Unraveling the time-scale teleconnections between soil moisture and vegetation

2020 ◽  
Author(s):  
Diego Bueso ◽  
Maria Piles ◽  
Gustau Camps-Valls
Keyword(s):  
Soil Moisture ◽  
International Workshop ◽  
Temporal Analysis ◽  
Earth Observation ◽  
Alternative Methods ◽  
Observation Data ◽  
Causal Relations ◽  
Modes Of Variability ◽  
Nonlinear Pca ◽  
Spatio Temporal

<p>Identifying causal relations from observational data is key to understand Earth system interactions. Extensions to spatio-temporal analysis at different scales are of vital importance for better understanding dynamical phenomenon of natural complex systems. Soil moisture-vegetation interactions constitute a central part of ecosystem functioning and health. Here we are interested in uncovering (potentially nonlinear) spatio-temporal causal relations at different time scales between two relevant Earth observation variables: soil moisture (SM) and vegetation optical depth (VOD). To aboard the complexity data problem, we extract relevant and expressive feature components with the nonlinear kernel-based dimensional reduction method ROCK-PCA in [1]. The method yields the main modes of variability of the variables that are then used to study causal relations. To infer causality relations we use the cross-information kernel Granger causality (XKGC) method introduced in [2], which accounts for nonlinear cross-relations between the involved variables and generalizes nonlinear GC methods. Results are succesfully compared to standard correlation analysis, transfer entropy and convergent cross-mapping alternative methods. In general XKGC identifies a sparser connectivity than correlation. Also, well-known wet and dry patterns are identified as reported in the literature, but other interesting unreported connections and spatio-temporal SM<-->VOD emerge.</p><p>REFERENCES<br>[1] D. Bueso, M. Piles and G. Camps-Valls, "Nonlinear PCA for Spatio-Temporal Analysis of<br>Earth Observation Data," in IEEE Transactions on Geoscience and Remote Sensing, accepted (2020).<br>[2] Brajard, J., Charantonis, A., Chen, C., & Runge, J. (Eds.). (2019). Proceedings of the<br>9th International Workshop on Climate Informatics: CI 2019 (No. NCAR/TN-561+PROC).</p>

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>

Physical factors of influence in the interactions between rainfall, evapotranspiration and soil moisture driving the spatio-temporal evolution of drought patterns in the Ebro Basin (NE Spain).

2021 ◽  
Author(s):  
Jaime Gaona ◽  
Pere Quintana-Seguí ◽  
Maria José Escorihuela
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Temporal Analysis ◽  
Drought Indices ◽  
Surface Model ◽  
Physical Factors ◽  
Spatio Temporal ◽  
The Impact

<p>Droughts in the Iberian Peninsula are a natural hazard of great relevance due to their recurrence, severity and impact on multiple environmental and socioeconomic aspects. The Ebro Basin, located in the NE of the Iberian Peninsula, is particularly vulnerable to drought with consequences on agriculture, urban water supply and hydropower. This study, performed within the Project HUMID (CGL2017-85687-R), aims at evaluating the influence of the climatic, land cover and soil characteristics on the interactions between rainfall, evapotranspiration and soil moisture anomalies which define the spatio-temporal drought patterns in the basin.</p><p>The onset, propagation and mitigation of droughts in the Iberian Peninsula is driven by anomalies of rainfall, evapotranspiration and soil moisture, which are related by feedback processes. To test the relative importance of such anomalies, we evaluate the contribution of climatic, land-cover and geologic heterogeneity on the definition of the spatio-temporal patterns of drought. We use the Köppen-Geiger climatic classification to assess how the contrasting climatic types within the basin determine differences on drought behavior. Land-cover types that govern the partition between evaporation and transpiration are also of great interest to discern the influence of vegetation and crop types on the anomalies of evapotranspiration across the distinct regions of the basin (e.g. forested mountains vs. crop-dominated areas). The third physical characteristic whose effect on drought we investigate is the impact of soil properties on soil moisture anomalies.</p><p>The maps and time series used for the spatio-temporal analysis are based on drought indices calculated with high-resolution datasets from remote sensing (MOD16A2ET and SMOS1km) and the land-surface model SURFEX-ISBA. The Standardized Precipitation Index (SPI), the EvapoTranspiration Deficit Index (ETDI) and the Soil Moisture Deficit Index (SMDI) are the three indices chosen to characterize the anomalies of the corresponding rainfall (atmospheric), evapotranspiration (atmosphere-land interface) and soil moisture (land) anomalies (components of the water balance). The comparison of the correlations of the indices (with different time lags) between contrasting regions offers insights about the impact of climate, land-cover and soil properties in the dominance, the timing of the response and memory aspects of the interactions. The high spatial and temporal resolution of remote sensing and land-surface model data allows adopting time and spatial scales suitable to investigate the influence of these physical factors with detail beyond comparison with ground-based datasets.</p><p>The spatial and temporal analysis prove useful to investigate the physical factors of influence on the anomalies between rainfall, evapotranspiration and soil moisture. This approach facilitates the physical interpretation of the anomalies of drought indices aiming to improve the characterization of drought in heterogeneous semi-arid areas like the Ebro River Basin.</p>

