Evaluating the usefulness of soil moisture (forecasts) in streamflow prediction for central Texas

The past decade has brought significant advancements in seasonal climate forecasting. However, water resources decision support and management continues to be based almost entirely on historical observations and does not take advantage of climate forecasts. This study builds on previous work that conditioned streamflow ensemble forecasts on observable climate indicators, such as the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) for use in a decision support model for the Highland Lakes multi-reservoir system in central Texas operated by the Lower Colorado River Authority (LCRA). In the current study, seasonal soil moisture is explored as a climate indicator and predictor of annual streamflow for the LCRA region. The main purpose of this study is to evaluate the correlation of fractional soil moisture with streamflow using the 1950-2000 Variable Infiltration Capacity (VIC) Retrospective Land Surface Data Set over the LCRA region. Correlations were determined by examining different annual and seasonal combinations of VIC modeled fractional soil moisture and observed streamflow. The applicability of the VIC Retrospective Land Surface Data Set as a data source for this study is tested along with establishing and analyzing patterns of climatology for the watershed study area using the selected data source (VIC model) and historical data. Correlation results showed potential for the use of soil moisture as a predictor of streamflow over the LCRA region. This was evident by the good correlations found between seasonal soil moisture and seasonal streamflow during coincident seasons as well as between seasonal and annual soil moisture with annual streamflow during coincident years. With the findings of good correlation between seasonal soil moisture from the VIC Retrospective Land Surface Data Set with observed annual streamflow presented in this study, future research would evaluate the application of NOAA Climate Prediction Center (CPC) forecasts of soil moisture in predicting annual streamflow for use in the decision support model for the LCRA.

Modeling and classifying variable width riparian zones utilizing digital elevation models, flood height data, digital soil data and national wetlands inventory : a new approach for riparian zone delineation

Riparian zones are dynamic, transitional ecosystems between aquatic and terrestrial ecosystems with well defined vegetation and soil characteristics. Development of an all-encompassing definition for riparian ecotones, because of their high variability, is challenging. However, there are two primary factors that all riparian ecotones are dependent on: the watercourse and its associated floodplain. Previous approaches to riparian boundary delineation have utilized fixed width buffers, but this methodology has proven to be inadequate as it only takes the watercourse into consideration and ignores critical geomorphology, associated vegetation and soil characteristics. Our approach offers advantages over other previously used methods by utilizing: the geospatial modeling capabilities of ArcMap GIS; a better sampling technique along the water course that can distinguish the 50-year flood plain, which is the optimal hydrologic descriptor of riparian ecotones; the Soil Survey Database (SSURGO) and National Wetland Inventory (NWI) databases to distinguish contiguous areas beyond the 50-year plain; and land use/cover characteristics associated with the delineated riparian zones. The model utilizes spatial data readily available from Federal and State agencies and geospatial clearinghouses. An accuracy assessment was performed to assess the impact of varying the 50-year flood height, changing the DEM spatial resolution (1, 3, 5 and 10m), and positional inaccuracies with the National Hydrography Dataset (NHD) streams layer on the boundary placement of the delineated variable width riparian ecotones area. The result of this study is a robust and automated GIS based model attached to ESRI ArcMap software to delineate and classify variable-width riparian ecotones.

Assessment of soil erosion vulnerability in rural communities in the vicinity of Motozintla de Mendoza, Chiapas, Mexico : a physical and sociological approach

Soil erosion is a natural geological phenomenon resulting from removal and transportation of soil particles by water, wind, ice and gravity. As soil erosion may be affected from cultural factors as well. The physical and social phenomena of soil erosion are researched in six communities in the upper part of Rio Grijalva Basin in the vicinity of Motozintla de Mendoza, Chiapas, Mexico. For this study, the USDA RUSLE model was applied to estimate soil erosion rates in the six communities based on the available data. The RUSLE model is based on soil properties, topography, and land cover and management factors. These results showed that estimated soil erosion rates ranged from a high of 2,050 metric ton ha-1 yr-1 to a low of 100 metric ton ha-1 yr-1. A survey concerning knowledge, attitudes and practices (KAP) related to soil erosion was also conducted in all 236 households in the six communities. The main findings of the KAP survey were: 69% of respondents did not know what soil erosion was, while over 40% of the population perceived that hurricanes are the biggest factors that cause soil erosion, and about 20 % of the interviewees said that the landslides are the consequences of the soil erosion. People in communities did not perceive cultural factors as important in conservation efforts for reduce vulnerability to erosion; therefore, the results obtained are suggested to be useful for informing efforts to educate stakeholders.

