Modeling Land-Cover using Bio-Climate Variables

MODELING LAND-COVER USING BIO-CLIMATE VARIABLES Vegetation (land-cover) is an inherent part of the climate system (M.-J. Gaillard et al. 2010). Natural, primarily climate-driven, vegetation and ecosystem processes interact with human land-use to determine vegetation patterns, stand structure and their development through time (e.g. Vitousek et al., 1997). The resulting land surface properties feedback on climate by modulating exchanges of energy, water vapour and greenhouse gases with the atmosphere. Regional Estimates of VEgetation Abundance from Large Sites (REVEALS) was introduced by M.-J. et al., (2010) as a new method to discuss issues related to pollenbased reconstruction of the past land-cover. REVEALS requires raw pollen counts, site radius, pollen productivity estimates (PPEs), and fall speed of pollen (FS) to estimate vegetation cover in percentages, (M.-J. et al., (2010)). The REVEALS model-based landcover reconstruction has been demonstrated to provide better estimates of regional vegetation/land-cover changes than the traditional use of pollen percentages. The LPJ (Lund Potsdam Jena) – GUESS (General Ecosystem Simulator) model (LPJGUESS, Smith et al., 2001) is a dynamic, process-based vegetation model optimized for application across a regional grid that simulates vegetation dynamics based on climate data input. From both REVEALS and LPJ-GUESS datasets, we have Plant Functional Types (PFTs) and Bio-climate variables. The aim is to use multiple linear regressions to find the relationship between these PFTs and the Bio-climate variables using the REVEALS dataset. Further, we will predict PFT values using the regression models and the REVEALS dataset and compare them to those in the LPJ-GUESS dataset. The PFTs will then be grouped into three different land-cover types: Ever-green canopy, Summer-green canopy and Open-land. Then these land-covers will be modelled using the bio-climate variables to provide a new way of modeling landcover or vegetation of the past. Conclusions It has been seen that bio-climate variables are important to the growth of plants thereby helping plants to produce pollens. Naturally, one should expect plants to grow well when they have their favourable climatic conditions. These include, soil water content, temperature, precipitation among others. Given a reliable and well-measured data of bioclimate variables and plant functional types, it is possible to use regression analysis to obtain a linear relationship between these plant functional types and the bio-climate variables. Consequently, it is feasible to model land-cover when we have bio-climate variables and plant functional types using multiple linear regressions

Natural Capital and Its Services in the Caatinga Phytogeographic Domain

A new millennium has begun, and a new paradigm in exploitation of natural resources to generate products has emerged. In this sense, a model in line with local environmental reality and based on the sustainable development goals (SDG) proposed by the UN is required. The document addresses the suitability of natural capitals in the Caatinga phytogeographic domain (CPD) for generating products, encompassing primary (solar energy and rainfall) and secondary (soil and vegetation) natural resources. These resources have well-defined scarcity (water, soil, and vegetation) and abundance (solar radiation) characteristics. Water is scarce due to uncertain rainfall and frequent droughts. Due to their fragility, soils, when shallow and/or with a low organic matter content, are susceptible to erosion and salinization. In turn, vegetation is scarce due to low potential for biomass production (50% of the area has a biomass production below 2 Mg ha-1. As for solar energy, there is no uncertainty regarding its occurrence in the CPD. Moreover, the region has the highest solar radiation intensities in Brazil, with daily averages ranging from 4.8 to 6.0 Kwh m-2 within a year. Therefore, a new model must be adopted, in which socioeconomic development is based on the potential of local natural resources. In short, abundant resources should be prioritized over scarce ones. Furthermore, exploitation must be democratized and not only in the hands of few privileged individual

Remote sensing and apparent electrical conductivity to characterize soil water content

