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List of figures

<table><tr><td>Figure 1. Example of Virtual Map Forum site view</td><td>22</td></tr><tr><td>Figure 2. Parts of the lake easily observable by field survey</td><td>25</td></tr><tr><td>Figure 3. Principle of echosounding detection of underwater bottom</td><td>28</td></tr><tr><td>Figure 4. Parts of the echoes used by the RoxAnn system (left) and an example of a classification diagram (right)</td><td>29</td></tr><tr><td>Figure 5. The three lakes in Norway where Green LiDAR measurements were performed and analysed</td><td>33</td></tr><tr><td>Figure 6. Bathymetric map generated from Green LiDAR (Coastal Zone Mapping and Imaging LiDAR sensor) from the upper end of Krøderen, where the River Krøderen flows from left into Lake Krøderen to the right</td><td>34</td></tr><tr><td>Figure 7. The HBV-model as implemented by Staudinger et al. (2015). The catchment processes are conceptualized as linear buckets</td><td>37</td></tr><tr><td>Figure 8. The principal differences between spatially lumped (left), semi-distributed (centre) and gridded (fully distributed) hydrological models</td><td>38</td></tr><tr><td>Figure 9. The principles of a gridded hydrological model, where the hydrological equations are solved in each of the cells</td><td>38</td></tr><tr><td>Figure 10. The figure shows a non-calibrated Soil and Water Assessment Tool (SWAT) model (upper part) and a calibrated SWAT model (lower part) where simulated (red) and observed (black) monthly streamflow are compared</td><td>39</td></tr><tr><td>Figure 11. Potential soil loss map of a basin according to the RUSLE method</td><td>44</td></tr><tr><td>Figure 12. Example of HYMO index development</td><td>51</td></tr><tr><td>Figure 13. Illustration of the decision-making process for including hydromorphology in the assessment of lake ecological status</td><td>64</td></tr><tr><td>Figure 14. Relationships between mean EQRs (rescaled Ecological Quality Ratios) and water level drawdown for three groups of organisms for the 12 lakes with data for all three groups, and their mean EQR and minimum EQR in non-regulated reference lakes (open dots) and regulated lakes (black dots)</td><td>67</td></tr></table>
List of tables

<table><tr><td>Table 1.</td><td>Examples of human activities and driving forces, pressures related to them, and the resulting impacts that may occur.</td><td>8</td></tr><tr><td>Table 2.</td><td>Equipment required for a field survey.</td><td>26</td></tr><tr><td>Table 3.</td><td>Overview of the sensors applied in each lake. CZMIL: Coastal Zone Mapping and Imaging LiDAR; MBES: multibeam echosounder; RIEGL VQ-840 G: integrated airborne laser scanning system for combined hydrographic and topographic surveying</td><td>33</td></tr><tr><td>Table 4.</td><td>Commonly applied models and references.</td><td>35</td></tr><tr><td>Table 5.</td><td>Results for the simulation of changes in selected hydrological indices at the two hydrological gauging stations Nordsetfoss and Brattset in Norway.</td><td>90</td></tr><tr><td>Table 6.</td><td>High status (reference condition) for hydromorphological quality elements in lakes</td><td>45</td></tr><tr><td>Table 7.</td><td>Different approaches that can be used to define reference conditions.</td><td>46</td></tr><tr><td>Table 8.</td><td>Maximum ecological potential (reference condition) for hydromorphological quality elements in HMWBs.</td><td>48</td></tr><tr><td>Table 9.</td><td>Summary of the impacts on aquatic biota caused by hydromorphological pressures</td><td>52</td></tr><tr><td>Table 10.</td><td>Overview of Member States' HYMO-specific biological assessment methods (with relationships demonstrated with HYMO metrics), together with HYMO pressure proxies.</td><td>57</td></tr><tr><td>Table 11.</td><td>Overview of Member States' biological assessment methods addressing HYMO pressures in addition to eutrophication (and demonstrating relationships with HYMO metrics).</td><td>58</td></tr><tr><td>Table 12.</td><td>Metrics included in HYMO-specific lake assessment methods. Metrics are grouped into categories according to Hering et al. (2006) and Kanninen et al. (2013).</td><td>58</td></tr><tr><td>Table 13.</td><td>Metrics included in lake assessment methods addressing HYMO as one of multiple pressures (and showing relationship with HYMO metrics).</td><td>60</td></tr><tr><td>Table 14.</td><td>Overview of common biological assessment methods addressing HyMo pressures (with relationships demonstrated with HYMO indices).</td><td>61</td></tr></table>
# Annexes

