Cover crops and precipitation influence soluble reactive phosphorus losses via tile drain discharge in an agricultural watershed

Subsurface tile drainage speeds water removal from agricultural fields that are historically prone to flooding. While managed drainage systems improve crop yields, they can also contribute tothe eutrophication of downstream ecosystems, as tile-drained systems are conduits for nutrients to adjacent waterways. The changing climate of the Midwestern US has already altered precipitation regimes which will likely continue into the future, with unknown effects on tile drain water and nutrient loss to waterways. Adding vegetative cover (i.e., as winter cover crops) is one approach that can retain water and nutrients on fields to minimize export via tile drains. In the current study, we evaluate the effect of cover crops on tile drain discharge and soluble reactive phosphorus (SRP) loads using bi-monthly measurements from 43 unique tile outlets draining fields with or without cover crops in two watersheds in northern Indiana. Using four water years of data (n = 844 measurements), we examined the role of short-term antecedent precipitation conditions and variation in soil biogeochemistry in mediating the effect of cover crops on tile drain flow and SRP loads. We observed significant effects of cover crops on both tile drain discharge and SRP loads, but these results were season and watershed specific. Cover crop effects were identified only in spring, where their presence reduced tile drain discharge in both watersheds and SRP loads in one watershed. Varying effects on SRP loads between watersheds were attributed to different soil biogeochemical characteristics, where soils with lower bioavailable P and higher P sorption capacity were less likely to have a cover crop effect. Antecedent precipitation was important in spring, and cover crop differences were still evident during periods of wet and dry antecedent precipitation conditions. Overall, we show that cover crops have the potential to significantly decrease spring tile drain P export, and these effects are resilient to a wide range of precipitation conditions.     

Watershed-scale vegetation,water quantity,and water quality responses to wildfire in the southern Appalachian mountain region,United States

Wildfires are landscape scale disturbances that can significantly affect hydrologic processes such as runoff generation and sediment and nutrient transport to streams. In Fall 2016, multiple large drought-related wildfires burned forests across the southern Appalachian Mountains. Immediately after the fires, we identified and instrumented eight 28.4–344 ha watersheds (four burned and four unburned) to measure vegetation, soil, water quantity, and water quality responses over the following two years. Within burned watersheds, plots varied in burn severity with up to 100% tree mortality and soil O-horizon loss. Watershed scale high burn severity extent ranged from 5% to 65% of total watershed area. Water quantity and quality responses among burned watersheds were closely related to the high burn severity extent. Total water yield (Q) was up to 39% greater in burned watersheds than unburned reference watersheds. Total suspended solids (TSS) concentration during storm events were up to 168 times greater in samples collected from the most severely burned watershed than from a corresponding unburned reference watershed, suggesting that there was elevated risk of localized erosion and sedimentation of streams. NO₃-N concentration, export, and concentration dependence on streamflow were greater in burned watersheds and increased with increasing high burn severity extent. Mean NO₃-N concentration in the most severely burned watershed increased from 0.087 mg L⁻¹ in the first year to 0.363 mg L⁻¹ (+317%) in the second year. These results suggest that the 2016 wildfires degraded forest condition, increased Q, and had negative effects on water quality particularly during storm events.     

Surficial and Deep Earth Material Prediction from Geochemical Compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.

     

Extracting drainage networks and their connectivity using LiDAR data

Policies, measures, and models geared towards flood prevention and managing surface waters benefit from high quality data on the presence and characteristics of drainage ditches. As a cost and labour effective alternative for acquiring such data through field surveys, we propose a method (a) to extract vector data representing ditch drainage networks based on local morphologic features derived from high resolution digital elevation models (DEM) and (b) to identify possible connections in the ditch network by calculating a probability of the connectivity using a logistic regression where the predictor variables are characteristics of the ditch centre lines or derived from the DEM. Using Light Detection and Ranging (LiDAR) derived DEMs with a 1 m resolution, the method was developed and tested for a mixed agricultural residential area in north‐eastern Belgium. The derived ditch segments had an error of omission of 8% and an error of commission of 5%. The original positional accuracy of the centre lines of the extracted ditches was 0.6 m and could be improved to 0.4 m by shifting each vertex to the position of the lowest LiDAR point located within a radius equal to the spatial resolution of the used DEM. About 69% of the false disconnections in the network were identified and corrected leading to a reduction of the unconnected parts of the ditch network by 71%. The extracted and connected network approximated the reference ditch network fairly well.     