2016 Fall.Includes bibliographical references.Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency while maintaining or increasing grain yield. As part of site-specific farming, PI needs to be explored, tested, and evaluated which continues to be a research issue. Neutron probes (NPs) have consistently been used for studies as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related with root water uptake. Crop yield has not been evaluated on a basis of SWC as explained by NPs in time and at different depths. One among many challenges in implementing PI is the reliable characterization of the soil water content (SWC) across spatially variable fields. For this purpose, commercial retailers are employing apparent soil electrical conductivity (ECa) to create irrigation prescription maps. However, the accuracy of this method has not been properly studied at the field scale. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC (3) to assess the potential of ECa measurement to characterize spatial distribution of SWC at field scale, and (4) to determine whether soil properties coupled with ECa could further improve the characterization of the SWC. For objectives 1 and 2, the study was conducted on maize (Zea Mays L.) irrigated in two fields in northern Colorado. Soil water data was collected at five soil depths (30, 60, 90, 120 and 150 cm), 21 and 12 times at Site I and II, respectively. Three vegetation indices were calculated on seven dates (Emergence to R3). Maize grain yield was harvested at the physiological maturity at each NPs location. Automated model selection of SWC readings to assess maize yield consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). For objectives 3 and 4, the study was conducted on two fields located in northeastern Colorado. In-field SWC was measured using neutron probes at 41 and 31 locations for Site I and II respectively. Soil ECa measurements were acquired using Geonics EM38-MK2 unit. In addition, cation exchange capacity, clay, organic matter and salt content were coupled with soil ECa to estimate SWC. Data analysis was performed using the statistical software R. Statistical correlations and multiple linear regressions were obtained from the properties that were statistically significant (p value < 0.05). Results of the study showed that the SWC readings at the 90 cm depth had the highest correlations with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status right before the reproductive growth stage (V9). Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management. Likewise, the SWC was found to be statistically different across ECa derived zones, indicating that ECa was able to accurately characterize average differences in SWC across management zones. Organic matter and salt content significantly improved the SWC assessment when combined with the ECa. The development of prescription maps for variable rate irrigation should be tailor made depending on the specific field characteristics influencing SWC

Issues Related to Incorporating Northern Peatlands into Global Climate Models

Northern peatlands cover ~3–4 million km2 (~10% of the land north of 45°N) and contain ~200–400 Pg carbon (~10–20% of total global soil carbon), almost entirely as peat (organic soil). Recent developments in global climate models have included incorporation of the terrestrial carbon cycle and representation of several terrestrial ecosystem types and processes in their land surface modules. Peatlands share many general properties with upland, mineral-soil ecosystems, and general ecosystem carbon, water, and energy cycle functions (productivity, decomposition, water infiltration, evapotranspiration, runoff, latent, sensible, and ground heat fluxes). However, northern peatlands also have several unique characteristics that will require some rethinking or revising of land surface algorithms in global climate models. Here we review some of these characteristics, deep organic soils, a significant fraction of bryophyte vegetation, shallow water tables, spatial heterogeneity, anaerobic biogeochemistry, and disturbance regimes, in the context of incorporating them into global climate models. With the incorporation of peatlands, global climate models will be able to simulate the fate of northern peatland carbon under climate change, and estimate the magnitude and strength of any climate system feedbacks associated with the dynamics of this large carbon pool

Potential net primary productivity in South America: application of a global model

We use a mechanistically based ecosystem simulation model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in South America. The Terrestrial Ecosystem Model (TEM) is designed to predict major carbon and nitrogen fluxes and pool sizes in terrestrial ecosystems at continental to global scales. Information from intensively studies field sites is used in combination with continental—scale information on climate, soils, and vegetation to estimate NPP in each of 5888 non—wetland, 0.5° latitude °0.5° longitude grid cells in South America, at monthly time steps. Preliminary analyses are presented for the scenario of natural vegetation throughout the continent, as a prelude to evaluating human impacts on terrestrial NPP. The potential annual NPP of South America is estimated to be 12.5 Pg/yr of carbon (26.3 Pg/yr of organic matter) in a non—wetland area of 17.0 ° 106 km2. More than 50% of this production occurs in the tropical and subtropical evergreen forest region. Six independent model runs, each based on an independently derived set of model parameters, generated mean annual NPP estimates for the tropical evergreen forest region ranging from 900 to 1510 g°m—2°yr—1 of carbon, with an overall mean of 1170 g°m—2°yr—1. Coefficients of variation in estimated annual NPP averaged 20% for any specific location in the evergreen forests, which is probably within the confidence limits of extant NPP measurements. Predicted rates of mean annual NPP in other types of vegetation ranged from 95 g°m—2°yr—1 in arid shrublands to 930 g°m@?yr—1 in savannas, and were within the ranges measured in empirical studies. The spatial distribution of predicted NPP was directly compared with estimates made using the Miami mode of Lieth (1975). Overall, TEM predictions were °10% lower than those of the Miami model, but the two models agreed closely on the spatial patterns of NPP in south America. Unlike previous models, however, TEM estimates NPP monthly, allowing for the evaluation of seasonal phenomena. This is an important step toward integration of ecosystem models with remotely sensed information, global climate models, and atmospheric transport models, all of which are evaluated at comparable spatial and temporal scales. Seasonal patterns of NPP in South America are correlated with moisture availability in most vegetation types, but are strongly influenced by seasonal differences in cloudiness in the tropical evergreen forests. On an annual basis, moisture availability was the factor that was correlated most strongly with annual NPP in South America, but differences were again observed among vegetation types. These results allow for the investigation and analysis of climatic controls over NPP at continental scales, within and among vegetation types, and within years. Further model validation is needed. Nevertheless, the ability to investigate NPP—environment interactions with a high spatial and temporal resolution at continental scales should prove useful if not essential for rigorous analysis of the potential effects of global climate changes on terrestrial ecosystems

