scholarly journals Soil Hydrology of Agricultural Landscapes: Quantitative Description, Research Methods, and Availability of Soil Water

2021 ◽  
Vol 54 (9) ◽  
pp. 1367-1374
Author(s):  
Ye. V. Shein ◽  
A. G. Bolotov ◽  
A. V. Dembovetskii
Keyword(s):  
Soil Water ◽  
Agricultural Landscape ◽  
Quantitative Description ◽  
Lower Boundary ◽  
Agricultural Landscapes ◽  
Soil Conditions ◽  
Moscow Oblast ◽  
Soil Hydrology ◽  
Physically Based ◽  
Physically Based Models

Abstract Soil hydrology has deep Russian roots, which are primarily related to the theory of soil hydrological constants and their practical application. These constants have been used to assess the hydrological soil conditions in stationary observations, for which attempts to arrange regular hydrological observations in the landscape faced impracticable complexity of work and calculations and provided unreliable quantitative predictions. At present, there are new opportunities for experimental research, digital analysis, and prediction of hydrological indicators of soils in the landscape. A new quantitative approach to the use of digital technologies for monitoring soil water and temperature in the soils of agricultural landscapes, their dynamics, and their probabilistic calculations has been developed. Based on the soil map, it is proposed to create an information and measurement system with the studied thermal and hydrophysical characteristics of soils using mathematical models to calculate the dynamics of moisture and temperature for given periods and conditions of different availability of heat and precipitation, which allows us to quantify the availability of moisture reserves in the soils of the agricultural landscape. This system of observations, assessment, and forecast includes the use of modern technologies for determining soil water content and temperature, the adaptation of predictive physically based models for calculating the dynamics of moisture reserves depending on the availability of precipitation and conditions at the lower boundary of soil profiles. The paper deals with the hydrological analysis of soils by the example of the agricultural landscape of the Zelenograd station of the Dokuchaev Soil Science Institute in the village of El’digino, Pushkino district, Moscow oblast.

scholarly journals Physics-Informed Data-Driven Models to Predict Surface Runoff Water Quantity and Quality in Agricultural Fields

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 200 ◽  
Author(s):  
Jing Liang ◽  
Wenzhe Li ◽  
Scott Bradford ◽  
Jiří Šimůnek
Keyword(s):  
Surface Water ◽  
Surface Runoff ◽  
Overland Flow ◽  
Data Driven ◽  
Water Quantity ◽  
Soil Conditions ◽  
Runoff Water ◽  
Physically Based ◽  
Physically Based Models ◽  
Water Quantity And Quality

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models (PBMs), which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with PBMs, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. Here we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport. A large number of numerical simulations was then carried out to develop a database containing information about the impact of various relevant factors on surface runoff quantity and quality, such as different weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices. Finally, the resulting database was used to train data-driven models. Several Machine Learning techniques were explored to find input-output functional relations. The results indicate that the Neural Network model with two hidden layers performed the best among selected data-driven models, accurately predicting runoff water quantity and quality over a wide range of parameters.

A study of hysteresis models for soil-water characteristic curves

2005 ◽  
Vol 42 (6) ◽  
pp. 1548-1568 ◽  
Author(s):  
Hung Q Pham ◽  
Delwyn G Fredlund ◽  
S Lee Barbour
Keyword(s):  
Soil Water ◽  
Unsaturated Soils ◽  
Engineering Practice ◽  
Characteristic Curves ◽  
Physically Based ◽  
Physically Based Models ◽  
Soil Water Characteristic Curves ◽  
Scanning Curves ◽  
Wetting Curve ◽  
Drying Curve

A review of hysteresis models for soil-water characteristic curves is presented. The models can be categorized into two groups: (i) domain models (or physically based models) and (ii) empirical models. Some models are capable of predicting scanning curves, while other models are capable of predicting the boundary wetting curve and the boundary drying curve. A comparison of the ability of five selected models to predict the boundary wetting curve showed that the Feng and Fredlund model with enhancements by Pham, Fredlund, and Barbour appears to be the most appropriate model for engineering practice. Another comparison among five physically based models for predicting scanning curves showed that the Mualem model-II gives the best overall prediction of scanning curves. The study showed that taking the effect of pore blockage into account does not always give a better prediction of hysteretic soil-water characteristic curves. A scaling method for estimating the initial drying curve, the boundary wetting curve, and the boundary drying curve is also presented in the paper.Key words: soil-water characteristic curve, hysteresis model, comparison, boundary curve, scanning curve, unsaturated soils.

scholarly journals A Technique to Evaluate Agricultural Landscape Changes on the Basis of GIS-Processed 1861 Demarcation Plans and Current Description of Kenozero National Park

