scholarly journals Long-term forest monitoring unravels constant mortality rise in European forests

2021 ◽  
Author(s):  
Jan-Peter George ◽  
Tanja GM Sanders ◽  
Mathias Neumann ◽  
Carmelo Cammalleri ◽  
Juergen V. Vogt ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Tree Mortality ◽  
Single Species ◽  
Forest Monitoring ◽  
Human Welfare ◽  
European Forests ◽  
Continental Scale ◽  
Crown Defoliation ◽  
Soil Moisture Anomaly

European forests are an important source for timber production, human welfare, income, protection and biodiversity. During the last two decades, Europe has experienced a number of droughts which were exceptionally within the last 500 years both in terms of duration and intensity and these droughts seem to left remarkable imprints in the mortality dynamics of European forests. However, systematic observations on tree decline with emphasis on single species together with high-resolution drought data has been scarce so far so that deeper insights into mortality dynamics and drought occurrence is still limiting our understanding at continental scale. Here we make use of the ICP Forest crown defoliation dataset, permitting us to retrospectively monitor tree mortality for four major conifers, two major broadleaves as well as a pooled dataset of nearly all minor tree species in Europe. In total, we analysed more than 3 million observations gathered during the last 25 years and employed a high-resolution drought index which is able to assess soil moisture anomaly based on a hydrological water-balance and runoff model every ten days globally. We found significant overall and species-specific increasing trends in mortality rates accompanied by decreasing soil moisture. A generalized linear model identified previous-year soil moisture anomaly as the most important driver of mortality patterns in European forests. Significant interactions appeared between previous-year soil moisture and stand water regime in conifers, strongly suggesting that conifers growing at productive sites are more vulnerable under drought. We conclude that mortality patterns in European forests are currently reaching a concerning upward trend which could be further accelerated by global change-type droughts.

scholarly journals Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

2019 ◽  
Vol 23 (1) ◽  
pp. 277-301 ◽  
Author(s):  
Bibi S. Naz ◽  
Wolfgang Kurtz ◽  
Carsten Montzka ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Resources ◽  
Data Assimilation ◽  
Land Surface ◽  
European Space Agency ◽  
Loop Model ◽  
Continental Scale ◽  
Community Land Model ◽  
Assimilation Experiment

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

scholarly journals Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

2018 ◽  
Author(s):  
Bibi S. Naz ◽  
Wolfgang Kurtz ◽  
Carsten Montzka ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Resources ◽  
Data Assimilation ◽  
Open Loop ◽  
Modeling Framework ◽  
High Resolution Data ◽  
Continental Scale ◽  
Community Land Model ◽  
Assimilation Experiment

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period from 2000 to 2006 with the Community Land Model, version 3.5 (CLM3.5) integrated with the Parallel Data Assimilation Framework (PDAF) at spatial resolution of 0.0275° (~ 3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans-Ertel Centre for Weather Research (HErZ). Our results show that estimates of soil moisture have improved, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm3/mm3 in open-loop simulations to 0.004 mm3/mm3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

scholarly journals Impact of ASAR soil moisture data on the MM5 precipitation forecast for the Tanaro flood event of April 2009

2011 ◽  
Vol 11 (12) ◽  
pp. 3135-3149 ◽  
Author(s):  
G. Panegrossi ◽  
R. Ferretti ◽  
L. Pulvirenti ◽  
N. Pierdicca
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ground Truth ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Flood Event ◽  
Strong Impact ◽  
Moisture Field ◽  
The Impact

Abstract. The representation of land-atmosphere interactions in weather forecast models has a strong impact on the Planetary Boundary Layer (PBL) and, in turn, on the forecast. Soil moisture is one of the key variables in land surface modelling, and an inadequate initial soil moisture field can introduce major biases in the surface heat and moisture fluxes and have a long-lasting effect on the model behaviour. Detecting the variability of soil characteristics at small scales is particularly important in mesoscale models because of the continued increase of their spatial resolution. In this paper, the high resolution soil moisture field derived from ENVISAT/ASAR observations is used to derive the soil moisture initial condition for the MM5 simulation of the Tanaro flood event of April 2009. The ASAR-derived soil moisture field shows significantly drier conditions compared to the ECMWF analysis. The impact of soil moisture on the forecast has been evaluated in terms of predicted precipitation and rain gauge data available for this event have been used as ground truth. The use of the drier, highly resolved soil moisture content (SMC) shows a significant impact on the precipitation forecast, particularly evident during the early phase of the event. The timing of the onset of the precipitation, as well as the intensity of rainfall and the location of rain/no rain areas, are better predicted. The overall accuracy of the forecast using ASAR SMC data is significantly increased during the first 30 h of simulation. The impact of initial SMC on the precipitation has been related to the change in the water vapour field in the PBL prior to the onset of the precipitation, due to surface evaporation. This study represents a first attempt to establish whether high resolution SAR-based SMC data might be useful for operational use, in anticipation of the launch of the Sentinel-1 satellite.

