Behavior Of Soil Moisture Research Articles

A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation

Accurate knowledge of the spatiotemporal behavior of soil moisture (SM) can greatly improve hydrological forecasting capability. While ground-based SM measurements exhibit greater accuracy, they tend to be sparse in space and available for limited periods. To overcome this, a viable alternative is space-borne microwave remote sensing because of the near real-time observational capability at the global scale. However, its direct applications have been limited because of the uncertainty associated and the coarse spatial resolution the products are available at. Considering its usefulness and limitations, this paper is intended to provide an extensive review of satellite-derived SM and its applications for assessing flood risk in sparsely gauged or ungauged catchments worldwide. Therefore, we aim to identify and discuss research gaps, and finally propose future research directions for improvement and hydrological applications of these SM products. The paper starts with a brief introduction to satellite-derived SM products and consists of three interdependent focal areas: evaluation, improvement, and application of the satellite SM for flood risk alleviation.

Temporal stability of soil moisture in banana cropping area in the Brazilian semiarid region

ABSTRACT Soil moisture is essential for agricultural production. Knowledge on its spatial-temporal variability is indispensable to support agriculture, and it is strongly influenced by cultural practices, soil cover conditions and irrigation methods. Thus, this study aimed to evaluate the temporal stability and spatial distribution of soil moisture as a function of the use of banana leaves as soil cover in a plot under conventional sprinkler irrigation and cultivated with banana in the Brazilian semiarid region. The study area was divided in two sectors, with and without covering using banana leaves. Soil moisture was monitored before and after irrigation, at 16 times, using FALKER HidroFarm sensors installed on a transect with 11 units spaced by 8 m, in each sector. The data were analyzed using descriptive statistics, temporal stability and Spearman correlation test. The morphology of the banana leaves and the irrigation system used contributed to lower soil moisture in the covered sector at all monitoring times. Variations in the physical-hydraulic properties of the soil promoted variations in soil moisture with the position of the sensors in the ground. The temporal stability technique allowed the identification of points that represent the mean behavior of soil moisture throughout the area. The use of banana leaf residues caused less soil wetting using sprinkler irrigation, indicating the need to pre-grind the material or use localized irrigation systems, practices that are not very accessible to small farmers.

2D-ERT monitoring of soil moisture seasonal behaviour in a river levee: A case study

We present a study of the seasonal behaviour of soil moisture in an old embankment levee by means of 2D DC-electrical resistivity tomography monitoring based on an embedded electrode installation.We were able to produce seasonal resistivity change models that are compensated for temperature effects using a seasonal temperature profile model. Time-lapse sections of percentage resistivity changes show spatial and temporal overall consistency with seasonal variations of soil temperature. Discrepancies are likely due to the pavement layer that is not well considered in the inversion process as well as to time-lapse inversion pitfalls. Furthermore, a detailed estimation of seasonal moisture content variations could not be given as an accurate calibration of the employed suction probes was not achievable.Nevertheless, the levee appears to have spatially consistent time variations in soil moisture, clearly influenced by both rainfall and water table and river levels. Future work on developing 3D acquisitions and adding embedded moisture content probes should prove effective to our monitoring design and give a more detailed understanding of the soil moisture seasonal behaviour in the studied stretch of levee.

Global environmental changes impact soil hydraulic functions through biophysical feedbacks.

Although only representing 0.05% of global freshwater, or 0.001% of all global water, soil water supports all terrestrial biological life. Soil moisture behaviour in most models is constrained by hydraulic parameters that do not change. Here we argue that biological feedbacks from plants, macro-fauna and the microbiome influence soil structure, and thus the soil hydraulic parameters and the soil water content signals we observe. Incorporating biological feedbacks into soil hydrological models is therefore important for understanding environmental change and its impacts on ecosystems. We anticipate that environmental change will accelerate and modify soil hydraulic function. Increasingly, we understand the vital role that soil moisture exerts on the carbon cycle and other environmental threats such as heatwaves, droughts and floods, wildfires, regional precipitation patterns, disease regulation and infrastructure stability, in addition to agricultural production. Biological feedbacks may result in changes to soil hydraulic function that could be irreversible, resulting in alternative stable states (ASS) of soil moisture. To explore this, we need models that consider all the major feedbacks between soil properties and soil-plant-faunal-microbial-atmospheric processes, which is something we currently do not have. Therefore, a new direction is required to incorporate a dynamic description of soil structure and hydraulic property evolution into soil-plant-atmosphere, or land surface, models that consider feedbacks from land use and climate drivers of change, so as to better model ecosystem dynamics.

