.

Figure 4. Watershed climatology: monthly average precipitation and evapotranspiration over the watershed.

Precipitation data comes from WorldClim, a global gridded dataset by researchers at the University of East Anglia (Harris et al. 2020). This dataset is based on downscaling and bias-correcting the CRU-TS dataset (Fick and Hijmans 2017), which is based on a large collection of station observations that span 1901–2018.

Evapotranspiration is even more difficult to estimate than precipitation. Here, we use a dataset, GLEAM v3.6B, which combines modeling and remote sensing data (Martens et al. 2017; Miralles et al. 2011).

It is difficult to estimate water cycle variables over large areas. So you should keep in mind that these estimates are uncertain (not perfect).

Terrestrial Water Storage Anomaly

The GRACE satellites provide information about changes in the amount of water over different locations on the Earth. Figure 3 shows the average terrestrial water storage anomaly over the watershed.

🖱️Mouse wheel to zoom Click & drag to pan 📱Pinch to zoom on mobile

Figure 3. GRACE terrestrial water storage anomaly from 2002 to 2025.

The GRACE satellites make highly accurate measurements of the Earth's gravitational field, and provide measurements of changes in the mass of water on a monthly time scale. These measurements do not tell us how much water there is in a region, but rather, how it the amount of water has changed compared to a baseline. The measurement includes all forms of water, including water in rivers, lakes, and reservoirs, soil moisture, groundwater, glaciers, snow, and ice. For an introduction to GRACE, see the section of my PhD thesis on Remote Sensing of the Water Cycle.

The total amount of water in the watershed appears to be trending downwards at a rate of -4.8 cm per decade (P < 0.01). This P-value suggests that the observed trend is statistically significant; it is unlikely the trend is due to random chance.

GRACE Terrestrial Water Storage Anomaly data is from the Center for Space Research at the University of Texas, Austin (Save et al. 2016, 2022).

Irrigation

The watershed had about 2.4 km² of land equipped for irrigation in 2005. This is about 0.0% of the watershed. Figure 5 shows the development of irrigated area from 1900 to 2005. These estimates are based on a global dataset published by an international team of researchers (Siebert et al. 2015).

Figure 5. Area equipped for irrigation in the watershed from 1900 to 2005

Irrigation brings many benefits for growing crops. In arid regions, it enables production where it would be otherwise impossible. In humid regions, irrigation can increase crop quality and yield, and provide more certainty against unpredictable climate. Yet, the expansion of irrigation can have impacts on watersheds that need to be carefully managed.

Increased irrigation usually requires more water to be withdrawn from rivers, lakes, or groundwater sources, which can reduce streamflow and alter natural flow patterns. This reduced flow can harm aquatic ecosystems and diminish the water available downstream for other uses. Irrigation often introduces fertilizers and pesticides into the watershed, and may increase soil erosion and sediment transport. When nutrients run off into rivers and streams, they can lead to nutrient pollution, algal blooms, and eutrophication. This plus chemical pesticides and herbicides negatively impacts aquatic life and water quality for human uses.

Dams

This watershed contains four dams identified in the Global Dam Watch database, with a total storage capacity of 57 million m³.

Information on these dams is listed in Table 4. The number and size of dams in a watershed is one measure of hydromodification, or how much the natural hydrologic cycle is influenced by human activities.

Table 4. Dams in the watershed. Unknown or missing data shown with a dash.

Dam Name Reservoir Name River Main Use Year Dam Height (m) Capacity (10⁶ m³) Latitude Longitude URL
45.452 15.504
45.614 15.478
Lokvarka Lokvarka Hydroelectricity Built 1953 52.0 36.9 45.360 14.718
20.0 45.234 15.230

Dams serve many useful purposes including electric generation, water supply, irrigation, flood control, and recreation. However, if not carefully planned and managed, dams can have a heavy environmental impact. Today, many governments are removing dams to bring rivers back to life.

For a detailed look at the impact of dams on people and the environment, I encourage you to read the book Silenced Rivers by Patrick McCully. To help conserve free-flowing rivers, consider supporting the nonprofit International Rivers.

Data on dams comes from the Global Dam Watch database, published in July 2024. For more details, see the journal article by lead researchers at McGill University (Lehner et al. 2024).

Longest River

The longest river is the watershed is 334 km long. It is shown highlighted in the map in Figure 5 below.

This is the longest continuous flowline in the source dataset, MERIT-Basins. It is not necessarily the mainstem of the river, or the one with the same name. When it comes to naming rivers, historical, legal, and cultural influences are also important.

Figure 5. The longest river that flows to the watershed outlet (highlighted).

