Dominik Michel

Climatological drought monitoring in Switzerland relies heavily on station-based precipitation and temperature data. Due to the high spatial variability and complexity of droughts, it is important to complement station-based drought indices with gridded information and to couple multiple drought indicators within the monitoring system. Here, long-term satellite-based drought parameters from the EUMETSAT SAF network are analyzed in terms of dry anomalies within their climatology’s, namely ASCAT soil water index (SWI), CM SAF land surface temperature (LST), complemented with NOAA vegetation data, and LSA SAF Meteosat evapotranspiration data. The upcoming EUMETSAT SAF climate data records on land surface temperature and evapotranspiration will cover for the first time the WMO climatological 30-year reference period. This study is the first study investigating the potential of those long-term data records for climate monitoring of droughts in Europe. The satellite datasets are compared with the standardized precipitation index (SPI), soil moisture observations from the SwissSMEX measurement network, with a modelled soil moisture index (SMI) based on observations, and with evapotranspiration measurements, focusing on the temporal dynamics of the anomalies. For vegetation and surface temperature, the dry years of 2003, 2015, and 2018 are clearly visible in the satellite data. CM SAF LSTs show strong anomalies at the beginning of the drought period. The comparison of in situ and modelled soil moisture and evapotranspiration measurements with the satellite parameters shows strong agreement in terms of anomalies. The SWI indicates high anomaly correlations of 0.56 to 0.83 with measurements and 0.63 to 0.76 with the SMI at grassland sites. The Meteosat evapotranspiration data strongly agree with the measurements, with anomaly correlations of 0.63 and 0.67 for potential and actual evapotranspiration, respectively. Due to the prevailing humid climate conditions at the considered sites, evapotranspiration anomalies during the investigated dry periods were mostly positive and thus not water limited, but were also a driver for soil moisture drought. The results indicate that EUMETSAT SAF satellite data can well complement the station-based drought monitoring in Switzerland with spatial information.

Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems.The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites are complemented with land evaporation measurements from mini-lysimeters.Here we analyze long-term satellite-based drought parameters, namely ASCAT Soil Water Index (SWI) derived from an H SAF test data set and LSA SAF Meteosat land evaporation products. We compare the satellite-based datasets with the SwissSMEX in-situ measurements of soil moisture and lysimeter land evaporation. The comparison of in-situ soil moisture and land evaporation data with the satellite parameters shows strong agreement in terms of anomalies. SWI indicates high correlations of 0.6 to 0.8 with in-situ measurements. The Meteosat land evaporation products strongly agree with measurements, with correlations of 0.7 and 0.9 for potential and actual land evaporation, respectively (Burgstall et al.).These analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding and for a better preparedness for future droughts.References:Burgstall, A. et al., Climatological drought monitoring in Switzerland using EUMETSAT SAF satellite products, Remote Sensing, in preparation.

With respect to ongoing discussions about the causes of energy imbalance and approaches to force energy balance closure, a method has been proposed that allows partial latent heat flux closure (Widmoser and Wohlfahrt, 2018). In the present paper, this method is applied to four measurement stations over grassland under humid and semiarid climates, where lysimeter (LY) and eddy covariance (EC) measurements were taken simultaneously. The results differ significantly from the ones reported in the literature. We distinguish between the resulting EC values being weakly and strongly correlated to LY observations as well as systematic and random deviations between the LY and EC values. Overall, an excellent match could be achieved between the LY and EC measurements after applying evaporation-linked weights. But there remain large differences between the standard deviations of the LY and adjusted EC values. For further studies we recommend data collected at time intervals even below 0.5 h. No correlation could be found between evaporation weights and weather indices. Only for some datasets, a positive correlation between evaporation and the evaporation weight could be found. This effect appears pronounced for cases with high radiation and plant water stress. Without further knowledge of the causes of energy imbalance one might perform full closure using equally distributed weights. Full closure, however, is not dealt with in this paper.

Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems. The country was affected by major drought and heatwave conditions in 2015 and 2018. While the meteorological conditions started to recover at the end of the summer, the soil moisture conditions (and runoff) continued to be anomalously low for most of the fall. This illustrates the decoupling between meteorological drought and soil moisture drought conditions related to the intrinsic memory of the soil.The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites were complemented with land evaporation measurements from mini-lysimeters.First, a quality assessment and inter-comparison of the in-situ soil moisture and land evaporation observations at 12 grassland sites revealed substantial discrepancies between different sensor types in terms of absolute values and data availability. A standard procedure for processing and interpreting the SwissSMEX data is thus being established. Second, analyses have been carried out comparing the SwissSMEX measurements with gridded remote-sensing and reanalysis products that provide near real time soil moisture data. In particular, the European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture product (ESA-CCI soil moisture) as well as the new ECMWF reanalysis ERA5 are considered. The seasonal evolution of the soil moisture anomalies (with respect to the long-term mean) show for 2020 two pronounced phases of dryness. These are consistently represented in SwissSMEX in-situ observations and ERA5. Also the other recent drought events of 2015 and 2018 show a similar temporal evolution in both datasets. The response of ESA-CCI surface soil moisture is less pronounced, more variable and also dependent on the measurement methodology, i.e., active or passive microwave remote sensing.These first analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding at the national scale and a better preparedness for future droughts.

Core Ideas: We saw generally high agreement between lysimeters, despite substantial design differences. Large‐lysimeter processing was optimized in light of highly resolved mini‐lysimeter data. Smoothing filter and setting of ET to zero in wet hours was useful for the large lysimeter. Lysimeter precipitation types were identified using an opto‐electric precipitation monitor. Protrusion of the large lysimeter may cause precipitation under‐catch by flow distortion. Weighing lysimeters are a well‐established means of accurately obtaining local‐scale estimates of actual evapotranspiration (ET), precipitation, and seepage within soils. At the Rietholzbach research catchment in northeastern Switzerland, two weighing lysimeters are in operation. One is a recently installed state‐of‐the‐art monolithic mini‐lysimeter with a tension‐controlled lower boundary; the other is a large backfilled free‐drainage lysimeter in operation since 1976. For this study, the mini‐lysimeter measurements were processed using the Adaptive‐Window and Adaptive‐Threshold (AWAT) filter. The resulting water‐balance estimates were then compared with those of the lower resolution large lysimeter, whose processing has remained unchanged since its installation. A number of additional, retrospectively applicable processing steps for the large lysimeter were then tested to mitigate the main sources of error for this instrument. Those found to be most beneficial were the application of a 10‐min moving average to the mass measurements and the setting of ET and condensation to zero in hours with liquid precipitation. In spite of the differences in design, a generally close agreement between the two lysimeters was observed, which was further improved with the optimized large‐lysimeter processing. A comparison of the lysimeter mass increases associated with liquid precipitation further revealed, however, that the large lysimeter experiences a previously unknown under‐catch of about 11.5%, which could also be important for other lysimeter facilities. This under‐catch led to a reduction in large‐lysimeter seepage during the analyzed period, although the ET flux was not found to be affected.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R² = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R² = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R² = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R² = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.