The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling

Remote Sensing of Environment 239 (2020) 111603

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The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling

T

Paul Schattana,b,∗, Gabriele Schwaizerc, Johannes Schöberd, Stefan Achleitnere a

Institute of Geography, University of Innsbruck, Innrain 52f, Innsbruck, Austria alpS GmbH, Grabenweg 68, Innsbruck, Austria c ENVEO IT GmbH, Fürstenweg 176, Innsbruck, Austria d TIWAG Tiroler Wasserkraft AG, Eduard-Wallnöfer-Platz 2, Innsbruck, Austria e Unit of Hydraulic Engineering, University of Innsbruck, Technikerstraße 13, Innsbruck, Austria b

ARTICLE INFO

ABSTRACT

Edited by Dr Emilio Chuvieco

A combined snow modelling approach integrating remote sensing data, in-situ data, and an improved hydrological model is presented. Complementary information sources are evaluated in terms of its value for constraining the model parameters and to overcome limitations of individual data such as inadequate scale representation. The study site consists of the Upper Fagge river basin in the Austrian Alps featuring the Weisssee Snow Research Site. The available remote sensing datasets include Terra MODIS based medium resolution and Landsat-7/8 and Sentinel-2A based high resolution fractional snow covered area maps. Recently, Sentinel-1 based wet snow covered area maps have become increasingly available. To the knowledge of the authors the first evaluation of their value for snow-hydrological modelling is presented. Besides conventional small footprint station data, in-situ time-series of snow water equivalent (SWE) of a Cosmic-Ray Neutron Sensor (CRNS) having a footprint of several hectares is additionally used. For including these data the model now provides respective outputs such as fractional snow cover, wet/dry snow surface and SWE areal means equivalent to the CRNS sensor footprint. By means of 40,000 model runs the high complementary value of representative SWE data and remote sensing information was assessed with most promising results achieved by combining high resolution fractional snow covered area maps with CRNS-SWE data. Regarding mean SWE or mean snow covered area in the catchment the ensemble spreads are reduced by two thirds compared to the results of a benchmark simulation based only on runoff for model calibration. Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain detection of the snow covered area from Sentinel-1 backscatter. The approach has high potential for water resources management in gauged and ungauged mountain basin and gives guidance for efficient data assimilation schemes.

Keywords: Cosmic-Ray Neutron Sensing High resolution optical snow cover fraction Sentinel-1 wet snow cover maps Snow hydrological modelling

1. Introduction The knowledge of the amount of water stored in the snowpack is of tremendous value not only for mountain regions, but also for many densely populated areas in the lowlands relying on snow fed mountain rivers (Viviroli et al., 2007; Sturm et al., 2017). Thereby, the snowpack acts as a seasonal water reservoir with the precipitation stored during the winter period being gradually released in spring and early summer (Verbunt et al., 2003; Viviroli et al., 2007). Mountain snowpacks are, however, very complex in its temporal and spatial features (Sturm et al., 1995). This considerably hampers the assessment of the snow water equivalent (SWE) in mountain regions. Multi-disciplinarity and

∗

innovative observational approaches allow for a better understanding of complex hydrological systems (Tauro et al., 2018). Thus, following the concept of complementary techniques (Sturm, 2015) the three legs of (1) in-situ data, (2) remote sensing, and (3) modelling and data assimilation are taken into account in this study. In-situ SWE data include information about the amount of water stored in the snow pack, are continuous in time, but represent only a small part of the area of interest. Snow pits, or snow cores, representing traditional snow sampling techniques, have a small measurement footprint, and are labour intensive, resulting in a large spacing and/or low repeat frequency of the surveys (Goodison et al., 1987; Stuefer et al., 2013; Kinar and Pomeroy, 2015).

Corresponding author at: Institute of Geography, University of Innsbruck, Innrain 52f, Innsbruck, Austria. E-mail address: [email protected] (P. Schattan).

https://doi.org/10.1016/j.rse.2019.111603 Received 22 January 2019; Received in revised form 25 October 2019; Accepted 9 December 2019 0034-4257/ © 2019 Elsevier Inc. All rights reserved.

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Widely used automated instruments like snow pillows (Johnson, 2004; Lundberg et al., 2010), snow scales (Smith et al., 2017), or SnowPackAnalyzer (Stähli et al., 2004) have a small measurement footprint introducing a considerable scale gap between the in-situ measurements, and the pixels of remote sensing or hydrological models. Furthermore, the small area around the instruments is affected by altered energy fluxes into the snowpack, and changes in local wind field (Lundberg et al., 2010). Snow pillows are prone to biases due to bridging caused by the development of thin ice layers (Johnson, 2004). Similarly, air gaps and wet snowfall may hamper the reliability of SPA measurements (Stähli et al., 2004; Lundberg et al., 2016). Thus, timevariant biases and a lack of representativeness are common (Molotch and Bales, 2006; Meromy et al., 2013; Grünewald and Lehning, 2015). Furthermore, this introduces a considerable scale gap between conventional in-situ measurements, and the pixels of remote sensing or hydrological models. A comparably novel measurement technique with the potential to bridge this gap is Cosmic-Ray Neutron Sensing (CRNS). Originally, CRNS was introduced for soil moisture monitoring (Zreda et al., 2008) at scales of several hectares (Desilets and Zreda, 2013; Köhli et al., 2015; Schrön et al., 2017). Recently, the technique was shown to be also suitable for in-situ SWE monitoring in both shallow (Desilets et al., 2010; Sigouin and Si, 2016), and mountain snowpacks (Schattan et al., 2017b). Remote sensing products have the clear advantage of covering larger areas. Furthermore, spaceborne remote sensing platforms are capable of operationally assessing the spatial patterns of snow. In satellite-based SWE products, however, mountain ranges are often masked out due to both the low sensor resolution of several kilometres and signal saturation (Rott et al., 2004; Liang et al., 2008; Clifford, 2010; Nolin, 2010; Dietz et al., 2012). The extent of snow covered area (SCA) has been monitored by optical satellite imaginary since decades (Dozier, 1989; Hall et al., 2002; Nolin, 2010; Dietz et al., 2012), though, there is a trade-off between sensor resolution and repeat frequency. While Landsat offers a spatial resolution of 30 m with a revisit time of 16 days, medium resolution sensors like e.g. Terra/Aqua MODIS, Suomi-NPP VIIRS, or Copernicus Sentinel-3A/B SLSTR/OLCI allow for daily data acquisition but the derived products have a spatial resolution of 1 km to 250 m. In combination, recent missions like Landsat-8 and Sentinel-2 reduce this trade-off offering high resolution images of 10 to 30 m spatial resolution with a revisit time of a few days only. Still, cloud coverage can significantly reduce the effective revisit time, especially in mountain regions. Another promising data source for mapping the extent of melting snow covered areas are high resolution Synthetic Aperture Radar (SAR) platforms like TerraSAR-X, or Sentinel-1. Change detection algorithms allow for the mapping of wet snow areas (Nagler and Rott, 2000; Nagler et al., 2016; Pettinato et al., 2013; RondeauGenesse et al., 2016). The resulting products are independent of the cloud coverage, but may include radar shadows in steep terrain, and are not able to discriminate dry snow from snow-free ground. SCA data can be used for SWE reconstruction (Durand et al., 2008; Schneider and Molotch, 2016), but only in a retrospective analysis. Snow modelling comprises a further complementary approach for assessing catchment-wide water resources such as SWE. Dedicated snow hydrological models with different degree of complexity exist (Essery et al., 2013; Avanzi et al., 2016). More complex models can resolve the processes occurring within the snowpack in a physically more meaningful way, but also require more precise input data. In complex mountainous terrain, this can constitute a serious drawback as uncertainties in the meteorological forcing can be substantial, most notably in the case of areal precipitation (Duethmann et al., 2013; Henn et al., 2018; Behrangi et al., 2018). River discharge is a valuable data source for calibrating hydrological models. It integrates, however, all relevant processes in the catchment. A hydrological basin comprises a complex system with different processes and states potentially compensating for each other. Thus, many parameter combinations may lead to good streamflow predictions (equifinality of model parameters