Spatial-temporal variability of soil moisture: Addressing the monitoring at the catchment scale

Soil moisture plays a fundamental role in the mass and energy balance between the land surface and the atmosphere, making its knowledge essential for several hydrological and climatic applications. The aim of this study is to extend the current knowledge of soil moisture spatial-temporal variability at the catchment scale (up to 500 km2). The main implication is to provide guidelines to obtain soil moisture values representative of the mean behaviour at the medium-sized river basin scale, which is useful for remote sensing validation analysis and rainfall-runoff modeling. To this end, 23 measurements campaigns were carried out during a time span of 14 months at 20 sites located within the Upper Chiascio River Basin, a catchment with a drainage area of about 460 km2 in the Umbria Region (central Italy). The data set allowed the analysis of both soil moisture temporal stability and its dynamics. On the basis of statistical and temporal stability approaches, it was investigated how factors such as climatic regime and geomorphology influence soil moisture behaviour. For the investigated area, the spatial variability of soil moisture was higher in dry periods with respect to wet periods, mainly due to the rainfall pattern characteristics during different periods of the year. Soil moisture values recorded during wet periods showed a better correlation than those recorded during dry periods. The maximum number of required samples, to obtain the mean areal soil moisture with an absolute error of 3% vol/vol, was found equal to 12. The temporal stability analysis showed that during wet periods just one “optimal” measurement point can provide values of soil moisture representative of the catchment-mean behaviour, while during dry periods the number of “optimal” measurement points became equal to two. Therefore, at the adopted spatial scale the use of a single measurement point can lead to significant errors. From the perspective of soil moisture dynamics, the decomposition of the spatial variance showed that the contribution of the time-invariant component (temporal mean of each site) was predominant on respect to the total spatial variance of absolute soil moisture data, for almost the whole observation period. Results provided guidance to optimize soil moisture sampling by performing targeted measurements at a few selected points representative of the catchment-mean behaviour.

Soil humidity and evapotranspiration under three coffee (Coffea arabica L.) planting densities at Naranjal experimental station (Chinchiná, Caldas, Colombia)

El café es un cultivo dependiente del agua disponible en el suelo, bien sea a través de la lluvia o del riego, ya que ésta tiene un marcado efecto en la productividad y la distribución de la cosecha. Conocer el efecto de la distancia entre plantas sobre el comportamiento de la humedad en el suelo y el consumo de agua, influye en las prácticas agronómicas del sistema productivo del café, entre ellas: la densidad de siembra, la fertilización, y las épocas de siembra. El estudio se realizó en la Estación Experimental Naranjal ubicada en Chinchiná (Caldas, Colombia) a 04° 58’ N, 75° 39’ O; 1,381 m de altitud y tuvo como objetivos conocer el comportamiento de la humedad en un Andisol sembrado con café en tres densidades de siembra, estimar la evapotranspiración de referencia (ETo) y evaluar la evapotranspiración del cultivo (ETc). La humedad del suelo se midió con una sonda de capacitancia de 100 cm con sensores espaciados cada 10 cm. La humedad en el perfil del suelo varió con la densidad de siembra y entre los periodos secos y húmedos considerados. La ETo y la ETc fueron influenciadas por las condiciones de clima, siendo mayores en la época seca y menores en la época de lluvias. La densidad de siembra afectó las propiedades hidráulicas del suelo. No obstante, por la capacidad de almacenar agua que caracteriza a los Andisoles, las plantas tuvieron agua disponible aún en las épocas secas. La sonda de capacitancia mostró ser un instrumento efectivo para el estudio del comportamiento del agua en los suelos de la región cafetera de Colombia, especialmente aquellos sensibles a las limitaciones ocasionadas por el déficit hídrico como producto de la variabilidad climática actual y futura.

Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition

Soil moisture (SM) is an essential component of the environmental and the agricultural system. Continuous monitoring and forecasting of soil moisture is a desirable strategy to understand the soil dynamics for proactive planning and decision-making measures for agriculture and related fields. In this study hybrid data-intelligent, extreme learning machine (ELM) models are designed and explored for monthly SM forecasting. The chaotic, complex and dynamical behavior of SM can compound the accuracy of data-driven models. Consequently, two versatile, computationally efficient and self-adaptive multi-resolution utilities namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the ensemble empirical mode decomposition (EEMD) algorithms are utilized to address these data non-stationarity issues, which if not resolved can lead to model prediction inaccuracies. The difference in these approaches is that, during the EEMD process, a Gaussian white noise is added to the intact (i.e., unresolved) time series only, while, the CEEMDAN requires sequential additions at each decomposition phase. Integration of these multi-resolution tools with the ELM model led to the hybrid CEEMDAN-ELM and the EEMD-ELM models, that were benchmarked with random forest (RF) equivalent models. Using WaterDyn model's hind-simulated SM data, these models were applied (without any climate inputs) to forecast the upper (0.2 m) and the lower layer (0.2–1.5 m depth) soil moisture in Australia's agricultural-hub, the Murray-Darling Basin. The standalone ELM and RF model has similar computation efficiency and model performances. However, despite the implementation of computationally expensive ensemble techniques (i.e., EEMD and CEEMDAN, the hybrid ensembles EEMD-ELM and CEEMDAN-ELM were highly efficient with improved performances. The research outcomes showed that the CEEMDAN-ELM model outperformed the alternative models at three (out of the seven) sites applied for upper layer SM forecasts, while the EEMD-ELM hybrid model was superior at all seven sites for the lower layer soil moisture forecasts. The study signifies the important role of the self-adaptive multi-resolution utility (CEEMDAN) hybridized with the ELM algorithm to potentially develop automated prediction systems for forecasting soil moisture, with potential applications in agriculture.

Understanding plot‐scale hydrology of Lesser Himalayan watershed—A field study and HYDRUS‐2D modelling approach

AbstractSoil moisture dynamics have a significant effect on overland flow generation. Catchment aspect is one of the major controlling factors of overland flow and soil moisture behaviour. A few experimental studies have been carried out in the uneven topography of the Himalayas. This study presents plot‐scale experiments using portable rainfall simulator at an altitude of 1,230 m above mean sea level and modelling of overland flow using observed datasets. Two plots were selected in 2 different aspects of Aglar watershed of Lesser Himalaya; the agro‐forested (AF) plot was positioned at the north aspect whereas the degraded (DE) plot was located at the south aspect of the hillslope. HS flumes and rain gauges were installed to measure the runoff at the outlet of the plot and the rainfall depth during rainfall simulation experiments. Moreover, 10 soil moisture sensors were installed at upslope and downslope locations of both the plots at 5, 15, 25, 35, and 45 cm depth from ground level to capture the soil moisture dynamics. The tests were conducted at intensities of 79.8 and 75 mm/hr in AF plot and 82.2 and 72 mm/hr in the DE plot during Test 1 and Test 2, respectively. The observed data indicate the presence of reinfiltration process only in the AF plot. The high water holding capacity and the presence of reinfiltration process results in less runoff volume in the AF plot compared with the DE plot. The Hortonian overland flow mechanism was found to be the dominant overland flow mechanism as only a few layers of top soil get saturated during all of the rainfall–runoff experiments. The runoff, rainfall, and soil moisture data were subsequently used to calibrate the parameters of HYDRUS‐2D overland flow module to simulate the runoff hydrograph and soil moisture. The components of hydrograph were evaluated in terms of peak discharge, runoff volume and time of concentration, the results were found to be within the satisfactory range. The goodness of fit of simulated hydrographs were more than 0.85 and 0.95 for AF and DE plot, respectively. The model produced satisfactory simulation results of soil moisture for all of the rainfall–runoff experiments. The HYDRUS‐2D overland flow module was found promising to simulate the runoff hydrograph and soil moisture in plot‐scale research.