Tree species and diversity effects on soil water seepage in a tropical plantation

Plant diversity has been shown to influence the water cycle of forest ecosystems by differences in water consumption and the associated effects on groundwater recharge. However, the effects of biodiversity on soil water fluxes remain poorly understood for native tree species plantations in the tropics. Therefore, we estimated soil water fluxes and assessed the effects of tree species and diversity on these fluxes in an experimental native tree species plantation in Sardinilla (Panama). The study was conducted during the wet season 2008 on plots of monocultures and mixtures of three or six tree species. Rainfall and soil water content were measured and evapotranspiration was estimated with the Penman-Monteith equation. Soil water fluxes were estimated using a simple soil water budget model considering water input, output, and soil water and groundwater storage changes and in addition, were simulated using the physically based one-dimensional water flow model Hydrus-1D. In general, the Hydrus simulation did not reflect the observed pressure heads, in that modeled pressure heads were higher compared to measured ones. On the other hand, the results of the water balance equation (WBE) reproduced observed water use patterns well. In monocultures, the downward fluxes through the 200 cm-depth plane were highest below Hura crepitans (6.13 mm day−1) and lowest below Luehea seemannii (5.18 mm day−1). The average seepage rate in monocultures (±SE) was 5.66 ± 0.18 mm day−1, and therefore, significantly higher than below six-species mixtures (5.49 ± 0.04 mm day−1) according to overyielding analyses. The three-species mixtures had an average seepage rate of 5.63 ± 0.12 mm day−1 and their values did not differ significantly from the average values of the corresponding species in monocultures. Seepage rates were driven by the transpiration of the varying biomass among the plots (r = 0.61, p = 0.017). Thus, a mixture of trees with different growth rates resulted in moderate seepage rates compared to monocultures of either fast growing or slow growing tree species. Our results demonstrate that tree-species specific biomass production and tree diversity are important controls of seepage rates in the Sardinilla plantation during the wet season.

Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions

Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected.

Building a soil spectral library for Tajikistan comparing local and global modeling approaches

Over the last ~20 years, soil spectral libraries storing near-infrared reflectance (NIR) spectra from diverse soil samples have been built for many places, since almost 10 years also for Tajikistan. Many calibration approaches have been reported and used for prediction from large and heterogeneous libraries, but most are hampered by the high diversity of the soils, where the mineral background is heavily influencing spectral features. In such cases, local learning strategies have the advantage of building locally adapted calibrations, which can deal better with nonlinearities. Therefore, it was our major aim to identify the most efficient approach to develop an accurate and stable locally weigthed calibration model using a spectral library compiled over the past years. Keywords: Tajikistan, Near-Infrared spectroscopy (NIRS), soil organic carbon, locally weighted regression, regional and local spectral library.

Inverse modelling of the 14C bomb pulse in stalagmites to constrain the dynamics of soil carbon cycling at selected European cave sites