(Beven, 2012)), including physically not meaningful parametrisations. In particular, simulated snow states may not reflect real situations. Combining hydrological modelling with complementary information sources comprises a strategy to balance the limitations of these three legs while taking advantage of their respective explanatory power. Constraining the model with additional observations allows to reduce the uncertainties raising from model structure and meteorological input data, and to increase the realism of the simulation results (Rakovec et al., 2016). For instance, a large explanatory power was found for soil moisture and evaporation datasets (Kunnath-Poovakka et al., 2016; López et al., 2017; Nijzink et al., 2018; Rakovec et al., 2016). Similarly, the additional consideration of snow data has the potential to significantly reduce the uncertainty in modelling mountain water resources. SCA maps are widely used for model calibration (Kirnbauer et al., 1994; Parajka and Blöschl, 2008; Finger et al., 2011; Besic et al., 2014; Duethmann et al., 2014; Bellinger et al., 2012; Franz and Karsten, 2013; Schöber et al., 2014; Schöber, 2014), and also increasingly for assimilating model states (Fletcher et al., 2012; Thirel et al., 2013; Stigter et al., 2017). Space-borne remote sensing products have the clear advantage of covering an entire basin on an operational basis. The adverse characteristics include the fact that only information on snow coverage is available. Thus, further enhancements were reported when including mass related datasets like glacier mass balance (Finger et al., 2011; Finger et al., 2015; Etter et al., 2017), in-situ SWE measurements (Besic et al., 2012; Schattan et al., 2017a), ALS or TLS based SWE maps (Schöber et al., 2014; Revuelto et al., 2016) into model calibration schemes. Despite this general agreement on the value of additional snow observations for assessing mountain water resources, there is no comprehensive analysis on the selection of data sources. Both remote sensing and in-situ data are often chosen without considering their suitability in terms of scale, accuracy, and measured variable. For instance, differences in the explanatory power of MODIS and Landsat based binary SCA maps are evident (Hanzer et al., 2016). Still, many studies use binary medium resolution SCA products, although processing approaches allowing for the production of daily fractional SCA (fSCA) maps (Salomonson and Appel, 2004; Painter et al., 2009) and new high spatial resolution platforms are available. Only few approaches include fSCA maps (Franz and Karsten, 2013; Duethmann et al., 2014; Schneider and Molotch, 2016), or high resolution SCA maps (Durand et al., 2008; Schattan et al., 2017a; Schöber, 2014; Schöber et al., 2014). It is very likely that the more accurate data increase the accuracy of the model results, but no comparative studies exist. The selection of data resources can, however, considerably reduce the value of the data for reducing model uncertainty, as the model performance is governed by the explanatory power of the calibration data rather than by model complexity (Finger et al., 2015). In-situ data with a small measurement footprint (as used e.g. by Besic et al., 2014) can introduce large uncertainties in the model results due to the scale mismatch between observation and model (Schattan et al., 2017a). The goal of the present study is thus to introduce a calibration framework to select the most suitable snow observations. The approach is exemplified by evaluating the value of large-footprint SWE data using CRNS, fractional high spatial resolution SCA data, and wet snow covered area data. It is hypothesized that these novel datasets feature a high explanatory value. The impact of the suitability of the data sources is assessed in terms of reducing the uncertainty and improving the realism of a medium complexity snow hydrological model. Finally, the impacts of complementary information on the estimation of water resources in the absence of streamflow data are addressed. 2. Study area and data 2.1. The Upper Fagge Basin To investigate the value of different snow datasets the Upper Fagge research basin (52 km2) was chosen, as the hydrology of the basin is 2

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Fig. 1. The study area upstream of the Gepatschalm gauging station: (a) General overview with glacierised areas in light blue. (b) Footprint of the snow pillow, the snow depth sensors and the SnowPackAnalyzer (SPA). (c) Footprint of the Cosmic-Ray Neutron Sensor (CRNS). The orthophoto in the background was acquired by the Province of the Tyrol in 2015.

strongly influenced by snow accumulation and melt. With an elevation range from 1895 to 3509 m.a.s.l., most parts of the catchment are located above the tree-line. As of 2006, 39 % of the total area was glacierised (Fig. 1a). After the peak of snow accumulation in April, the runoff generation is dominated by snow melt until July, and by glacier melt thereafter.

to June 2014, and from October 2014 to June 2016. While the footprint of the SPA and the two SH gauges is much smaller than the 50 × 50m model unit (Fig. 1b), the CRNS signal averages over the area of several model units (Fig. 1c). Moreover, based on TLS campaigns with accompanying snow density measurements between October 2014 and June 2016, a total of 17 SWE maps in 1 × 1m resolution is available (Schattan et al., 2017b; Fey et al., 2019). SWE is calculated based on the automated measurements of snow density and the average of both SH gauges (SWESPA). The bulk LWC measurement can be used to evaluate snow wetness at the point scale (LWCSPA). Furthermore, SWE is inferred from changes in CRNS neutron count rates (SWECRNS). In contrast, the TLS based SWE dataset (SWETLS) is discontinuous in time but reflects the spatial variability of the area surrounding the Weisssee AWS (Schattan et al., 2017b). For comparison with space-borne fSCA products, selected TLS scenes were processed to 1 × 1m binary SCA maps (fSCATLS). The threshold to classify a pixel as snow-covered was set to a SH value higher than 0.03 m to reduce misclassification due to measurement uncertainty. These very high resolution binary maps were then aggregated by averaging to fSCA maps at the resolution of the respective remote sensing product. The major difference between the SWE datasets lies in the spatial scale of the measurement. While the footprint of SWESPA is much smaller than the grid cell of the model, the footprint of SWECRNS comprises around 80 grid cells (Table 1, Fig. 1). SWESPA and SWECRNS are used for model calibration. The TLS based data are used as a reference for evaluating both calibration data (in-situ and remote sensing) and model results.

2.2. Meteorological data The snow hydrological model requires the meteorological variables (i) air temperature [°C], (ii) precipitation [mm], (iii) relative humidity [%], (iv) wind speed [ms−1], and (v) global radiation [Wm−2] as input. The meteorological input is derived from the gridded 1 × 1km ZAMG INCA analysis product consisting of hourly datasets of temperature, precipitation, relative humidity, wind speed, and global radiation (Haiden et al., 2011). This dataset also integrates information from meteorological stations, including the automated weather station at the Weisssee Snow Research Site. Inverse Distance Weighting (IDW) is used to interpolate the data to the grid of the snow module. Additionally, a moving window approach considering the 24 nearest grid cells is used to extract local lapse rates of precipitation and temperature for every time-step. If the Pearson coefficient of the regression is lower than the threshold of 0.4, default lapse rates are used. These are based on previous calibration experiments including ALS SWE data and high resolution SCA maps (Schöber et al., 2014). 2.3. Discharge data Being of major importance for the natural inflow to the Gepatsch reservoir with an installed electric capacity of 325 to 392 MW, the Gepatschalm gauging station at the basin outlet is operated by the local hydropower company (TIWAG, Tiroler Wasserkraft AG). Hourly discharge data are available throughout the calibration and validation period from 10/2013 to 09/2016. The discharge data represents a bulk response of all relevant processes in the catchment and is used for model calibration (Table 1).