Figure 6 shows the elevation profile of the longest river reach. Elevations on the plot are in meters above mean sea level. The river begins at 978 m, and the outlet is at 94 m.

🖱️Mouse wheel to zoom Click & drag to pan 📱Pinch to zoom on mobile

Figure 6. Elevation profile of the longest river reach.

Elevation data for the profile plot is from MERIT-DEM (Yamazaki et al. 2017). Actual distances on the plot may be underestimated somewhat. This is because river paths are based on a grid or raster data, which simplifies the meandering path of real-world rivers.

Topography

Terrain elevations in the watershed range from 94 m to 1,400 m above sea level. The average, or mean, elevation is 350 m. Figure 7 shows a distribution of terrain elevations in the watershed.

Figure 7. Distribution of terrain elevations in the watershed.
Scale for x-axis: Linear   Log

Elevation data is provided by EarthEnv (Amatulli et al. 2021), and is based on the Global Multi-resolution Terrain Elevation Data 2010 dataset (GMTED2010). Statistics are based on gridded elevation data with pixels that are about 1 km on a side. Because of the size of the pixels, some smoothing takes place, and so the statistics reported above may not capture the true minimum and maximum elevation.

Endangered Species

The watershed is home to 95 threatened freshwater species. Table 5 shows the species that are endangered, vulnerable, or near-threatened, based on assessments by the International Union for the Conservation of Nature, or IUCN. Data comes from the IUCN Red List.

For a photo of the species, hover the mouse over Scientific Name in the table, or tap on mobile. Click the link under Status to view the IUCN assessment. Here you can find out more about the reasons the species decline, view maps of the species distribution, and read about any conservation measures being taken.

Table 5. Threatened species in the watershed.

