Large earthquakes can increase the amount of water feeding stream flows, raise groundwater levels, and thus grant plant roots more access to water in water-limited environments. We examine growth and photosynthetic responses of Pine plantations to the Maule M-w 8.8 earthquake in headwater catchments of Chile's Coastal Range. We combine high-resolution wood anatomic (lumen area) and biogeochemical (delta 13C of wood cellulose) proxies of daily to weekly tree growth sampled from trees on floodplains and close to ridge lines. We find that, immediately after the earthquake, at least two out of six tree trees on valley floors had increased lumen area and decreased delta 13C, while trees on hillslopes had a reverse trend. Our results indicate a control of soil water on this response, largely consistent with models that predict how enhanced postseismic vertical soil permeability causes groundwater levels to rise on valley floors, but fall along the ridges. Statistical analysis with boosted regression trees indicates that streamflow discharge gained predictive importance for photosynthetic activity on the ridges, but lost importance on the valley floor after the earthquake. We infer that earthquakes may stimulate ecohydrological conditions favoring tree growth over days to weeks by triggering stomatal opening. The weak and short-lived signals that we identified, however, show that such responses are only valid under water-limited, rather than energy-limited tree, growth. Hence, dendrochronological studies targeted at annual resolution may overlook some earthquake effects on tree vitality.

Deriving soil moisture content (SMC) at the regional scale with different spatial and temporal land cover changes is still a challenge for active and passive remote sensing systems, often coped with machine learning methods. So far, the reference measurements of the data-driven approaches are usually based on point data, which entails a scale gap to the resolution of the remote sensing data. Cosmic Ray Neutron Sensing (CRNS) indirectly provides SMC estimates of a soil volume covering more than 1 ha and vertical depth up to 80 cm and is thus able to narrow this scale gap. So far, the CRNS-based SMC has only been used as validation source of remote sensing based SMC products. Its beneficial large sensing volume, especially in depth, has not been exploited yet. However, the sensing volume of the CRNS, which is changing with hydrological conditions, bears challenges for the comparison with remote sensing observations. This study, for the fist time, aims to understand the direct linkage of optical (Sentinel 2) and SAR (Sentinel 1) data with CRNS-based SMC. Thereby, the CRNS-based SMC is obtained by an experimental CRNS cluster that covers the high temporal and spatial SMC variability of an entire pre-alpine subcatchment. Using different Random Forest regressions, we analyze the potentials and limitations of both remote sensing sensors to follow the CRNS-based SMC signal. Our results show that it is possible to link the CRNS-based SMC signal with SAR and optical remote sensing observations via Random Forest modelling. We found that Sentinel 2 data is able to separate wet from dry periods with a R2 of 0.68. It is less affected by the changing soil volume that contributes to the CRNS-based SMC signal and it is able to assign a land cover specific SMC distribution. However, Sentinel 2 regression models are not accurate (R2 < 0.21) in mapping the CRNSbased SMC for the frequently mowed grassland areas of the study site. It requires soil type and topographical information to accurately follow the CRNS-based SMC signal with Random Forest regression. Sentinel 1 data instead is affected by the changing soil volume that contributes to the CRNS-based SMC signal. It has reasonable model performance (R2 = 0.34) when the CRNS data correspond to surface SMC. Also for Sentinel 1 the retrieval is impacted by the mowing activities at the test site. When separating the CRNS data set into dry and wet periods, soil properties and topography are the main drivers of SMC estimation. Sentinel 1 or Sentinel 2 data add the existing temporal variability to the regression models. The analysis underlines the need of combining optical and SAR observations (Sentinel 1, Sentinel 2) as well as soil property and topographical information to understand and follow the CRNS-based SMC signal for different hydrological conditions and land cover types.