2.5. Snow covered area products Three different types of satellite based SCA products are used in the experiment for model calibration and validation. This includes high resolution fSCA (fSCAhighRes), medium resolution fSCA (fSCAmedRes) and SAR based wSCA. The fSCAhighRes maps were generated from a set of manually selected Landsat-7, Landsat-8 and Sentinel-2A scenes over the area of interest acquired between October 2013 and July 2016. The selection includes 14 scenes for model calibration period and 4 scenes for validation. The data were radiometrically calibrated and converted to top of atmosphere reflectances using the parameters provided in the associated metadata, keeping the original map projection of the data. To reduce the illumination effect on the reflectance data caused by topography, the top of atmosphere reflectances were topographically corrected using the method of Ekstrand (1996). If clouds obscure parts of a scene, such areas were masked manually. Also gaps in the input satellite data were masked. The total no-data area including all masks ranges between 0.05 % on 2016-04-29 and 30.46 % on 2015-05-28. The snow mapping approach of Salomonson and Appel (2006) was applied to

2.4. In-situ snow observations The Weisssee automatic weather station (AWS) is located in the central part of the research basin at an elevation of 2480 m.a.s.l. (Fig. 1). All in-situ SWE measurements were conducted at the station or in its close vicinity. The site is equipped with standard meteorological sensors, and a SnowPackAnalyzer (SPA) measuring snow density and liquid water content (LWC) accompanied by two ultra-sonic snow height (SH) gauges. In addition, a CRS1000 neutron detector (Hydroinnova LLC, USA), as used in the COSMOS-network (Zreda et al., 2012), was installed. Continuous CRNS measurements exist from March 2014 3

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Table 1 Overview of the data used for calibration and validation of the snow hydrological model. Q

fSCAmedRes

Type Sensor

Discharge Gauge

Terra MODIS

Variable Unit Temporal resolution (Calib) Temporal resolution (Valid) Spatial resolution Scale of measurement Scale of comparison Evaluation against

Q m3s−1 1h 1h Bulk 52 m2 -

349 scenes 220 scenes 250 m > Model 250 m fSCAhighRes

fSCAhighRes Remote Sensing Data Landsat 7 Landsat 8 Sentinel-2A fSCA % 14 scenes 4 scenes 50 m < Model fSCATLS

Usage

Calibration

retrieve the fractional SCA per pixel. On glaciers identified by the Randolph Glacier Inventory v6.0 (RGI, 2017), a binary discrimination of snow and ice areas is added when parts of the glaciers became snow free. The resulting high resolution snow maps from Landsat and Sentinel-2 data were bilinearly resampled to the grid size of the snow model (50 × 50m). The 250 and 500 m bands of Terra MODIS data were used to generate daily fractional snow cover maps (fSCAmedRes) over the area of interest using a multi-spectral linear unmixing approach applied on the geolocated top of atmosphere reflectance values. Local end-members for the multi-spectral unmixing were selected based on a binary preclassification of snow and snow free areas using the normalized difference snow index (Riggs et al., 1994). Clouds were masked using the MOD35_L2 product provided by NASA. Daily products for the full alpine area are available since October 2012. The product is used in its original 250 × 250m resolution with the model results being aggregated for comparison. Scenes with the entire basin being masked due to cloud cover were excluded. The number of scenes with data coverage over the study area is 349 for the calibration period and 220 for the validation period. Within this selection, the total data gaps, summing up cloud cover and other masks, are 24.23 % and 30.29 %, respectively. Sentinel-1A SLC data were used for the generation of wet snow cover maps over the area of interest during the melting seasons of the years 2015 and 2016 using the method described by Nagler et al. (2016). Dry snow and snow free pixels have very similar backscattering characteristics in the C-band SAR and are combined in one class. Forested pixels as well as pixels affected by radar shadow or foreshortening were masked. The total number of masked pixels is in the range of 5 to 10 %. The wet snow cover maps were generated with 100 m pixel size and were oversampled to the grid size of the snow model (50×50 m) using the nearest neighbour sampling method. 19 scenes are available during the calibration period, and 5 during the validation period. The fSCA datasets fSCAmedRes and fSCAhighRes differ in spatial scale and temporal coverage (Table 1). The scale of comparison with the model is adapted to the spatial scale of the data to reduce uncertainty caused by differences in spatial resolution. In addition to snow presence or absence, wSCA data include the complementary information of the hydrologically relevant presence of meltwater in the snowpack. All remote sensing datasets are used for model calibration.

wSCA

SWESPA

Sentinel-1A wSCA binary 19 scenes 5 scenes 100 m > Model 50 m fSCAhighRes LWCSPA

SWECRNS In-Situ SWE Data CRS1000

SPA2

1h 1h 1 m2 ≪Model SWETLS

SWE mm 12 h 12 h 200,000 m2 ≫Model 200,000 m2

SWETLS VZ-6000 VZ-4000

11 scenes 6 scenes 1 m2 > Model Validation

techniques (Sturm, 2015), and the integration of space-borne data with in-situ measurement techniques. The general overview of the data types and their potential for calibrating and validating the hydrological model is presented in Fig. 2. Detailed information can be found in Table 1. The core of the framework comprises a snow hydrological model with interfaces to the different snow datasets. In a first step, the data is evaluated against the reference datasets listed in Table 1. In a second step, a calibration experiment based on 40,000 Monte Carlo model runs is set up to test the value of different snow data for constraining model parameters. All simulations start with a warm up period from 10/2012 to 09/2013. The period 10/2013 to 09/2015 is used for calibration, and the following hydrological year from 10/2015 to 09/2016 for validation. The firn storages were initialised by a deterministic model run with standard parameters used in the operational mode of the model (Schöber et al., 2014) from 10/2003 to 09/2012. During calibration, individual efficiency criteria are calculated and stored. To evaluate the value of different combinations of calibration data (see Table 2), for each combination the individual criteria Ei are first combined into one overall Efficiency criterion Ecombined (Eq. (1)). Being based on KGE, Ecombined (Schattan et al., 2017a; Nijzink et al., 2018) is dominated by the element with the poorest fit and has its optimum at unity. In the next step, all simulations are ranked by Ecombined, and the best 40 runs are selected. The ranking by a combined efficiency value has been shown to be a suitable method to compare different realizations of a quasi multi objective calibration and to evaluate whether the selected model results are physically meaningful (Finger et al., 2011, 2015; Schöber, 2014).

Ecombined = 1

(E1

1)2 + (E2

1) 2 + ...+ (En

1)2

(1)

To assess the realism of the hydrological model, an evaluation of different variables representing dominant processes in the basin is performed. The explanatory power of additional snow datasets is evaluated against a benchmark calibration using runoff only (set B). The temporal evolution of the model state variables, and their spread among the 40 best model runs give insights into both the ability of the model to provide a physically meaningful representation of the underlying processes, and the associated model uncertainty. Furthermore, boxplots of ensemble spread and efficiency criteria summarise the uncertainty and the accuracy of the simulated states. The other sets (1 to 3) of the experiment aim at evaluating different aspects of using the data for model calibration. The first set focuses on space-borne data. Combination 1 uses runoff and fSCAmedRes data, as has been used in many previous studies, to evaluate the improvement of the combinations 2 (using fSCAhighRes) and 3 (using wSCA). The complementarity of fSCAhighRes and wSCA is tested in combination 4. In the second set, in-situ SWE data is added to combination 2. Thereby, the value of SWECRNS (combination 6) is compared against

3. Methods 3.1. Experimental setup The main aim of the calibration framework is to test the suitability of different snow observations with regard to spatial scale and explanatory value. The concept is based on the use of complementary 4

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Fig. 2. Calibration and validation concept based on the complementary legs of discharge data, space-borne snow covered area (SCA) data and in-situ snow water equivalent (SWE) data.

