Uncertainty assessment of spatial soil information

Uncertainty is present in our daily lives. It affects our decisions on what to do. The weather forecast might tell us that there is a 60% chance that it will rain: we take umbrellas. If it says that the chance of rain is only 10% we might decide to leave our umbrellas at home and risk getting wet. More seriously, farmers want to know the likelihood of disease in their crops and the deficiencies in plant nutrients in the soil. These are matters that affect profit and loss of farm business. Agencies responsible for public health and environmental protection need to weigh the risk of doing nothing in the face of uncertain threats against the cost of acting unnecessarily to counter them when the threats are almost non-existent. There are many examples of decision making problems involving uncertain soil information. They include the remediation of polluted soil, the prevention of soil erosion, and the mitigation of pesticide leaching. They are practical matters, not purely academic exercises in statistics. All measurements of soil properties (and other environmental variables) contain error in the sense that they depart from the true values. That error arises from imperfections in the analytical instruments, from the people who use them and from errors that occur during the processing of the recorded data to make them suitable for storage in information databases. Short-range spatial variation is another source of error, given that soil samples are never returned to where they were taken and sampling locations have positional error. Soil taken from location s and analysed in the laboratory might differ substantially from the soil at location s + h, even if |h| is as small as a few decimeters. Composite soil sampling can diminish these differences, but some error inevitably persists because even such a composite is still only a sample of all the soil at that site. All this means that we can never be sure about the true state of the soil: we, the producers and users of soil information, are to some extent uncertain. Uncertainty tends to increase when measurements of basic soil properties are used to obtain derived ones via pedotransfer functions or mechanistic models of dynamic soil processes, for example. Interpolation from measurements to create maps of soil properties adds to the errors of measurement and so too increases uncertainties. We must conclude that considerable uncertainty is often associated with the information that is stored in soil databases and presented in various forms, including maps. This does not mean that the information is of no value; uncertainty is not the same as ignorance. In many cases we do know a great deal about the soil, but we must also acknowledge that the information is not perfect. Some numerical expression of the uncertainty is important because it is needed to determine whether the information is sufficiently accurate for the purpose that a user has in mind. Soil data of too poor a quality might lead to flawed decisions with serious undesirable consequences, both economic and environmental. For instance, the European legislation on the use of pesticides in agriculture depends crucially on the leaching potential of these substances to the ground- and surface-water, which in turn depends importantly on soil properties. In these circumstances users should be aware of the quality of the soil information so that they can be sure that it is sufficiently reliable for their purposes. Ideally they should account for the uncertainty of the information when making their decisions. This chapter (i) provides a statistical definition of uncertainty in soil information; (ii) extends this definition to uncertainty in spatial soil information; (iii) reviews methods that are used to quantify uncertainty in soil information, while paying attention to different sources of uncertainty; (iv) shows how uncertainty in soil information propagates through subsequent analyses; and (v) explains how uncertainty information can be used in decision making. It focuses on the quantification of uncertainty of soil properties that are measured and recorded on continuous scales: properties such as pH, particle-size distribution, and soil organic matter content. The chapter also addresses uncertainty of categorical variables, such as soil type and diagnostic properties recorded as present or absent, i.e. binary variables. It begins with defining uncertainty in a single soil measuremen

Demography and Evolutionary Ecology of the Carnivorous Subshrub Drosophyllum lusitanicum (L.) Link (Drosophyllaceae)

Natural disturbances occur in various ecosystems and have resulted in the evolution of life histories to buffer or even benefit from disturbance regimes. However, human activities increasingly interact with natural disturbances, posing potentially significant threats to the viability of disturbance-adapted species and therefore causing biodiviersity loss. With fires regularly affecting 50 % of the Eath’s surface, such compounded effects of disturbance interactions are particularly prominent in fire-prone ecosystems. Using the rare carnivorous subshrub Drosophyllum lusitanicum (Drosophyllaceae), endemic to Mediterranean heathlands under increasing human pressure in the southwestern Iberian Peninsula and northern Morocco, this doctoral work illustrates how interactions between fire and small-scale human disturbances affect population dynamics and the potential evolutionary trajectory of populations. Greenhouse and in-situ field experiments and stochastic demographic models quantified biological and ecological characteristics of the study species that could be linked to an important, positive role of recurrent fires in population dynamics. At the same time, population censuses across the species range revealed that small-scale human disturbances related to removal of competitively superior shrub neighbors significantly increased the probability of population occurrence and the abundance of several life-cycle stages. Subsequently, stochastic integral projection models confirmed that moderate interactions between human and fire disturbances may significantly improve species viability in the absence of fires. However, a crucial finding of this work was that frequent human disturbances as well as frequent interactions between fires and chronic vegetation removal may be detrimental to population viability because the two fundamentally different disturbance types exert opposing selection pressures on populations. These findings are of potentially great importance for the management of disturbance-adapted species because they highlight the importance of including compounding effects of environmental drivers into demographic models and the need to consider the local disturbance history when designing conservation strategies of species exposed to various disturbance types