2021 ◽  
pp. 221-232
Author(s):  
Aleksandr Vladimirovich Kozykin
Keyword(s):  
19Th Century ◽  
Land Management ◽  
National Park ◽  
Agricultural Landscape ◽  
Structural Characteristics ◽  
Land Development ◽  
Agricultural Landscapes ◽  
Soil Conditions ◽  
Landscape Changes ◽  
The 19Th Century

In many Russian nature reserves traditional landscapes are objects of important historical and cultural heritage. To preserve and restore them one needs to deeply understand their development, formation and degradation processes. In the north of European Russia agricultural landscapes are often covered with forests and lose their features when agricultural activity decreases. However, structural characteristics of these forests as a rule tell us about their development and peculiarities of successions. The study aims at creating a technique to estimate the scope of former agricultural land development, model historical transformation of agricultural landscapes and identify plots of slash and burn, shifting, two and three field agriculture judging by structural characteristics of post-agrarian forests. Aided by GIS the study compares raster analogs of land demarcation plans of the second half of the 19th century and vector layers of present day forests with attributive data on the forest structure. The use of cartographic forest data and inventory forest characteristics to compare with former land management documents related to the plot named have not been found in studies before. High precision of present day land management provides for permitted comparability with old demarcation plans and allows one to use inventory data for inter-landscape differentiation of agricultural landscapes in the 19th century. The study covers a model plot within Kenozero National Park (Arkhangelsk Region) addressing 1861 demarcation plans and 2014 forests GIS developed by Arkhangelsk branch of Roslesinforg. GIS processing of 19th century and present day demarcation plans provides for modeling agricultural landscape changes in relation to separate plots, trace the influence of soil conditions and elements of agrarian use on topological and inventory changes of emerging forests and reconstruct the biodiversity of ecosystems in the past.

scholarly journals Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin

2014 ◽  
Vol 7 (3) ◽  
pp. 1115-1136 ◽  
Author(s):  
M. Guimberteau ◽  
A. Ducharne ◽  
P. Ciais ◽  
J. P. Boisier ◽  
S. Peng ◽  
...  
Keyword(s):  
Soil Water ◽  
Water Budget ◽  
River Discharge ◽  
Amazon Basin ◽  
Water Storage ◽  
Dry Season ◽  
Soil Water Storage ◽  
Soil Hydrology ◽  
Physically Based ◽  
Diffusion Scheme

Abstract. This study analyzes the performance of the two soil hydrology schemes of the land surface model ORCHIDEE in estimating Amazonian hydrology and phenology for five major sub-basins (Xingu, Tapajós, Madeira, Solimões and Negro), during the 29-year period 1980–2008. A simple 2-layer scheme with a bucket topped by an evaporative layer is compared to an 11-layer diffusion scheme. The soil schemes are coupled with a river routing module and a process model of plant physiology, phenology and carbon dynamics. The simulated water budget and vegetation functioning components are compared with several data sets at sub-basin scale. The use of the 11-layer soil diffusion scheme does not significantly change the Amazonian water budget simulation when compared to the 2-layer soil scheme (+3.1 and −3.0% in evapotranspiration and river discharge, respectively). However, the higher water-holding capacity of the soil and the physically based representation of runoff and drainage in the 11-layer soil diffusion scheme result in more dynamic soil water storage variation and improved simulation of the total terrestrial water storage when compared to GRACE satellite estimates. The greater soil water storage within the 11-layer scheme also results in increased dry-season evapotranspiration (+0.5 mm d−1, +17%) and improves river discharge simulation in the southeastern sub-basins such as the Xingu. Evapotranspiration over this sub-basin is sustained during the whole dry season with the 11-layer soil diffusion scheme, whereas the 2-layer scheme limits it after only 2 dry months. Lower plant drought stress simulated by the 11-layer soil diffusion scheme leads to better simulation of the seasonal cycle of photosynthesis (GPP) when compared to a GPP data-driven model based on eddy covariance and satellite greenness measurements. A dry-season length between 4 and 7 months over the entire Amazon Basin is found to be critical in distinguishing differences in hydrological feedbacks between the soil and the vegetation cover simulated by the two soil schemes. On average, the multilayer soil diffusion scheme provides little improvement in simulated hydrology over the wet tropical Amazonian sub-basins, but a more significant improvement is found over the drier sub-basins. The use of a multilayer soil diffusion scheme might become critical for assessments of future hydrological changes, especially in southern regions of the Amazon Basin where longer dry seasons and more severe droughts are expected in the next century.