Thus, prior to interpreting the exploratory value of individual snow datasets, the suitability of the calibration datasets in terms of spatial scale and pattern representation is assessed. For that purpose, the individual datasets are compared against independent snow observations (see above and Table 1). First, the high resolution fSCA product is evaluated against TLS based fSCA maps. For a better comparison, the space-borne fSCA product is masked to the extent of the TLS data. Two dates where a TLS campaign closely matches in time with a satellite acquisition were chosen. In peak accumulation conditions, a TLS campaign was conducted on 2015-04-09 (Fig. 4a,b), while a Landsat-8 scene was acquired the following day in the morning (Fig. 4-c). During melt-out, both TLS and Landat-7 data were available for 2015-06-05 (Fig. 4d–f). Based on this assessment, the medium resolution product can be evaluated against the more detailed remote sensing data. In this way, the characteristics of basin-wide snow patterns can be interpreted. For three dates with different snowline altitudes (2015-04-10, 2015-06-05, and 2015-07-15) Landsat-7 and 8 based fSCAhighRes maps are aggregated to the resolution of the MODIS based product. The plausibility of the Sentinel-1 based wSCA maps is evaluated using snow cover masks based on fSCATLS and fSCAhighRes data and point-scale LWCTLS observations. The in-situ SWE observations (SWESPA and SWECRNS) are compared against SWETLS.

Table 2 Overview of the experimental setups of the hydrological model.

0 1 2 3 4 5 6 7 8 9 10 11

Set

Q

B 1 1 1 1 2 2,3 2 3 3 3 3

x x x x x x x x

fSCAmedRes x

x

fSCAhighRes

x x x x x x x

wSCA

x x

SWESPA

x

SWECRNS

x

x x

x

x x x

SWETLS

including conventional small-footprint measurements of SWESPA (combination 5). Combination 7 extends combination 4 by SWECRNS featuring observations describing the bulk runoff response, total snow coverage, wet snow coverage, and large-footprint SWE data. The aim of the third set is to test the value of the snow observations for modelling the water resources in the absence of streamflow observations. The benchmark run and combination 6 are used as a reference. Combination 8 uses only MODIS data (fSCAmedRes) as is common practise. In contrast, combinations 9 to 11 use the innovative SWECRNS data alone (combination 9), and in conjunction with spaceborne data (fSCAhighRes in combination 10, fSCAhighRes and wSCA in combination 11).

3.3. Snow hydrological model A medium complexity snow module based on the SES model (Asztalos, 2004; Asztalos et al., 2007), being part of the modular framework for modelling the runoff response in the Inn River basin (Fig. 3), was chosen. The distributed snow module, set up at a 50 × 50m resolution, is coupled one-way to a semi-distributed soil module from the HQsim model (Achleitner et al., 2012; Bellinger et al., 2012; Kleindienst, 1996) with 167 hydrological response units (HRUs) (Schattan et al., 2017a). The simulation of snow and ice processes on the ground is based

3.2. Assessing the calibration datasets All calibration datasets are in a way based on transfer functions with inherent assumptions and associated uncertainties. Furthermore, the scale of the data differs substantially (see Table 1). As a consequence, the data might not always fully reflect the true state of the snowpack. 5

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Gridded Meteo Input

Meteo Station Data

(ZAMG INCA) [1 x 1 km]

(135 Stations)

Canopy Module

Canopy Module

(Energy Balance)

(Simplified)

[50 x 50 m, or HRU]

[HRU]

Snow Melt

Snow Melt

(Surface Energy Balance)

(Temperature-Index)

[50 x 50 m, or HRU]

[HRU]

approximated by an empirical function (Webster et al., 2017). The canopy is connected to the ground layer via unloading of the intercepted precipitation, considering both rain and snow, and an altered energy input including short-wave, long-wave, and turbulent fluxes. The resulting meltwater outflow and potential evapotranspiration are aggregated to HRUs, and serves as an input for the soil module. There, soil infiltration and surface runoff are separated by a parametrization of contributing area (Achleitner et al., 2012). A closed form equation is used to calculate soil hydraulic conductivity as a function of current soil moisture and saturated hydraulic conductivity (van Genuchten, 1980). The runoff is concentrated according to the timearea method (Morgali and Linsley, 1965) and routed based on a nonlinear storage release with adaptive time-steps for each channel segment. Flow velocities are calculated using an equation for steep channels (Rickenmann, 1996).

Soil Storage Runoff Concentration Runoff Concentration

[HRU]

[Parallel Nash Cascades]

3.4. Interfaces between the model and observed snow data

Hydrological Routing [Stream Segments]

To evaluate model results with regard to observed discharge and snow data, a number of output modules have been implemented in the hydrological model. These comprise discharge, point-scale SWE, CRNS based SWE, fSCA, and wSCA. For comparing discharge data, a time-series of simulated discharge can be exported at a given river segment. The Kling-Gupta-Efficiency (KGE) (Kling et al., 2012) is used to evaluate the simulations as it combines r2, a ratio of the coefficients of variation, and a bias ratio (Eq. (2)):

1D Hydraulic Routing (Inn River) Flood Forecasting Water Resource Management Fig. 3. Modular framework for modelling the runoff response in the Inn River basin. The modules marked in blue are used in this study.

upon a simplified energy and mass balance scheme with up to three layers representing bare ice, firn, and snow. The bare ice layer is initialised with glacier outlines based on the third Austrian Glacier Inventory (AGI-III) (Fischer et al., 2015). The firn layer is initialised by a model run starting in 09/2003 and is practically only present on glaciers. For all layers, the topographically induced effects of selfshading and terrain shading on short-wave irradiance are explicitly accounted for. Snow albedo is altered with solar angle and energy input into the snowpack (Asztalos, 2004). In general, the model assumes each layer to be homogeneous within one grid cell, and thus uses the albedo values for new snow, old snow, bare ice, or the snow-free surface. In the case of SWE being below a defined threshold, however, shallow pack albedo values are used to account for mixed pixel situations, i.e. the grid cell being partly snow-free. Incoming long wave radiation is estimated from air temperature, relative humidity, and cloudiness (Blöschl et al., 1991a). The combined effects of preferential deposition during a precipitation event, and gravitational or wind induced snow redistribution are parametrized based on slope and curvature of the terrain (Blöschl et al., 1991b). Outgoing long wave radiation is calculated based on the assumption of a snow temperature of 0° C and a high emissivity (Blöschl and Kirnbauer, 1991). Turbulent fluxes and associated sublimation are parametrized based on a wind function (Blöschl and Kirnbauer, 1991; Blöschl et al., 1991a). The snow accumulation season is assumed to start on October 1. By that date, 90 % of the seasonal snow is converted into firn, while firn exceeding 3000 mm of water equivalent is converted into ice (Asztalos, 2004). A bulk parametrization is used for processes associated with thermal conductivity and liquid water storage capacity (Braun, 1985; Blöschl and Kirnbauer, 1991). Linear storages are used to route the liquid water through the individual layers. The parameters Leaf-Area-Index, roughness height, and snow-free albedo needed for calculating potential evapotranspiration (Monteith, 1965) are derived from the ECOCLIMAP II dataset (Faroux et al., 2013). An additional canopy layer is considered in forested areas. The energy and mass balance, including sublimation and potential evaporation, is based on the one layer scheme implemented in Snowpack (Gouttevin et al., 2015). The energy balance is, however, not completely resolved but the long wave radiation within the canopy is

KGE = 1

(r

1) 2 + (

µs µo

s

1) 2 + (

µs o

µo

1)2 (2)

where r is the correlation between the simulated (s) and observed discharge (o), μ is the mean discharge [m3s−1], and σ is the standard deviation of the discharge [m3s−1]. One or more points can be selected to export a number of predefined model states, i.e. the modelled SWE, of the corresponding grid cell. Due to the model assumption of homogeneity within one model unit, subgrid variability is neglected. This module is used to compare modelled SWE at the Weisssee AWS against SWESPA. For SWE data, KGE is used as an evaluation criterion to account also for differences in temporal dynamics. Based on the spatial weighting function developed for calibrating CRNS against soil moisture (Köhli et al., 2015), a module was implemented to export a time-series of SWE that is equivalent to the CRNS signal. A weighted average SWE with time-varying weights reflecting the distance to the CRNS location, the absolute air humidity, and the presence or absence of snow is calculated. The assumptions regarding the moisture of the surface, and the behaviour of the signal during the melt-out phase in early summer are based on empirical findings from the site (Schattan et al., 2017b). To calculate the weighting function (Köhli et al., 2015), surface moisture is assumed to be 99% in snowcovered conditions, and 20% if the pixel is snow-free. In the case of melting snow, defined by the presence of liquid water exceeding the threshold of water holding capacity, the SWE value of the grid cell is limited to 200 mm. Observed and modelled SWECRNS is compared by means of KGE. As at the location and with the detector used, the CRNS derived SWE estimate is considerably less stable for SWE exceeding 400 mm (Schattan et al., 2017, 2019), periods with observed SWECRNS above this threshold are excluded (see Fig. 7a). A binary map is produced to compare the model against the wSCA product. Snow-covered pixels with a liquid water content above 5%, corresponding to the wet snow class (Fierz et al., 2009; Techel and Pielmeier, 2011), are classified as wet snow. All other grid cells, i.e. snow-covered pixel not meeting this condition and snow-free pixels, are assumed to reflect the dry snow or snow-free category of the product. 6

