Proxy For Soil Moisture Research Articles

Shifting trait coordination along a soil‐moisture‐nutrient gradient in tropical forests

Abstract Soil nutrients and water availability are strong drivers of tropical tree species distribution across scales. However, the physiological mechanisms underlying environmental filtering along these gradients remain incompletely understood. Previous studies mostly focused on univariate variation in structural traits, but a more integrative approach combining multiple physiological traits is needed to fully portray species functional strategies. We measured nine leaf functional traits related to trees' resource capture and hydraulic strategies for 552 individuals belonging to 21 tropical tree species across an environmental gradient in Amazonian forests. Our sampling included generalist and specialist species from terra firme (TF) and seasonally flooded (SF) forests. We tested the influence of the topographic wetness index, a proxy for soil moisture and nutrient gradients, on each trait separately and on the trait integration through multivariate indices computed from the eigenvalues of a principal component analysis on the traits of the species. Finally, we evaluated intraspecific trait variability (ITV) for generalists and specialists by calculating the coefficient of variation for each trait. Results showed that (1) the environment had a greater influence on trait syndromes than single trait variation. Moreover, (2) SF specialist species expressed a stronger leaf trait coordination than TF specialist species. Furthermore, (3) the ability of generalist species to occupy a broader range of environments was not reflected by a larger ITV than specialist species but by the capacity to change trait coordination across environments. Our work highlights the need to investigate functional strategies as multidimensional syndromes in physiological trait space to fully understand and predict species distribution along environmental gradients. Read the free Plain Language Summary for this article on the Journal blog.

Modelling landscape-scale occurrences of common grassland species in a topographically complex mountainous environment

Mountainous regions typically harbour high plant diversity but are also characterised by low sampling intensity. Coarse-scale species distribution models can provide insights into the distribution of poorly sampled species, but the required bioclimatic data are often limited in these landscapes. In comparison, several environmental factors that vary over relatively fine scales in mountain environments (e.g. measures of topography) can be quantified from remotely-sensed data, and can potentially provide direct and indirect measures of biologically-relevant habitat characteristics in mountains. Therefore, in this study, we combine field-sampled floristic data with environmental predictors derived from remotely-sensed data, to model the ecological niches of 19 montane plant species in the Maloti-Drakensberg mountains, South Africa. The resulting models varied considerably in their performance, and species showed generally inconsistent responses to environmental predictors, with altitude and distance to watershed being most frequently included in models. These results highlight the species-specificity of the forb species’ environmental tolerances and requirements, suggesting that environmental change may result in re-shuffling of community composition, instead of intact communities shifting along gradients. Furthermore, while the relatively high importance of altitude (a proxy for temperature) and topographic wetness index (a proxy for soil moisture) suggest that the flora of this region will be sensitive to shifts in temperature and rainfall patterns, several non-climatic environmental variables were also influential. Our findings indicate that local response to climate change in mountains might be especially constrained by soil type and topographic variables, supporting the important influence of non-climatic factors in microclimatic refugia dynamics.

Predawn leaf water potential of grapevines is not necessarily a good proxy for soil moisture

BackgroundIn plant water relations research, predawn leaf water potential (Ψpd) is often used as a proxy for soil water potential (Ψsoil), without testing the underlying assumptions that nighttime transpiration is negligible and that enough time has passed for a hydrostatic equilibrium to be established. The goal of this research was to test the assumption Ψpd = Ψsoil for field-grown grapevines.ResultsA field trial was conducted with 30 different cultivars of wine grapes grown in a single vineyard in arid southeastern Washington, USA, for two years. The Ψpd and the volumetric soil water content (θv) under each sampled plant were measured multiple times during several dry-down cycles. The results show that in wet soil (Ψsoil > − 0.14 MPa or relative extractable water content, θe > 0.36), Ψpd was significantly lower than Ψsoil for all 30 cultivars. Under dry soil conditions (Ψsoil < − 0.14 MPa or θe < 0.36) Ψpd lined up better with Ψsoil. There were differences between cultivars, but these were not consistent over the years.ConclusionThese results suggest that for wet soils Ψpd of grapevines cannot be used as a proxy for Ψsoil, while the Ψpd = Ψsoil assumption may hold for dry soils.

