In their study, Dong and Ochsner (2018) used an extensive dataset of 18 cosmic-ray neutron rover surveys to assess the influence of precipitation and soil texture on mesoscale soil moisture patterns. Based on their analysis, they concluded that soil texture, represented by sand content, often exerts a stronger influence on mesoscale soil moisture variability than precipitation, represented by the antecedent precipitation index. However, we consider that Dong and Ochsner (2018) made a mistake in their calculation of volumetric soil moisture, such that their analysis on the influence of soil texture on soil moisture is not valid. This result does however not bring into question the paper’s conclusion on the influence of soil texture on mesoscale soil moisture patterns.
Floating photovoltaic solar energy (FPV) are solar photovoltaic systems that float on bodies of water. They are a rapidly expanding renewable energy source emerging as an alternative to land-intensive ground-mounted solar arrays. The applications of this technology are commonly explored through technical potential assessments; a vital step in the development of renewable energy resources that allow for the identification of feasible installation sites and provide an estimation of costs, power generation, and capacity (Lee and Roberts 2018). These assessments are carried out to aid planners, policymakers, and other decision-makers in predicting and achieving goals related to the development of renewable energy; however, some considerations may be overlooked. Assessing the technical potential of solar energy without a standardized methodological framework for site selection may lead to an inconsistent range of generation outcomes, adding to the confusion and lack of confidence that has been a significant barrier to the growth of renewable energy in recent years (Seetharaman 2019) This study systematically reviewed criteria in the published literature emphasizing assessment of FPV technical potential and related siting studies, especially those using geographic technologies. The systematic review was performed in alignment with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Preliminary findings of this systematic review suggest that FPV site selection criteria can be categorized into economic, social, environmental, and technical considerations. We aim to elucidate which criteria within each classification are important to include in an FPV siting study based on the current literature. Results from this analysis will inform the standardization of a site-selection framework used in future technical potential studies, which may, in turn, improve the accuracy of generation estimates and offer a meaningful and realistic idea of how FPV installations could transform the direction of renewable energy science and development.
Understanding future land-use related water demand is important for planners and resource managers in identifying potential shortages and crafting mitigation strategies. This is especially the case for regions dependent on limited local groundwater supplies. For the groundwater dependent Central Coast of California, we developed two scenarios of future land use and water demand based on sampling from a historic land change record: a business-as-usual scenario (BAU; 1992–2016) and a recent-modern scenario (RM; 2002–2016). We modeled the scenarios in the stochastic, empirically based, spatially explicit LUCAS state-and-transition simulation model at a high resolution (270-m) for the years 2001-2100 across 10 Monte Carlo simulations, applying current land zoning restrictions. Under the BAU scenario, regional water demand increased by an estimated ~ 222.7 Mm by 2100, driven by the continuation of perennial cropland expansion as well as higher than modern urbanization rates. Since 2000, mandates have been in place restricting new development unless adequate water resources could be identified. Despite these restrictions, water demand dramatically increased in the RM scenario by 310.6 Mm by century’s end, driven by the projected continuation of dramatic orchard and vineyard expansion trends. Overall, increased perennial cropland leads to a near doubling to tripling perennial water demand by 2100. Our scenario projections can provide water managers and policy makers with information on diverging land use and water use futures based on observed land change and water use trends, helping better inform land and resource management decisions.
We present novel in-field vegetation fire observations, and the analyses used to process the data, using brightness temperatures recorded by longwave infrared camera and thermal image velocimetry. The brightness temperatures from a wind-driven stubble wheat fire were obtained in video format with a 60 frames per second (fps) acquisition rate. Multi-level sonic anemometers mounted on a 10m in-fire tower were used for in-situ measurements of turbulent velocity and air temperatures, while fuel level air and flame temperatures were collected by an array of thermocouples. The camera’s image pixel resolution was adequate to resolve dynamics and in accordance with the in-fire thermocouple spacing distances. The in-situ and remotely measured flaming zone dynamics were derived using two different methodologies, Thermal Image Velocimetry (TIV) and Image Segmentation (IS). The results highlight spatial and spectral information of coherent turbulent and mean velocity structures. The power spectra decomposition of the thermal image velocimetry showed similar spectral characteristics to the sonic velocity measurements during the fire passage under the tower with a similar inertial subrange slope. This result reveals plausible evidence of interaction between the flaming zone and wind turbulence for a prescribed rapidly moving stubble wheat fire. This research presents a new field measurement methodology for understanding fire-atmospheric interactions between the flaming zone and the immediate overlying atmospheric turbulent boundary layer.