Like many other regions in central Europe, Germany experienced sequential summer droughts from 2015 to 2018. As one of the environmental consequences, river nitrate concentrations have exhibited significant changes in many catchments. However, catchment nitrate responses to the changing weather conditions have not yet been mechanistically explored. Thus, a fully distributed, process-based catchment Nitrate model (mHM-Nitrate) was used to reveal the causal relations in the Bode catchment, of which river nitrate concentrations have experienced contrasting trends from upstream to downstream reaches. The model was evaluated using data from six gauging stations, reflecting different levels of runoff components and their associated nitrate-mixing from upstream to downstream. Results indicated that the mHM-Nitrate model reproduced dynamics of daily discharge and nitrate concentration well, with Nash-Sutcliffe Efficiency >= 0.73 for discharge and Kling-Gupta Efficiency >= 0.50 for nitrate concentration at most stations. Particularly, the spatially contrasting trends of nitrate con-centration were successfully captured by the model. The decrease of nitrate concentration in the lowland area in drought years (2015-2018) was presumably due to (1) limited terrestrial export loading (ca. 40 % lower than that of normal years 2004-2014), and (2) increased in-stream retention efficiency (20 % higher in summer within the whole river network). From a mechanistic modelling perspective, this study provided insights into spatially heterogeneous flow and nitrate dynamics and effects of sequential droughts, which shed light on water -quality responses to future climate change, as droughts are projected to be more frequent.

Increased rates of glacier retreat and thinning need accurate local estimates of glacier elevation change to predict future changes in glacier runoff and their contribution to sea level rise. Glacier elevation change is typically derived from digital elevation models (DEMs) tied to surface change analysis from satellite imagery. Yet, the rugged topography in mountain regions can cast shadows onto glacier surfaces, making it difficult to detect local glacier elevation changes in remote areas. A rather untapped resource comprises precise, time-stamped metadata on the solar position and angle in satellite images. These data are useful for simulating shadows from a given DEM. Accordingly, any differences in shadow length between simulated and mapped shadows in satellite images could indicate a change in glacier elevation relative to the acquisition date of the DEM. We tested this hypothesis at five selected glaciers with long-term monitoring programmes. For each glacier, we projected cast shadows onto the glacier surface from freely available DEMs and compared simulated shadows to cast shadows mapped from ∼40 years of Landsat images. W validated the relative differences with geodetic measurements of glacier elevation change where these shadows occurred. We find that shadow-derived glacier elevation changes are consistent with independent photogrammetric and geodetic surveys in shaded areas. Accordingly, a shadow cast on Baltoro Glacier (the Karakoram, Pakistan) suggests no changes in elevation between 1987 and 2020, while shadows on Great Aletsch Glacier (Switzerland) point to negative thinning rates of about 1 m yr−1 in our sample. Our estimates of glacier elevation change are tied to occurrence of mountain shadows and may help complement field campaigns in regions that are difficult to access. This information can be vital to quantify possibly varying elevation-dependent changes in the accumulation or ablation zone of a given glacier. Shadow-based retrieval of glacier elevation changes hinges on the precision of the DEM as the geometry of ridges and peaks constrains the shadow that we cast on the glacier surface. Future generations of DEMs with higher resolution and accuracy will improve our method, enriching the toolbox for tracking historical glacier mass balances from satellite and aerial images.

In this work, we present a comprehensive evaluation of a stochastic multi-site, multi-variate weather generator at the scale of entire Germany and parts of the neighbouring countries covering the major German river basins Elbe, Upper Danube, Rhine, Weser and Ems with a total area of approximately 580,000 km(2). The regional weather generator, which is based on a first-order multi-variate auto-regressive model, is setup using 53-year long daily observational data at 528 locations. The performance is evaluated by investigating the ability of the weather generator to replicate various important statistical properties of the observed variables including precipitation occurrence and dry/wet transition probabilities, mean daily and extreme precipitation, multi-day precipitation sums, spatial correlation structure, areal precipitation, mean daily and extreme temperature and solar radiation. We explore two marginal distributions for daily precipitation amount: mixed Gamma-Generalized Pareto and extended Generalized Pareto. Furthermore, we introduce a new procedure to estimate the spatial correlation matrix and model mean daily temperature and solar radiation. The extensive evaluation reveals that the weather generator is greatly capable of capturing most of the crucial properties of the weather variables, particularly of extreme precipitation at individual locations. Some deficiencies are detected in capturing spatial precipitation correlation structure that leads to an overestimation of areal precipitation extremes. Further improvement of the spatial correlation structure is envisaged for future research. The mixed marginal model found to outperform the extended Generalized Pareto in our case. The use of power transformation in combination with normal distribution significantly improves the performance for non-precipitation variables. The weather generator can be used to generate synthetic event footprints for large-scale trans-basin flood risk assessment.