Water Body Size Research Articles

Traits related to distributional range shifts of marine fishes.

In the context of global change, reviewing the relationships between marine fish traits and their range shifts is required to (1) identify ecological generalizations regarding the influence of traits on range shifts at a global scale and (2) investigate the rationale behind trait inclusion in models describing those relationships. We systematically searched for studies on marine fish assemblages that identified distributional shifts and analyzed the relationship between fish traits and these shifts. We reviewed 29 papers and identified 11 shift type characterizations and 41 traits, noting significant variation in measurement methods and model types used to describe their relationships. We identified global trait redundancies in the relationship between fish traits and latitudinal range shifts. These trends are related to the fishes' latitudinal range, trophic level, water column habitat, body size, size-at-settlement, growth rate, and larval swimming ability. The first four traits, along with fish bottom habitat, biogeographic affinity, diet, and thermal affinity, also showed significant relationships across four ways to characterize horizontal range shifts of fish species. The significance of these traits suggests their relevance in range shifting, regardless of the analyses conducted, biogeographic realm, and range shift type. However, trait redundancies require further consideration, mainly because some traits show opposing relationships in different studies, and important biogeographic research gaps limit global generalizations about the trait-range shift relationship. Half of the studies analyzed provided a rationale for 23 out of 41 traits. We also provide guidelines for future work to better understand the influence of traits on fish range shifts.

TSAE-UNet: A Novel Network for Multi-Scene and Multi-Temporal Water Body Detection Based on Spatiotemporal Feature Extraction

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km2 each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet.

Water-level changes impact angler effort in a large lake: Implications for climate change

Climate change is expected to influence aquatic habitats and associated fish populations, yet we know little about the impact on recreational anglers. Our goal was to explore whether interannual fluctuations in waterbody surface area and other explanatory variables could be used as indicators of changes in angler fishing effort. Our approach leveraged a combination of remotely sensed waterbody surface area, environmental and fish population data, and onsite angler survey monitoring data for Devils Lake, North Dakota, USA during the open-water fishing period (May 1st to August 31st) for 9 years (1992–2021). The information was used to develop a dynamic waterbody size-angler effort model. Changes in waterbody surface area reliably predicted changes in angler effort (r2 = 0.60). Increases in waterbody surface area led to increases in angler effort, and decreases in waterbody surface area led to decreases in angler effort. Our findings show promise that remotely sensed fluctuations in waterbody surface area could be used as an indicator of interannual angler effort dynamics. Dynamic waterbody size-angler effort models could provide managers the ability to predict changes in angler effort via climate-related hydrological cycles that affect the size and distribution of waterbodies on the landscape.

How does urban form explain water cooling effect heterogeneity: A case study in Wuhan, China

Water bodies in urban areas are considered to be natural and effective cooling factors that can help mitigate the urban heat island (UHI) effect. Previous studies highlighted the cooling effect is determined by the size and landscape of the water bodies. However, it is unclear how the urban form surrounding water bodies influences the water cooling effect received by human activity space. Using high resolution data and a multiple linear regression model, the effect of various 2D/3D urban form factors on the average water cooling intensity (AWCI) and water cooling intensity (WCI) at the fine scale were examined in Wuhan City on hot days. We further verified the spatial heterogeneity of the relationship between urban form factors and WCI according to the distance to and size of adjacent water bodies. The AWCI of the entire surrounding area were correlated with building morphology indicators. Urban form indicators effectively explained the variations of WCI at the fine scale, indicating that a mixture of large building volume density (BVD) with small building density (BD), and an aggregated impervious surface area (ISA) with a complex shape were conducive to WCI. Additionally, building morphology had a larger impact on the cooling effect around small water bodies and in the outer buffer area of water bodies. In comparison, the cooling effect of the area surrounding large water bodies was more affected by the landscape of the ISA. We therefore propose a differentiated urban planning strategy to take full use of the limited water cooling effect.

