Yield forecasting can give early warning of food risks and provide theoretical support for food security planning. Climate change and land use change directly influence the regional yield and planting area of maize, but few existing studies have examined their synergistic impact on maize production. In this study, we combine system dynamic (SD), the future land use simulation (FLUS) and a statistical crop model to predict future maize yield variation in response to climate change and land use change. Specifically, SD predicts the future land use demand, FLUS simulates future spatial land use patterns, and a statistical maize yield model based on regression analysis is utilized to adjust the per hectare maize yield under four representative concentration pathways (RCPs). A phaeozem region in central Jilin Province of China is taken as a case study. The results show that the future land use pattern will significantly change from 2030 to 2050. Although the cultivated land is likely to reduce by 862.84 km2, the total maize yield in 2050 will increase under all four RCP scenarios due to the promotion of per hectare maize yield. RCP4.5 will be more beneficial to maize production than other scenarios, with a doubled total yield in 2050. Notably, the yield gap between different counties will be further widened, which necessitates the differentiated policies of agricultural production and farmland protection, e.g., strengthening cultivated land protection and crop management in low-yield areas, as well as taking adaptation and mitigation measures to coordinate climate change and crop production.
Over the last nearly five decades, optical remote sensing has played a key role in monitoring and quantifying global change, plant diversity, and vegetation functioning across Earth’s terrestrial biomes. As a key tool for researchers, land managers, and policy makers, optical remote sensing facilitates scaling, mapping, and characterizing surface properties over large areas and through time. In addition, steady technological improvements have led to transformational changes in our ability to understand ecosystem state and change, particularly through the expansion of high spectral resolution (i.e. spectroscopic) remote sensing platforms. Point and imaging spectroscopy systems have been used across a range of scales, vegetation types, and biomes to infer plant diversity, leaf traits, and ecosystem functioning. However, despite the acknowledged utility of spectroscopic systems, data availability has been limited to smaller geographic regions given a number of technical challenges, including issues related to data volume and limited spatial coverage by previous Earth Observing (EO) missions (i.e. Hyperion). The NASA Surface Biology and Geology (SBG) mission is designed to fill this gap in ecosystem monitoring. As part of the Space-based Imaging Spectroscopy and Thermal pathfindER (SISTER) and Modeling end-to-end traceability (MEET) SBG efforts, we used field, unoccupied aerial system (UAS), and airborne imagery (from NASA’s AVIRIS-NG plafrom) to evaluate the impacts of proposed and theoretical sensor instrument properties on the retrieval of vegetation reflectance across tundra, shrub, and treeline ecosystems in Alaska. Existing observations and open-source tools are used for the simulation of surface reflectance under a range of atmospheric conditions, vegetation types, and different sensor properties. We find that retrieval uncertainty is reduced across all surface types with increasing detector signal-to-noise (SNR) but also key differences across different plant types. Results were also strongly tied to sun-sensor geometry and atmospheric state. Through this exercise we highlight key outcomes to consider for the SBG mission to optimize surface reflectance retrieval in high latitudes that will help to minimize errors in down-stream algorithms, such as functional trait retrievals.
The future uncertainty and complexity of alternative socioeconomic and climatic scenarios challenge the model-based analysis of sustainable development. Obtaining robust insights requires a systematic processing of uncertainty and complexity not only in input assumptions, but also in the diversity of model structures that simulates the multisectoral dynamics of human and Earth system interactions. Here, we implement the global change scenarios, i.e., the Shared Socioeconomic Pathways and the Representative Concentration Pathways, in a feedback-rich, integrated assessment model of human-Earth system dynamics, called FeliX, to serve two aims: (1) to provide modellers with well-defined steps for the adoption of established scenarios in new integrated assessment models; (2) to explore the impacts of model uncertainty and its structural complexity on the projection of these scenarios for sustainable development. Our modelling shows internally consistent scenario storylines across sectors, yet with quantitatively different realisations of these scenarios compared to other integrated assessment models due to the new model’s structural complexity. The results highlight the importance of enumerating global change scenarios and their uncertainty exploration with a diversity of models of different input assumptions and structures to capture a wider variety of future possibilities and sustainability indicators.
