## 2 The Alpine Environment Context
### Required Accuracy of Measurements  
Soil moisture measurements are difficult because an _in situ_ measurement very often disturbs the soil matrix and therefore heavily influences the measurement itself, while remote sensing techniques can typically only measure surface wetness. For most practical problems, an absolute accuracy in the percentage level is sufficient, albeit a better relative accuracy (in order to identify changes) is highly desirable.  
Since soil moisture is on the interface between atmosphere and subsurface, it influences both domains through energy and water partitioning. For the overall hydrological behavior of a catchment, the exact small-scale spatial pattern is infeasible to account for in a hydrological model, but the spatial statistics can be determined and modeled in a parameterized way. This can have an important influence on flood response to extreme rainfall. Required resolution depends very much on the application and topographical features, but ideally the spatial statistics can be determined over a wide range of scales to find optimal, area-specific parameterizations [23].  
On the atmospheric side, patterns in soil moisture play an important role in the feedbacks of energy and mass from the land to the atmosphere. Understanding of the small-scale processes is important for parameterizations that allow the modeling of large-scale processes. All in all, measurements of soil moisture at spatial scales from 10 cm (e.g., interactions with vegetation) to 1 km (e.g., hill slope average behavior) can provide interesting new insights into the complex processes, at work between atmosphere and subsurface, that evade understanding when observed through point measurements and satellite based measurements, see e.g., [14, 24].  
With HS and SWE, the situation is similar. In principle, any snow measurement is a good measurement since insufficient spatial and temporal coverage is available, particularly in mountainous areas [8]. The required spatial resolution is dependent on the application. It has been shown that changes in the dominating processes typically occur in the range of tens of meters [25] and at fine scale resolution (<5 m), surface roughness (features such as rocks and bushes) dominate the snow distribution [26], especially for small snow amounts. In order to simulate such fine (point-scale) resolution spatial variability, numerical models require grid resolutions of a few meters [21, 27]. For the assessment of water resources available in a catchment, a higher resolution is required in order to understand the processes, and hence understand that larger snow accumulations will remain for longer, affecting water availability later in the season. For the prediction of snow hydrology based on topology alone, fine (point) scale measurements cannot, however, be directly related to terrain and must be smoothed to 200-500 m grid cells, following an approach also used in semi-distributed hydrological models [28, 29].  
For assessing spatial snow distribution on a scale of a few meters, an accuracy of 0.1 m is a useful benchmark [6], which would correspond to a range between 20 to 30 mm SWE for typical mean densities in alpine snow covers. Note that snow height determines most of the spatial variability in SWE since density typically does not have a pronounced spatial variability [6]. In the assessment of slope stability (avalanche danger), small scale spatial variability on the scale of \(<\)1 m is influential on avalanche release, as is (if such measurements are possible) layer thickness (mm scale) [30]. HNS is one of the few simple snow parameters, which is indicative of avalanche danger [5]. Good estimates of snow fall amounts are therefore very useful. The RMSE of such measurements should exceed 0.06 m [31].