Multi-parameter Inversion of Tunnels Based on Deep Learning Constraints

When tunnels traverse unfavorable geological formations, they often encounter the risk of sudden water-related incidents, posing a severe threat to the safety of construction workers and property. Consequently, there is an urgent demand for conducting research on the early detection of potential water-related disasters ahead of tunnel excavation. Among the various advanced exploration techniques, the DC resistivity method has proven to be more cost-effective and highly responsive to water-bearing structures, making it a prevalent choice for addressing sudden water hazards in real tunnel projects. This study introduces an innovative electrical approach underpinned by deep learning constraints, fusing resistivity and chargeability to pinpoint the location and size of underground water bodies. More specifically, we’ve designed a learning model rooted in the traditional U-Net convolutional network architecture, featuring a single encoder for feature extraction and a dual decoder for output. When compared to conventional linear inversion techniques and standalone parameter-based deep learning inversion methods, our proposed joint inversion approach exhibits superior performance.

A contrarian growth: The spatiotemporal dynamics of open-surface water bodies on the northern slope of Kunlun Mountains

Identifying and monitoring open-surface water bodies are significant for understanding regional water resource distribution and evolving trends. This understanding carries profound value for comprehensively assessing the effects of climate change and human social activities on water resources and maintaining water security. This study pioneers’ improvements in surface water detection rules with the central aim of constructing a Method with Enhanced Impurity Control, specifically tailored for complex environmental scenarios (OA = 98.97 % ± 0.06). This is achieved by incorporating intricate exponential constraints and targeted data masks. Our analysis is grounded in the examination of 21,709 preprocessed high-quality images, covering the period from 1990 to 2021 across the northern slope of the Kunlun Mountains. Through this comprehensive study, we have gleaned insightful results regarding the spatiotemporal dynamics of open-surface water bodies. In the past 32 years, the surface water body area on the northern slope of the Kunlun Mountains has exhibited a significantly increasing trend. Specifically, the size of permanent water bodies expanded by 71.03 % (with a 95 % confidence interval: ± 27.73 %), while the area of seasonal water bodies grew by a substantial 170.63 % (with a 95 % confidence interval: ± 17.50 %). Open-surface water bodies display discernible spatiotemporal differences. Stable and continuous permanent water bodies are predominantly in mountainous areas, whereas seasonal water bodies with pronounced fluctuations are chiefly found in desert-oasis areas. Climate change is the dominant factor that affects the changes of water bodies on the surface of mountain regions, while human social activities also have a significant impact on desert-oasis areas. The improved methodology adopted in this study offers a robust and scalable framework that can be tailored for studying changes in open-surface water bodies in intricate environments. We performed a comprehensive analysis of the changes and drivers of open-surface water bodies on the north side of the Kunlun Mountains over time and space. This work is aimed at providing data support for scientifically appraising the development potential of water resources in Southern Xinjiang and addressing the disparity between water resource supply and demand.

Effects of Environmental Factors on the Distribution and Diversity of Aquatic Oligochaetes

The aim of our study was to detect the actual distribution of oligochaete species and to identify their ecological differentiation with respect to environmental factors: altitude, temperature, oxygen concentration, conductivity, total organic carbon, and waterbody type. Although widespread, differentiation of oligochaete communities in four waterbody types and altitudinal groups can be observed through alpha and beta diversity. Their differences were analyzed using MANOVA, while the ecological preferences of species were presented with logistic Gaussian regression analyses. The highest number of the species of Oligochaeta was recorded in oligochaete communities in medium and large rivers. Total beta diversity decreased with the decreasing of waterbody size, the increasing of size of the substrate particles, river flow velocity, as well as altitude. Communities from small mountain rivers and streams and large and medium rivers with coarser substrate differed from other oligochaete communities. When coarser substrate was prevalent in smaller and medium rivers, a domination of a certain family was observed: Lumbriculidae (>800 m a.s.l.), Propappidae and Enchytraeidae (500–800 m), and Naididae (<500 m a.s.l.). Common species of Oligochaeta, with significantly overlapping ranges in running waters in Serbia, still show a clear grouping with respect to preference for certain types of waterbodies.