The ferrimagnetic (FM) and antiferromagnetic (AFM) particles of iron oxides are considered pedogenic and climatic indicators due to their enrichment with comparable increasing in rainfall and temperature. However, the opposite changes in rainfall and temperature result in rapid change of relative humidity (RH), which could lead to their competition and transformation. We examined two soil sequences undergone contrary climate development on the eastern edge of the Tibetan Plateau. The dry and warm climate with low RH favors the coordinative enrichment of AFM hematite and FM particles, while the wet and cool climate with high RH mainly produces goethite but leads to competition between low content AFM hematite and FM particles. The outcome well interprets the changing relationship between color and magnetism in soils and sediments, and suggests that temperature is as important as precipitation in paleoclimate reconstruction based on iron oxides, especially during strong dry-wet cycles and climate pattern shifts.
Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.
This article provides a commentary about the state of integrated, coordinated, open, and networked (ICON) principles in Earth and Planetary Science Processes (EPSP) and discussion on the opportunities and challenges of adopting them. This commentary focuses on the challenges with current inclusive, equitable, and accessible science and highlights how research undertaken in the earth and planetary surface processes community currently benefit from and would be able to grow as a discipline with more directed implementation of ICON principles.
The severity and frequency of wildfires have risen dramatically in recent years, drawing attention to the term ‘wildland-urban interface’ (WUI), the region where man-made constructions meet flammable vegetation. Herein, we mapped a finer-scale, novel linear WUI for California (CA) based on the intersection of boundaries of wildland vegetation and building footprint. The direct intersection is referred to as a direct WUI, whereas the intersection at 100-m is known as an indirect WUI. More fires were ignited closer to direct WUI than indirect WUI due to their proximity to communities. However, the overlap of past fire perimeters with indirect WUI is greater than that with direct WUI which shows that more areas were burned in the indirect WUI due to embers transported by strong wind gusts during large wildfires. The study’s findings will help land managers and policymakers in controlling fire dangers, land-use planning, and reducing threats to fire-prone communities.
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions often posed by earth and environmental scientists with the aid of a real-world modelling experiment. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where DL models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.
The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53°C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (20002015) permafrost area on the QTP is 1.04 × 106 km2 (0.801.28 × 106 km2), and the average MAGT and ALT are -1.35 ± 0.42°C and 2.3 ± 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (20612080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP.
Extensive loss of salt marshes in back-barrier tidal embayments is undergoing worldwide as a consequence of land-use changes, wave-driven lateral marsh erosion, and relative sea-level rise compounded by mineral sediment starvation. However, how salt-marsh loss affects the hydrodynamics of back-barrier systems and feeds back into their morphodynamic evolution is still poorly understood. Here we use a depth-averaged numerical hydrodynamic model to investigate the feedback between salt-marsh erosion and hydrodynamic changes in the Venice Lagoon, a large microtidal back-barrier system in northeastern Italy. Numerical simulations are carried out for past morphological configurations of the lagoon dating back up to 1887, as well as for hypothetical scenarios involving additional marsh erosion relative to the present-day conditions. We demonstrate that the progressive loss of salt marshes significantly impacted the Lagoon hydrodynamics, both directly and indirectly, by amplifying high-tide water levels, promoting the formation of higher and more powerful wind waves, and critically affecting tidal asymmetries across the lagoon. We also argue that further losses of salt marshes, partially prevented by restoration projects and manmade protection of salt-marsh margins against wave erosion, which have been put in place over the past few decades, limited the detrimental effects of marsh loss on the lagoon hydrodynamics, while not substantially changing the risk of flooding in urban lagoon settlements. Compared to previous studies, our analyses suggest that the hydrodynamic response of back-barrier systems to salt-marsh erosion is extremely site-specific, depending closely on the morphological characteristics of the embayment as well as on the external climatic forcings.