Type Scientific Name Common Name Status Presence
mammals Mustela lutreola European Mink Critically Endangered ⧉ Extinct
molluscs Hauffenia tovunica Unknown Critically Endangered ⧉ Extant
molluscs Kerkia kusceri Unknown Critically Endangered ⧉ Extant
molluscs Tanousia zrmanjae Unknown Critically Endangered ⧉ Possibly Extinct
molluscs Sadleriana cavernosa Unknown Critically Endangered ⧉ Extant
fish Telestes polylepis Croatian Dace Critically Endangered ⧉ Extant
fish Huso huso Beluga Critically Endangered ⧉ Extinct
molluscs Hadziella rudnicae Unknown Critically Endangered ⧉ Extant
molluscs Iglica velkovrhi Unknown Critically Endangered ⧉ Extant
fish Anguilla anguilla European Eel Critically Endangered ⧉ Extant
molluscs Unio nanus Unknown Endangered ⧉ Extant
plants Bryum versicolor Unknown Endangered ⧉ Extinct
fish Telestes karsticus Karst Dace Endangered ⧉ Extant
birds Falco cherrug Saker Falcon Endangered ⧉ Extant
molluscs Anodonta exulcerata Unknown Endangered ⧉ Possibly Extant
molluscs Unio crassus Thick Shelled River Mussel Endangered ⧉ Possibly Extant
molluscs Unio elongatulus Unknown Endangered ⧉ Possibly Extant
molluscs Vinodolia fiumana Unknown Endangered ⧉ Extant
molluscs Congeria jalzici North Dinaric Cave Clam Endangered ⧉ Extant
crayfish Austropotamobius pallipes White-clawed Crayfish Endangered ⧉ Extant
molluscs Pseudanodonta complanata Depressed River Mussel Endangered ⧉ Extant
molluscs Dalmatinella fluviatilis Unknown Endangered ⧉ Extant
amphibians Pelophylax shqipericus Albanian Water Frog Vulnerable ⧉ Presence Uncertain
molluscs Kerkia brezicensis Unknown Vulnerable ⧉ Extant
molluscs Paladilhiopsis grobbeni Unknown Vulnerable ⧉ Extant
molluscs Unio vicarius Unknown Vulnerable ⧉ Possibly Extant
molluscs Belgrandiella superior Unknown Vulnerable ⧉ Extant
plants Damasonium polyspermum Starfruit Vulnerable ⧉ Extant
molluscs Belgrandiella globulosa Unknown Vulnerable ⧉ Extant
fish Sabanejewia larvata Italian Golden Loach Vulnerable ⧉ Extant
molluscs Belgrandiella croatica Unknown Vulnerable ⧉ Extant
birds Acrocephalus paludicola Aquatic Warbler Vulnerable ⧉ Extant
amphibians Triturus carnifex Italian Crested Newt Vulnerable ⧉ Extant
shrimps Troglocaris kapelana Unknown Vulnerable ⧉ Extant
molluscs Bythinella robiciana Unknown Vulnerable ⧉ Extant
molluscs Acroloxus tetensi Unknown Vulnerable ⧉ Extant
birds Calidris ferruginea Curlew Sandpiper Vulnerable ⧉ Extant
molluscs Belgrandiella crucis Unknown Vulnerable ⧉ Extant
molluscs Belgrandiella substricta Unknown Vulnerable ⧉ Extant
fish Hucho hucho Danube Salmon Vulnerable ⧉ Extant
birds Anser erythropus Lesser White-fronted Goose Vulnerable ⧉ Extant
molluscs Bythinella kapelana Unknown Vulnerable ⧉ Extant
molluscs Sadleriana supercarinata Unknown Vulnerable ⧉ Extant
birds Aythya ferina Common Pochard Vulnerable ⧉ Extant
molluscs Marstoniopsis croatica Jamski Marstoniopsis Vulnerable ⧉ Extant
birds Pluvialis squatarola Grey Plover Vulnerable ⧉ Extant
molluscs Zospeum exiguum Unknown Vulnerable ⧉ Extant
molluscs Belgrandiella schleschi Unknown Vulnerable ⧉ Extant
molluscs Paladilhiopsis insularis Unknown Vulnerable ⧉ Extant
molluscs Iglica langhofferi Unknown Vulnerable ⧉ Extant
molluscs Hauffenia media Unknown Vulnerable ⧉ Extant
molluscs Belgrandia stochi Unknown Vulnerable ⧉ Extant
molluscs Iglica gracilis Vitka Iglica Vulnerable ⧉ Extant
fish Umbra krameri European Mudminnow Vulnerable ⧉ Extant
crayfish Astacus astacus Noble Crayfish Vulnerable ⧉ Extant
birds Clanga clanga Greater Spotted Eagle Vulnerable ⧉ Extant
molluscs Plagigeyeria jalzici Unknown Vulnerable ⧉ Extant
molluscs Sphaerium rivicola River Orb Mussel Vulnerable ⧉ Extant
birds Podiceps auritus Horned Grebe Vulnerable ⧉ Extant
amphibians Salamandra salamandra Common Fire Salamander Vulnerable ⧉ Extant
molluscs Hadziella krkae Krška Hadžijel Vulnerable ⧉ Extant
molluscs Euglesa pseudosphaerium False-orb Pea Mussel Vulnerable ⧉ Possibly Extant
birds Calidris falcinellus Broad-billed Sandpiper Vulnerable ⧉ Extant
amphibians Proteus anguinus Olm Vulnerable ⧉ Extant
molluscs Bythinella angelitae Unknown Vulnerable ⧉ Extant
molluscs Iglica luxurians Bujna Iglica Near Threatened ⧉ Extant
fish Chondrostoma nasus Common Nase Near Threatened ⧉ Extant
molluscs Phreatica bolei Unknown Near Threatened ⧉ Extant
birds Aythya nyroca Ferruginous Duck Near Threatened ⧉ Extant
fish Chelon labrosus Thicklip Grey Mullet Near Threatened ⧉ Extant
molluscs Istriana mirnae Mirna mud snail Near Threatened ⧉ Extant
crabs Potamon fluviatile Unknown Near Threatened ⧉ Extant
fish Chelon auratus Golden Grey Mullet Near Threatened ⧉ Extant
plants Elatine alsinastrum Whorled Elatin Near Threatened ⧉ Extant
fish Dicentrarchus labrax European Sea Bass Near Threatened ⧉ Extant
fish Alburnus arborella Italian Bleak Near Threatened ⧉ Extant
molluscs Lanzaia rudnicae Rudnica lanzae Near Threatened ⧉ Extant
fish Chelon ramada Thinlip Grey Mullet Near Threatened ⧉ Extant
plants Baldellia ranunculoides Lesser Water-plantain Near Threatened ⧉ Extant
birds Numenius arquata Eurasian Curlew Near Threatened ⧉ Extant
fish Chelon saliens Leaping Mullet Near Threatened ⧉ Extant
mammals Lutra lutra Eurasian Otter Near Threatened ⧉ Extant
birds Calidris canutus Red Knot Near Threatened ⧉ Extant
birds Vanellus vanellus Northern Lapwing Near Threatened ⧉ Extant
birds Arenaria interpres Ruddy Turnstone Near Threatened ⧉ Extant
birds Circus macrourus Pallid Harrier Near Threatened ⧉ Extant
fish Romanogobio benacensis Italian Gudgeon Near Threatened ⧉ Extant
shrimps Troglocaris planinensis Unknown Near Threatened ⧉ Extant
molluscs Iglica hauffeni Unknown Near Threatened ⧉ Extant
fish Cottus metae Sava Sculpin Near Threatened ⧉ Extant
birds Limosa lapponica Bar-tailed Godwit Near Threatened ⧉ Extant
birds Limosa limosa Black-tailed Godwit Near Threatened ⧉ Extant
birds Calidris alpina Dunlin Near Threatened ⧉ Extant
birds Gallinago media Great Snipe Near Threatened ⧉ Extant
fish Barbus plebejus Italian Barbel Near Threatened ⧉ Extant

