scholarly journals Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content

2009 ◽  
Vol 11 (3-4) ◽  
pp. 237-251 ◽  
Author(s):  
Amin Elshorbagy ◽  
Ibrahim El-Baroudy
Keyword(s):  
Soil Moisture ◽  
Polynomial Regression ◽  
Time Lag ◽  
Soil Layer ◽  
Mining Industry ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Atmospheric Conditions ◽  
Complex Behaviour ◽  
Near Surface

Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.

scholarly journals Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

2016 ◽  
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Column ◽  
Regional Analysis ◽  
Soil Layer ◽  
Model Performance ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

scholarly journals A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

2017 ◽  
Vol 18 (3) ◽  
pp. 837-843 ◽  
Author(s):  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Sarith P. P. Mahanama
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Time Estimates ◽  
Data Driven Approach ◽  
Global Surface

Abstract NASA’s Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2–3 days and a latency of 24 h. Here, to enhance the utility of the SMAP data, an approach is presented for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

scholarly journals Novel Cosmic Ray Neutron Sensor Accurately Captures Field-Scale Soil Moisture Trends under Heterogeneous Soil Textures

Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3038
Author(s):  
Kade D. Flynn ◽  
Briana M. Wyatt ◽  
Kevin J. McInnes
Keyword(s):  
Soil Moisture ◽  
Water Conservation ◽  
Cosmic Ray ◽  
Atmospheric Conditions ◽  
Rainfall Runoff ◽  
Field Scale ◽  
Near Surface ◽  
Heterogeneous Soil ◽  
In Situ Sensors

Soil moisture is a critical variable influencing plant water uptake, rainfall-runoff partitioning, and near-surface atmospheric conditions. Soil moisture measurements are typically made using either in-situ sensors or by collecting samples, both methods which have a small spatial footprint or, in recent years, by remote sensing satellites with large spatial footprints. The cosmic ray neutron sensor (CRNS) is a proximal technology which provides estimates of field-averaged soil moisture within a radius of up to 240 m from the sensor, offering a much larger sensing footprint than point measurements and providing field-scale information that satellite soil moisture observations cannot capture. Here we compare volumetric soil moisture estimates derived from a novel, less expensive lithium (Li) foil-based CRNS to those from a more expensive commercially available 3He-based CRNS, to measurements from in-situ sensors, and to four intensive surveys of soil moisture in a field with highly variable soil texture. Our results indicate that the accuracy of the Li foil CRNS is comparable to that of the commercially available sensors (MAD = 0.020 m3 m−3), as are the detection radius and depth. Additionally, both sensors capture the influence of soil textural variability on field-average soil moisture. Because novel Li foil-based CRNSs are comparable in accuracy to and much less expensive than current commercially available CRNSs, there is strong potential for future adoption by land and water managers and increased adoption by researchers interested in obtaining field-scale estimates of soil moisture to improve water conservation and sustainability.

scholarly journals Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean

2021 ◽  
Author(s):  
Johannes Vogel ◽  
Eva Paton ◽  
Valentin Aich
Keyword(s):  
Soil Moisture ◽  
Eastern Mediterranean ◽  
Time Lag ◽  
Mediterranean Ecosystems ◽  
Western Mediterranean ◽  
Bivariate Analysis ◽  
Spatial And Temporal Variability ◽  
Ecosystem Vulnerability ◽  
Dry Conditions ◽  
The Mediterranean

Abstract. Mediterranean ecosystems are particularly vulnerable to climate change and the associated increase in climate extremes. This study investigates extreme ecosystem responses evoked by climatic drivers in the Mediterranean Basin for the time span 1999–2019 with a specific focus on seasonal variations, as the seasonal timing of climatic anomalies is considered essential for impact and vulnerability assessment. A bivariate vulnerability analysis is performed for each month of the year to quantify which combinations of the drivers temperature (obtained from ER5 Land) and soil moisture (obtained from ESA CCI and ERA5 Land) lead to extreme reductions of ecosystem productivity using the fraction of absorbed photosynthetically active radiation (FAPAR; obtained from Copernicus Global Land Service) as a proxy. The bivariate analysis clearly showed that, in many cases, it is not just one but a combination of both drivers that causes ecosystem vulnerability. The overall pattern shows that Mediterranean ecosystems are prone to three soil moisture regimes during the yearly cycle: They are vulnerable to hot and dry conditions from May to July, to cold and dry conditions from August to October, and to cold conditions from November to April, illustrating the shift from a soil moisture-limited regime in summer to an energy-limited regime in winter. In late spring, a month with significant vulnerability to hot conditions only often precedes the next stage of vulnerability to both hot and dry conditions, suggesting that high temperatures lead to critically low soil moisture levels with a certain time lag. In the eastern Mediterranean, the period of vulnerability to hot and dry conditions within the year is much longer than in the western Mediterranean. Our results show that it is crucial to account for both spatial and temporal variability to adequately assess ecosystem vulnerability. The seasonal vulnerability approach presented in this study helps to provide detailed insights regarding the specific phenological stage of the year in which ecosystem vulnerability to a certain climatic condition occurs.