The decomposition of soil organic matter (SOM) is temperature dependent, but its response to a future warmer climate remains equivocal. Enhanced rates of decomposition of SOM under increased global temperatures might cause higher CO2 emissions to the atmosphere, and could therefore constitute a strong positive feedback. The magnitude of this feedback however remains poorly understood, primarily because of the difficulty in quantifying the temperature sensitivity of stored, recalcitrant carbon that comprises the bulk (>90%) of SOM in most soils. In this study we investigated the effects of climatic conditions on soil carbon dynamics using the attenuation of the 14C ‘bomb’ pulse as recorded in selected modern European speleothems. These new data were combined with published results to further examine soil carbon dynamics, and to explore the sensitivity of labile and recalcitrant organic matter decomposition to different climatic conditions. Temporal changes in 14C activity inferred from each speleothem was modelled using a three pool soil carbon inverse model (applying a Monte Carlo method) to constrain soil carbon turnover rates at each site. Speleothems from sites that are characterised by semi-arid conditions, sparse vegetation, thin soil cover and high mean annual air temperatures (MAATs), exhibit weak attenuation of atmospheric 14C ‘bomb’ peak (a low damping effect, D in the range: 55–77%) and low modelled mean respired carbon ages (MRCA), indicating that decomposition is dominated by young, recently fixed soil carbon. By contrast, humid and high MAAT sites that are characterised by a thick soil cover and dense, well developed vegetation, display the highest damping effect (D = c. 90%), and the highest MRCA values (in the range from 350 ± 126 years to 571 ± 128 years). This suggests that carbon incorporated into these stalagmites originates predominantly from decomposition of old, recalcitrant organic matter. SOM turnover rates cannot be ascribed to a single climate variable, e.g. (MAAT) but instead reflect a complex interplay of climate (e.g. MAAT and moisture budget) and vegetation development.

Development of a high- versus low-pathogenicity model of the free-living amoeba Naegleria fowleri

Species in the genus Naegleria are free-living amoebae of the soil and warm fresh water. Although around 30 species have been recognized, Naegleria fowleri is the only one that causes primary amoebic meningoencephalitis (PAM) in humans. PAM is an acute and fast progressing disease affecting the central nervous system. Most of the patients die within 1-2 weeks of exposure to the infectious water source. The fact that N. fowleri causes such fast progressing and highly lethal infections has opened many questions regarding the relevant pathogenicity factors of the amoeba. In order to investigate the pathogenesis of N. fowleri under defined experimental conditions, we developed a novel high- versus low-pathogenicity model for this pathogen. We showed that the composition of the axenic growth media influenced growth behaviour and morphology, as well as in vitro cytotoxicity and in vivo pathogenicity of N. fowleri. Trophozoites maintained in Nelson's medium were highly pathogenic for mice, demonstrated rapid in vitro proliferation, characteristic expression of surface membrane vesicles and a small cell diameter, and killed target mouse fibroblasts by both contact-dependent and -independent destruction. In contrast, N. fowleri cultured in PYNFH medium exhibited a low pathogenicity, slower growth, increased cell size and contact-dependent target cell destruction. However, cultivation of the amoeba in PYNFH medium supplemented with liver hydrolysate (LH) resulted in trophozoites that were highly pathogenic in mice, and demonstrated an intermediate proliferation rate in vitro, diminished cell diameter and contact-dependent target cell destruction. Thus, in this model, the presence of LH resulted in increased proliferation of trophozoites in vitro and enhanced pathogenicity of N. fowleri in mice. However, neither in vitro cytotoxicity mechanisms nor the presence of membrane vesicles on the surface correlated with the pathologic potential of the amoeba. This indicated that the pathogenicity of N. fowleri remains a complex interaction between as-yet-unidentified cellular mechanisms.

A model-data comparison of Holocene timberline changes in the Swiss Alps reveals past and future drivers of mountain forest dynamics

Mountain vegetation is strongly affected by temperature and is expected to shift upwards with climate change. Dynamic vegetation models are often used to assess the impact of climate on vegetation and model output can be compared with paleobotanical data as a reality check. Recent paleoecological studies have revealed regional variation in the upward shift of timberlines in the Northern and Central European Alps in response to rapid warming at the Younger Dryas/Preboreal transition ca. 11700years ago, probably caused by a climatic gradient across the Alps. This contrasts with previous studies that successfully simulated the early Holocene afforestation in the (warmer) Central Alps with a chironomid-inferred temperature reconstruction from the (colder) Northern Alps. We use LandClim, a dynamic landscape vegetation model to simulate mountain forests under different temperature, soil and precipitation scenarios around Iffigsee (2065m a.s.l.) a lake in the Northwestern Swiss Alps, and compare the model output with the paleobotanical records. The model clearly overestimates the upward shift of timberline in a climate scenario that applies chironomid-inferred July-temperature anomalies to all months. However, forest establishment at 9800 cal. BP at Iffigsee is successfully simulated with lower moisture availability and monthly temperatures corrected for stronger seasonality during the early Holocene. The model-data comparison reveals a contraction in the realized niche of Abies alba due to the prominent role of anthropogenic disturbance after ca. 5000 cal. BP, which has important implications for species distribution models (SDMs) that rely on equilibrium with climate and niche stability. Under future climate projections, LandClim indicates a rapid upward shift of mountain vegetation belts by ca. 500m and treeline positions of ca. 2500m a.s.l. by the end of this century. Resulting biodiversity losses in the alpine vegetation belt might be mitigated with low-impact pastoralism to preserve species-rich alpine meadows.

Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition

Ecosystem management policies increasingly emphasize provision of multiple, as opposed to single, ecosystem services. Management for such "multifunctionality" has stimulated research into the role that biodiversity plays in providing desired rates of multiple ecosystem processes. Positive effects of biodiversity on indices of multifunctionality are consistently found, primarily because species that are redundant for one ecosystem process under a given set of environmental conditions play a distinct role under different conditions or in the provision of another ecosystem process. Here we show that the positive effects of diversity (specifically community composition) on multifunctionality indices can also arise from a statistical fallacy analogous to Simpson's paradox (where aggregating data obscures causal relationships). We manipulated soil faunal community composition in combination with nitrogen fertilization of model grassland ecosystems and repeatedly measured five ecosystem processes related to plant productivity, carbon storage, and nutrient turnover. We calculated three common multifunctionality indices based on these processes and found that the functional complexity of the soil communities had a consistent positive effect on the indices. However, only two of the five ecosystem processes also responded positively to increasing complexity, whereas the other three responded neutrally or negatively. Furthermore, none of the individual processes responded to both the complexity and the nitrogen manipulations in a manner consistent with the indices. Our data show that multifunctionality indices can obscure relationships that exist between communities and key ecosystem processes, leading us to question their use in advancing theoretical understanding-and in management decisions-about how biodiversity is related to the provision of multiple ecosystem services.

Tracking water pathways in steep hillslopes by d18O depth profiles of soil water

Assessing temporal variations in soil water flow is important, especially at the hillslope scale, to identify mechanisms of runoff and flood generation and pathways for nutrients and pollutants in soils. While surface processes are well considered and parameterized, the assessment of subsurface processes at the hillslope scale is still challenging since measurement of hydrological pathways is connected to high efforts in time, money and personnel work. The latter might not even be possible in alpine environments with harsh winter processes. Soil water stable isotope profiles may offer a time-integrating fingerprint of subsurface water pathways. In this study, we investigated the suitability of soil water stable isotope (d18O) depth profiles to identify water flow paths along two transects of steep subalpine hillslopes in the Swiss Alps. We applied a one-dimensional advection–dispersion model using d18O values of precipitation (ranging from _24.7 to _2.9‰) as input data to simulate the d18O profiles of soil water. The variability of d18O values with depth within each soil profile and a comparison of the simulated and measured d18O profiles were used to infer information about subsurface hydrological pathways. The temporal pattern of d18O in precipitation was found in several profiles, ranging from _14.5 to _4.0‰. This suggests that vertical percolation plays an important role even at slope angles of up to 46_. Lateral subsurface flow and/or mixing of soil water at lower slope angles might occur in deeper soil layers and at sites near a small stream. The difference between several observed and simulated d18O profiles revealed spatially highly variable infiltration patterns during the snowmelt periods: The d18O value of snow (_17.7 ± 1.9‰) was absent in several measured d18O profiles but present in the respective simulated d18O profiles. This indicated overland flow and/or preferential flow through the soil profile during the melt period. The applied methods proved to be a fast and promising tool to obtain time-integrated information on soil water flow paths at the hillslope scale in steep subalpine slopes.