Literature widely recognize the strong influence of urban green spaces in the microclimatic regulation and its potential applications to mitigate warming in cities. Promote viable actions to the climate change adaptation from cities through vegetation and help to palliate the urban heat island effect (UHI) to reduce health risk during extreme heat episodes, requires accurate criteria for each context in its different scales. This study presents a multi-scale approach to quantify the influence of urban green spaces on climate behavior of the Viladecans-Gava-Castelldefels conurbation in the metropolitan area of Barcelona. For this purpose, first, air (Ta) and surface (Ts) temperature of 124 points located in the interior and surroundings of seven green spaces are registered through field measurement campaigns during day and night between July 26 and August 4 of 2018. Then, Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8 and Sentinel 2 data imagery for a clear-sky day nearby to the measurement days are retrieved and complemented with the NDVI of the spring-summer period of 2018 (1m) available in the Cartographic and Geographic Institute of Catalonia (ICGC). Analytical methods departed from the UHI characterization of the three-municipal area, resulted in 1.63°C LST increase in the urban Corine land cover (CLC) in relation with the rural at the whole ambit. Then, an OLS model to predict LST is constructed with NDVI and distance to parks (spaces with NDVI>=0.30) in the whole ambit (R²=0.59) and in the urban area (R²=0.47). At this point, results indicate that increase a tenth of NDVI reduces 1.15°C the LST of the whole ambit and 0.73°C on the urban area (p<0.01); while for each 100m further from parks, the LST rises 0.61°C for the whole ambit and 1.81°C on urban area (p<0.01). Particularly for the seven study cases, field measurements registered coincident spatial distribution with LST and NDVI, as well as highlighted the UHI effect during night. The quantification of the intensity and extent of the cooling effect of the study cases, registered a maximum cooling intensity of 2.7°C with a 300m buffer area; as well as the cooling effect calculation through concentric rings resulted between 40 to 130m extents from the parks boundaries and cooling intensity from 0.29 to 2.15°C. In conclusion, even when the multiple-scale analysis present coincidences and discrepancies between the different approaches, the models and methods applied in this study resulted in values that allow starting to talk about adequate actions to adapt to climate change in the context of the metropolitan area of Barcelona. The present study is part of the “Urban-CLIMPLAN. The urban heat island: effects on climate change and modeling for territorial and urban planning strategies. Application to the metropolitan region of Barcelona”, financed by the Ministry of Economy of Spain (MINECO) and the European Regional Development Fund (ERDF).
Observations reveal end of summer Arctic sea ice extent is declining at an accelerating rate. Model projections underestimate this decline and continue to have a broad spread in forecasted September sea ice extent. This suggests some important summer processes, such as melt pond formation and evolution, may not be properly represented in current models. Melt ponds form on the sea ice surface as snow melts, and pools in low lying areas on the sea ice surface. The evolution of the ponds depends on snow depth, ice thickness, and surface conditions. Melt water may spread across a level surface, or be confined to depressions between sea ice ridges. Ponds decrease the albedo of the surface and enhance the positive ice albedo feedback, accelerating further melt. Until recently, Arctic-wide observations of individual melt ponds were not available. ICESat-2, a photon counting laser altimeter launched in 2018, provides high resolution detail of sea ice and snow topography due to its unique combination of a small footprint (~12 m) and high-resolution along-track sampling (0.7 m). The green laser (532 nm) is able to penetrate water, enabling melt pond depth measurements. We have developed methods to track the melt pond surface and bathymetry in ICESat-2 data to determine melt pond depth. We also track melt pond evolution through application of a sea ice classification algorithm to 10 m resolution Sentinel-2 imagery. The combination of these two datasets allows for an evolving, three-dimensional view of the melting sea ice surface. We focus on the evolution of summer melt on multiyear ice in the Central Arctic north of Greenland and Canada in 2020. Our findings are put in context of existing literature on melt pond depth, volume, and evolution. We also discuss our results in relation to the melt pond fraction north of the Fram Strait, where we expect different ice conditions in the vicinity of the 2020 MOSAiC field studies. Observational data products comprising melt pond fraction and pond depth are being developed for public distribution. These products may be of interest to those studying under-ice light and biology, as well as modelers who are interested in understanding the evolution of melt pond parameters for model initialization and validation.