scholarly journals The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Antonio J. Mendoza-Fernández ◽  
Fabián Martínez-Hernández ◽  
Esteban Salmerón-Sánchez ◽  
Francisco J. Pérez-García ◽  
Blas Teruel ◽  
...  
Keyword(s):  
Plant Communities ◽  
Ecological Niche ◽  
Agricultural Landscape ◽  
Agricultural Landscapes ◽  
Eastern Coast ◽  
Ecological Value ◽  
Distribution Models ◽  
European Continent ◽  
Major Interest ◽  
Maytenus Senegalensis

Maytenus senegalensis subsp. europaea is a shrub belonging to the Celastraceae family, whose only European populations are distributed discontinuously along the south-eastern coast of the Iberian Peninsula, forming plant communities with great ecological value, unique in Europe. As it is an endangered species that makes up plant communities with great palaeoecological significance, the development of species distribution models is of major interest under different climatic scenarios, past, present and future, based on the fact that the climate could play a relevant role in the distribution of this species, as well as in the conformation of the communities in which it is integrated. Palaeoecological models were generated for the Maximum Interglacial, Last Maximum Glacial and Middle Holocene periods. The results obtained showed that the widest distribution of this species, and the maximum suitability of its habitat, occurred during the Last Glacial Maximum, when the temperatures of the peninsular southeast were not as contrasting as those of the rest of the European continent and were favored by higher rainfall. Under these conditions, large territories could act as shelters during the glacial period, a hypothesis reflected in the model’s results for this period, which exhibit a further expansion of M. europaea’s ecological niche. The future projection of models in around 2070, for four Representative Concentration Pathways according to the fifth report of the Intergovernmental Panel on Climate Change, showed that the most favorable areas for this species would be Campo de Dalías (southern portion of Almería province) as it presents the bioclimatic characteristics of greater adjustment to M. europaea’s ecological niche model. Currently, some of the largest specimens of the species survive in the agricultural landscapes in the southern Spain. These areas are almost totally destroyed and heavily altered by intensive agriculture greenhouses, also causing a severe fragmentation of the habitat, which implies a prospective extinction scenario in the near future.

scholarly journals Predation on Drosophila suzukii within Hedges in the Agricultural Landscape

Insects ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 305
Author(s):  
Alexandra Siffert ◽  
Fabian Cahenzli ◽  
Patrik Kehrli ◽  
Claudia Daniel ◽  
Virginie Dekumbis ◽  
...  
Keyword(s):  
Agricultural Landscape ◽  
Vital Role ◽  
Agricultural Landscapes ◽  
Gut Content ◽  
Drosophila Suzukii ◽  
Natural Habitats ◽  
Host Fruit ◽  
Molecular Gut Content Analysis ◽  
Wild Fruits ◽  
Diverse Community

The invasive Drosophila suzukii feeds and reproduces on various cultivated and wild fruits and moves between agricultural and semi-natural habitats. Hedges in agricultural landscapes play a vital role in the population development of D. suzukii, but also harbor a diverse community of natural enemies. We investigated predation by repeatedly exposing cohorts of D. suzukii pupae between June and October in dry and humid hedges at five different locations in Switzerland. We sampled predator communities and analyzed their gut content for the presence of D. suzukii DNA based on the COI marker. On average, 44% of the exposed pupae were predated. Predation was higher in dry than humid hedges, but did not differ significantly between pupae exposed on the ground or on branches and among sampling periods. Earwigs, spiders, and ants were the dominant predators. Predator communities did not vary significantly between hedge types or sampling periods. DNA of D. suzukii was detected in 3.4% of the earwigs, 1.8% of the spiders, and in one predatory bug (1.6%). While the molecular gut content analysis detected only a small proportion of predators that had fed on D. suzukii, overall predation seemed sufficient to reduce D. suzukii populations, in particular in hedges that provide few host fruit resources.

scholarly journals A Data-Driven Method for the Temporal Estimation of Soil Water Potential and Its Application for Shallow Landslides Prediction

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1208
Author(s):  
Massimiliano Bordoni ◽  
Fabrizio Inzaghi ◽  
Valerio Vivaldi ◽  
Roberto Valentino ◽  
Marco Bittelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Potential ◽  
Soil Water ◽  
Temporal Trends ◽  
Soil Water Potential ◽  
Shallow Landslides ◽  
Water Dynamics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Physically Based

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

scholarly journals Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 708
Author(s):  
Phanthasin Khanthavong ◽  
Shin Yabuta ◽  
Hidetoshi Asai ◽  
Md. Amzad Hossain ◽  
Isao Akagi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Distribution ◽  
Water Status ◽  
Soil Layer ◽  
Shoot Biomass ◽  
Soil Conditions ◽  
Crop Adaptation ◽  
Soil Moisture Status ◽  
Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

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