Topographic Wetness Index as a Proxy for Soil Moisture in a Hillslope Catena: Flow Algorithms and Map Generalization

Topographic wetness index (TWI) is used as a proxy for soil moisture, but how well it performs across varying timescales and methods of calculation is not well understood. To assess the effectiveness of TWI, we examined spatial correlations between in situ soil volumetric water content (VWC) and TWI values over 5 years in soils at 42 locations in an agroforestry catena in Fayetteville, Arkansas, USA. We calculated TWI 546 ways using different flow algorithms and digital elevation model (DEM) preparations. We found that most TWI algorithms performed poorly on DEMs that were not first filtered or resampled, but DEM filtration and resampling (collectively called generalization) greatly improved the TWI performance. Seasonal variation of soil moisture influenced TWI performance which was best when conditions were not saturated and not dry. Pearson correlation coefficients between TWI and grand mean VWC for the 5-year measurement period ranged from 0.18 to 0.64 on generalized DEMs and 0.15 to 0.59 for on DEMs that were not generalized. These results aid management of crop fields with variable moisture characteristics.

Spatially differentiated effects of local moisture deficit and increased global temperature on hot extreme occurrences

This study compares the relative importance of global temperature change and local moisture deficit in influencing the frequency of hot extremes on a global scale. For the first time, the wavelet decomposed GRACE terrestrial water storage is applied in examining the relationship between soil moisture (θ) and the number of hot days in the hottest month (NHD). It reveals stronger θ-NHD relationships over larger areas than the selected commonly used soil moisture proxies (i.e., standardised precipitation index and a GLDAS model-derived product). During 1985–2015, local moisture deficit played a more important role in influencing hot extreme occurrences in the regions with relatively flat topography, thick soil and where inter-annual rainfall variability is large. They cover an area 1.6 times larger than the areas (e.g., mountain ranges, deserts) where the global temperature change has posed a stronger influence. Under a continuing increase of greenhouse gas forcing, global actions in reducing emissions will support combating the expansion of hot extremes. However, this study shows that necessary attention should also be directed to mitigating the moisture deficit exacerbating hot extremes, e. g., through regional adaptive land management.

Residual-Oriented Optimization of Antecedent Precipitation Index and Its Impact on Flood Prediction Uncertainty

Antecedent moisture conditions are essential in explaining differences in the translation of flood-producing precipitation to floods. This study proposes an empirical residual-oriented antecedent precipitation index (RAPI) to estimate and further link antecedent moisture conditions with flood predictive uncertainty. By developing a fully kernel-based residual error model without functional presumptions, the proposed RAPI is calibrated as the regressor of the deterministic model residual. Furthermore, the MI-LXPM algorithm is applied to search for optimal parameters in mixed-integer constraints. The rationality of the proposed framework is demonstrated by its application to a case study in South-East China. The quality of probabilistic streamflow predictions is then quantified using reliability, precision, and the NSE of the prediction mean. The results show that the RAPI closely connects to the uncertainty of hourly flood prediction as a proxy of antecedent soil moisture. The influence of RAPI is mainly on the precision and unbiasedness of flood prediction. Compared with the deterministic model output, the RAPI provides a better flood prediction of small to median flood events as a regressor. Along with the proposed date-driven residual error model, the framework can be applied to any pre-calibrated hydrological model and help modelers achieve high-quality probability flood prediction.