Assessment of the Zooplankton Community and Water Quality in an Artificial Freshwater Lake from a Semi-Arid Area (Irbid, Jordan)

Zooplankton play a crucial role in aquatic food chains and contain many species, which could be bioindicators of water quality and ecosystem health. The ecological impacts of eutrophication on zooplankton composition in freshwater lakes have recently gained wide interest. Geographic location and water-body size influence zooplankton diversity in freshwaters; meanwhile, less is known about the composition and dynamic of the zooplankton community and their relationship with the trophic status in artificial water in semi-arid areas. The present study aimed to assess the physical–chemical parameters and to document the seasonal distribution of zooplankton species and their relationship with environmental factors and trophic state in the artificial freshwater lake JUST, in a semi-arid area. The high concentrations of nutrients and the trophic level index (TLI) classified the lake as eutrophic–hypertrophic. The zooplankton in the JUST lake were composed of twenty-six species, with eleven Rotifera, ten Copepoda, and five Cladocera. Copepoda was numerically the most abundant taxon, accounting for 64% of the total zooplankton abundance, in both seasons. However, the second most abundant taxon in summer was Rotifera (28.26%) while in winter it was Cladocera (25.88%). The community structure seemed to be influenced, most likely, by trophic state, phytoplankton abundance, water temperature, dissolved oxygen, and nutrient loading. The zooplankton were largely dominated by bioindicator species of high trophic levels. Zooplankton could be used as a tool to monitor the trophic state of the lake. For sustainable development, the introduction of phytoplanktivorous, aquaculture species, such as carp and koi, will strengthen the top-down control of the phytoplankton concentration, leading to a reduced trophic state.

Waterbody size predicts bank- and boat-angler efforts

Bank- and boat-angler efforts are logistically difficult and costly to estimate, preventing landscape-scale estimates that are required to address current and future challenges (e.g., climate change, invasive species) for inland recreational fisheries. Using a large Nebraska, USA, recreational fishery dataset (N = 67 waterbodies), we demonstrate that waterbody size can be used to predict bank- and boat-angler efforts across a heterogeneous landscape of extra small (< 104 ha) and large (> 647 ha) waterbodies. Bank and boat anglers respond to waterbody size, however these relationships appear to be unique between the two angler types. Boat-angler efforts increased as a function of waterbody size, whereas bank-angler efforts increased as a function of waterbody size for extra small waterbodies but not for large waterbodies. The ability to connect waterbody size and angler effort will be important for continued effective inland fisheries management.

Mapping Irish Water Bodies: Comparison of Platforms, Indices and Water Body Type

Accurate monitoring of water bodies is essential for the management and regulation of water resources. Traditional methods for measuring water quality are always time-consuming and expensive; furthermore, it can be very difficult capture the full spatiotemporal variations across regions. Many studies have shown the possibility of remote-sensing-based water monitoring work in many areas, especially for water quality monitoring. However, the use of optical remotely sensed imagery depends on several factors, including weather, quality of images and the size of water bodies. Hence, in this study, the feasibility of optical remote sensing for water quality monitoring in the Republic of Ireland was investigated. To assess the value of remote sensing for water quality monitoring, it is critical to know how well water bodies and the existing in situ monitoring stations are mapped. In this study, two satellite platforms (Sentinel-2 MSI and Landsat-8 OLI) and four indices for separating water and land pixel (Normalized Difference Vegetation Index—NDVI; Normalized Difference Water Index—NDWI; Modified Normalized Difference Water Index—MNDWI; and Automated Water Extraction Index—AWEI) have been used to create water masks for two scenarios. In the first scenario (Scenario 1), we included all pixels classified as water, while for the second scenario (Scenario 2) accounts for potential land contamination and only used water pixels that were completed surround by other water pixels. The water masks for the different scenarios and combinations of platforms and indices were then compared with the existing water quality monitoring station and to the shapefile of the river network, lakes and coastal and transitional water bodies. We found that both platforms had potential for water quality monitoring in the Republic of Ireland, with Sentinel-2 outperforming Landsat due to its finer spatial resolution. Overall, Sentinel-2 was able to map ~25% of the existing monitoring station, while Landsat-8 could only map ~21%. These percentages were heavily impacted by the large number of river monitoring stations that were difficult to map with either satellite due to their location on smaller rivers. Our results showed the importance of testing several indices. No index performed the best across the different platforms. AWEInsh (Automated Water Extraction Index—no shadow) and Sentinel-2 outperformed all other combinations and was able to map over 80% of the area of all non-river water bodies across the Republic of Ireland. While MNDWI was the best index for Landsat-8, it was the worst performer for Sentinel-2. This study showed that optical remote sensing has potential for water monitoring in the Republic of Ireland, especially for larger rivers, lakes and transitional and coastal water bodies.