Stable carbon isotopes (δ<Superscript>13</Superscript>C) of total organic carbon and long-chain <Emphasis Type="Italic">n</Emphasis>-alkanes as proxies for climate and environmental change in a sediment core from Lake Petén-Itzá, Guatemala

Sediment core PI-6 from Lake Petén Itzá, Guatemala, possesses an ~85-ka record of climate and environmental change from lowland Central America. Variations in sediment lithology suggest large and abrupt changes in precipitation during the last glacial and deglacial periods, and into the early Holocene. We measured stable carbon isotope ratios of total organic carbon and long-chain n-alkanes from the core, the latter representing a largely allochthonous (terrestrial) source of organic matter, to reveal past shifts in the relative proportion of C₃–C₄ terrestrial biomass. We sought to test whether stable carbon isotope results were consistent with other paleoclimate proxies measured in the PI-6 core, and if extraction and isotope analysis of n-alkanes is warranted. The largest δ¹³C variations are associated with Heinrich Events. Carbon isotope values in sediments deposited during the last glacial maximum indicate moderate precipitation with little fluctuation. The deglacial was a period of pronounced climate variability, e.g. a relatively warm and moist Bølling–Allerød, but a cool and dry Younger Dryas. Arid periods of the deglacial were inferred from samples with high δ¹³C values in total organic carbon, which reflect times of greater proportions of C₄ plants. These inferences are supported by stable isotope measurements on ostracod shells and relative abundance of grass pollen from the same depths in core PI-6. Similar trends in carbon stable isotopes measured on bulk organic carbon and n-alkanes suggest that carbon isotope measures on bulk organic carbon in sediments from this lake are sufficient to infer past climate-driven shifts in local vegetation.     

Comparing the Validity and Reliability of Place Attachment Across Cultures

Researchers often measure human–place bonds via place attachment scales across a variety of settings. However, scale use does not always include an evaluation of the scales’ psychometric properties, especially in multisite studies. Failure to consider a place attachment scale’s measurement properties makes both validity and reliability assumptions and may lead to improper data interpretation. Hence, this investigation assessed a place attachment scale across three sites via data collected on site in natural resource protected areas in Colorado, Minnesota, and Germany. A series of confirmatory factor analyses assessed the hypothesized two-dimensional (i.e., place identity and place dependence) model, Cronbach’s alphas calculated a measure of internal consistency, and a multigroup procedure cross-validated the scale. Some items did not load on the hypothesized dimension and the pattern of factor loadings was not equivalent across settings, suggesting assessment of place attachment scales may be necessary when used in new contexts.     

Preface

正This special issue of the Journal of Resources and Ecology focuses on food safety in China,a problem that has been of great concern to the public in recent years and the object of several new laws and institutional reforms.This collection presents the work of researchers who have participated in the Food Safety Working Group of the Forum on Health,Environment and     

Climate change impacts on the energy system: a review of trends and gaps

Major transformation of the global energy system is required for climate change mitigation. However, energy demand patterns and supply systems are themselves subject to climate change impacts. These impacts will variously help and hinder mitigation and adaptation efforts, so it is vital they are well understood and incorporated into models used to study energy system decarbonisation pathways. To assess the current state of understanding of this topic and identify research priorities, this paper critically reviews the literature on the impacts of climate change on the energy supply system, summarising the regional coverage of studies, trends in their results and sources of disagreement. We then examine the ways in which these impacts have been represented in integrated assessment models of the electricity or energy system.Studies tend to agree broadly on impacts for wind, solar and thermal power stations. Projections for impacts on hydropower and bioenergy resources are more varied. Key uncertainties and gaps remain due to the variation between climate projections, modelling limitations and the regional bias of research interests. Priorities for future research include the following: further regional impact studies for developing countries; studies examining impacts of the changing variability of renewable resources, extreme weather events and combined hazards; inclusion of multiple climate feedback mechanisms in IAMs, accounting for adaptation options and climate model uncertainty.     

Adaptation and poverty reduction in Mozambique: an opportunity for developing countries to lead§

Climate change disproportionately impacts the world’s poorest countries. A recent World Bank report highlighted that over 100 million people are at risk of falling into extreme poverty as a result of climate change. There is currently a lack of information about how to simultaneously address climate change and poverty. Climate change challenges provide an opportunity for those impacted most to come up with new and innovative technologies and solutions. This article uses an example from Mozambique where local and international partners are working side-by-side, to show how developing countries can simultaneously address climate change and poverty reduction using an ecosystem-based adaptation approach. Using ecosystem-based adaptation, a technique that uses the natural environment to help societies adapt to climate change, developing countries can lead the way to improve climate adaptation globally. This paradigm shift would help developing countries become leaders in ecosystem-based adaptation and green infrastructure techniques and has implications for climate policy worldwide.

POLICY RELEVANCE

The Paris Agreement resulting from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of Parties (COP 21) in December 2015 was rightly lauded for its global commitment to cut greenhouse gas emissions. However, COP 21 was also historic because of its call for non-party stakeholders to address climate change, inclusion of a global goal of ‘enhancing adaptive capacity, strengthening resilience and reducing vulnerability’, and the United States’ commitment of $800 million to adaptation funding. The combination of recognizing the need for new stakeholders to commit to climate change adaptation, the large impact climate change will have on the developing world, and providing access to funds for climate change adaptation creates a unique opportunity for developing countries to pave the way in adaptation policies in practices. Currently, developing countries are creating National Adaptation Plans (NAPs) for the UNFCCC. Through including a strong component of ecosystem-based adaptation in NAPs, developing countries can shape their countries’ policies, improve local institutions and governments, and facilitate a new generation of innovative leaders. Lessons learned in places like Mozambique can help lead the way in other regions facing similar climatic risks.