Substrate Composition and Depth Affect Soil Moisture Behavior and Plant-Soil Relationship on Mediterranean Extensive Green Roofs

The Mediterranean basin is extremely vulnerable to climate change, and one of the areas most impacted by human water demand. Yet the green roofs increasingly created both for aesthetic reasons and to limit pollution and urban runoff are themselves very water-demanding. Successful green roof installation depends on the establishment of the vegetation, and the substrate is the key element: it conserves water, and provides the nutrients and physical support indispensable for plant growth. Since typical Mediterranean plant communities require no maintenance, this study seeks to develop techniques for creating maintenance- and watering-free horizontal green roofs for public or private buildings in a Mediterranean context. The innovative aspect of this study lies in creating two soil mixes, fine elements (clay and silt) and coarse elements (pebbles of all sizes), in two different thicknesses, to assess vegetation development. Monitoring of substrate moisture was carried out and coupled with local rainfall measurements during summer and autumn. As expected, substrate moisture is mainly influenced by substrate depth (the deeper, the moister) and composition (the finer the particles (clays and silts), the higher the moisture content). Vegetation cover impacts moisture to a lesser extent but is itself affected by the composition and depth of the substrates. These results are subsequently discussed with relation to the issue of sustainable green roofs in Mediterranean climates. Considering applications of our results, for an optimal colonization of a Mediterranean vegetation, a substrate thickness of 15 cm composed mainly of fine elements (75% clay-silt and 25% pebble-sand) would be recommended in green roofs.

Estimating Soil Moisture Patterns with Remote Sensing and Terrain Data at the Small Catchment Scale

Core Ideas The FCM SEA was used for sampling and spatial estimation of soil moisture patterns. Multispectral remote sensing and terrain data were combined to guide the sampling and estimation. Selected vegetation patterns and terrain data provided reasonable estimates of soil moisture. The FCM SEA was stable to explain about 50% of the total observed variance. The FCM SEA was superior to an approach driven solely by terrain data. Detailed information on the temporal and spatial evolution of soil moisture patterns is of fundamental importance to improve runoff prediction, optimize irrigation management and to enhance crop forecasting. However, obtaining representative soil moisture measurements at the catchment scale is challenging because of the dynamic spatial and temporal behavior of soil moisture. High‐resolution remote sensing data provide detailed spatial information about catchment characteristics (e.g., terrain and land use) that can be used as proxies to estimate soil moisture. We assessed the potential use of combined multitemporal multispectral remote sensing (RS) and terrain data for estimating spatial soil moisture patterns at the small catchment scale. The fuzzy c‐means sampling and estimation approach (FCM SEA) was applied to conduct a sensor (proxy) directed (guided) sampling and to reconstruct multitemporal soil moisture patterns based on time domain reflectometry measurements. A comprehensive soil moisture database for the Schäfertal catchment, located in central Germany, was used to test, validate, and compare the FCM SEA performances of the combined remote sensing data with those of a benchmark approach driven solely by terrain data. Results from the study show that a FCM SEA model that integrates bi‐temporal RS imagery and terrain data was more effective in estimating spatial soil moisture patterns relative to the benchmark model. It outperformed the benchmark model in 58% of the cases and was stable to explain about 50% of the total observed variance for a range of different catchment moisture conditions. This was achieved with only a small sample size (n = 30). The results of this study are promising because they highlight the importance of considering multitemporal RS and terrain data and demonstrate how in situ sensors can be optimally placed to enable cost‐efficient monitoring and prediction of spatial soil moisture patterns at the small catchment scale.

Effects of micro-topography and vegetation type on soil moisture in a large gully on the Loess Plateau of China

Abstract Large gullies occur globally and can be classified into four main micro-topographic types: ridges, plane surfaces, pipes and cliffs. Afforestation is an effective method of controlling land degradation worldwide. However, the combined effects of afforestation and micro-topography on the variability of soil moisture remain poorly understood. The primary objectives of this study were to determine whether afforestation affects the spatial pattern of the root-zone (0–100 cm) soil moisture and whether soil moisture dynamics differ among the micro-topographic types in gully areas of the Chinese Loess Plateau. The results showed that in the woodland regions, the spatial mean moisture values decreased by an average of 6.2% and the spatial variability increased, as indicated by the standard deviation (17.1%) and the coefficient of variation (22.2%). In general, different micro-topographic types exerted different influences on soil moisture behavior. The plane surface presented the largest average soil moisture values and the smallest spatial variability. The lowest soil moisture values were observed in the ridge, mainly due to the rapid drainage of these areas. Although pipe woodland region can concentrate surface runoff during and after rainfall, the larger trees growing in these areas can lead to increased soil moisture evapotranspiration.

Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation

A neural network (NN) soil moisture retrieval product computed from the synergy of AMSR-E brightness temperature and ASCAT backscatter observations is evaluated against in situ soil moisture observations from the International Soil Moisture Network (ISMN). The skill of the NN retrieval is compared to that of the ESA-CCI soil moisture retrieval as well as modeled soil moisture fields from ERA-interim/land. The NN retrieval is able to capture the observed soil moisture temporal variations with a station-average correlation and anomaly correlation of 0.45 and 0.35, respectively. For most ground stations the model obtained a higher temporal correlation skill, with average correlation and anomaly correlation values of 0.53 and 0.46, respectively. For stations in data-sparse regions, the NN retrieval showed a slightly better performance than the model, illustrating the potential of soil moisture retrievals to inform models in data-sparse areas. A time series analysis further showed that the retrieval is well able to capture soil moisture variability outside of active precipitation phases, such as the soil moisture behavior during the dry down phase. Compared to the ESA-CCI retrieval, the NN methodology obtained higher correlations, as a result of its ability to use the complementary information provided by the active and passive microwave sensors. A global evaluation of the retrieval errors through a triple collocation analysis with SMOS and GLDAS soil moisture estimates showed that the errors are high in energy limited regions, where the NN retrieval appears to be lacking input information. In the data-sparse regions of Africa and South America, the triple collocation analysis confirmed the higher skill of the NN retrieval compared to the model.

Evaluation of soil moisture‐precipitation feedback at different time scales over Asia

ABSTRACTThe soil moisture‐precipitation (SM‐P) feedback process can affect the water and energy cycles at local to regional scale, subsequently affecting the water distribution in space and time. This study investigates SM‐P feedback at different time scales over Asia during boreal summer season based on the covariation between SM and P. In this article the positive (negative) feedback means positive (negative) statistical correlations between SM‐P. This is based on the statistical relationship and do not reflect physical process associated between SM and P. The Global Land Data Assimilation System (GLDAS) data, Climate Forecast System Reanalysis (CFSR) data and regional climate model, version 4 (RegCM4) simulations are adopted for analysis in this study. The results indicate a negative SM‐P relationship at a seasonal time scale over mid‐high latitudes of the study area whereas a positive but weak and spatial scattered correlation pattern is observed at the interannual time scale. A positive relationship is diagnosed over India, Indochina, and southeast China regions at seasonal (though weak) and interannual time scales. The positive and/or negative SM‐P correlation patterns observed at different time scales depends on the memory associated with seasonal precipitation and soil moisture. The strength of the SM‐P relationship seems to be influenced by the threshold behaviour of soil moisture, especially at the interannual time scale. This indicates that beyond a certain threshold in soil moisture anomalies likely to have significant influence on precipitation.

Spatial variability of SEBAL estimated root-zone soil moisture across scales

ABSTRACTThis study investigated the spatial scaling behaviour of root-zone soil moisture obtained from optical/thermal remote-sensing observations. The data for this study were obtained from Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites on five different dates between early spring (April) and fall (September) in the years from 2000 to 2004 in the semi-arid middle Rio Grande Valley of New Mexico. Soil moisture data were obtained using the Surface Energy Balance Algorithm for Land (SEBAL) algorithm. The data were spatially aggregated and checked for power-law behaviour over a range of scales from 30 m to 15 km for Landsat and from 1 to 28 km for MODIS images. Results of this study demonstrate that power-law scaling of soil moisture in the middle Rio Grande area holds up to 1 km2 pixel size, but is no longer valid beyond that scale. Whereas previous studies have studied soil moisture in the top 5 cm of the soil using radar and point measurements, our study uses SEBAL to estimate root-zone soil moisture. Our study is consistent with these previous studies in showing that variation in root-zone soil follows an empirical power law for pixel sizes of up to about 106 m2 and that there is an apparent break in the scaling at larger scales.

