Seasonal control of spatial distribution of soil moisture on a steep hillslope

2018 ◽  
Vol 79 (3) ◽  
pp. 556-565
Author(s):  
Xiaoyi Dong ◽  
Eunhyung Lee ◽  
Yongseok Gwak ◽  
Sanghyun Kim
Keyword(s):  
Soil Moisture ◽  
Environmental Factors ◽  
Temporal Variation ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ecological Processes ◽  
Moisture Profile ◽  
Humid Forest ◽  
Control Factors ◽  
Spatio Temporal

Abstract Spatio-temporal variation in soil moisture plays an important role in hydrological and ecological processes. In the present study, we investigated the effect of environmental factors on variation in soil moisture at a hillslope scale. The relationships among various environmental factors, including soil properties, topographic indices, and vegetation of a humid forest hillslope, and soil moisture distributions were evaluated based on soil moisture data collected at 18 sampling locations over three seasons (spring, rainy, and autumn) at depths of 10, 30, and 60 cm. In order to evaluate the multi-dimensional data sets without the interaction among factors, the principal component regression (PCR) model was applied to identify the factors controlling the spatio-temporal variation in soil moisture. The effects on soil texture and topography were significant in spring. In addition, clay and sand appeared as critical control factors for the study area in all seasons. The transitional control patterns in the soil moisture profile indicated that the control varied depending on features, such as total amount, intensity, and duration, of rainfall events in spring and during the rainy season. The transitional control pattern for autumn showed that vegetation and local slope controlled transitions in topography.

scholarly journals Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5211
Author(s):  
Maedeh Farokhi ◽  
Farid Faridani ◽  
Rosa Lasaponara ◽  
Hossein Ansari ◽  
Alireza Faridhosseini
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Surface Soil ◽  
Root Zone ◽  
Analytical Relationship ◽  
Moisture Profile ◽  
Essential Variable ◽  
Potential Benefits ◽  
Spatio Temporal ◽  
Hydrological Prediction

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.

Short-term growth (RNA/DNA ratio) of yellow perch (Perca flavescens) in relation to environmental influences and spatio-temporal variation in a shallow fluvial lake

2007 ◽  
Vol 64 (12) ◽  
pp. 1646-1655 ◽  
Author(s):  
Hélène Glémet ◽  
Marco A Rodríguez
Keyword(s):  
Temporal Variation ◽  
Water Level ◽  
Yellow Perch ◽  
Perca Flavescens ◽  
Negative Influence ◽  
Water Levels ◽  
Short Term ◽  
Ecological Processes ◽  
Flood Pulse Concept ◽  
Spatio Temporal

Shallow fluvial lakes are heterogeneous ecosystems in which marked spatio-temporal variation renders difficult the analysis of key ecological processes, such as growth. In this study, we used generalized additive modelling of the RNA/DNA ratio, an index of short-term growth, to investigate the influence of environmental variables and spatio-temporal variation on growth of yellow perch (Perca flavescens) in Lake St. Pierre, Quebec, Canada. Temperature and water level had seemingly stronger effects on short-term growth than seasonal change or spatial variation between and along the lakeshores. Consistent with previous studies, the maximum RNA/DNA ratio was found at 20.5 °C, suggesting that our approach provides a useful tool for estimating thermal optima for growth in the field. The RNA/DNA ratio showed a positive relationship with water level, as predicted by the flood pulse concept, a finding with implications for ecosystem productivity in fluvial lakes. The RNA/DNA ratio was more variable along the north than the south shore, possibly reflecting exposure to more differentiated water masses. The negative influence of both high temperatures and low water levels on growth points to potential impacts of climatic change on fish production in shallow fluvial lakes.

Spatio-temporal variation of soil moisture and its influencing factors in Chinese typical forest ecosystems

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
常清青，何洪林，牛忠恩，任小丽，张黎，孙婉馨，朱晓波 CHANG Qingqing
Keyword(s):  
Soil Moisture ◽  
Temporal Variation ◽  
Influencing Factors ◽  
Forest Ecosystems ◽  
Spatio Temporal

Seasonal forecast of agricultural impacts of droughts in Mexico through a principal component regression based approach

2020 ◽  
Author(s):  
Roberto Real-Rangel ◽  
Adrián Pedrozo-Acuña ◽  
Agustín Breña-Naranjo
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Principal Component Regression ◽  
Principal Component ◽  
Drought Monitoring ◽  
Drought Forecasting ◽  
Moisture Index ◽  
The North ◽  
Wide Range ◽  
Monitoring And Forecasting

<p>Drought monitoring and forecasting allows to adopt mitigating actions in early stages of an event to reduce the vulnerability of a wide range of environmetal, economical and social sectors. In Mexico, various drought monitoring systems on national and regional scale perform a follow up of these events, such as the Drought Monitor in Mexico, and the North American Drought Monitor, but seasonal drought forecasting is still a pending task. This study aims at fill this gap applying a methodology that uses data derived from a globally available atmospheric reanalysis product and a principal component regression based model oriented to predict drought impacts in rainfed crops associated to deficits in the soil moisture, estimated by means of the standardized soil moisture index (SSI). Using the state of Guanajuato (Center-North of Mexico) as a study case, the model generated yielded RSME values of 0.74 using regional and global hydrological, climatic and atmospheric variables as predictors with a lead-time of 4 months.</p>

Spatio-temporal variation of karst ecosystem service value and its correlation with ambient environmental factors

2010 ◽  
Vol 18 (1) ◽  
pp. 189-197
Author(s):  
Ming-Yang ZHANG ◽  
Ke-Lin WANG ◽  
Hui-Yu LIU ◽  
Hong-Song CHEN ◽  
Chun-Hua ZHANG ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Temporal Variation ◽  
Ecosystem Service ◽  
Ecosystem Service Value ◽  
Service Value ◽  
Karst Ecosystem ◽  
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.

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