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

scholarly journals An Indirect Approach Based on Long Short-Term Memory Networks to Estimate Groundwater Table Depth Anomalies Across Europe With an Application for Drought Analysis

2021 ◽  
Vol 3 ◽  
Author(s):  
Yueling Ma ◽  
Carsten Montzka ◽  
Bagher Bayat ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
Indirect Method ◽  
Short Term Memory ◽  
Groundwater Table ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Continental Scale ◽  
Groundwater Table Depth ◽  
Long Short Term Memory

The lack of high-quality continental-scale groundwater table depth observations necessitates developing an indirect method to produce reliable estimation for water table depth anomalies (wtda) over Europe to facilitate European groundwater management under drought conditions. Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-short-term dependencies in the input-output relationship, which have been observed in the response of groundwater dynamics to atmospheric and land surface processes. Here, we introduced different input variables including precipitation anomalies (pra), which is the most common proxy of wtda, for the networks to arrive at improved wtda estimates at individual pixels over Europe in various experiments. All input and target data involved in this study were obtained from the simulated TSMP-G2A data set. We performed wavelet coherence analysis to gain a comprehensive understanding of the contributions of different input variable combinations to wtda estimates. Based on the different experiments, we derived an indirect method utilizing LSTM networks with pra and soil moisture anomaly (θa) as input, which achieved the optimal network performance. The regional medians of test R2 scores and RMSEs obtained by the method in the areas with wtd ≤ 3.0 m were 76–95% and 0.17–0.30, respectively, constituting a 20–66% increase in median R2 and a 0.19–0.30 decrease in median RMSEs compared to the LSTM networks only with pra as input. Our results show that introducing θa significantly improved the performance of the trained networks to predict wtda, indicating the substantial contribution of θa to explain groundwater anomalies. Also, the European wtda map reproduced by the method had good agreement with that derived from the TSMP-G2A data set with respect to drought severity, successfully detecting ~41% of strong drought events (wtda ≥ 1.5) and ~29% of extreme drought events (wtda ≥ 2) in August 2015. The study emphasizes the importance to combine soil moisture information with precipitation information in quantifying or predicting groundwater anomalies. In the future, the indirect method derived in this study can be transferred to real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture and precipitation observations or respective information from weather prediction models.

scholarly journals Improving Numerical Weather Predictions of Summertime Precipitation over the Southeastern United States through a High-Resolution Initialization of the Surface State

2011 ◽  
Vol 26 (6) ◽  
pp. 785-807 ◽  
Author(s):  
Jonathan L. Case ◽  
Sujay V. Kumar ◽  
Jayanthi Srikishen ◽  
Gary J. Jedlovec
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Weather Prediction ◽  
Southeastern United States ◽  
Model Verification ◽  
Sea Surface ◽  
Convective Precipitation ◽  
Numerical Weather

Abstract It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h−1 matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.

scholarly journals Observed Soil Moisture–Precipitation Feedback in Illinois: A Systematic Analysis over Different Scales

2016 ◽  
Vol 17 (6) ◽  
pp. 1645-1660 ◽  
Author(s):  
H. M. Duerinck ◽  
R. J. van der Ent ◽  
N. C. van de Giesen ◽  
G. Schoups ◽  
V. Babovic ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Time Scale ◽  
Regional Analysis ◽  
Precipitation Variability ◽  
Climate Indices ◽  
Back Trajectory ◽  
Strong Positive Correlation ◽  
Coupled Models ◽  
Continental Scale ◽  
State Average

Abstract The lack of understanding on the soil moisture–precipitation feedback mechanisms remains a large source of uncertainty for land–atmosphere coupled models. Previous observation-based studies on the soil moisture–precipitation feedback in Illinois have shown contradictory results. This paper extends earlier research by providing a more holistic analysis considering different scales based on an 11-yr (2003–13) hourly soil moisture dataset, which makes it possible to revisit the disputed hypothesis on the correlation between warm-season soil moisture and subsequent precipitation. This study finds a strong positive correlation between late spring/early summer state-average soil moisture at the root-zone depths and subsequent state-average summer precipitation. On the daily to weekly time scale, however, no relation is found. Moreover, regional analysis suggests that precipitation variability over central Illinois can be best explained by the soil moisture variability in northwest Illinois. Using a back-trajectory method [Water Accounting Model-2 layers (WAM-2layers)] from May to July, the evaporative sources of precipitation in Illinois are identified. The pattern of the source regions shows little interannual variability, while the strength of the sources changes significantly and the Gulf of Mexico contributes more during wet years. However, strong influences (teleconnections) of sea surface temperatures on the subsequent precipitation variability in Illinois are not found on a seasonal scale. The long time scale of the soil moisture–precipitation correlation and the weak influences of SSTs and climate indices may suggest that precipitation variability in spring/summer in Illinois is mostly related to continental-scale soil moisture–precipitation feedback.

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