Meltwater infiltration and refreezing in snow and firn are important processes for Greenland Ice Sheet mass balance, acting to reduce meltwater runoff and mass loss. To advance understanding of meltwater retention processes in firn, we deployed vertical arrays of time-domain reflectometry sensors and thermistors to continuously monitor meltwater infiltration, refreezing, and wetting-front propagation in the upper 4 m of snow and firn over the 2016 melt season at DYE-2, Greenland. The dataset provides a detailed record of the co-development of the firn wetting and thawing fronts through the melt season. These data are used to constrain a model of firn thermodynamics and hydrology that is then used in simulations of the long-term firn evolution at DYE-2, forced by ERA5 climate reanalyses over the period 1950-2020. Summer 2016 meltwater infiltration reached a depth of 1.8 m below the surface, which is close to the modelled long-term mean at this site. Modelled meltwater infiltration increased at DYE-2 from 1990-2020, driving increases in firn density, ice content, and temperature; 10-m firn temperatures increased by 1°C per decade over this period. Modelled meltwater infiltration reached 6 to 7 m depth during extreme melt seasons in Greenland such as 2012 and 2019, causing 3-4°C increases in 10-m firn temperatures which persist for several years. A similar event occurred in 1968 in the model reconstructions. These deep infiltration events strongly impact the firn at DYE-2, and may be more influential than the background warming trend in governing meltwater retention capacity in the Greenland percolation zone.
For over 150 years, plans to divert Arctic Ocean-draining rivers southwards in order to relieve an ongoing water supply crisis in central Asia have been discussed. Recent insights have identified the importance of freshwater in regulating the role of heat associated with intruding intermediate depth Atlantic water in driving Arctic Ocean sea ice decline. Here we assess the potential impact of the redirection of the Ob’, Yenisey, Northern Dvina and Pechora rivers on upper ocean density structure, and by implication, the aerial sea ice extent. A simple 1D model is applied in which freshwater content of the upper ocean water column is reduced to mimic the diversion of the rivers, and the impact on water column stratification assessed. The results show that the impact is dependent on distribution of riverine freshwater in the upper water column. If the impact of reduced freshwater is spread through the entire water column, down to the Atlantic Water Layer, the level of stratification is reduced by an average of 28%, more than the seasonal variability in stratification. However, if the changes were limited to the surface layer, the resultant reduction in stratification is less, only 17%, but the direct entrainment of deeper, warmer waters is found to occur. At a time when climate change and population growth put increasing pressure on water resources, these results show the sensitivity of a region critical to global weather and climate to anthropogenic attempts to resolve water resource issues many thousands of kilometres away.
Spatial econometric models estimated on the big geo-located point data have at least two problems: limited computational capabilities and inefficient forecasting for the new out-of-sample geo-points. This is because of spatial weights matrix W defined for in-sample observations only and the computational complexity. Machine learning models suffer the same when using kriging for predictions; thus this problem still remains unsolved. The paper presents a novel methodology for estimating spatial models on big data and predicting in new locations. The approach uses bootstrap and tessellation to calibrate both model and space. The best bootstrapped model is selected with the PAM (Partitioning Around Medoids) algorithm by classifying the regression coefficients jointly in a non-independent manner. Voronoi polygons for the geo-points used in the best model allow for a representative space division. New out-of-sample points are assigned to tessellation tiles and linked to the spatial weights matrix as a replacement for an original point what makes feasible usage of calibrated spatial models as a forecasting tool for new locations. There is no trade-off between forecast quality and computational efficiency in this approach. An empirical example illustrates a model for business locations and firms’ profitability.