scholarly journals Seasonal ecosystem vulnerability to climatic anomalies in the Mediterranean

2021 ◽  
Vol 18 (22) ◽  
pp. 5903-5927
Author(s):  
Johannes Vogel ◽  
Eva Paton ◽  
Valentin Aich
Keyword(s):  
Soil Moisture ◽  
Eastern Mediterranean ◽  
Time Lag ◽  
Mediterranean Ecosystems ◽  
Western Mediterranean ◽  
Bivariate Analysis ◽  
Spatial And Temporal Variability ◽  
Ecosystem Vulnerability ◽  
Dry Conditions ◽  
The Mediterranean

Abstract. Mediterranean ecosystems are particularly vulnerable to climate change and the associated increase in climate anomalies. This study investigates extreme ecosystem responses evoked by climatic drivers in the Mediterranean Basin for the time span 1999–2019 with a specific focus on seasonal variations as the seasonal timing of climatic anomalies is considered essential for impact and vulnerability assessment. A bivariate vulnerability analysis is performed for each month of the year to quantify which combinations of the drivers temperature (obtained from ERA5-Land) and soil moisture (obtained from ESA CCI and ERA5-Land) lead to extreme reductions in ecosystem productivity using the fraction of absorbed photosynthetically active radiation (FAPAR; obtained from the Copernicus Global Land Service) as a proxy. The bivariate analysis clearly showed that, in many cases, it is not just one but a combination of both drivers that causes ecosystem vulnerability. The overall pattern shows that Mediterranean ecosystems are prone to three soil moisture regimes during the yearly cycle: they are vulnerable to hot and dry conditions from May to July, to cold and dry conditions from August to October, and to cold conditions from November to April, illustrating the shift from a soil-moisture-limited regime in summer to an energy-limited regime in winter. In late spring, a month with significant vulnerability to hot conditions only often precedes the next stage of vulnerability to both hot and dry conditions, suggesting that high temperatures lead to critically low soil moisture levels with a certain time lag. In the eastern Mediterranean, the period of vulnerability to hot and dry conditions within the year is much longer than in the western Mediterranean. Our results show that it is crucial to account for both spatial and temporal variability to adequately assess ecosystem vulnerability. The seasonal vulnerability approach presented in this study helps to provide detailed insights regarding the specific phenological stage of the year in which ecosystem vulnerability to a certain climatic condition occurs.

scholarly journals An Analytical Model of Evaporation Efficiency for Unsaturated Soil Surfaces with an Arbitrary Thickness

2011 ◽  
Vol 50 (2) ◽  
pp. 457-471 ◽  
Author(s):  
Olivier Merlin ◽  
Ahmad Al Bitar ◽  
Vincent Rivalland ◽  
Pierre Béziat ◽  
Eric Ceschia ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Analytical Model ◽  
Land Surface ◽  
Soil Layer ◽  
Soil Evaporation ◽  
Atmospheric Conditions ◽  
Potential Evaporation ◽  
Evaporative Demand ◽  
Soil Layers ◽  
Arbitrary Thickness

Abstract Analytical expressions of evaporative efficiency over bare soil (defined as the ratio of actual to potential soil evaporation) have been limited to soil layers with a fixed depth and/or to specific atmospheric conditions. To fill the gap, a new analytical model is developed for arbitrary soil thicknesses and varying boundary layer conditions. The soil evaporative efficiency is written [0.5 − 0.5 cos(πθL/θmax)]P with θL being the water content in the soil layer of thickness L, θmax being the soil moisture at saturation, and P being a function of L and potential soil evaporation. This formulation predicts soil evaporative efficiency in both energy-driven and moisture-driven conditions, which correspond to P < 0.5 and P > 0.5, respectively. For P = 0.5, an equilibrium state is identified when retention forces in the soil compensate the evaporative demand above the soil surface. The approach is applied to in situ measurements of actual evaporation, potential evaporation, and soil moisture at five different depths (5, 10, 30, 60, and 100 cm) collected in summer at two sites in southwestern France. It is found that (i) soil evaporative efficiency cannot be considered as a function of soil moisture only because it also depends on potential evaporation, (ii) retention forces in the soil increase in reaction to an increase of potential evaporation, and (iii) the model is able to accurately predict the soil evaporation process for soil layers with an arbitrary thickness up to 100 cm. This new model representation is expected to facilitate the coupling of land surface models with multisensor (multisensing depth) remote sensing data.

scholarly journals Responses of vertical soil moisture to rainfall pulses and land uses in a typical loess hilly area, China