Newly initiated carbon stock, organic soil accumulation patterns and main driving factors in the High Arctic Svalbard, Norway

High latitude organic soils form a significant carbon storage and deposition of these soils is largely driven by climate. Svalbard, Norway, has experienced millennial-scale climate variations and in general organic soil processes have benefitted from warm and humid climate phases while cool late Holocene has been unfavourable. In addition to direct effect of cool climate, the advancing glaciers have restricted the vegetation growth, thus soil accumulation. Since the early 1900’s climate has been warming at unprecedented rate, assumingly promoting organic soil establishment. Here we present results of multiple organic soil profiles collected from Svalbard. The profiles have robust chronologies accompanied by soil property analyses, carbon stock estimations and testate amoeba data as a proxy for soil moisture. Our results reveal relatively recent initiation of organic soils across the Isfjorden area. The initiation processes could be linked to glacier retreat, and improvement of growing conditions and soil stabilization. Carbon stock analyses suggested that our sites are hot spots for organic matter accumulation. Testate amoebae data suggested drying of soil surfaces, but the reason remained unresolved. If continued, such a process may lead to carbon release. Our data suggest that detailed palaeoecological data from the Arctic is needed to depict the on-going processes and to estimate future trajectories.

Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies

This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 0.02 units per year while that of NADI-MERRA–2 was 0.01 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.

High‐Resolution Soil Moisture Evolution in Hyper‐Arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the Southern Arabian Peninsula

AbstractWe present an analysis of a 3‐year time series of Synthetic Aperture Radar (SAR) imagery covering the southern Arabian Peninsula, spanning two devastating typhoons that impacted the region in 2018. The limited vegetation and precipitation in the area allows for clear separation of soil moisture changes from other factors that typically also affect microwave imagery. We generate soil moisture proxies from Interferometric SAR (InSAR) coherence and compare them with a range of other soil moisture products. We estimate the decay timescale over which the surface returns to its pre‐storm state for each event and data type, and find that the radar coherence‐based timescales tend to be longer than those for the other products. We also find that the coherence‐based timescales of the two events differ from each other in a manner that is consistent with seasonal temperature fluctuations and the total rainfall that occurred each time. The higher spatial resolution of the InSAR coherence results and differing sensitivity to the vertical distribution of soil moisture complements the other more temporally dense products, particularly in regions of high heterogeneity along the edge of the storm and within regions where the surface properties change dramatically over small spatial scales. As the frequency and strength of tropical cyclones increase, and as already scarce water resources are utilized further in arid regions, InSAR coherence data constitutes another tool that researchers can use to expand our understanding of these challenges.

Topographic Wetness Index as a Proxy for Soil Moisture: The Importance of Flow‐Routing Algorithm and Grid Resolution

AbstractThe Topographic Wetness Index (TWI) is a commonly used proxy for soil moisture. The predictive capability of TWI is influenced by the flow‐routing algorithm and the resolution of the Digital Elevation Model (DEM) that TWI is derived from. Here, we examine the predictive capability of TWI using 11 flow‐routing algorithms at DEM resolutions 1–30 m. We analyze the relationship between TWI and field‐quantified soil moisture using statistical modeling methods and 5,200 study plots with over 46 000 soil moisture measurements. In addition, we test the sensitivity of the flow‐routing algorithms against vertical height errors in DEM at different resolutions. The results reveal that the overall predictive capability of TWI was modest. The highest r2 (23.7%) was reached using a multiple‐flow‐direction algorithm at 2 m resolution. In addition, the test of sensitivity against height errors revealed that the multiple‐flow‐direction algorithms were also more robust against DEM errors than single‐flow‐direction algorithms. The results provide field‐evidence indicating that at its best TWI is a modest proxy for soil moisture and its predictive capability is influenced by the flow‐routing algorithm and DEM resolution. Thus, we encourage careful evaluation of algorithms and resolutions when using TWI as a proxy for soil moisture.