Surface and profile soil moisture spatio-temporal analysis during an excessive rainfall period in the Southern Great Plains, USA

CATENA ◽  
2009 ◽  
Vol 78 (2) ◽  
pp. 159-169 ◽  
Author(s):  
Gary C. Heathman ◽  
Myriam Larose ◽  
Michael H. Cosh ◽  
Rajat Bindlish
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Temporal Analysis ◽  
Southern Great Plains ◽  
Spatio Temporal

scholarly journals Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin

2013 ◽  
Vol 17 (4) ◽  
pp. 1401-1414 ◽  
Author(s):  
M. Nied ◽  
Y. Hundecha ◽  
B. Merz
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Large Scale ◽  
Principal Component ◽  
Temporal Analysis ◽  
Elbe River ◽  
High Soil Moisture ◽  
Elbe River Basin ◽  
Spatio Temporal ◽  
The Elbe River

Abstract. Floods are the result of a complex interaction between meteorological event characteristics and pre-event catchment conditions. While the large-scale meteorological conditions have been classified and successfully linked to floods, this is lacking for the large-scale pre-event catchment conditions. Therefore, we propose classifying soil moisture as a key variable of pre-event catchment conditions and investigating the link between soil moisture patterns and flood occurrence in the Elbe River basin. Soil moisture is simulated using a semi-distributed conceptual rainfall-runoff model over the period 1951–2003. Principal component analysis (PCA) and cluster analysis are applied successively to identify days of similar soil moisture patterns. The results show that PCA considerably reduced the dimensionality of the soil moisture data. The first principal component (PC) explains 75.71% of the soil moisture variability and represents the large-scale seasonal wetting and drying. The successive PCs express spatially heterogeneous catchment processes. By clustering the leading PCs, we identify large-scale soil moisture patterns which frequently occur before the onset of floods. In winter, floods are initiated by overall high soil moisture content, whereas in summer the flood-initiating soil moisture patterns are diverse and less stable in time.

scholarly journals Semantic Earth Observation Data Cubes

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 102 ◽  
Author(s):  
Hannah Augustin ◽  
Martin Sudmanns ◽  
Dirk Tiede ◽  
Stefan Lang ◽  
Andrea Baraldi
Keyword(s):  
Data Retrieval ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Cubes ◽  
Sensory Data ◽  
Main Challenge ◽  
Data Content ◽  
Spatio Temporal ◽  
Earth Observation Data

There is an increasing amount of free and open Earth observation (EO) data, yet more information is not necessarily being generated from them at the same rate despite high information potential. The main challenge in the big EO analysis domain is producing information from EO data, because numerical, sensory data have no semantic meaning; they lack semantics. We are introducing the concept of a semantic EO data cube as an advancement of state-of-the-art EO data cubes. We define a semantic EO data cube as a spatio-temporal data cube containing EO data, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Here we clarify and share our definition of semantic EO data cubes, demonstrating how they enable different possibilities for data retrieval, semantic queries based on EO data content and semantically enabled analysis. Semantic EO data cubes are the foundation for EO data expert systems, where new information can be inferred automatically in a machine-based way using semantic queries that humans understand. We argue that semantic EO data cubes are better positioned to handle current and upcoming big EO data challenges than non-semantic EO data cubes, while facilitating an ever-diversifying user-base to produce their own information and harness the immense potential of big EO data.

scholarly journals Understanding terrestrial water storage variations in northern latitudes across scales

2018 ◽  
Vol 22 (7) ◽  
pp. 4061-4082 ◽  
Author(s):  
Tina Trautmann ◽  
Sujan Koirala ◽  
Nuno Carvalhais ◽  
Annette Eicker ◽  
Manfred Fink ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Liquid Water ◽  
Spatial Scales ◽  
Water Storage ◽  
Earth Observation ◽  
High Latitudes ◽  
Terrestrial Water Storage ◽  
Northern Latitudes ◽  
Terrestrial Water ◽  
Spatio Temporal

Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid- to high latitudes. To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales. Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid- to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model–data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on inter-annual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short- and long-term variability of water resources.

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