Main hydrological disasters that occur in the Republic of Moldova are floods. In the last 70 years (1947-2015) floods caused losses of 583 mil. US$. Thereby, a proportion of 55% of the damage was determined by flash floods and 40% - by fluvial floods. Increasing human impact on environment is considered to be the main factor for modifications of flood runoff regimes. Moldova features intense economic activity resulting in the fact that over 60% of the territory is used for agriculture and almost all rivers’ morphology is heavily modified. Therefore, the evaluation of anthropogenic impact on floods generation and propagation processes is of particular interest. Environmental change scenario modelling was performed to analyze the modification of flood wave hydrograph features with the help of the JAMS/J2000 hydrological model. 11 pilot basins have been utilized for this analysis, which represent different parts of the country. Assessment of land cover changes impact over the past 3 decades on flood runoff was performed by consecutive change of land cover layer for 1982 and 2013 for each flood event. The results showed that land cover changes caused a slight decrease of flood runoff in the northern part of the country (-4-10%) due to processes of basins naturalization in areas that were left fallow with undergoing natural succession. In the central and southern rivers basins an increase of flood features could be identified (+2-35%) because of intensified agricultural and urbanization processes. Assessment of reservoir impact on floods was performed on example of the Byc River, which is subject to change due to the Ghidighici reservoir. Thus, first, hydrological model was calibrated and validated for the Byc River natural runoff period and later applied for runoff simulation for the reservoir operation period. Differences of real and naturalized modeled runoff showed that Ghidighici reservoir causes a significant decrease of flood runoff which is from 3 up to 20 times lower than in the case of natural runoff modeling. Resulted hydrological models are well suited for land planning, hydraulic engineering and flood control. They can serve as support for authorities in decision making in flood protection and water resources management.
The process of scientific visualization often involves making design choices- colors being one one them, to effectively communicate and highlight features in the data (e.g. high/low temperatures). Using the techniques of registration and image tracking, which are widely used in Augmented Reality (AR) applications to anchor digital content to the real world, an iPad/iPhone application has been developed that visualizes hand colored earth science datasets. The application would scan a student’s hand-colored page of a rectangular image of some global dataset, obtain the colors used, and convert that to an AR interactive, 3D globe with the dataset in study, animated with the students’ colors. This exercise could also be used to educate students about different map projections and is a flexible, customizable, inexpensive tool for teachers to teach a variety of geoscience topics. This engaging interactive environment could help instill a sense of ownership of the data and encourage the student to be more engaged with the science being presented.
Sea Level Rise is a global concern that has varying and potentially serious consequences on the local level. Although national policies and international frameworks can help to reduce the severity of sea level rise by limiting future emissions, in the current policy framework in the United States, local governments hold most of the responsibility for protecting their communities from flooding and the impacts of future sea level rise, with limited assistance from state and federal governments. Recognizing that local governments are largely driven by their community’s needs and desires, a survey of 500 persons affiliated with coastal communities across the east coast of the United States was conducted to identify public perceptions on the relative priority of sea level rise planning, components that should be included in local plans, protection priorities, funding mechanisms, methods to resolve conflict, and acceptable adaptation responses. The information from this survey was then provided to representatives from six local governments in a barrier island New Jersey to help identify its appropriateness and usefulness for local planning. In addition to discussing the key findings of the public survey, an overview of the responses from government officials will be presented, with an emphasis on comparing and contrasting the viewpoints of public officials and members of the public to help foster stronger collaborations among all members of a community (residents, businesses, utilities, governments and others) to help address local adaptation in light of this complex issue.
Convective aggregation is an important atmospheric phenomenon which frequently occurs in idealised models in radiative-convective equilibrium (RCE), where the effects of land, rotation, sea surface temperature gradients, and the diurnal cycle are often removed. This aggregation is triggered and maintained by self-generated radiatively driven circulations, for which longwave feedbacks are essential. Many questions remain over how important the driving processes of aggregation in idealized models are in the real atmosphere. We approach this question by adding a continentally-sized, idealized tropical rainforest island into an RCE model to investigate how land-sea contrasts impact convective aggregation and its mechanisms. We show that convection preferentially forms over the island persistently in our simulation. This is forced by a large-scale thermally driven circulation. First, a sea-breeze circulation is triggered by the land-sea thermal contrast, driven by surface sensible heating. This sea-breeze circulation triggers convection which then generates longwave heating anomalies. We find that these longwave heating anomalies are essential for maintaining the aggregation of convection over the island through mechanism denial tests. We also show, by varying the island size, that the aggregated convective cluster appears to have a maximum spatial extent of 10,000 km. These results highlight that the mechanisms of idealized aggregation remain relevant when land is included in the model, and therefore these mechanisms could help us understand convective organization in the real-world.