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Fig. 4. Comparing high resolution fSCA products with TLS data: (a) TLS based binary SCA map in 1 × 1m resolution (2015-04-09). (b) TLS based fSCA resampled to 50 × 50m resolution (2015-04-09) (c) Landsat-8 based fSCA product (2015-04-10), masked to the extent of the TLS data. (d) TLS based binary SCA in 1 × 1m resolution (2015-06-05). (e) TLS based fSCA resampled to 50 × 50m resolution (2015-06-05) (f) Landsat-7 based fSCA product (2015-06-05), masked to the extent of the TLS data.

Based on a contingency table, the accuracy criterion (ACC) (Zappa, 2008) is used to compare the simulated and the satellite based wSCA maps (Eq. (3)):

ACC =

(n11 + n 00) n xx

value of 100%. As contingency table based approaches are suitable for binary data only, in previous studies fSCA was evaluated against the mean absolute error (MAE) (Franz and Karsten, 2013), or the root mean squared error (RMSE) (Duethmann et al., 2014). To be comparable with the other criteria having their optimum at unity, the following fSCA efficiency (EfSCA) based on RMSE subtracted by 1 was used (Eq. (4)):

(3)

where the sum of correct wet snow covered pixels n11, and correct dry snow pixels n00 is divided by the total number of valid grid cells nxx excluding no-data areas. As the model doesn’t continuously simulate the snow cover fraction of a grid cell, a simplified scheme is used to export fSCA. A fSCA value of 50% is assigned to grid cells where the model simulates a shallow snowpack representing a mixture of snow covered and snow-free areas in the pixel. In contrast, a snow-free grid cell with a fSCA value of 0% is defined by the absence of snow and firn, thus being covered by bare ice or bare ground. The remaining grid cells are classified with the fSCA

EfSCA = 1

(

fSCAobs )2

(fSCAsim n

)

(4)

where n is the number of valid observations, excluding grid cells with data gaps in the observation (no-data areas). The model output can be compared directly to fSCAhighRes maps, without further processing. In the case of fSCAmedRes maps, the model results are aggregated to the 250 × 250m resolution of the remote sensing product first. 7

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4. Results

pronounced in December, or January. To a certain degree, fSCA data can also serve as a reference for the Sentinel-1 based wSCA maps. A comparison of both products for different dates between late April and mid-July is shown in Fig. 6. In Fig. 6a a comparison with TLS data is shown, whereas in Fig. 6b the data for the entire basin is compared with fSCAhighRes data. Both the fSCATLS and the fSCAhighRes data are not differentiating between dry and wet snow, mapping rather the total snow extent. Areas with fSCA below 50 % are therefore masked in green and the wSCA maps are evaluated only for areas with snow coverage. Fig. 6c shows LWCSPA data where the grey bars mark the dates shown in panels (a) and (b). Snow station data prove melt conditions for more than a week prior 27 April 2015. SWE was still in the order of 500 mm (Fig. 7) and LWC was between 4 and 5%. This confirms a wet snowpack around the station. According to the optical image, the higher elevation zones are snow covered, but not mapped as wet snow in the wSCA product, which is plausible. Melt conditions were dominating between 27 April and 29 May 2015 only interrupted by one snow accumulation event on May 21. LWC was at the station around 8 % on May 20 but decreased with the later snow accumulation and reached 5 % on May 29. In the following days prior to melt-out on 15 June 2015 LWC reached maximum values of 10 to 12 %. Thus, the catchment area was almost entirely covered by wet snow on May 29. The widespread occurrence of dry snow in elevations between above the snowline and zones classified as melting snow is, however, highly unlikely. This phenomenon is observable at all dates plotted in Fig. 6, and can be interpreted as an underestimation of wSCA in the lowest snow covered elevation bands. The temporal evolution of the snow parameters SWE, fSCA and wSCA gives further insights into the characteristics of the different datasets used within this study. Fig. 7 illustrates the time-series of (i) SWE measurements at the Weisssee Snow Research Site (Fig. 7a), (ii) fSCA and wSCA values in a 250 m radius around the Weisssee Snow Research site (Fig. 7b), and (iii) basin-wide averages of snow coverage (Fig. 7c). Using TLS based SWE (SWETLS, green triangle) as a reference, SWESPA (orange line) largely overestimates snow accumulation in the 2014/15 winter season. In the following season (2015/16), in contrast, the performance is considerably better. Still, SWE is underestimated in November and December, and overestimated in May. Thus, biases between area averages of SWE and SWESPA are not stable in time, hampering upscaling procedures. In both winter seasons, melt-out at the AWS is very fast, while it is less abrupt if considering a 250 m buffer. The KGE-values for SWESPA and SWECRNS are 0.60 and 0.95, respectively. This underlines the disadvantages associated with the assumption of a single point measurement being representative for the grid cell around it. SWECRNS represents the areal evolution of SWE well, but is very noisy above a threshold of approximately 400 mm (see also Schattan et al., 2017b). In a temporal perspective, the remotely sensed fSCA values from high and medium resolution retrievals in the 250 m radius around the Weisssee AWS (blue triangles, and black crosses in Fig. 7b) correspond well to the SWE dynamics (Fig. 7a). SWE measurements, TLS based fSCA (green triangles), and fSCAhighRes indicate a longer snow covered period than fSCAmedRes showing a very fast decrease of snow cover in spring. At basin scale this effect is averaged out, but it is observable as noise in the temporal evolution of the signal (Fig. 7c). A clear advantage of the medium resolution data is the daily revisit time allowing for capturing a large portion of snow dynamics in the basin. The RMSE of the fSCAhighRes data is 0.07 when using fSCATLS as a reference. For fSCAmedRes the RMSE is higher being 0.12 when using fSCAhighRes as a reference and 0.17 when using fSCATLS as reference. In the case of wSCA (red triangles), a systematic underestimation during melt-out is apparent in both basin average (Fig. 7c), and in the 250 m radius surrounding the Weisssee AWS (Fig. 7b). Dry snow conditions during snow accumulation in the winter season 2015/16 are, however, plausible, as is the onset of snow melt in 2015 and 2016.