Solid Earth ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 595-608 ◽  
Author(s):  
Y. Yu ◽  
W. Wei ◽  
L. D. Chen ◽  
F. Y. Jia ◽  
L. Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Time Lag ◽  
Soil Layer ◽  
Growing Season ◽  
Vegetation Restoration ◽  
Pinus Tabulaeformis ◽  
Heavy Rainfall Event ◽  
Hydrological Process ◽  
Hilly Area ◽  
Moisture Variation

Abstract. Soil moisture plays a key role in vegetation restoration and ecosystem stability in arid and semiarid regions. The response of soil moisture to rainfall pulses is an important hydrological process, which is strongly influenced by land use during the implementation of vegetation restoration. In this study, vertical soil moisture variations of woodland (Pinus tabulaeformis), native grassland (Stipa bungeana), shrubland (Hippophea rhamnoides), cropland (Triticum aestivum) and artificial grassland (Onobrychis viciaefolia) in five soil profiles were monitored in a typical loess hilly area during the 2010 growing season. The results demonstrated that rainfall pulses directly affected soil moisture variation. A multi-peak pattern of soil moisture appeared during the growing season, notably in the surface soil layer. Meanwhile, the response of each vegetation type to rainfall was inconsistent, and a time-lag effect before reaching the peak value was detected, following each heavy rainfall event. The response duration of soil moisture, however, varied markedly with the size of rainfall events. Furthermore, higher soil water content was detected in grassland and shrubland. Woodland was characterized by relatively lower soil moisture values throughout the investigation period. Our research suggests that vegetation restoration efforts should give priority to grassland and shrubland at the research site. We suggest that more studies should be focused on the characteristics of community structure and spatial vegetation distribution on soil moisture dynamics, particularly within the grass and shrub ecosystems.

scholarly journals Responses of vertical soil moisture to rainfall pulses and land uses in a typical loess hilly area, China

2014 ◽  
Vol 6 (2) ◽  
pp. 3111-3139 ◽  
Author(s):  
Y. Yu ◽  
W. Wei ◽  
L. D. Chen ◽  
L. Yang ◽  
F. Y. Jia ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Time Lag ◽  
Soil Layer ◽  
Growing Season ◽  
Vegetation Restoration ◽  
Pinus Tabulaeformis ◽  
Heavy Rainfall Event ◽  
Hydrological Process ◽  
Hilly Area ◽  
Moisture Variation

Abstract. Soil moisture plays a key role in vegetation restoration and ecosystem stability in arid and semiarid regions. The response of soil moisture to rainfall pulses is an important hydrological process, which is strongly influenced by land use during the implementation of vegetation restoration measures. In this study, vertical soil moisture variations of woodland (Pinus tabulaeformis), native grassland (Stipa bungeana), shrubland Hippophea rhamnoides), cropland (Triticum aestivum) and artificial grassland (Onobrychis viciaefolia) in five soil profiles were monitored in a typical loess hilly area during the 2010 growing season. The results demonstrated that rainfall pulses directly affected soil moisture variation. A multi-peak pattern of soil moisture appeared during the growing season, notably in the surface soil layer. Meanwhile, the response of each vegetation type to rainfall was inconsistent, and a time-lag effect before reaching the peak value was detected, following a heavy rainfall event. The response duration of soil moisture, however, varied markedly with the size of rainfall events. Furthermore, higher soil water content was detected in grassland and shrubland. Woodland was characterized by relatively lower soil moisture values throughout the investigation period. Our research suggests that vegetation restoration efforts should give priority to grassland and shrubland at the research site. We suggest that more studies should be focused on the characteristics of community structure and spatial vegetation distribution on soil moisture dynamics, particularly within the grass and shrub ecosystems.

scholarly journals Analyzing Spatio-Temporal Factors to Estimate the Response Time between SMOS and In-Situ Soil Moisture at Different Depths

2020 ◽  
Vol 12 (16) ◽  
pp. 2614
Author(s):  
Christoph Herbert ◽  
Miriam Pablos ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
José Martínez-Fernández
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Response Time ◽  
Temporal Variability ◽  
Characteristic Time ◽  
Time Lag ◽  
Near Surface ◽  
Time Length ◽  
Spatio Temporal

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

scholarly journals Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations

2017 ◽  
Vol 21 (4) ◽  
pp. 2203-2218 ◽  
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Soil Column ◽  
Regional Analysis ◽  
Soil Layer ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Near Surface ◽  
Southern Us

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources is used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multimodel ensemble means and the performance of individual models are assessed at a monthly timescale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the amount of both near-surface and soil column soil moisture in the western US and underestimate it in the eastern US. There are large variations across models, especially for the near-surface soil moisture. There are significant regional variations in performance as well. Results of a regional analysis show that in the deeper soil layers, the CMIP5 soil moisture simulations tend to be most skillful in the southern US. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

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