Contribution of soil moisture variations to high temperatures over different climatic regimes

The coupling of soil moisture to climatic variables highly depends on soil moisture variability. Therefore, the activities affecting the soil moisture availability and dynamics such as land use/cover change and agricultural practices influence land-atmosphere interactions. Using soil moisture proxies, modeling studies have proven significant coupling of soil water variations to near-surface atmospheric temperature in some regions. However, these works have mostly applied the precipitation-based indices at a specific time scale. In the present study, the concurrent and lagged associations of the soil wetness variations and the high temperature index (HTI) across different climates in Iran were evaluated using Spearman’s rank test. Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) calculated at one-month and six-month scales were employed as soil moisture surrogates. When seasonal aridity index (AI) range was from 0.2 to 1.0 (transitional condition), SPEI1 and SPI1 were correlated significantly (p < 0.05) with HTI at 83.0 and 42.4 % of cases, respectively. However, when AI was below 0.2 (water-limited condition) or over 2.0 (energy-limited condition), the topsoil moisture indices showed an insignificant association with HTI at the majority of cases. A significant negative association existed between SPEI6 and HTI at most dry cases in which soil surface is dry but subsoil appears not to be fully depleted. Therefore, the topsoil and subsoil moisture variations are likely to affect high temperature occurrence under transitional and dry conditions, respectively. The insignificant lagged correlations found for most areas indicate poor linear relationship between high temperature occurrence and soil water dynamics in the preceding month over the study area. Furthermore, when AI exceed 0.2, ENSO-related soil drought is likely to cause a stronger soil moisture-temperature coupling. Overall, the role of evapotranspiration and soil moisture depth should be considered for assessing soil moisture dynamics and land-atmosphere interactions. Our findings may be helpful for adopting soil moisture-based adaptive measures to negate the impacts of high temperatures on agricultural products.

Influence of environmental factors on autotrophic, soil and ecosystem respirations in Canadian boreal forest

Ecosystem respiration (Reco) and its components, the autotrophic respiration (Ra) and soil respiration (Rs) are the essential indicators of the global carbon cycle. They are represented as functions of either temperature or soil moisture, or a combination of both in the widely-used Earth System Models (ESMs). Thus, it is difficult to evaluate the influence of other environmental factors (such as, precipitation, soil temperature, dissolved oxygen level and oxidation reduction potential (ORP)) on Ra, Rs and Reco. Here we introduced microbially mediated, detailed carbon cycle processes within our mechanistic model to address this issue. Dominance analysis using a multivariate approach was performed to find out the influence of individual environmental factors on Ra, Rs and Reco in the cold climate regions of Athabasca River Basin (ARB), Canada. Contribution of the 6 predictor variables, including air temperature, precipitation, soil temperature, water-filled pore space (WFPS) used as a proxy of soil moisture, dissolved oxygen level, and ORP, on Ra, Rs and Reco were estimated based on the R2 values originated from multiple regression analyses. Our results showed that the prevailing temperature (both air and soil) and dissolved oxygen levels are the major influencing factors on Ra, Rs and Reco. WFPS is found to be the least influential factor on respiration estimation. Output of this study can be used to consider the crucial roles of environmental drivers in Ra, Rs and Reco estimation in the development of future ESMs.

Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition

Soil moisture controls environmental processes and species distributions, but it is difficult to measure and interpolate across space. Topographic Wetness Index (TWI) derived from digital elevation model is therefore often used as a proxy for soil moisture. However, different algorithms can be used to calculate TWI and this potentially affects TWI relationship with soil moisture and species assemblages.To disentangle insufficiently-known effects of different algorithms on TWI relation with soil moisture and plant species composition, we measured the root-zone soil moisture throughout a growing season and recorded vascular plants and bryophytes in 45 temperate forest plots. For each plot, we calculated 26 TWI variants from a LiDAR-based digital terrain model and related these TWI variants to the measured soil moisture and moisture-controlled species assemblages of vascular plants and bryophytes.A flow accumulation algorithm determined the ability of the TWI to predict soil moisture, while the flow width and slope algorithms had only a small effects. The TWI calculated with the most often used single-flow D8 algorithm explained less than half of the variation in soil moisture and species composition explained by the TWI calculated with the multiple-flow FD8 algorithm. Flow dispersion used in the FD8 algorithm strongly affected the TWI performance, and a flow dispersion close to 1.0 resulted in the TWI best related to the soil moisture and species assemblages. Using downslope gradient instead of the local slope gradient can strongly decrease TWI performance.Our results clearly showed that the method used to calculate TWI affects study conclusion. However, TWI calculation is often not specified and thus impossible to reproduce and compare among studies. We therefore provide guidelines for TWI calculation and recommend the FD8 flow algorithm with a flow dispersion close to 1.0, flow width equal to the raster cell size and local slope gradient for TWI calculation.