4.1. Assessing the calibration datasets The evaluation of the high resolution fSCA product is illustrated in Fig. 4. In the first column the original binary TLS based SCA maps, in the second column the TLS based fSCA maps at 50 × 50m resolution, and in third column the remote sensing based fSCAhighRes maps are shown. Photographs taken at the dates of the TLS campaigns illustrate the snow conditions (Fig. 4d and h). During peak accumulation, both data sources (TLS and Landsat-8) agree on a very high snow cover fraction close to 100 %. Small snow free areas in the binary TLS SCA map (Fig. 4a), like the cleared access road or rough areas with rock outcrops (Fig. 4d), are reflected in fSCA values below 100 % in the aggregated TLS data (Fig. 4b). These details are not reproduced in the Landsat-8 based fSCA map where all pixels are classified as fully snow covered (Fig. 4c). The mean fSCA for the scene is 0.99 for fSCAhighRes and 0.98 for the original binary TLS data. The difference between the binary TLS data and the aggregated data is in the range of 0.5 % of fSCA (0.980 and 0.975, respectively). In early June, when snow-free patches are revealing differences in snow deposition patterns and surface energy fluxes, the heterogeneity of the snow cover is considerably higher. The larger patterns of the TLS based fSCA maps (Fig. 4f) are well reflected in the Landsat-7 based product (Fig. 4g), especially where larger areas are either entirely snow covered, or snow-free. The transition zones are, however, captured less precisely. The Landsat-7 based map tends to overestimate fSCA in subsets with small fractions of snow-free areas as shown in Fig. 4h, but to otherwise underestimate fSCA where small snow-covered patches are present in snow-free areas. This corresponds with the findings presented above for peak accumulation conditions. Still, for both dates the overall agreement of the space-borne and the TLS based fSCA maps is very high. The difference in mean fSCA for the scene is larger than for the scene in April being 0.65 for fSCAhighRes and 0.59 for the original binary TLS data (0.60 for the resampled TLS data). In Fig. 5, the evaluation of the medium resolution product is shown. In the first row, the false colour Landsat scenes, in the second the aggregated Landsat based fSCA maps, and in the third row the MODIS based data are shown. For peak accumulation conditions (2015-04-10), both products consistently map a high snow coverage in the basin (Fig. 5b–c). Differences appear only in individual grid cells where fSCAmedRes maps show a full snow cover of 100 %, whereas in the aggregated high resolution data a small snow-free fraction in the range of a few percent is present. During snow melt (2015-06-05), agreement in the general pattern is high, but differences are obvious in the transition zone between generally snow-free and snow-covered areas (Fig. 5d–f). While the spatial pattern of snow coverage is more fragmented in the aggregated fSCAhighRes data, it appears very smooth in the medium resolution map. Hydrologically important differences include an overestimation of snow coverage in steep areas, or an underestimation at the glacier tongue of Gepatschferner. Another remarkable underestimation of snow coverage is visible in mid-July, where small-scale snow patches in the Western part of the basin (Fig. 5g), being covered well by the high resolution product (Fig. 5h), are entirely snow-free in fSCAmedRes data (Fig. 5i). Besides that, the false colour composites illustrate the combined impact of steep terrain and low solar angles on optical remote sensing. In mid-July the scene is well illuminated (Fig. 5g). In addition, snow is only present in the highest elevation zones where effects of terrain shading are low. The half shades like on the plateau of Gepatschferner, located in the South-Eastern part of the basin, can be processed well by applying topographical corrections. In April (Fig. 5a), however, many fully shaded areas exist resulting in a large fraction of the map being subject to major processing uncertainties. It is noteworthy that this in many cases affects steep slopes which are particularly interesting in terms of gravitational snow transport. This issue is even more 8

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Fig. 5. Comparing medium resolution fSCA products with high resolution fSCA data: (a) Landsat-8 false colour composite (2015-04-10). (b) Landsat-8 based fSCA product (2015-04-10) (c) MODIS based fSCA prouct (2015-04-10). (d) Landsat-8 false colour composite (2015-06-05). (e) Landsat-8 based fSCA product (2015-0605). (f) MODIS based fSCA product (2015-06-05). (g) Landsat-8 false colour composite (2015-07-15). (h) Landsat-8 based fSCA product (2015-07-15). (i) MODIS based fSCA product (2015-07-15).

4.2. Benchmark calibration using runoff only

mm in the calibration and validation period, comparably large spreads of the Q25/Q75 values of SWE are the case, ranging between 86 to 184 and 78 to 159 mm respectively. The simulations of basin-wide fSCA and wSCA agree well with the observations, except for the delayed melt-out in summer 2014 (Fig. 8c). For the fSCA the spread among simulation runs is smaller than for basin-wide SWE. The mean fSCA of 0.04 to 0.05 is similar in the calibration and validation, with a range between 0.010 to 0.07 and 0.015 to 0.08 for the 25 to 75 percentile of the fSCA. In addition, the RMSE of simulated fSCA using the High Resolution fSCA as ground truth is shown. Mean RSME are found to be 0.11 and 0.06 associated with a small spread among the runs. In contrast, a considerably larger spread is found for wSCA. The simulation results agree with regard to the onset of melt as seen in wSCA data, but follow the optical data in the melt-out phase. Intermediate melt-events like in October and November 2015 are reproduced in the model, but to a smaller extent than reported in fSCAmedRes data. This supports the assessment of wSCA and fSCAmedRes data presented above. Similar to the results at basin scale (Fig. 8b), a

Fig. 8 shows the results of the best 40 runs when using discharge only in the benchmark calibration. Besides daily time series of discharge, SWE fSCA and wSCA, each of the parameters is evaluated with regard to their ensemble spread in the calibration and validation period. Considering the discharge hydrograph, the seasonal dynamics are well captured whereas the model partly over- or underestimates at event scale (daily discharges) (Fig. 8a). The box plots on the right hand side indicate that the given spread is similar for the discharge time series in the calibration and validation period. Mean discharge of 0.53 to 0.54 m3s−1 are found to have as well similar ranges between Q25 and Q75 (0.18 to 0.19 and 0.75 to 0.78) in the calibration and validation period respectively. The mean KGE of 0.89 obtained in the calibration drops to 0.80 in the validation, showing as well a larger spread of the Q25/Q75 range of KGE increasing form 0.88/0.90 to 0.77/0.85. The simulated average SWE in the upper Fagge basin (Fig. 8b) show a wide spread among the best runs. With the mean SWE of 137 mm and 122 9

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Fig. 6. Sentinel-1 based wSCA classification in areas identified as snow covered by (a) Terrestrial Laser Scanning, and (b) high resolution optical imagery. (c) Timeseries of point-scale liquid water content measurements at the Weisssee Snow Research Site.

Fig. 7. Comparing the time series of snow data from different data sources at the location of the Weisssee Snow Research Site: (a) Snow Water Equivalent (SWE). (b) Mean Snow Covered Area (SCA) in a 250 m radius around the station. (c) Mean Snow Covered Area (SCA) in the entire Upper Fagge Basin.

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Fig. 8. Benchmark calibration using runoff only: (a) Daily Runoff. (b) Mean Snow Water Equivalent (SWE) in the Upper Fagge Basin. (c) Mean Snow Covered Area (SCA) in the Upper Fagge Basin. (d) Snow Water Equivalent (SWE) at the Snow Research Site. (e) Mean Snow Covered Area (SCA) in a 250 m radius around the Snow Research Site.; Box plots of ensemble spreads at each hydrograph include the mean value (green triangle), median value (orange line) and 25/75 percent quantiles.

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large range of simulated SWE at the Weisssee Snow Research Site (Fig. 8d) is the case. In the calibration and validation period, the mean SWE of 72 and 56 mm had a spread of percentiles 25 and 75 in the range of 7.7 to 124 mm and 6.7 to 92 mm respectively. The mean KGE using the TLS based dataset as ground truth are 0.47 and 0.52 for the calibration and validation period, having as well a substantial spread among the simulation runs. Compared to CRNS and TLS based measurements, the model tends to overestimate SWE but is quite close to the SPA based SWE data. Still, the range of simulated values covers all in-situ SWE measurements. The fSCA and wSCA values in a 250 m radius around the station are in good agreement with the measurements, but show a large uncertainty in melting periods (Fig. 8e).