Visible near infra-red (VisNIR) spectroscopy for predicting soil organic carbon in Ethiopia

Over the past few decades, the advantages of the visible-near infra-red (VisNIR) diffuse reflectance spectrometer (DRS) method have enabled prediction of soil organic carbon (SOC). In this study, SOC was predicted using regression models for samples taken from three sites (Gununo, Maybar and Anjeni) in Ethiopia. SOC was characterized in laboratory using conventional wet chemistry and VisNIR-DRS methods. Principal component analysis (PCA), principal component regression (PCR) and partial least square regression (PLS) models were developed using Unscrambler X 10.2. PCA results show that the first two components accounted for a minimum of 96% variation which increased for individual sites and with data treatments. Correlation (r), coefficient of determination (R2) and residual prediction deviation (RPD) were used to rate four models built. PLS model (r, R2, RPD) values for Anjeni were 0.9, 0.9 and 3.6; for Gununo values 0.6, 0.3 and 1.2; for Maybar values 0.6, 0.3 and 0.9, and for the three sites values 0.7, 0.6 and 1.5, respectively. PCR model values (r, R2, RPD) for Anjeni were 0.9, 0.8 and 2.7; for Gununo values 0.5, 0.3 and 1; for Maybar values 0.5, 0.1 and 0.7, and for the three sites values 0.7, 0.5 and 1.2, respectively. Comparison and testing of models shows superior performance of PLS to PCR. Models were rated as very poor (Maybar), poor (Gununo and three sites) and excellent (Anjeni). A robust model, Anjeni, is recommended for prediction of SOC in Ethiopia.

The role of climate-driven chemical weathering on soil production

Climate plays an important role in controlling rates of weathering and weathered regolith production. Regolith production functions, however, seldom take climate parameters into account. Based on a climate-dependent weathered regolith production model, at low denudation rates, relative regolith thicknesses are less sensitive to changes in precipitation rates, while at high denudation rates, small changes in climatic parameters can result in complete stripping of hillslopes. This pattern is compounded by the long residence times and system response times associated with low denudation rates, and vice versa. As others have shown, the transition between regolith-mantled and bedrock slopes is dependent on the ratio of denudation to production. Here, we further suggest that this is itself a function of precipitation rate and temperature. We suggest that climatic parameters can be easily incorporated into existing soil production models and that such additions improve the predictive power of soil production models. (C) 2013 Elsevier B.V. All rights reserved.

Modelling susceptibility of grassland soil to macropore flow

Investigating preferential flow, including macropore flow, is crucial to predicting and preventing point sources of contamination in soil, for example in the vicinity of pumping wells. With a view to advancing groundwater protection, this study aimed (i) to quantify the strength of macropore flow in four representative natural grassland soils on the Swiss plateau, and (ii) to define the parameters that significantly control macropore flow in grassland soil. For each soil type we selected three measurement points on which three successive irrigation experiments were carried out, resulting in a total of 36 irrigations. The strength of macropore flow, parameterized as the cumulated water volume flowing from macropores at a depth of 1 m in response to an irrigation of 60 mm h−1 intensity and 1 h duration, was simulated using the dual-permeability MACRO model. The model calibration was based on the key soil parameters and fine measurements of water content at different depths. Modelling results indicate high performance of macropore flow in all investigated soil types except in gleysols. The volume of water that flowed from macropores and was hence expected to reach groundwater varied between 81% and 94% in brown soils, 59% and 67% in para-brown soils, 43% and 56% in acid brown soils, and 22% and 35% in gleysols. These results show that spreading pesticides and herbicides in pumping well protection zones poses a high risk of contamination and must be strictly prohibited. We also found that organic carbon content was not correlated with the strength of macropore flow, probably due to its very weak variation in our study, while saturated water content showed a negative correlation with macropore flow. The correlation between saturated hydraulic conductivity (Ks) and macropore flow was negative as well, but weak. Macropore flow appears to be controlled by the interaction between the bulk density of the uppermost topsoil layer (0–0.10 m) and the macroporosity of the soil below. This interaction also affects the variations in Ks and saturated water content. Further investigations are needed to better understand the combined effect of all these processes including the exchange between micropore and macropore domains.