Tropical mountain glaciers are an important water resource and highly impacted by recent climate change. Tropical mountain glaciation also occurred in the recent and deep past, which presents opportunities for better validating paleoclimate simulations in continental interiors and mountainous regions but requires bridging global model scales (100s of km) with the ~ 1–10 km scale of glaciers when paleotopography is poorly known. Here we hindcast tropical mountain glaciation in pre-industrial time by using global climate model meteorology to force standalone simulations in its land component that use high resolution topography to resolve selected tropical mountain glaciers. These simulations underestimate observed equilibrium line altitudes (ELA) by 249 ± 330 m, but the simulated ELA and snow lines capture observed inter-mountain ELA variability. Error in large-scale model precipitation and ELA reconstruction uncertainty are the main contributors to this bias.
The grandest geotourism attractions in the southern hemisphere, in the nineteenth century were the siliceous Pink and White Terraces, the lost New Zealand Eighth Wonder of the World. In 1886, the Tarawera eruption buried the terraces. In the absence of a government survey or evidence of their locations; public debate over their survival ensued until the 1940s. Recently, a unique survey was uncovered and led researchers at last to the Terrace locations. Early colonial visitors were told by traditional landowners, that the major White Terrace spring erupted in strong easterly winds. Having researched the Pink and White Terraces for some years, this 1859 report puzzled me, as it did Ferdinand Hochstetter to whom the first report was made in 1859. From previous studies in automotive crankcase ventilation, I could see a potential causal pathway for these east-wind spring eruptions. After examining the topography of the White Terrace spring, embankment and apron: I suggest the puzzling eruptions were a product of three phenomenae: the Venturi and Coandă effects, with Bernoulli’s principle. This paper presents the evidence for the presence of Venturi and Coandă effects at the Lake Rotomahana Basin. More importantly, it discusses how these effects contributed to postulated spring eruptions during the 1886 eruptions; which created so far unexplained water ponding around the Pink, Black and White Terrace locations. These surface waters contribute to the new paradigm for the Rotomahana Basin during the 1886 eruptions; where the topographic changes lead today’s researchers to the lost Terrace locations around the shores of the new Lake Rotomahana.
Extreme weather conditions are associated with a variety of water quality issues that can pose harm to humans and aquatic ecosystems. Under dry extremes, contaminants become more concentrated in streams with a greater potential for harmful algal blooms, while wet extremes can cause flooding and broadcast pollution. Developing appropriate interventions to improve water quality in a changing climate requires a better understanding of how extremes affect watershed processes, and which places are most vulnerable. We developed a Soil and Water Assessment Tool model of the Cape Fear River Basin (CFRB) in North Carolina, USA, representing contemporary land use, point and non-point sources, and weather conditions from 1979 to 2019. The CFRB is a large and complex river basin undergoing urbanization and agricultural intensification, with a history of extreme droughts and floods, making it an excellent case study. To identify intervention priorities, we developed a Water Quality Risk Index (WQRI) using the load average and load variability across normal conditions, dry extremes, and wet extremes. We found that the landscape generated the majority of contaminants, including 90.1% of sediment, 85.4% of total nitrogen, and 52.6% of total phosphorus at the City of Wilmington’s drinking water intake. Approximately 16% of the watershed contributed most of the pollutants across conditions—these represent high priority locations for interventions. The WQRI approach considering risks to water quality across different weather conditions can help identify locations where interventions are more likely to improve water quality under climate change.