effect of adding SWESPA to the objective function is shown in Fig. 9f. In combination, runoff, fSCAhighRes, and SWESPA, considerably reduce the range of simulated SWE values to 3 to 56 and 3 to 45 mm having a mean value of 30 and 24 mm. The simulation results closely follow the observed SWE data. SWESPA which is, however, partly biased towards higher SWE values. As a consequence, the uncertainty of this in-situ observational dataset is fully propagated into the simulation results. The resulting mean KGE for this case (0.73/0.73) is thus somewhat lower than for case (e) in Fig. 9. Furthermore, the given spread for the KGE in the calibration and validation period increases (0.66 to 0.83 and 0.63 to 0.87). Using SWECRNS instead of SWESPA, further tightens the range of SWE estimations (Fig. 9g). In addition, the simulations match well with both the TLS data points, and the CRNS SWE in the validation period. Mean SWE values drop to 14 and 10 mm. The mean KGE in the calibration period increases to 0.77, whereas only a slight reduction to 0.78 is found in the validation period compared to case (e) in Fig. 9 (including wSCA). Further, a very narrow spread is the case for both periods ranging from 0.74 to 0.79 and from 0.74 to 0.82. A further improvement is visible in the years 2015 and 2016 if wSCA and SWECRNS are both added to the objective function (Fig. 9h). Still, the formal performance of the simulations in the calibration period reduces a bit down to a mean KGE of 0.72, most likely to some larger deviations in 2014. In the validation period the mean KGE of 0.84 is the highest of all cases shown in Fig. 9. Summarizing the results obtained for the snow research site Weisssee, it can be said that the tested complementary selection of different datasets is able to reduce modelling uncertainties. These improvements come along with a reduced uncertainty in other snow state variables, as illustrated in Fig. 10. The ranges of basin mean SWE, basin-wide averages of fSCA and wSCA, and the average fSCA and wSCA in a 250 m radius around the weather station are considerably tighter than for the benchmark calibration runs. The only variable still showing a rather high degree of uncertainty is wSCA at basin-level. Overall, also the runoff simulations are in good agreement with measured data, though with a slightly higher range of uncertainty than in the benchmark calibration.

4.3. The value of SCA products As shown above, the exclusive use of discharge for calibration introduces a non-negligible uncertainty in the simulated snow states on basin as well as on local (snow research site) scale. Using one or more snow products in addition to runoff is expected to improve the calibration. Thus, the model performance of SWE at the Weisssee Snow Research Site is analysed. Besides analysing the spread of simulation results, a direct comparison to observations, including TLS based SWE maps is made (Fig. 9). For an easier comparison, the SWE time series based on the runoff-only benchmark calibration is shown in the first row of Fig. 9a. Each time series of SWE is accompanied by a box plot of the ensemble spread (25 and 75 quantiles) in the calibration and validation period. Second, the mean KGE and its spread (including 25 and 75 quantiles) using the TLS based SWE as a reference are shown. Including fSCAmedRes into the calibration only slightly reduces the bandwidth of SWE values from the best 40 model runs (Fig. 9b). It increases, however, the tendency to overestimate SWE at the research site. Accordingly, the mean values in the calibration and validation period drop including a reduction of the spread of the results. Mean SWE values are reduced from 73/56 mm to 53/44 mm. The spread reduces to quantile range (Q25/75) 8 to 84 and 11 to 63 mm in the calibration and validation period. The use of runoff and fSCAhighRes has a similar effect (Fig. 9c). Compared to both the benchmark and the calibration including fSCAmedRes data, the range of possible values is further reduced. Mean SWE is thereby 48 and 38 mm having a Q25/75 spread of 5 to 84 and 6 to 61 mm. Simultaneously, simulated SWE are at the higher end of the bandwidth marked by the benchmark simulations, resulting in a clear positive bias. Thus for the cases in Fig. 9a–c the reduction of bandwidth in the model runs causes a simultaneous drop in the mean KGE (from 0.47/0.52 to 0.37/0.34 and 0.38/0.39 respectively). Combining runoff observations with wSCA data results in an opposite behaviour (Fig. 9d). The range gets again narrower, but SWE is underestimated in most periods. Resulting mean SWE are 36 and 25 mm. Still, calculated KGE is slightly higher again (0.47/0.50). Both effects of narrow bandwidth and underestimations are likely due to biases in the remote sensing data. In the optical data, fSCA is often biased towards 100 %, whereas wSCA clearly underestimates patchy snow cover in the melting season. Having these limitations in mind, runoff was combined with both, fSCAhighRes and wSCA data (Fig. 9e). This combination clearly outperforms the selections based on a single remote sensing product. Mean SWE is found to be 40 and 29 mm for calibration and validation periods, where both have a reduced spread (1 to 73 and 1 to 58 mm) compared to the baseline scenario. A substantial improvement is observed looking at the KGE, being 0.68 and 0.81 for calibration and validation. Even the spread of KGE reduces to values of Q25/Q75 being 0.67/0.74 and 0.75/ 0.89. Moreover, the simulations match very well with SWE observed by TLS and CRNS. This confirms the usefulness of complementary spaceborne datasets. The integration of space-borne and in-situ data is expected to further improve the model results. The value of SWESPA and SWECRNS is illustrated by combining these datasets with runoff and fSCAhighRes. The

4.4. Implications for water resource management in gauged and ungauged basins In the so far presented results, the focus has been put on KGE as a time series performance indicator and the capturing of snow and runoff dynamics in general. Still, some water resource application such as operation of annual reservoirs, the simulation of annual runoff is as well of importance. Thus, getting the seasonal or annual discharge right is of similar (or even higher) value than the prediction of the interannual dynamics. Aiming to get annual runoff right leaves the question on what to do in ungauged basins or basins without direct runoff measurements. The application of models with a best possible representation of the snow state variables seems to be a promising way to get reliable results on annual runoff. To test this assumption, different calibration strategies in the upper Fagge basins were applied. The benchmark cases shown in Fig. 11 a and b used either runoff and runoff, fSCAhighRes, and SWECRNS respectively. In the calibration, annual runoff was used as optimization goal. The remaining model setups shown in Fig. 11c–f mimic ungauged basins where snow measurements and maps were used exclusively for calibration. For runoff evaluation, measured and simulated runoff time series were cumulated for hydrological years starting in October. Besides the cumulated runoff time series being predicted, all results are evaluated for the ensemble spread illustrated as boxplots. Further, the mean KGE and its spread is shown. Obviously, the results from the calibration runs including runoff are very well calibrated and perform well in the calibration as well as in the validation period. Some underestimation in the first two years as well as some overestimation in the last two years is observable. The spread of the ensemble is in the range of 50 m3s−1 for both the calibration and the validation period. The results of the calibration with runoff, 12

Fig. 9. Snow Water Equivalent (SWE) time series and Box plots of ensemble spreads at the Snow Research Site. The applied calibration procedures are based on (a) runoff only (benchmark calibration). (b) Runoff, and medium resolution fSCA (MODIS). (c) Runoff, and high resolution fSCA (Landsat 7/8 and Sentinel-2). (d) Runoff and wSCA (Sentinel-1). (e) Runoff, high resolution fSCA (Landsat 7/8 and Sentinel-2), and wSCA (Sentinel1). (f) Runoff, high resolution fSCA (Landsat 7/8 and Sentinel-2), and SPASPA. (g) runoff, high resolution fSCA (Landsat 7/8 and Sentinel-2), and SPACRNS. (h) Runoff, high resolution fSCA (Landsat 7/8 and Sentinel-2), wSCA (Sentinel-1), and SPACRNS.

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Fig. 10. Calibration using runoff, high resolution fractional snow covered area (fSCA), wet snow covered area (wSCA), and Cosmic-Ray Neutron Sensing (CRNS) SWE: (a) Daily Runoff. (b) Mean Snow Water Equivalent (SWE) in the Upper Fagge Basin. (c) Mean Snow Covered Area (SCA) in the Upper Fagge Basin. (d) Snow Water Equivalent (SWE) at the Snow Research Site. (e) Mean Snow Covered Area (SCA) in a 250 m radius around the Snow Research Site. Box plots of ensemble spreads at each hydrograph include the mean value (green triangle), median value (orange line) and 25/75 percent quantiles.

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Fig. 11. Calibration results with regard to cumulated simulated streamflow.