The presence of endangered species in a watershed provides important information about ecosystem health and biodiversity. Many factors can threaten endangered species within watersheds:

Water management plays an important role in protecting endangered species. This includes maintaining adequate streamflows, protecting riparian zones and wetlands, controlling pollution sources, and preserving habitat connectivity throughout the watershed.

What Can I Do?

If you're concerned about endangered species in your watershed, there are many ways you can help. Support organizations working on species conservation and habitat protection, or local watershed councils and land trusts in your area. Many watersheds have dedicated conservation groups focused on protecting local rivers, wetlands, and wildlife. Search for [your watershed name] + "conservation," "river keeper," or "watershed association."

You can also take direct action in your community. Participate in river cleanups, plant native vegetation along stream banks, reduce pesticide and fertilizer use, and support land use policies that protect riparian corridors and wetlands.

If you own property near streams or wetlands, consider conservation easements through organizations like the Land Trust Alliance.

Report wildlife sightings to citizen science platforms like iNaturalist or eBird. Your observations can contribute to scientific understanding and conservation planning. Finally, use your voice: contact local officials to support clean water regulations, habitat protection, and sustainable development practices that consider watershed health and wildlife needs.

References

Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5(1), 180040. https://doi.org/10.1038/sdata.2018.40

Fick, S.E., and R.J. Hijmans. 2017. “WorldClim 2: New 1‐km Spatial Resolution Climate Surfaces for Global Land Areas.” International Journal of Climatology 37 (12): 4302–15. https://doi.org/10.1002/joc.5086

Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3

The IUCN Red List of Threatened Species. (n.d.). IUCN Red List of Threatened Species. https://www.iucnredlist.org

Lehner, B., Beames, P., Mulligan, M. et al. The Global Dam Watch database of river barrier and reservoir information for large-scale applications. Scientific Data 11, 1069 (2024). https://doi.org/10.1038/s41597-024-03752-9

Liu, L., X. Cao, S. Li, and N. Jie. “A 31-Year (1990–2020) Global Gridded Population Dataset Generated by Cluster Analysis and Statistical Learning.” Scientific Data 11, no. 1 (January 24, 2024): 124. https://doi.org/10.1038/s41597-024-02913-0

Martens, B., Miralles, D.G., Lievens, H., van der Schalie, R., de Jeu, R.A.M., Fernández-Prieto, D., Beck, H.E., Dorigo, W.A., and Verhoest, N.E.C. 2017. GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geoscientific Model Development, 10, 1903–1925. https://doi.org/10.5194/gmd-10-1903-2017

Miralles, D.G., Holmes, T.R.H., de Jeu, R.A.M., Gash, J.H., Meesters, A.G.C.A., Dolman, A.J. 2011. Global land-surface evaporation estimated from satellite-based observations, Hydrology and Earth System Sciences, 15, 453–469. https://doi.org/10.5194/hess-15-453-2011

Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. (2022) The global 2000-2020 land cover and land use change dataset derived from the Landsat archive: first results. Frontiers in Remote Sensing. https://doi.org/10.3389/frsen.2022.856903

Save, H., S. Bettadpur, and B.D. Tapley (2016), High resolution CSR GRACE RL05 mascons, Journal of Geophysical Research Solid Earth, 121. https://doi.org/10.1002/2016JB013007

Save, H., 2020, "CSR GRACE and GRACE-FO RL06 Mascon Solutions v02." https://doi.org/10.15781/cgq9-nh24

Siebert, S., M. Kummu, M. Porkka, P. Döll, N. Ramankutty, and B. R. Scanlon. “A Global Data Set of the Extent of Irrigated Land from 1900 to 2005.” Hydrology and Earth System Sciences 19, no. 3 (March 25, 2015): 1521–45. https://doi.org/10.5194/hess-19-1521-2015.

Yamazaki, D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O’Loughlin, J.C. Neal, C.C. Sampson, S. Kanae, & P.D. Bates. (2017). A high‐accuracy map of global terrain elevations. Geophysical Research Letters, 44, 5844–5853. https://doi.org/10.1002/2017GL072874.