Using remote sensing to assess peatland resilience by estimating soil surface moisture and drought recovery

Peatland areas provide a range of ecosystem services, including biodiversity, carbon storage, clean water, and flood mitigation, but many areas of peatland in the UK have been degraded through human land use including drainage. Here, we explore whether remote sensing can be used to monitor peatland resilience to drought. We take resilience to mean the rate at which a system recovers from perturbation; here measured literally as a recovery timescale of a soil surface moisture proxy from drought lowering. Our objectives were (1) to assess the reliability of Sentinel-1 Synthetic Aperture Radar (SAR) backscatter as a proxy for water table depth (WTD); (2) to develop a method using SAR to estimate below-ground (hydrological) resilience of peatlands; and (3) to apply the developed method to different sites and consider the links between resilience and land management. Our inferences of WTD from Sentinel-1 SAR data gave results with an average Pearson's correlation of 0.77 when compared to measured WTD values. The 2018 summer drought was used to assess resilience across three different UK peatland areas (Dartmoor, the Peak District, and the Flow Country) by considering the timescale of the soil moisture proxy recovery. Results show clear areas of lower resilience within all three study sites, which often correspond to areas of high drainage and may be particularly vulnerable to increasing drought severity/events under climate change. This method is applicable to monitoring peatland resilience elsewhere over larger scales, and could be used to target restoration work towards the most vulnerable areas.

Evaluating the Utility of Drought Indices as Soil Moisture Proxies for Drought Monitoring and Land–Atmosphere Interactions

AbstractThere are a variety of metrics that are used to monitor drought conditions, including soil moisture and drought indices. This study examines the relationship between in situ soil moisture, NLDAS-2 soil moisture, and four drought indices: the standardized precipitation index, the standardized precipitation evapotranspiration index, the crop moisture index, and the Palmer Z index. We evaluate how well drought indices and the modeled soil moisture represent the intensity, variability, and persistence of the observed soil moisture in the southern Great Plains. We also apply the drought indices to evaluate land–atmosphere interactions and compare the results with soil moisture. The results show that the SPI, SPEI, and Z index have higher correlations with 0–10-cm soil moisture, while the CMI is more strongly correlated with 0–100-cm soil moisture. All the drought indices tend to overestimate the area affected by moderate to extreme drought conditions. Significant drying trends from 2003 to 2017 are evident in SPEI, Z index, and CMI, and they agree with those in the observed soil moisture. The CMI captures the intra- and interannual variability of 0–100-cm soil moisture better than the other drought indices. The persistence of CMI is longer than that of 0–10-cm soil moisture and shorter than that of 0–100-cm soil moisture. Model-derived soil moisture does not outperform the CMI in the 0–100-cm soil layer. The Z index and CMI are better drought indices to use as a proxy for soil moisture when examining land–atmosphere interactions while the SPI is not recommended. Soil type and climate affect the relationship between drought indices and observed soil moisture.

Rainstorm‐generated sediment yield model based on soil moisture proxies (SMP)

AbstractThis study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC‐I‐AMC‐III) by coupling the improved initial abstraction (Ia‐λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness‐of‐fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE‐observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process.

Assessment of Native Radar Reflectivity and Radar Rainfall Estimates for Discharge Forecasting in Mountain Catchments with a Random Forest Model

Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 &lt; NSE &lt; 0.75; 0.72 &lt; KGE &lt; 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available.