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fSCAhighRes, and SWECRNS (Fig. 11b) lead to mean KGE's of 0.90/0.79 being equivalent to the runoff only calibration having a KGE of again 0.91/0.79. When excluding runoff from the objective function, the ensemble spread increases, while the overall performance in terms of KGE is still good. The largest overestimation of the cumulated runoff can be observed when calibrating the model using fSCAmedRes only (Fig. 11c). The KGE values decrease to 0.74 for the calibration period and 0.72 for the validation period. The ensemble spread increases to 70 and 64 m3s−1, respectively. Similarly, the calibration using the combination of SWECRNS with fSCAhighRes and wSCA (Fig. 11f) results in overestimating the cumulated runoff sums. The ensemble spread is high (85 m3s−1 during calibration and 83 m3s−1 validation), while the KGE-values are low (0.75 and 0.71, respectively). The performance increases, however, when using only SWECRNS for calibrating the model (Fig. 11d). The overestimation is smaller, as are the values of ensemble spread (70 and 65 m3s−1 for the calibration and validation periods, respectively) and KGE (0.79 and 0.76). The best results are achieved by combining fSCAhighRes with SWECRNS (Fig. 11e). The ensemble spread decreases to 65 (calibration) and 60 (validation) m3s−1 and the KGE values are 0.83 for both calibration and validation period.

several hectares, SWECRNS represents the mean conditions well, but is less robust in situations with high snow amounts. This is basically caused by the non-linearity of the function relating neutron counts to SWE, combined with higher measurement uncertainties in the presence of deep snowpacks (Schattan et al., 2017b). As the statistical counting error corresponds to the square root of the count rate, a more sensitive detector could possibly increase the effective measurement range. The exact threshold is also site-specific, as neutron count rates change with altitude, geomagnetic cutoff rigidity, and local characteristics (Desilets et al., 2006, 2010). The simulation results underline that the uncertainties of individual snow datasets propagate into hydrological modelling. For example, the model tends to overestimate SWE when trained with runoff and fSCAmedRes, but to underestimate it when trained with runoff and wSCA. The uncertainty of fSCAhighRes does not lead to model biases in terms of SWE, as the snow hydrological model component only differentiates between fSCA values of 0, 50 and 100 %. However, the data would most likely not be precise enough to calibrate or validate more detailed subpixel schemes. Similarly, the medium resolution fSCA maps are still a valuable data source for monitoring entire mountain ranges, or for constraining the parameters of more conceptual large-scale hydrological models. The results prove that, as shown for evapotranspiration and soil moisture (Nijzink et al., 2018), integrating data from complementary information sources is also suitable to effectively reduce the uncertainty in snow hydrological modelling. This is backed by the good results of the combination of runoff, fSCAhighRes, and wSCA. The fSCAhighRes dataset outperforms the commonly used coarser MODIS based data (fSCAmedRes). A further improvement can be achieved by combining space-borne and in-situ data. Again, the selection of data source and their suitability in terms of spatial scale and accuracy matters. While conventional in-situ datasets with a small footprint such as snow pillows (Molotch and Bales, 2006; Meromy et al., 2013), buried CosmicRay Neutron probes (Besic et al., 2014), or SWESPA (Schattan et al., 2017a) introduce model biases, combining remote sensing data with SWECRNS is more robust. This approves the positive impact of the intermediate scale footprint of CRNS for constraining model parameters, as shown before for soil moisture (Rivera Villarreyes et al., 2014; Baatz et al., 2017), and snow (Schattan et al., 2017a). In the presence of discharge measurements positive impacts are limited to physically more meaningful model representations. In extreme situations, this can considerably improve the model performance in terms of flood forecasts (Schöber, 2014). Besides that, the highest potential was identified for otherwise ungauged river basins. When intentionally leaving out discharge observations, simulated cumulative discharges are still very close to measured values. Besides calibration and validation of hydrological models, the limitations of single dataset approaches as well as the improvements achieved by integrating complementary data also have implications for data assimilation schemes (Fletcher et al., 2012; Thirel et al., 2013; Stigter et al., 2017), or terrainbased downscaling of fSCA data (e.g., Cristea et al., 2017).

5. Discussion In all remote sensing products, a high uncertainty was identified in mixed pixel situations (snow covered and snow-free, see Figs. 5 and 6). At the basin scale, the medium resolution fSCA product reflects the snow coverage adequately. Otherwise, substantial misclassification may occur on the local scale. In situations with a high overall snow coverage, the product overestimates fSCA. This can be even more pronounced in global products like MOD10A2, which e.g. in Nepal had a classification accuracy of only around 85 % if compared to Landsat-8 based data (Stigter et al., 2017). In particular, seasonal changes in snow albedo, and regional differences in snow-free albedo may result in systematic biases in classifying SCA (Härer et al., 2018). Underestimation during snow melt could be reduced by more physically based fSCA processing approaches like MODSCAG, accounting e.g. for the temporal evolution of snow albedo (Painter et al., 2009). In principle, the same is true for fSCAhighRes. However, due to its high spatial resolution, the aggregation of values allows for more robust estimations of fSCA in smaller areas (Härer et al., 2018). Even higher discrepancies are found in the Sentinel-1 based wSCA product. A likely explanation is that in alpine areas snow melt usually goes along with patchy snow cover. It is known that the wet snow mapping approach is limited for patchy snow conditions, as mixed pixels are often dominated by the bare soil component and thus classified as snowfree (Nagler et al., 2016). The number of misclassified pixels is differing, but may affect substantial areas (e.g., around 12.5 km2 as on 2015-07-04, see Fig. 6b). A clear advantage of Sentinel-1 is its high spatial resolution allowing for retrievals in steep terrain. This can be underlined by the fact that, despite the steep topography of the basin, the effective spatial coverage of the wSCA maps (excluding all masked areas) is higher than the one of both optical products (fSCAhighRes and fSCAmedRes). Depending on the acquisition geometry relative to the surface topography, the high resolution of Sentinel-1 can on the other side also be a drawback. This results in repetitive data gaps due to radar shadows, usually located close to mountain ridges. Though, as presented above, also optical data face systematic issues in steep areas due to low illumination in the winter period. Another point is that, in general, cloud cover can be assumed as randomly distributed in space, but its probability is considerably higher in areas close to mountain peaks (Crawford et al., 2013). A complementary view on the snowpack is given by in-situ SWE measurements. As with remotely sensed data, also these datasets include uncertainties. Due to its small measurement footprint, the representativeness of SWESPA is changing in time. Featuring a footprint of

6. Conclusions and outlook A calibration framework to select the most suitable snow data for reducing the uncertainty of a hydrological model is presented. It is shown that all analysed snow related data sources feature both uncertainties and valuable information. If relying on one data source only, errors and biases propagate into hydrological modelling. As the biases are not stable over space and time, a best practise is to use suitable data in terms of scale and accuracy. The combination of complementary datasets, and, in particular, the integration of remote sensing and insitu data are suitable approaches to cope with this issue. Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain 16

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detection of the snow covered area from Sentinel-1 backscatter. Most robust results are achieved by combining high spatial resolution remote sensing data (based on Landsat 7/8 and Sentinel-2) with CRNS, pointing out the high complementary value of these datasets. In combination, representative SWE data together with reliable snow covered area maps allow for refining the representation of snow variables, and for improving the estimation of water resources in snow fed mountain basins. Due to its modest power consumption and its low need for maintenance, CRNS is a promising technique for remote mountain regions. As a result of the Landsat and Copernicus programmes, high resolution satellite data are available with comparably frequent revisit times. In combination, these datasets are of high potential for operational water resource management, allowing for more reliable forecasts in regions with limited accessibility, or where discharge measurements are uncertain due to e.g. changing stream locations. The present approach can be extended to other geographical regions and hydrologically relevant data other than snow. In future research, the value of these data should be investigated in more diverse, and larger basins. For watersheds with a lower portion of annual precipitation stored in snow, the additional consideration of soil moisture, which is measured by CRNS in summer, should be investigated. The high explanatory power of the presented combination of in-situ and remote sensing data is also highly relevant for other approaches than model calibration and validation, like data assimilation schemes.

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