A new model of Holocene peatland net primary production, decomposition, water balance, and peat accumulation

Peatland carbon and water cycling are tightly coupled, so dynamic modeling of peat accumulation over decades to millennia should account for carbon-water feedbacks. We present initial results from a new simulation model of long-term peat accumulation, evaluated at a wellstudied temperate bog in Ontario, Canada. The Holocene Peat Model (HPM) determines vegetation community composition dynamics and annual net primary productivity based on peat depth (as a proxy for nutrients and acidity) and water table depth. Annual peat (carbon) accumulation is the net balance above- and below-ground productivity and litter/peat decomposition – a function of peat hydrology (controlling depth to and degree of anoxia). Peat bulk density is simulated as a function of degree of humification, and affects the water balance through its influence on both the growth rate of the peat column and on peat hydraulic conductivity and the capacity to shed water. HPM output includes both time series of annual carbon and water fluxes, peat height, and water table depth, as well as a final peat profile that can be “cored” and compared to field observations of peat age and macrofossil composition. A stochastic 8500-yr, annual precipitation time series was constrained by a published Holocene climate reconstruction for southern Quebec. HPM simulated 5.4 m of ´ peat accumulation (310 kg C m−2 ) over 8500 years, 6.5% of total NPP over the period. Vascular plant functional types accounted for 65% of total NPP over 8500 years but only 35% of the final (contemporary) peat mass. Simulated age-depth and carbon accumulation profiles were compared to a radiocarbon dated 5.8 m, c.9000-yr core. The simulated core was younger than observations at most depths, but had a similar overall trajectory; carbon accumulation rates were generally higher in the simulation and were somewhat more variable than observations. HPM results were sensitive to centuryscale anomalies in precipitation, with extended drier periods (precipitation reduced ∼10%) causing the peat profile to lose carbon (and height), despite relatively small changes in NP

Integrated model for the hydro-mechanical effects of vegetation against shallow landslides

Shallow landslides are instability events that lead to dramatic soil mass wasting in sloping areas and are commonly triggered by intense rainfall episodes. Vegetation may reduce the likelihood of slope failure through different hydro-mechanical mechanisms that take place at the soil-plant-atmosphere interface. However, while vegetation’s mechanical contribution has been widely recognized, its hydrological effects have been poorly quantified. In addition, most of the existing models lack a holistic approach, require difficult to measure parameters or are commercially based, making them hardly transferable to land planners and other researchers.In this paper an integrated, robust and reproducible model framework is proposed and evaluated with the aim of assessing the hydro-mechanical effects of different vegetation types on slope stability using easily measureable and quantifiable input parameters. The output shows that the model framework is able to simulate the hydro-mechanical effects of vegetation in a realistic manner and that it can be readily applied to any vegetation, soil and climate types. It also demonstrates that vegetation has positive hydro-mechanical effects against shallow landslides, where plant biomass and evapotranspiration play an important role

Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet

Leaf area index (LAI) and water content (WC) in the root zone are two major hydro-meteorological parameters that exhibit a dominant control on water, energy and carbon fluxes, and are therefore important for any regional eco-hydrological or climatological study. To investigate the potential for retrieving these parameter from hyperspectral remote sensing, we have investigated plant spectral reflectance (400-2,500 nm, ASD FieldSpec3) for two major agricultural crops (sugar beet and spring barley) in the mid-latitudes, treated under different water and nitrogen (N) conditions in a greenhouse experiment over the growing period of 2008. Along with the spectral response, we have measured soil water content and LAI for 15 intensive measurement campaigns spread over the growing season and could demonstrate a significant response of plant reflectance characteristics to variations in water content and nutrient conditions. Linear and non-linear dimensionality analysis suggests that the full band reflectance information is well represented by the set of 28 vegetation spectral indices (SI) and most of the variance is explained by three to a maximum of eight variables. Investigation of linear dependencies between LAI and soil WC and pre-selected SI's indicate that: (1) linear regression using single SI is not sufficient to describe plant/soil variables over the range of experimental conditions, however, some improvement can be seen knowing crop species beforehand; (2) the improvement is superior when applying multiple linear regression using three explanatory SI's approach. In addition to linear investigations, we applied the non-linear CART (Classification and Regression Trees) technique, which finally did not show the potential for any improvement in the retrieval process

ECOSSE: Estimating Carbon in Organic Soils - Sequestration and Emissions: Final Report

Background Climate change, caused by greenhouse gas ( GHG) emissions, is one of the most serious threats facing our planet, and is of concern at both UK and devolved administration levels. Accurate predictions for the effects of changes in climate and land use on GHG emissions are vital for informing land use policy. Models which are currently used to predict differences in soil carbon (C) and nitrogen (N) caused by these changes, have been derived from those based on mineral soils or deep peat. None of these models is entirely satisfactory for describing what happens to organic soils following land-use change. Reports of Scottish GHG emissions have revealed that approximately 15% of Scotland's total emissions come from land use changes on Scotland's high carbon soils; the figure is much lower for Wales. It is therefore important to reduce the major uncertainty in assessing the carbon store and flux from land use change on organic soils, especially those which are too shallow to be deep peats but still contain a large reserve of C. In order to predict the response of organic soils to external change we need to develop a model that reflects more accurately the conditions of these soils. The development of a model for organic soils will help to provide more accurate values of net change to soil C and N in response to changes in land use and climate and may be used to inform reporting to UKGHG inventories. Whilst a few models have been developed to describe deep peat formation and turnover, none have so far been developed suitable for examining the impacts of land-use and climate change on the types of organic soils often subject to land-use change in Scotland and Wales. Organic soils subject to land-use change are often (but not exclusively) characterised by a shallower organic horizon than deep peats (e.g. organo-mineral soils such as peaty podzols and peaty gleys). The main aim of the model developed in this project was to simulate the impacts of land-use and climate change in these types of soils. The model is, a) be driven by commonly available meteorological data and soil descriptions, b) able to simulate and predict C and N turnover in organic soils, c) able to predict the impacts of land-use change and climate change on C and N stores in organic soils in Scotland and Wales. In addition to developing the model, we have undertaken a number of other modelling exercises, literature searches, desk studies, data base exercises, and experimentation to answer a range of other questions associated with the responses of organic soils in Scotland and Wales to climate and land-use change. Aims of the ECOSSE project The aims of the study were: To develop a new model of C and N dynamics that reflects conditions in organic soils in Scotland and Wales and predicts their likely responses to external factors To identify the extent of soils that can be considered organic in Scotland and Wales and provide an estimate of the carbon contained within them To predict the contribution of CO 2, nitrous oxide and methane emissions from organic soils in Scotland and Wales, and provide advice on how changes in land use and climate will affect the C and N balance In order to fulfil these aims, the project was broken down into modules based on these objectives and the report uses that structure. The first aim is covered by module 2, the second aim by module 1, and the third aim by modules 3 to 8. Many of the modules are inter-linked. Objectives of the ECOSSE project The main objectives of the project were to: Describe the distribution of organic soils in Scotland and Wales and provide an estimate of the C contained in them Develop a model to simulate C and N cycling in organic soils and provide predictions as to how they will respond to land-use, management and climate change using elements of existing peat, mineral and forest soil models Provide predictive statements on the effects of land-use and climate change on organic soils and the relationships to GHG emissions, including CO 2, nitrous oxide and methane. Provide predictions on the effects of land use change and climate change on the release of Dissolved Organic Matter from organic soils Provide estimates of C loss from scenarios of accelerated erosion of organic soils Suggest best options for mitigating C and N loss from organic soils Provide guidelines on the likely effects of changing land-use from grazing or semi-natural vegetation to forestry on C and N in organic soils Use the land-use change data derived from the Countryside Surveys of Scotland and Wales to provide predictive estimates for changes to C and N balance in organic soils over time