Relationships between soil water content, evapotranspiration, and irrigation measurements in a California drip-irrigated Pinot noir vineyard

The Central Valley of California relies on irrigation for crop production, but water resources, particularly groundwater, have reached a critical state due to extended drought periods and overuse by irrigated agriculture. The purpose of the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX) project is to improve efficiency in vineyard irrigation through field measurements and modeling efforts using remote sensing. In this study, we analyze multi-year timeseries of ground-based soil moisture and evapotranspiration (ET) measurements collected during GRAPEX to identify patterns that may inform future irrigation recommendations based on remotely sensed ET. The study focuses on field data collected in two adjacent Pinot noir vineyards near Lodi, CA from 2013 to 2017. The data used for this analysis are reference evapotranspiration (ETref), actual evapotranspiration (ETa), mean daytime soil water content (SWC) measured to a depth of 90 cm, precipitation, and irrigation. The relationship between SWC and the ratio of ETa/ETref (used as a soil moisture proxy indicator) changes throughout the spring and summer months due to advancing phenological stages and management practices. In early spring, when the interrow cover crop is the primary source of ET, ETa is strongly correlated with SWC. As the vine canopy grows in, the strong correlation breaks down as the vines begin to access to water beyond the depth of the soil moisture sensors. As the soil profile dries out during the summer, the correlation between ETa and SWC once again emerges as the vines become strongly dependent on irrigation. The dynamic interaction between the upper and lower root zone profile and evaporative demand means that the key to understanding vineyard water status using relatively shallow SWC observations is to use them in conjunction with ETa data, so that when both SWC and ETa timeseries are highly correlated vine water status is well defined. This suggests, delaying the initiation of irrigation during the period of rapid vine growth until a clear relationship between SWC and ETa emerges would both reduce the total amount of water used and give the grower more control over vine growth and grape quality affected by water status.

Rainfall-Induced Variation of Seismic Waves Velocity in Soil and Implications for Soil Response: What the ARGONET (Cephalonia, Greece) Vertical Array Data Reveal

ABSTRACTWe apply interferometry by deconvolution to compute the shear-wave velocity in shallow sediments (0–83.4 m) based on earthquake records from a vertical accelerometric array (ARGOstoli Network [ARGONET]) on Cephalonia Island, Greece. Analysis of the time variation of measured values reveals a cyclical pattern, which correlates negatively to rainfall and a soil moisture proxy. The pattern includes a sharp reduction in velocity at the beginning of rainy seasons and a gradual rise toward dry periods, the overall variation being around 20%–25% within the shallowest depth interval examined (0–5.6 m) and estimated to reach 40% within the top 2 m. The variation itself and its amplitude are verified by surface-wave dispersion analysis, using ambient vibration data. Synthetic standard spectral ratios suggest that this seasonal effect leaves an imprint on soil response, causing differences in the level of high-frequency ground motion between dry and rainy seasons, and this is verified by earthquake records. Furthermore, the near-surface velocity decrease due to soil saturation can be of the same order of magnitude as the nonlinear coseismic variation, masking the physical process of the nonlinear response of the site due to weak-to-strong-motion shaking. Thus, seasonal variations of seismic-wave velocities in shallow sediments may be important for a number of site-effect related topics, such as high-frequency ground-motion variability, soil anisotropy, kappa measurements, nonlinear site response, and so on.

A geospatial method for estimating soil moisture variability in prehistoric agricultural landscapes.

Prehistoric peoples chose farming locations based on environmental conditions, such as soil moisture, which plays a crucial role in crop production. Ancestral Pueblo communities of the central Mesa Verde region became increasingly reliant on maize agriculture for their subsistence needs by AD 900. Prehistoric agriculturalists (e.g., Ancestral Pueblo farmers) were dependent on having sufficient soil moisture for successful plant growth. To better understand the quality of farmland in terms of soil moisture, this study develops a static geospatial soil moisture model, the Soil Moisture Proxy Model, which uses soil and topographic variables to estimate soil moisture potential across a watershed. The model is applied to the semi-arid region of the Goodman watershed in the central Mesa Verde region of southwestern Colorado. We evaluate the model by comparing the Goodman watershed output to two other watersheds and to soil moisture sensor values. The simple framework can be used in other regions of the world, where water is also an important limiting factor for farming. The general outcome of this research is an improved understanding of potential farmland and human-environmental relationships across the local landscape.