Uncertainty in soil physical data at river basin scale

International audienceFor hydrological modelling studies at the river basin scale, decision makers need guidance in assessing the implications of uncertain data used by modellers as an input to modelling tools. Simulated solute transport through the unsaturated zone is associated with uncertainty due to spatial variability of soil hydraulic properties and derived hydraulic model parameters. In general for modelling studies at the river basin scale spatially available data at various scales must be aggregated to an appropriate scale. Estimating soil properties at unsampled points by means of geostatistical techniques require reliable information on the spatial structure of soil data. In this paper this information is assessed by reviewing current developments in the field of soil physical data uncertainty and adopting a classification system. Then spatial variability and structure is inspected by reviewing experimental work on determining spatial length scales for soil physical (and soil chemical) data. Available literature on spatial length scales for soil physical- and chemical properties is reviewed and their use in facilitating change of spatial support discussed. Uncertainty associated to the derivation of hydraulic properties from soil physical properties in this context is also discussed

Uncertainties in the Hydrological Modelling Using Remote Sensing Data over the Himalayan Region

Himalayas the “roof of the world” are the source of water supply for major South Asian Rivers and fulfill the demand of almost one sixth of world’s humanity. Hydrological modeling poses a big challenge for Himalayan River Basins due to complex topography, climatology and lack of quality input data. In this study, hydrological uncertainties arising due to remotely sensed inputs, input resolution and model structure has been highlighted for a Himalayan Gandak River Basin. Firstly, spatial input DEM (Digital Elevation Model) from two sources SRTM (Shuttle Radar Topography Mission) and ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) with resolutions 30m, 90m and 30m respectively has been evaluated for their delineation accuracy. The result reveals that SRTM 90m has best performance in terms of least area delineation error (13239.28 km2) and least stream network delineation error. The daily satellite precipitation estimates TRMM 3B42 V7 (Tropical Rainfall Monitoring Mission) and CMORPH (Climate Prediction Center MORPHing Technique) are evaluated for their feasibly over these terrains. Evaluation based on various scores related to visual verification method, Yes/no dichotomous, and continuous variable verification method reveal that TRMM 3B42 V7 has better scores than CMORPH. The effect of DEM resolution on the SWAT (Soil Water Assessment Tool) model outputs has been demonstrated using sixteen DEM grid sizes (40m-1000m). The analysis reveals that sediment and flow are greatly affected by the DEM resolutions (for DEMs>300m). The amount of total nitrogen (TN) and total phosphorous (TP) are found affected via slope and volume of flow for DEM grid size ≥150m. The T-test results are significant for SWAT outputs for grid size >500m at a yearly time step. The SWAT model is accessed for uncertainty during various hydrological processes modeling with different setups/structure. The results reflects that the use of elevation band modeling routine (with six to eight elevation bands) improves the streamflow statistics and water budgets from upstream to downstream gauging sites. Also, the SWAT model represents a consistent pattern of spatiotemporal snow cover dynamics when compared with MODIS data. At the end, the uncertainty in the stream flow simulation for TRMM 3B42 V7 for various rainfall intensity has been accessed with the statistics Percentage Bias (PBIAS) and RSR (RMSE-observations Standard Deviation Ratio). The results found that TRMM simulated streamflow is suitable for moderate (7.5 to 35.4 mm/day) to heavy rainfall intensities (35.5 to 124.4 mm/day). The finding of the present work can be useful for TRMM based studies for water resources management over the similar parts of the world

Pedotransfer functions in Earth system science: challenges and perspectives

Soil, through its various functions, plays a vital role in the Earth's ecosystems and provides multiple ecosystem services to humanity. Pedotransfer functions (PTFs) are simple to complex knowledge rules that relate available soil information to soil properties and variables that are needed to parameterize soil processes. In this paper, we review the existing PTFs and document the new generation of PTFs developed in the different disciplines of Earth system science. To meet the methodological challenges for a successful application in Earth system modeling, we emphasize that PTF development has to go hand in hand with suitable extrapolation and upscaling techniques such that the PTFs correctly represent the spatial heterogeneity of soils. PTFs should encompass the variability of the estimated soil property or process, in such a way that the estimation of parameters allows for validation and can also confidently provide for extrapolation and upscaling purposes capturing the spatial variation in soils. Most actively pursued recent developments are related to parameterizations of solute transport, heat exchange, soil respiration and organic carbon content, root density and vegetation water uptake. Further challenges are to be addressed in parameterization of soil erosivity and land use change impacts at multiple scales. We argue that a comprehensive set of PTFs can be applied throughout a wide range of disciplines of Earth system science, with emphasis on land surface models. Novel sensing techniques provide a true breakthrough for this, yet further improvements are necessary for methods to deal with uncertainty and to validate applications at global scale

