Spatio-temporal analysis of drought in a typical plain region based on the soil moisture anomaly percentage index

2017 ◽  
Vol 576 ◽  
pp. 752-765 ◽  
Author(s):  
Yun Mao ◽  
Zhiyong Wu ◽  
Hai He ◽  
Guihua Lu ◽  
Huating Xu ◽  
...  
2021 ◽  
Author(s):  
Jaime Gaona ◽  
Pere Quintana-Seguí ◽  
Maria José Escorihuela

<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>


CATENA ◽  
2009 ◽  
Vol 78 (2) ◽  
pp. 159-169 ◽  
Author(s):  
Gary C. Heathman ◽  
Myriam Larose ◽  
Michael H. Cosh ◽  
Rajat Bindlish

2020 ◽  
Author(s):  
Diego Bueso ◽  
Maria Piles ◽  
Gustau Camps-Valls

<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>


2013 ◽  
Vol 17 (4) ◽  
pp. 1401-1414 ◽  
Author(s):  
M. Nied ◽  
Y. Hundecha ◽  
B. Merz

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.


2012 ◽  
Vol 9 (9) ◽  
pp. 10053-10094
Author(s):  
M. Nied ◽  
Y. Hundecha ◽  
B. Merz

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 to classify soil moisture as a key variable of pre-event catchment conditions and to investigate 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 the spatial heterogeneous antecedent catchment conditions. By clustering the leading PCs, we detected 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. The results underline the importance of large-scale pre-event catchment conditions in flood initiation.


2009 ◽  
Vol 129 (10) ◽  
pp. 1778-1784
Author(s):  
Yasuaki Uehara ◽  
Keita Tanaka ◽  
Yoshinori Uchikawa ◽  
Bong-Soo Kim

2010 ◽  
Vol 17 (4) ◽  
pp. 770-775
Author(s):  
Ren YANG ◽  
Zhi-Yuan REN ◽  
Qian XU ◽  
Mei-Xia WANG

Sign in / Sign up

Export Citation Format

Share Document