Due to an imbalance of incoming and outgoing radiation at the top of the atmosphere, excess heat has been accumulating in Earth’s climate system in recent decades, driving global warming and climatic changes. To date it has not been quantified how much of this excess heat is being used for the melting of ground ice in the terrestrial permafrost region. Here, we diagnose changes in sensible and latent heat contents in the northern terrestrial permafrost region from ensemble simulations of a numerical permafrost model. We find that about 3.9 (+1.5|-1.7) ZJ of heat, of which 1.7 (+1.4|-1.5) ZJ (45%) were used to melt ground ice, were taken up by permafrost from 1980 to 2018. This suggests that permafrost is a persistent heat sink similar in magnitude to other components of the cryosphere that requires an explicit consideration in assessments of the Earth’s energy imbalance.
To satisfy increasing global agricultural demand, the expansion of irrigation is an important intensification measure. At the same time, unsustainable water abstractions and cropland expansion pose a threat to biodiversity and ecosystem functioning. Irrigation potentials are influenced by local biophysical irrigation water availability and competition of different water users. Because water abstractions for various human uses along the river divert the river flow, it is also important to consider competing water uses when estimating irrigation potentials. Using a novel river routing routine that considers economic criteria of water allocation via a productivity ranking of grid cells and both land and water sustainability criteria, we estimate global irrigation potentials at a halfdegree spatial resolution. We show that there are considerable potentials to expand irrigation without harming the environment, but not necessarily at the places where irrigation is taking place today. In terms of potentially irrigated areas on current cropland, 711 Mha could be sustainably irrigated when only considering biophysical criteria. Of these, only 254 Mha have a yield value gain of more than 500 USD/ha and would be economically viable to be irrigated. The open-source data processing routine is a valuable aggregation and disaggregation tool for the use of hydrological inputs within land-system models that do not have a highly resolved representation of land use. The potentials can be aggregated to different simulation level units (e.g. basin level or country level) while maintaining biophysical and economic consistency.
Mangrove ecosystems are an essential component of tropical and subtropical urban coastal regions where they provide critical ecosystem services and ensures climate mitigation while playing a pivotal role in the livelihoods of coastal communities. However, growing anthropogenic pressures from rampant urbanization and infrastructural demands are leading to an unparalleled loss and degradation of mangrove cover especially in coastal cities of the global south. Addressing the immediate need for monitoring, protection and restoration of the ecologically stressed mangroves, this study uses earth observations, machine learning and cloud computing methods for advancing timely and accurate spatiotemporal mangrove mapping and change detection. Image classification through four different models i.e. Classification and Regression Trees (CART), Random Forest (RF), Gradient Tree Boost (GTB) and Support Vector Machine (SVM) was performed using Google Earth Engine to classify mangrove extent along the coastal regions of Mumbai, India. Spatially explicit temporal trend in mangrove extent was studied and used to estimate the rate of change of mangrove extent over a period of 30 years. Accuracy assessment was conducted to validate the robustness of trained classifier models alongside their comparative performance. Classification accuracies on the order of 95% were achieved through the machine learning-based classifier models in distinguishing mangrove areas from other land cover types. The time-series analysis combined with image classification reveals the pattern and causes of spatiotemporal changes in mangrove cover and highlights the hotspots of mangrove loss and gain. This approach can aid stakeholders in the management and restoration of mangrove ecosystems through periodic and cost-effective monitoring of mangrove cover particularly in data deficient coastal cities. The outcomes of the study will contribute towards efficient decision-making in achieving the localization of Sustainable Development Goals 6 and 11 of the United Nations.
Mangrove forests, occupying tropical and subtropical coastal areas, serve as coast protector, water filter, spot of attraction and an exceptional carbon reservoir. Despite these extraordinary ecological and economic functions, our understanding of the mangroves is limited because field survey is hard to conduct in the intertidal mangrove habitats. While satellite remote sensing provides good spatial and temporal coverages globally, data availability at the tropical latitudes is limited due to frequent cloud contamination. The quickly emerging unmanned aerial vehicle (UAV) technique enables data collection under almost all weather conditions, thus provides new opportunities. By mounting light detection and ranging (LiDAR) sensors on UAVs, the 3D structure of mangroves can be accurately characterized even at individual tree level. The basic tree parameters such as tree height and crown size can be easily extracted, which then enables the estimation of biomass, carbon stock and other important ecological indices. This study uses high density (>100 pt/m2) UAV-LiDAR data collected at four consecutive years to detect the growth rate and pattern of mangroves. The change detection is done at individual tree level. The dependence of mangrove growth pattern on tree species and clumpingness are analyzed. The output will allow scientists and environment managers to know mangroves better and to manage them effectively and efficiently.