Remote sensing and GIS tool application for tracing the water body of Bangalore area

To comprehend the function of the water bodies at various sizes, such as regional and global, for example, in biogeochemical cycles, it is necessary to more correctly estimate the size, form, and extent of the water bodies. Excluding the few areas with precise maps, scaling upward to vast area regions relies on statistical estimates of the number and size of lakes, rivers, and other water bodies, which explains why estimates are wrong. In comparison to the currently available conventional and frequently used methods of mapping land surface water (LSW), remote sensing offers several extra advantages since it is a more accurate, efficient, and reliable information source with the ability to make multiple high-frequency and repeatable quantities of observations. This project describes the development and technological evolution of the Water Body Map (WBM), which employs an automated algorithm to interpret multi-temporal Landsat images from the USGS database. In this project, we used Landsat 5 band photographs to define a water body map without any cloud cover. Permanent water bodies were separated from temporary water-covered areas by estimating the frequency of water bodies in overlapping, multi-temporal Landsat images. In contrast to past studies, the WBM now makes a more precise distinction between rivers and floodplains by looking at the frequency of water bodies. As a result, it is clear that using multi-temporal images from sources is equally as important for mapping global water bodies as analysis at a higher resolution. The classification's accuracy was confirmed in Hokkaido (Japan) and the United States of America using databases of actual water bodies. Around 70% of lakes N1 km2 have relative water area errors of b25%, and the commission error was diminishing and nearly nonexistent. The Water Body Mapping's overall accuracy should be helpful for much larger scale research projects in hydrology, biogeochemistry, and climate systems, among other things. It can also include a quantification of the kinds of water bodies that are currently present around. Some smaller water bodies were overlooked, primarily because shoreline pixels were not included. The primary goal of this project was to estimate the accuracy of mapping water bodies using Landsat 5 TM data by employing straightforward digital image processing techniques.

Secretive marsh bird occupancy across a spectrum of hydroelectric reservoir management in western montane Canada

AbstractDam construction projects have created opportunities for water security, targeted flood protection, and energy production but at the cost of increasing anthropogenic pressure on affected aquatic ecosystems. Wetland ecosystems are often among the most vulnerable, as underlying hydrological regimes influence overall wetland structure and function. Marsh bird species are wetland and riparian habitat specialists, many of which are experiencing population declines across North America. We examined how the alteration of hydrological regimes for generating hydroelectric power affected the occurrence of secretive marsh bird species in the western montane region of British Columbia, Canada. We established survey stations in wetlands across 2 regions, the West Kootenay and the Columbia Wetlands, sampling across a spectrum of hydrological regimes and other potentially relevant factors. At each station, we assessed wetland occupancy during the breeding season using broadcast‐callback surveys focused on 5 secretive marsh bird species: American bittern (Botaurus lentiginosus), American coot (Fulica americana), pied‐billed grebe (Podilymbus podiceps), sora (Porzana carolina), and Virginia rail (Rallus limicola). Additionally, we measured vegetation structure and the proximity and size of nearby water bodies for each survey station. We then used occupancy models to assess important correlates behind wetland occupancy for these marsh bird species, considering water management operations, regional differences, and local‐ and landscape‐level wetland characteristics. Secretive marsh bird species were more likely to occupy wetlands in areas with less frequently altered hydrological regimes. Occupancy models highlighted local‐ and landscape‐level characteristics as important correlates for wetland occupancy by marsh birds. Wetlands with frequently altered hydrological regimes had more open water cover and less tall vegetation present, conditions that were negatively associated with occupancy. Altered wetlands in this study were farther from the next nearest wetland, which was also negatively associated with occupancy. We suggest reservoir management is altering vegetation communities within these wetlands, indirectly promoting lower occupancy of secretive marsh bird species.