The occurrence and origin of salinity in non-coastal groundwater in the Waikato region

Aims The aims of this project are to describe the occurrence, and determine the origin of non-coastal saline groundwater in the Waikato region. High salinity limits the use of the water for supply and agricultural use. Understanding the origin and distribution of non-coastal salinity will assist with development and management of groundwater resources in the Waikato. Method The occurrence of non-coastal groundwater salinity was investigated by examining driller’s records and regional council groundwater quality information. Selected wells were sampled for water quality analyses and temperatures were profiled where possible. Water quality analyses include halogens such as chloride, fluoride, iodide and bromide. Ratios of these ions are useful to differentiate between geothermal and seawater origins of salinity (Hem, 1992). Other ionic ratio approaches for differentiating sources and influences on salinity such as those developed by Alcala and Emilio (2008) and Sanchez-Martos et al., (2002), may also be applied. Potential sources of salinity include seawater, connate water, geothermal and anthropogenic influences. The hydrogeologic settings of saline occurrence were also investigated, to explore the potential to predict further occurrence. Results Numerous occurrences of non-coastal saline groundwater have been observed in the Waikato region. Where possible, wells with relatively high total dissolved solids (TDS) were selected for further investigation. Several groundwater samples are moderately saline and exceed the TDS drinking water aesthetic guideline of 1,000 g m-3 (Ministry of Health, 2008). Selected ion ratios (predominantly halogens) were used to assist in differentiating between influences on salinity such as seawater and geothermal. Bromide to iodide ratios, in particular, infer a greater geothermal influence on salinity, although other ratios are not definitive. The anomalously elevated salinity observed appears natural but nevertheless has constrained localised groundwater resource development for dairy factory, industrial and prison water supply use. Further work may show some relationship with geology or tectonics, which could assist prediction of inland saline groundwater occurrence

Application of a Spatially Explicit, Agent-Based Land Use Conversion Model to Assess Water Quality Outcomes under Buffer Policies

Land use changes within watersheds have spatially explicit dynamics and involve decision making by individuals. The role of the spatial dimension of human behavior and its impact on land use change has been analyzed using agent-based modelling approaches. Agent-based land use change has received a significant theoretical attention; however, these models lack empirical implementation and testing due to the lack of spatial modelling tools and data that can capture human land use dynamics.;This research presents a methodology for projecting land use conversions through the implementation of a spatially explicit agent-based simulation model in the Opequon Creek watershed of Berkeley County, West Virginia. Empirical estimates for factors that influence the land use conversion probability are captured using a spatial logistic regression model. Then, agentbased probabilistic land use conversion (APLUC) model is programmed on Python language within a geographic information system (GIS) to explore the impacts of policies on land use conversion decisions using estimates from actual land use change from 2001-2011. A series of model runs are executed under buffer policy scenarios. Three policy scenarios are developed: (1) a scenario where there is no policy implemented, (2) a scenario where 50 ft buffer zones are applied to all streams, and (3) a scenario where 50 ft buffers are applied only on critical source areas (CSAs) watersheds. The land use patterns project in APLUC model are driven by individual land conversion decisions over 50 model runs of 10 iterations each under each policy scenario. The APLUC model is validated at sub-basin level and outcomes are analyzed to identify the influence of various land use policies on land use patterns. The results show that a 50 ft buffer policy everywhere in watershed, greatly reduced the residential land use conversions. Spatial patterns generated under a 50 ft buffer policy in CSAs only showed that future projected land use changes occurred close to major highways. In the baseline policy, most conversions occurred near existing residential land use and urban centers. Results from the APLUC model also suggests that forest is serving as distant amenity for residential land conversion.;Finally, the impacts of these three policies on water quality are estimated using an ArcSWAT model, a graphical user interface for SWAT (Soil and Water Assessment Tool). This model indicates that the 50 ft buffer policy in CSAs is most effective among the three policies in reducing the pollutant loads. This study suggests that carefully designed policies, which discourage residential land use conversion in CSAs, result in less pollutant loads by shifting the location of residential conversion to less critical areas where agricultural land is dominant in the watershed