Potential drought stress in a Swiss mountain catchment-Ensemble forecasting of high mountain soil moisture reveals a drastic decrease, despite major uncertainties

Climate change is expected to profoundly influence the hydrosphere of mountain ecosystems. The focus of current process-based research is centered on the reaction of glaciers and runoff to climate change; spatially explicit impacts on soil moisture remain widely neglected. We spatio-temporally analyzed the impact of the climate on soil moisture in a mesoscale high mountain catchment to facilitate the development of mitigation and adaptation strategies at the level of vegetation patterns. Two regional climate models were downscaled using three different approaches (statistical downscaling, delta change, and direct use) to drive a hydrological model (WaSiM-ETH) for reference and scenario period (1960–1990 and 2070–2100), resulting in an ensemble forecast of six members. For all ensembles members we found large changes in temperature, resulting in decreasing snow and ice storage and earlier runoff, but only small changes in evapotranspiration. The occurrence of downscaled dry spells was found to fluctuate greatly, causing soil moisture depletion and drought stress potential to show high variability in both space and time. In general, the choice of the downscaling approach had a stronger influence on the results than the applied regional climate model. All of the results indicate that summer soil moisture decreases, which leads to more frequent declines below a critical soil moisture level and an advanced evapotranspiration deficit. Forests up to an elevation of 1800 m a.s.l. are likely to be threatened the most, while alpine areas and most pastures remain nearly unaffected. Nevertheless, the ensemble variability was found to be extremely high and should be interpreted as a bandwidth of possible future drought stress situations.

Break detection of annual Swiss temperature series

[1] Instrumental temperature series are often affected by artificial breaks (“break points”) due to (e.g.,) changes in station location, land-use, or instrumentation. The Swiss climate observation network offers a high number and density of stations, many long and relatively complete daily to sub-daily temperature series, and well-documented station histories (i.e., metadata). However, for many climate observation networks outside of Switzerland, detailed station histories are missing, incomplete, or inaccessible. To correct these records, the use of reliable statistical break detection methods is necessary. Here, we apply three statistical break detection methods to high-quality Swiss temperature series and use the available metadata to assess the methods. Due to the complex terrain in Switzerland, we are able to assess these methods under specific local conditions such as the Foehn or crest situations. We find that the temperature series of all stations are affected by artificial breaks (average = 1 break point / 48 years) with discrepancies in the abilities of the methods to detect breaks. However, by combining the three statistical methods, almost all of the detected break points are confirmed by metadata. In most cases, these break points are ascribed to a combination of factors in the station history.

Validation of a single item to assess daytime sleepiness for the Swiss Transplant Cohort Study

Context-Daytime sleepiness in kidney transplant recipients has emerged as a potential predictor of impaired adherence to the immunosuppressive medication regimen. Thus there is a need to assess daytime sleepiness in clinical practice and transplant registries.Objective-To evaluate the validity of a single-item measure of daytime sleepiness integrated in the Swiss Transplant Cohort Study (STCS), using the American Educational Research Association framework.Methods-Using a cross-sectional design, we enrolled a convenience sample of 926 home-dwelling kidney transplant recipients (median age, 59.69 years; 25%-75% quartile [Q25-Q75], 50.27-59.69), 63% men; median time since transplant 9.42 years (Q25-Q75, 4.93-15.85). Daytime sleepiness was assessed by using a single item from the STCS and the 8 items of the validated Epworth Sleepiness Scale. Receiver operating characteristic curve analysis was used to determine the cutoff for the STCS daytime sleepiness item against the Epworth Sleepiness Scale score.Results-Based on the receiver operating characteristic curve analysis, a score greater than 4 on the STCS daytime sleepiness item is recommended to detect daytime sleepiness. Content validity was high as all expert reviews were unanimous. Concurrent validity was moderate (Spearman ϱ, 0.531; P< .001) and convergent validity with depression and poor sleep quality although low, was significant (ϱ, 0.235; P<.001 and ϱ, 0.318, P=.002, respectively). For the group difference validity: kidney transplant recipients with moderate, severe, and extremely severe depressive symptom scores had 3.4, 4.3, and 5.9 times higher odds of having daytime sleepiness, respectively, as compared with recipients without depressive symptoms.Conclusion-The accumulated evidence provided evidence for the validity of the STCS daytime sleepiness item as a simple screening scale for daytime sleepiness.