Land use/land cover (LULC) change could adversely affect watershed health by elevating nutrients and sediment levels and intensifying the risk of flooding. In this study, a spatially-explicit LULC change modeling framework was coupled with the Chesapeake Bay Watershed Model (CBWM) to investigate the impact of LULC change on nutrients (total nitrogen and total phosphorous), sediment and runoff volume in the watersheds surrounding Virginia’s Shenandoah National Park, U.S. Four stakeholder-informed scenarios alongside a Recent Trends LULC change scenario were studied. The stakeholder-informed LULC change scenarios, which differed in consideration of future planning and population growth, were developed through several meetings with stakeholders. To develop the Recent Trends, the historical LULC trend from 2001 to 2011 was analyzed. Using 2011 as a baseline scenario, the spatio-temporal patterns of LULC change were estimated as influenced by physiographic and socio-economic drivers 50 years in the future (2061). The projected LULCs were fed into the CBWM to predict the change in average annual loading of nutrients, sediment and runoff volume. While the changes in loads at the full study area were not substantial (< 0.9%), changes became more pronounced at finer spatial scales. Expectedly, the LULC change scenario with ad-hoc planning and high population growth resulted in the largest increase in runoff volume. However, the scenario with ad-hoc planning and low population growth showed the largest increase in the simulated pollutants. This was because while this scenario projected less development, it projected more increases in agricultural LULCs that export more nutrients and sediment than other changing LULCs. This implied that sole land use planning based on urban development is not sufficient for watershed protection and agricultural LULCs need to be incorporated in concert in our future planning. This further suggested that land use planning plays a more critical role than population growth rate in water quality management. The results have implications for the Chesapeake Bay total maximum daily load and could help well-informed future land use planning and watershed protection by incorporating the impact of future LULC change on water quality and quantity.
The vertical accuracy of eight different freely accessible DEMs has been evaluated across different physiographic divisions and the river basins of Nepal. Results revealed that MERIT is superior to other DEMs (RMSE 9m) in the low-lying Terai plains of Nepal where the elevation range is lower. In High mountains and High Himalayas having higher elevation range, SRTM90m outperformed all its counterparts. Meanwhile, in Siwalik and middle mountains, both SRTM90m and HYDROSHEDS exhibited almost similar RMSE indicating their compatible uses in these regions. Meanwhile, the accuracy assessment across different river basins of Nepal discerned that the accuracy of SRTM90m was above others in larger river basins like Koshi (RMSE 224m), Narayani (RMSE 215m), and Karnali (RMSE 265m) where the range of elevation is greater. In the smaller to medium-sized basins like Kankai, Kamala, Bagmati, West Rapti, and Babai, HYDROSHEDS was preferable along with SRTM90m. Based on different error statistics, the DEMs were ranked in order of their accuracy.
High-resolution seafloor mapping provides insights into the dynamics of past ice-sheets/ice-shelves on high-latitude continental margins. Geological/geophysical studies in the Arctic Ocean suggest widespread Pleistocene ice grounding on the Chukchi–East Siberian continental margin. However, flow directions, timing, and behavior of these ice masses are not yet clear due to insufficient data. We present a combined seismostratigraphic and morphobathymetric analysis of the Chukchi Rise off the northwestern Chukchi margin using the densely acquired sub-bottom profiler (SBP) and multibeam echosounder (MBES) data. Comparison with deeper airgun seismic records shows that the SBP data cover most of the glaciogenic stratigraphy possibly spanning ca. 0.5–1 Ma. Based on the stratigraphic distribution and geometry of acoustically transparent glaciogenic diamictons, the lateral and vertical extent of southern-sourced grounded ice became smaller over time. The older deposits are abundant as debris lobes on the slope contributing to a large trough mouth fan, whereas younger till wedges are found at shallower depths. MBES data show two sets of mega-scale lineations indicating at least two fast ice-streaming events of different ages. Contour-parallel recessional morainic ridges mark a stepwise retreat of the grounded ice margin, likely controlled by rising sea levels during deglaciation(s). The different inferred directions of ice advances and retreats reflect complex geomorphic settings on the borderland. The overall picture shows that the Chukchi Rise was an area of intense interaction(s) of different ice-sheets/ice-shelves. In addition to glaciogenic deposits, we identify a number of related or preceding seabed features including mounds, gullies/channels, and sediment waves.