Calibration of Noah Soil Hydraulic Property Parameters Using Surface Soil Moisture from SMOS and Basinwide In Situ Observations

AbstractSoil hydraulic properties (SHPs) control infiltration and redistribution of moisture in a soil column. The Noah land surface model (LSM) default simulation selects SHPs according to a location’s mapped soil texture class. SHPs are instead estimated at seven sites in North America through calibration. A single-objective algorithm minimizes the root-mean-square difference (RMSD) between simulated surface soil moisture and observations from 1) a dense network of in situ probes, 2) Soil Moisture Ocean Salinity (SMOS) satellite retrievals, and 3) SMOS retrievals adjusted such that their mean equals that of the in situ network. Parameters are optimized in 2012 and validated in 2013 against the in situ network. RMSD and unbiased RMSD (ubRMSD) assess resulting surface soil moisture behavior. At all sites, assigning SHP parameters from a different soil texture than the one that is mapped decreases the RMSD by an average of 0.029 cm3 cm−3. Similar improvements result from calibrating parameters using in situ network data (0.031 cm3 cm−3). Calibrations using remotely sensed data show comparable success (0.029 cm3 cm−3) if the SMOS product has no bias. Calibrated simulations are superior to texture-based simulations in their ability to decrease ubRMSD at times of year when the default simulation is worst. Changes to both RMSD and ubRMSD are small when the default simulation is already good. Most calibrated simulations have higher runoff ratios than do texture-based simulations, a change that warrants further evaluation. Overall, parameter selection using SMOS data shows good potential where biases are low.

Modelling the root zone soil moisture using artificial neural networks, a case study

Surface soil moisture constitutes a major component in the Earth’s water cycle. In many cases, modelling and predicting soil moisture represent a serious problem in water resources field due to the problematic measurements or lack of measurements, etc. Data-driven models such as artificial neural networks (ANN) have been characterized as a robust tool to overcome these shortcomings. This study aims to identify the optimum ANNs to model the root zone soil moisture (up to 2 m depth) in the upper reach of the Spree River using the synthetic soil moisture data from SWAT model. Thus, three different approaches were developed and compared to determine the highest performing method. These networks can be broadly categorized into dynamic, static, and statistical neural networks, which are layer recurrent network (LRN), feedforward (FF), and radial basis networks, respectively. Data sets of precipitation and antecedent soil moisture were selected based on quantification of cross-, auto-, and partial autocorrelation coefficients to represent the best behaviour of root soil moisture. The time series data were subdivided into two subsets: one for network training and the second for network testing. The determination coefficient (R 2), root-mean-square error, and Nash–Sutcliffe efficiency were employed to test the goodness of fit between the actual and modelled data. Results show that, among the used methods, the LRN and FF networks have the top performance criteria, showing a reliable ability to be used as estimator for the soil moisture in this catchment.

Disentangling the Effects of Land Management and Soil Heterogeneity on Soil Moisture Dynamics

Core Ideas We performed time series analysis of existing large soil moisture data sets. Observed behavior was decomposed into respective contributions of single processes. Considering input signal transformation instead of mass flux considerations. Our approach opens many new possibilities to analyze complex soil system behavior. Soil moisture is the essential control of water and energy dynamics at arable sites. Time series of soil moisture reflect the interplay of various processes, each of which influences the overall soil moisture dynamics. In this study we tested an approach to break down observed soil moisture behavior into the respective contributions of individual processes. We applied a principal component analysis to soil moisture time series from a field experiment comprising two crop rotation systems and two different soil tillage practices. We concentrated on 57 soil moisture time series measured over nearly 4 yr at 12 plots and five soil depths, down to 1.5 m. About 77.9% of the variance was reflected by the first component being almost identical to a time series of averaged soil moisture. It described the effect of the meteorological boundary conditions. The second component described the effect of the input signal damping increasing with soil depth and accounted for 7.8% of total variance. The signal transformation over depth proved to be more or less uniform throughout the test site, despite considerable soil heterogeneity. Another 3.6% of the total variance (third component) was unambiguously explained by the different cropping systems. On the contrary, different soil tillage practices had no significant effect. The suggested approach opens up many possibilities to analyze and better understand complex soil system behavior. The data‐based approach of time series analysis provides model‐independent, quantitative information about the key factors and processes controlling soil water dynamics. Hence, it is especially valuable for model building, calibration, and evaluation.