## Annex 1. Application of hydrological modelling in Southern and Central Europe

Considering the changes in the water cycle induced by climate change, the frequency and intensity of flood events and prolonged dry periods, the development of adequate techniques to model the quantity of available water is essential. The inputs of hydrological models are not always accurate and certain, given the uncertainties linked to the simplification of physical meteorological processes. To reduce these uncertainties several studies have used terrestrial water storage (TWS) data that can be obtained from Gravity Recovery and Climate Experiment (GRACE) (Soltani et al., 2021). TWS refers to the total vertically integrated water content in an area regardless of the reservoir (surface water, groundwater, soil moisture and permafrost, snow and ice, or wet biomass) (Ahmed et al., 2016). Among the different models used for the definition of the hydrological balance and runoff, the one most used in Europe (Andersen et al., 2005; Hinderer et al., 2006; Weise et al., 2009; Li et al., 2012; Springer A., 2019; Lenczuk et al., 2020; Wang at el., 2020), is the mesoscale Hydrological Model (mHM) GRACE satellite-based TWS anomalies. Through satellite data relating to TWS obtained through GRACE, it is possible to obtain a deeper understanding of hydrological dynamics (Moravec et al., 2019). The GRACE mission has been active since 2002 and has two satellites in polar orbit for monitoring the earth continuously with an altitude ~ 500 km, by means of a joint project between the National Aeronautics and Space Administration (NASA) in the USA and the German Aerospace Center (DLR) in Germany (Tapley et al., 2004). The variability in gravity field solutions mapped by the GRACE satellite is directly related to the redistribution of mass at or near the Earth's surface. The largest signals observable in the GRACE data are coming from spatial and temporal variations in TWS. The traditional spherical harmonics solutions produced from GRACE are typically obtained through an optimization of the gravity field data at the global scale and are generated by a number of processing centres around the world. Alternatives to this global approach include so-called "regional techniques", for which many variants exist but whose common trait is that they only use the gravity data collected over the area of interest to generate the solution (Klees et al., 2008).

Using GRACE TWS data in hydrological models is not easy as errors and assimilation techniques must be managed correctly. Recently the application of GRACE TWS was made for: i) monitoring TWS change; ii) evaluating hydrological components; iii) drought analysis and iv) detecting glacier mass balance. GRACE TWS data can also be used by advanced statistical and numerical analyses to improve the output of hydrological models by reducing the input uncertainty resulting from climate data. GRACE TWS data resolution is in the order of a day or a month, depending on the method of data analysis. The spatial resolution is a few hundred kilometres; for this reason it performs poorly for small basins (Soltani et al., 2021). To isolate the hydrological part arising from the signal from the GRACE satellites, several methods have been used which all consider that the storage of water in the continents and its changes in time and space can be obtained from gravimetric observations of the satellites. The use of GRACE satellite data in hydrological models can effectively separate TWS into its components to improve its resolution and to constrain model simulations and their parameters through data assimilation (Soltani et al., 2021).
## Annex 2. Application of hydrological modelling in Norway

The precipitation-runoff model HYPE (Lindström et al., 1997) has been tested for the purpose of supporting the implementation of the EU WFD with hydrological data in Norway (Schönfelder et al., 2017; Adera et al., 2018). HYPE is a process-based semi-distributed rainfall runoff model which has been developed at SMHI (Swedish Meteorological and Hydrological Institute). The model is open-source and based on the concept of HBV (Bergström, 1992). It can be used to predict discharges in gauged and ungauged basins, as well as water quality parameters. Further information on HYPE can be found on the HYPE wiki (http://www.smhi.net/hype/wiki/doku.php).

The performance of the model was tested by comparing model output with observations at selected sites across Norway. The studies concluded that HYPE was capable of simulating seasonal and low flow conditions with acceptable quality (Schönfelder et al., 2017; Adera et al., 2018). The studies also confirmed that HYPE seems to be well suited to simulate the hydrology in unregulated basins, i.e., to simulate the reference hydrological conditions.

Based on a well-calibrated hydrological model, a set of different hydrological indices can be derived, which was exemplified with selected indices of ecological relevance (Table 14).

<table><caption>**Table 14.** Results for the simulation of changes in selected hydrological indices at two hydrological gauging stations Nordsetfoss and Brattset in Norway.</caption><thead><tr><th rowspan="2">Parameter</th><th colspan="3">Nordsetfoss</th><th colspan="3">Brattset</th></tr><tr><th>Index unregulated</th><th>Index regulated</th><th>Rel. change of hydrol. Index</th><th>Index unregulated</th><th>Index regulated</th><th>Rel. change of hydrol. Index</th></tr></thead><tbody><tr><td>Average runoff [m³/s]</td><td>85.6</td><td>42.6</td><td>-50 %</td><td>31.3</td><td>9.8</td><td>-69 %</td></tr><tr><td>Annual 1 day max [m³/s]</td><td>500.3</td><td>190.5</td><td>62 %</td><td>274.5</td><td>143.8</td><td>-48 %</td></tr><tr><td>Annual 30-day max [m³/s]</td><td>274.9</td><td>89.8</td><td>-67 %</td><td>122.4</td><td>41.4</td><td>-66 %</td></tr><tr><td>Annual highest 7-day average flow [m³/s]</td><td>417.3</td><td>153.4</td><td>-63 %</td><td>205.2</td><td>84.7</td><td>-59 %</td></tr><tr><td>Number of rises [-]</td><td>20.9</td><td>1.9</td><td>-91 %</td><td>25.9</td><td>6.4</td><td>-75 %</td></tr><tr><td>Number of falls [-]</td><td>14.0</td><td>1.8</td><td>-87 %</td><td>19.7</td><td>5.0</td><td>-75 %</td></tr></tbody></table>

Source: Schönfelder et al., 2017

In the context of the EU WFD, hydrological models have been used mainly to simulate the hydrological status and changes in rivers in Norway (Table 14). We foresee a wider use of hydrological models to simulate the present, future or historical conditions in lakes and reservoirs. The reasons for the relatively less common use of hydrological models to assess, for instance, water level changes in lakes and reservoirs are; i) the regulated water bodies are usually affected by hydropower regulations, and it has often been difficult to gain access to data on the operation of the hydropower plants, ii) many of the regulated lakes have been subject to regulation for many years (some since ~ 1900) and sometimes there is limited information about the situation before the lakes were regulated, iii) in general less attention, including research, has been given to assessing the HYMO pressures on lakes and their subsequent ecological effect.
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