Implications of submonthly oxygen and carbon isotope variations in late Pleistocene Melanopsis shells for regional and local hydroclimate in the upper Jordan River valley

AbstractMany water-stressed regions of the globe have a highly seasonal precipitation regime. However, seasonality in the past and under changing climates is little studied. Submonthly records of sclerochronological δ18O and δ13C values of Melanopsis shells from the Jordan River Dureijat archaeological site (JRD) in the upper Jordan River valley presented here document the hydrology of paleo-Lake Hula. These records were assessed for changes in seasonal hydrology in the lake and compared with modern shells collected from present-day waterbodies in northern Israel and with models of δ18Oshell. Results from shells in sediments dating from the last glacial maximum (LGM) to the Bølling-Allerød imply changes in waterbody size that qualitatively parallel changes in the late Pleistocene Lake Lisan levels; Hula Lake was well buffered when Lake Lisan stood at a high stand and poorly buffered when water levels were lower. Furthermore, data from shells dated to the LGM suggest inflowing water with lower δ18O values than local rainfall, providing evidence for a greater proportion of snow in the catchment than today. Reconstruction of water δ18O and mixing-model calculations suggest that snowmelt contribution to spring water during the LGM may have been more than twice the amount in the modern-day catchment.

Factors influencing the home range of freshwater fishes

AbstractInitial investigations of home range for freshwater fish noted that it scaled allometrically, with additional studies having linked home range to variations in water body size. Here, we revisit the analysis of factors influencing freshwater fish home range sizes using an expanded dataset incorporating more recent telemetry data and a meta‐analysis approach to evaluate the influence of fish length, water body size, trophic guild and latitude on the home range size of fishes in lotic and lentic systems. With the expanded data set we reaffirm that length has a significant effect on the home range size in both lentic and lotic environments, with the effect of length being more pronounced in lotic environments. Similarly, we determined water body size also significantly influences home range size and is the key determinant of home range size in both lentic and lotic environments. We also demonstrate the importance of trophic guild and latitude as determinants of home range in lentic and lotic environments. The factors influencing home range size in both environments were common (i.e. fish length, water body size, trophic guild and latitude), but varied in importance weight between the environments. The results of this study help illuminate the weight of importance of factors influencing the home range of freshwater fishes and provide useful information for conservation and environmental management planning purposes.

Evaporation From Six Water Bodies of Various Sizes in East Asia: An Analysis on Size Dependency

AbstractOne pond and five lakes in East Asia with area from 0.05 to 3,000 km2 were comparatively investigated to reveal the size‐dependency of the evaporation and its radiation and aerodynamic contributions. The available energy controls on the annual evaporation are independent on the sizes of the six water bodies, but the ratio of evaporation to Penman's open water evaporation decreases with areas from almost unity at the small pond to around 0.8 at the two large lakes. The aerodynamic related parameters of three combination equations can be well represented by the water body areas with a power law, which is attributed to the decreasing mass transfer coefficient with the fetch of the water body. The increasing size‐dependent wind speed offsets the effect of mass transfer coefficient, making the energy partitioning and evaporation rate size independent. Results imply that the energy‐based equations can be used for evaporation without considering the water body size in the study area. The size‐dependent parameters of the combination equations are required to be further investigated with more lakes.

Integrating community science and agency‐collected monitoring data to expand monitoring capacity at large spatial scales

AbstractMonitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency‐collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons (Gavia immer) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency‐collected data, if appropriate frameworks are developed to integrate these data sources.

Remote Sensing and Ecological Variables Related to Influenza A Prevalence and Subtype Diversity in Wild Birds in the Lluta Wetland of Northern Chile.