Large earthquakes rapidly denude hillslopes by triggering thousands of coseismic landslides. The sediment produced by these landslides is initially quickly mobilised from the landscape by an interconnected cascade of processes. This cascade can dramatically but briefly enhance local erosion rates. Hillslope and channel processes, such as landsliding and debris flows, interact to influence the total mass, calibre, and rate of sediment transport through catchments. Calculating the sediment budget of an earthquake lends insight into the nature of these interactions. Using satellite imagery derived landslide inventories, channel surveys and a literature review combined with a Monte Carlo simulation approach we present a constrained sediment budget of the first decade after the 2008 Mw7.9 Wenchuan earthquake. With this sediment budget we demonstrate that debris flows are dominant process for delivering sediment into channels and that large volumes of sediment remain in the landscape. In our study area over 88% (469 Mega tonnes) of the coseismically generated sediment remains on the hillslopes in 2018. Of the 12% of the sediment that was mobilised, 69% (40.7 14 Mt) was mobilised by debris flows. Despite the large proportion of sediment remaining on the hillslope, the frequency of debris flows declined significantly over our observation period. The reduction in debris-flow frequency is not correlated to reductions in the frequency of triggering storms, suggesting changes in the mechanical properties of hillslope sediment may drive this observation. The stabilisation of coseismically generated sediment greatly extends its residence time and may influence catchment sediment yields for centuries or millennia.
A flowslide overriding liquefied substrate can vastly enhance its disaster after failure initiation, due to rapid velocity and long-runout distance during landslides mobilized into flows. It is crucial to provide improved understanding to the mechanism of these catastrophic flowslides for hazard mitigation and risk assessment. This study focuses on the Saleshan landslide of Gansu in China, which is a typically catastrophic flowslide overrode a liquefied sand substrate. Geomorphologic and topographic maps along with analysis of seismic signals confirm its dynamic features and mobilized behaviors. ERT surveying detected abundant groundwater in the landslide, which is fundamental to its rapid long-runout distance. Particle size distributions and triaxial shear behaviors affirmed more readily liquefied behavior of superficial loess and underlying alluvial sand than red soil sandwiched them. We also examined the liquefaction susceptibility of the alluvial sand under loading impact at undrained and drained conditions. The alluvial sand is readily liquefied in the undrained condition while it is difficult at drained condition due to rapid water pore pressure dissipation. The results showed that the landslide experienced a sudden transformation from slide on the steep slope where it originated to flow on a nearly flat terrace with abundant groundwater that it overrode. This transformation can be attributed to the liquefied alluvial sand substrate enhancing the whole landslide body mobility. Along with recent, similar findings from landslides worldwide, substrate liquefaction may present a widespread, significant increase in landslide hazard and consequent mobility and our study reveals conditions necessary for this phenomenon to occur.
Global water use for food production needs to be reduced to remain within planetary boundaries, yet the financial feasibility of crucial measures to reduce water use is poorly quantified. Here, we introduce a novel method to compare the costs of water conservation measures with the added value that reallocation of water savings might generate if used for expansion of irrigation. Based on detailed water accounting through the use of a high-resolution hydrology-crop model, we modify the traditional cost curve approach with an improved estimation of demand, increasing marginal cost per water conservation measure combination and add a correction to control for impacts on downstream water availability. We apply the method to three major river basins in the Indo-Gangetic plain (Indus, Ganges and Brahmaputra), a major global food producing region but increasingly water stressed. Our analysis shows that at basin level only about 10% (Brahmaputra) to just over 20% (Indus and Ganges) of potential water savings would be realised; the equilibrium price for water is too low to make the majority of water conservation measures cost effective. The associated expansion of irrigated area is moderate, about 7% in the Indus basin, 5% in the Ganges and negligible in the Brahmaputra, but farmers’ gross profit increases more substantially, by 11%. Increasing the volumetric cost of irrigation water influences supply and demand in a similar way and has little influence on water reallocation. Controlling for the impact on return flows is important and more than halves the amount of water available for reallocation.