Plant biomass and soil moisture dynamics: analytical results

In semi-arid ecosystems, successful use of the limited water resources is of central importance in determining the evolutionary trends of the vegetation. The competition between different species and individuals for this resource is driven by variations in physiology and metabolic regulation strategies, expressed by such parameters as rooting depth, wilting point or stomatal opening, among others. It is typically not practical to simulate the full evolutionary dynamics of every plant individual in the landscape because of the difficulties introduced by the spatial heterogeneity, as well as the many timescales involved, ranging from hourly up to intergenerational. Instead, the amount of biomass of a given species assimilated per unit area of the landscape may serve as a proxy for its competitiveness and evolutionary success. It is the behaviour of the biomass, which must be described probabilistically due to the stochasticity of the rainfall, which is the subject here. This paper develops a new analytical description of the stationary and transient joint behaviour of plant biomass and soil moisture. Additionally, the effects of climatic fluctuations are considered, including the important case of a bi-seasonal climate regime consisting of alternating wet and dry seasons, which is characteristic of many ecosystems of interest.

Analysis of Temporal Stability of Observed Soil Moisture under Plantation Forest in Western Ghats of India

The recognition of temporally stable location with respect to soil moisture content is of importance for soil water management decisions, especially at watershed scale. And most importantly, the profile soil moisture which play a vital role in generation of runoff in a watershed. In this regard, it is very essential to understand the behavior of soil moisture for their vertical and horizontal stability. To this end, a watershed located in the Western Ghats close to Kodigibail Village, Siddapur Taluk, Uttara Kannada District of Karnataka State, India, was selected for monitoring of soil moisture. The soil moisture was measured at weekly time step for a period of Jan 206 to Dec 2009. The measured soil moisture values were used for evaluation following issues (a) temporal and spatial variation of the observed soil moisture and (b) stability of soil moisture for both vertical and horizontal scale. The results obtained through the analysis of the observed soil moisture revealed that, the soil moisture content in the watershed exhibit both vertical and horizontal stability and can be considered as representative of the watershed.

Irrigation with seawater as a strategy for the environmental management of abandoned solar saltworks: A case-study in SE Spain based on soil–vegetation relationships

Groundwater withdrawal by desalination plants might lead to a drop of the piezometric levels of aquifers, causing negative impacts, especially in water-dependent ecosystems. Management strategies are necessary to overcome these potential problems, particularly in abandoned solar saltworks—wetlands which are particularly prone to degradation by desiccation. This paper reports the soil–vegetation relationships in one of these wetlands (Agua Amarga salt marsh, SE Spain), in which a pioneer programme of irrigation with seawater is applied to correct the environmental impacts of two desalination plants. Depth of water level and soil Eh, bulk density, moisture and salinity were determined in December 2011 and June and October 2012, in 63 plots from 12 different environments/types of vegetation. Multivariate analyses were used to identify gradients, and a model of soil–vegetation relationships was elaborated to contribute to the management of the marsh. Two different situations were identified: the periphery (drier and less saline soils) and the abandoned saline ponds (wetter and more saline soils). The three environments/types of vegetation of the periphery (Suaeda vera, Lygeum spartum and Limonium spp. stands) were separated according to the seasonal behaviour of soil salinity and moisture, regardless of the irrigation programme. Hence, modifications in the vegetation distribution should be mainly attributable to environmental factors, including possible consequences of climate change. The nine environments/types of vegetation of the abandoned saline ponds (Sarcocornia fruticosa, Arthrocnemum macrostachyum growing in patches with and without the presence of seedlings, bare soils among A. macrostachyum patches, mixed stands with S. fruticosa and A. macrostachyum, Salicornia patula, flooded ponds with Ruppia maritima, Phragmites australis and a burned zone) were separated mainly according to soil moisture, water level in the soil profile, Eh and salinity. In this zone, vegetation can be used as a bioindicator to detect the consequences of the irrigation programme, which should be carefully managed if biodiversity and habitat variety have to be preserved. Our results indicate that the amount of seawater pumped is a critical issue and must be planned taking into account not only the vegetation but also the current soil properties such as bulk density (that influences water infiltration and retention) and salinity (mainly determined by the salts that remain in the soils due to the former use of the ponds as solar saltworks).