The Lluta River is the northernmost coastal wetland in Chile, representing a unique ecosystem and an important source of water in the extremely arid Atacama Desert. During peak season, the wetland is home to more than 150 species of wild birds and is the first stopover point for many migratory species that arrive in the country along the Pacific migratory route, thereby representing a priority site for avian influenza virus (AIV) surveillance in Chile. The aim of this study was to determine the prevalence of influenza A virus (IAV) in the Lluta River wetland, identify subtype diversity, and evaluate ecological and environmental factors that drive the prevalence at the study site. The wetland was studied and sampled from September 2015 to October 2020. In each visit, fresh fecal samples of wild birds were collected for IAV detection by real-time RT-PCR. Furthermore, a count of wild birds present at the site was performed and environmental variables, such as temperature, rainfall, vegetation coverage (Normalized Difference Vegetation Index-NDVI), and water body size were determined. A generalized linear mixed model (GLMM) was built to assess the association between AIV prevalence and explanatory variables. Influenza positive samples were sequenced, and the host species was determined by barcoding. Of the 4349 samples screened during the study period, overall prevalence in the wetland was 2.07% (95% CI: 1.68 to 2.55) and monthly prevalence of AIV ranged widely from 0% to 8.6%. Several hemagglutinin (HA) and neuraminidase (NA) subtypes were identified, and 10 viruses were isolated and sequenced, including low pathogenic H5, H7, and H9 strains. In addition, several reservoir species were recognized (both migratory and resident birds), including the newly identified host Chilean flamingo (Phoenicopterus chilensis). Regarding environmental variables, prevalence of AIV was positively associated with NDVI (OR = 3.65, p < 0.05) and with the abundance of migratory birds (OR = 3.57, p < 0.05). These results emphasize the importance of the Lluta wetland as a gateway to Chile for viruses that come from the Northern Hemisphere and contribute to the understanding of AIV ecological drivers.

Scaling waterbody carbon dioxide and methane fluxes in the arctic using an integrated terrestrial-aquatic approach

In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO2 and CH4 fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO2 emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km2) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km2) contributed the majority of carbon emissions from waterbodies. Waterbody CH4 emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO2 and CH4 emissions by 26%–79% and 8%–53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic.

Revealing the response of urban heat island effect to water body evaporation from main urban and suburb areas

The urban heat island (UHI) effect is accelerated with urbanization and climate change, thus threatening human survival. The evaporation from water body (E) takes away energy through heat absorption process, thereby effectively play a role in temperature cooling and UHI effect alleviation. However, the response of UHI effect to urban E is still lacks study in the current UHI research. To address this issue, this work proposes a customized water body evaporation model in urban areas. The newly developed urban E model considers the contribution of anthropogenic heat flux (AHF) to the energy balance in urban areas. Meanwhile, AHF is also used to enhance the simulation of the water heat storage change (G) for urban water body. Validation results in two megacities in China indicate that the developed urban E model which considered AHF in the energy balance equation significantly improves the simulation performance of E in the main urban area (the root mean square error (RMSE) significantly decreased by 26.6 W/m2 compared with the original Penman formula for E in the main urban area (Eu) simulation). The consideration of AHF in the G determination improves the simulation performance of E in the deep water body (the RMSE significantly decreased by 33.3 W/m2 compared to the AHF-Penman model that do not considering G for E simulation in deep water body). The developed urban E model is further used to evaluate the response of UHI to the Eu and E in the suburban area (Es). It is found that Eu effectively alleviates UHI, while Es aggravate UHI. Moreover, the cooling effect of E in the main urban areas (ΔTau) and suburbs (ΔTas) are increased with urbanization. The increasing rate of ΔTau is higher than ΔTas, indicate the contribution of evaporation cooling to the UHI alleviation is increased with urbanization. Further analysis demonstrate the urbanization process can explain approximately 90% of the enhanced ability of E to mitigate the UHI effect. Correlation analysis shows that the mitigation capability of E to UHI effect is mainly controlled by the volume and surface size of water body. Finally, future climate scenario-based urban E forecast confirms that ΔTau and ΔTas will continue to rise with climate change. The average increasing rate are 0.018 °C/year and 0.013 °C/year for ΔTau and ΔTas, respectively, under the three representative concentration pathways. The increasing rate of ΔTau is larger than ΔTas, suggesting the mitigation of the UHI effect will benefit more from urban E under the future climate change. Generally, our findings highlight that the mitigation of the UHI effect mainly benefits from E in the main urban area rather than E in the suburban area. This study gains insight into E in urban areas, including its algorithm, interaction with the UHI effect, and responses to urbanization and climate change. The results of this study provide a good scientific basis for urban landscape water planning.

Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes

AbstractSmall waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities.