High Spatial Variability Research Articles

Temperature Sensitivity of Soil Respiration in Grasslands under the Temperate Continental Climate Zone: Analysis of 25-Year Monitoring Data

Field observations of soil respiration (SR) in different types of terrestrial ecosystems seem to be very relevant, since the SR rate is characterized by high temporal and spatial variability. The intra-annual dynamics of SR is determined mainly by the change in hydrothermal conditions during the year and is often described using a temperature sensitivity coefficient (Q10), which usually has a fixed value in many of the models used. The aim of this study was to assess the seasonal and interannual dynamics of SR temperature sensitivity in two grasslands in the southern part of Moscow region (temperate continental climate) based on continuous 25-year year-round measurements of CO2 emissions from soils. Grasslands were formed on two different types of soils: soddy-podbur sandy soil (Entic Podzol (Arenic)) and gray loamy soil (Haplic Luvisol (Loamic)). The SR rate was measured continuously from December 1997 to November 2022 with an interval of 7–10 days using the closed static chamber method. The temperature sensitivity of SR, estimated from the entire set of data, had higher values on Haplic Luvisol compared to Entic Podzol (3.47 vs 2.59). The values of Q10 for SR in both types of soils in dry years were 1.2–1.4 times lower than in years with a normal moisture level. The interannual variability of Q10 values in grassland ecosystems was 21–36%, depending on the temperature range that was taken into account. A significant positive correlation between Q10 values in the temperature range ≥1°С and humidity indices was found in both grasslands. To obtain more accurate forecasts of the C balance in ecosystems, a differentiated approach should be applied by integrating different values of temperature coefficients for SR into the models.

Temperature Sensitivity of Soil Respiration in Grasslands in Temperate Continental Climate Zone: Analysis of 25-Year-Long Monitoring Data

Field observations of soil respiration (SR) in different types of terrestrial ecosystems are very relevant because of high temporal and spatial variations of SR rate. The intra-annual dynamics of SR is mainly determined by the changes in hydrothermal conditions during the year and is often described with temperature sensitivity coefficient (Q10), which usually has a fixed value in many of the used models. This study is focused on the assessment of seasonal and interannual dynamics of SR temperature sensitivity in two grasslands in the southern Moscow oblast (temperate continental climate) based on continuous 25-year-long all-year-round measurements of CO2 emission from soils. The grasslands have been formed on two different soil types: sandy soddy-podbur (Entic Podzol (Arenic)) and gray loamy soil (Haplic Luvisol (Loamic)). The SR rate has been continuously measured from December 1997 to November 2022 with an interval of 7–10 days using the technique of closed static chambers. The temperature sensitivity of SR, estimated from the entire set of data, is higher in Haplic Luvisol as compared with Entic Podzol (3.47 vs. 2.59). The Q10 values for SR in both soils are 1.2–1.4-fold lower in dry years as compared with wet years. The interannual variation of Q10 values in grassland ecosystems amounts to 21–36% depending on the considered temperature range. A statistically significant positive correlation between the Q10 values in the temperature range ≥1°С and wetness indices is observable in both grasslands. A differentiated approach integrating different values of temperature coefficients for SR into the used models is necessary to improve the predictions of C budget in ecosystems.

Investigating paleoclimate and current climatic controls at Lago Argentino using sediment pixel intensity time series

The quantity and characteristics of sediment deposited in lakes are affected by climate to varying extents. As sediment is deposited, it provides a record of past climatic or environmental conditions. However, determining a direct relationship between specific climatic variables and measurable sediment properties, for instance between temperature and sediment optical reflectance, is complex. In this study, we investigate the suitability of sediment reflectance, recorded as sediment pixel intensity (PxI), as a paleoclimate proxy at a large ice-contact lake in southern Patagonia, Lago Argentino. We also evaluate whether sediment PxI can be used to investigate the present-day climatic drivers of sedimentation across Lago Argentino. First, we show that sediment PxIs relate to underlying sediment composition, and are significantly correlated with XRF-derived major element composition. Secondly, we find that PxIs correlate with both austral summer temperatures and wind speeds, but not with precipitation. PxI timeseries reach the p<0.1 correlation significance threshold for use as a paleo-wind proxy in as many as 6 cores and a paleo-temperature proxy in up to 4 cores. However, high spatial variability and the non-unique relationship between PxI and both temperature and wind speed challenges the necessary assumption of stationarity at Lago Argentino. While not suitable as a paleoclimatic proxy, correlations between PxI and instrumental climate data do chronicle current climatic controls on sediment deposition at Lago Argentino: high summer temperatures enhance settling of coarse, optically dark grains across the lake basin by promoting ice melt and lake stratification, while high wind speeds reduce the settling of fine, optically bright grains in the ice-proximal regions by transporting sediment-rich waters away from the glacier fronts. The assumptions required for quantitative paleoclimatic reconstruction must be carefully evaluated in complex lacustrine environments, but records unsuitable for use as proxies might nevertheless yield valuable information about the drivers of modern sedimentary transport and deposition.

Interactions between light and wave exposure differentially affect epilithic algal biomass and productivity in two large lakes of different trophy

Light and nutrient availability determine the depth distribution of littoral attached algae, but erosional forces, such as wind waves, structure shallow, nearshore habitats. We measured the biomass and productivity of algae on cobbles (epilithon) in the wave-influenced erosional zones of two large lakes with contrasting trophic status, eutrophic Lake Erie and oligotrophic Lake Huron. We used fetch and wind speed to create an index of wave exposure for 10 sample sites and used regression models to determine the variation in algal biomass and productivity explained by physicochemical parameters. Lake Erie epilithon had greater biomass (average = 59–249 mg chlorophyll a m−2) and higher productivity (76–92 mg C m−2 hr−1) than Lake Huron epilithon (1–147 mg m−2 and 14–57 mg C m−2 hr−1, respectively). In Lake Erie, light and wave exposure had significant and opposite effects on algal biomass and productivity. Maximum algal biomass occurred between 1.5 and 3 m in Lake Erie, where the effects of waves begin to diminish and light was still relatively abundant. Conversely, Lake Huron biomass was uniformly low along the depth gradient. Although depth or wave exposure did not explain variation in biomass, Lake Huron epilithic productivity was best explained by the interaction between depth and wave exposure. Rocky nearshore areas in large lakes are dynamic habitats for attached algae, leading to high spatial variation in biomass and productivity. Understanding how physicochemical dynamics influence benthic algal biomass and productivity are necessary to predict their distributions in a changing world.

Role of mean and variability change in changes in European annual and seasonal extreme precipitation events

Abstract. The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in extreme precipitation event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions are from either of them (mean or variability) to the change in precipitation extremes is largely unknown. This is, however, relevant for a better understanding of how and why climate extremes change. For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used: a set of 50 members with historical and RCP8.5 forcing and a 35-member (700-year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme-event occurrence into contributions from a change in mean climate or a change in variability. The results show that the contributions from a change in variability are in parts equally important to changes in the mean and can even exceed them. The level of contributions shows high spatial variation, which underlines the importance of regional processes for changes in extremes. While over Scandinavia or central Europe the mean influences the increase in extremes more, the increase is driven by changes in variability over France, the Iberian Peninsula, and the Mediterranean. For annual extremes, the differences between the ratios of contribution of mean and variability are smaller, while on seasonal scales the difference in contributions becomes larger. In winter (DJF) the mean contributes more to an increase in extreme events, while in summer (JJA) the change in variability drives the change in extremes. The level of temporal aggregation (3, 24, 72 h) has only a small influence on annual and winterly extremes, while in summer the contribution from variability can increase with longer durations. The level of extremeness for the event definition generally increases the role of variability. These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

Two contrasting years of continuous N[formula omitted]O and CO[formula omitted] fluxes on a shallow-peated drained agricultural boreal peatland

Drained agricultural boreal peatlands comprise a large source of nitrous oxide (N2O) and carbon dioxide (CO2) but a small sink or source of methane (CH4). N2O fluxes have high spatial and temporal variability and are often measured with the chamber technique. Therefore, continuous measurements of N2O fluxes are needed to better understand how N2O emissions are triggered and to reduce the uncertainty of annual N2O budget estimations. Here we present a two-year-long time series of continuous measurements of CO2 and N2O fluxes of a shallow-peated drained agricultural boreal peatland cultivated for grass silage. The fluxes were measured with the area-averaging eddy covariance technique. Several N2O peak events were observed throughout all seasons. The peaks were associated with meteorological or management events, such as soil thawing or freezing, precipitation, fertilization and glyphosate application. The annual N2O budget was 4.74 ±0.47 and 6.08 ±0.49 kg N2O-N ha−1 y−1 in 2020 and 2021, respectively. The annual CO2 budget, comprising the sum of net ecosystem exchange and biomass export, was 3.70 ±0.22 and 5.54 ±0.33 t CO2-C ha−1 y−1 in 2020 and 2021, respectively. The N2O budget during the first, warmer winter was 106% higher than during the second, meteorologically more typical winter, due to the higher frequency of soil freezing–thawing cycles. The average annual N2O budget was 36%–50% lower than the IPCC Emission Factor (EF) while the CO2 budget was in accordance with the IPCC EF. CO2 emissions dominated the total CO2-eq emissions of our site but N2O also had a significant contribution of 12%. Our results also suggest that glyphosate application enhanced N2O emissions in the last quarter of 2021. However, the full rotation should be measured to confirm whether there is a need to re-evaluate the N2O IPCC EF for ‘grassland drained boreal’ land-use class.

Trait driven or neutral: understanding the change in functional trait diversity during early plant succession using Price partitions

Habitat filtering, species interactions and neutral colonization as well as extinction dynamics govern the sequence of community assembly and functional diversity (FD) during primary plant succession. To study the factors that influence changes in FD we here use data on plant seed size, seed numbers and specific leaf area from 107 study plots along a 7 year sequence of primary succession (2005–2011) in a 6 ha German artificial catchment. We show that the temporal variability in functional diversity can be partitioned into the effects of trait expression, species richness and plant cover. We observed a dominant role of species richness and community composition on FD. Trade-offs in the influence of species richness and plant cover tended to decrease the change in FD. Average FD steadily increased during the first 4 years of succession (2005–2008). The degree of annual changes in FD were highly plot specific. Average change in FD was comparatively low during the first 4 years and later high. Soil characteristics and light conditions did not significantly influence the detectable change in functional diversity. We conclude that the high plot-specific spatial variability of the annual changes in FD transformed the initially catchment-wide homogeneous distribution of plant species into a mosaic of very different local plant communities. Our partitioning results also indicate that the successional sequences in FD are in accordance with a hidden Markov series.

Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack

Snow water equivalent (SWE) is a critical input for weather, climate, and water resource management models at local to global scales. Despite its importance, global SWE measurements that are accurate, consistent, and at sufficiently high spatiotemporal resolutions are not currently available. L-band interferometric synthetic aperture radar (InSAR) techniques have been used to measure SWE at local to regional scales, and two upcoming L-band SAR satellite missions have renewed interest in these techniques to provide regular SWE measurements at the global scale. However, previous research demonstrating the capabilities of L-band InSAR-SWE measurement has been limited to mountain or tundra snowpack regimes. Here we examine the feasibility of applying the same techniques over a prairie snowpack, which are typically characterized by shallow snow depths (mean snow depth of 0.22 m in this study), exposed agricultural vegetation, and high spatial variability over short distances. Our study area in central Montana, USA (47.060, -109.951) was a validation site for NASA SnowEx 2021, as part of the UAVSAR snow timeseries. Airborne L-band SAR imagery was acquired by the UAVSAR platform while concurrent snow measurements were collected using uncrewed aerial vehicle (UAV)-based LiDAR, UAV-based photogrammetry, and ground-based manual techniques. This validation dataset enables an investigation of the effects of sub-pixel snow cover heterogeneity and exposed agricultural vegetation stubble on SAR data and the resulting SWE estimations. Results based on repeated application of the Kolmogorov–Smirnov test show that UAVSAR VV phase change is sensitive to differences in snow cover but relatively unaffected by differences in agricultural stubble height. However, we did not find similarly definitive results when we used the same phase change data to estimate SWE. Although broad spatial patterns were similar in both LiDAR-derived and InSAR-derived SWE estimates, considerable differences in the two estimates were apparent in areas with large sub-pixel snow depth variability. Our results indicate that additional work is necessary to derive accurate SWE estimates in prairie environments. Regular measurements from L-band SAR satellites will provide an excellent opportunity to refine InSAR-based snow estimation techniques over shallow, heterogeneous snowpacks.

Calibration of Sensor Network for Outdoor Measurement of PM2.5 on High Wood-Heating Smoke in Temuco City

In order to ascertain the spatial and temporal changes in the air quality in Temuco City, Chile, we created and installed a network of inexpensive sensors to detect PM2.5 particulate matter. The 21 measurement points deployed were based on a low-cost Sensiron SPS30 sensor, complemented with temperature and humidity sensors, an Esp32 microcontroller card with LoRa and WiFi wireless communication interface, and a solar charging unit. The units were calibrated using an airtight combustion chamber with a Grimm 11-E as a reference unit. The calibration procedure fits the parameters of a calibration model to map the raw low-cost particle-material measurements into reliable calibrated values. The measurements showed that the concentrations of fine particulate material recorded in Temuco present a high temporal and spatial variability. In critical contamination episodes, pollution reaches values as high as 354 µg/m3, and at the same time, it reaches 50 µg/m3 in other parts of the city. The contamination episodes show a similar trend around the city, and the peaks are in the time interval from 07:00 PM to 1:00 AM. In the winter, this time of day coincides with when families are usually home and there are low temperatures outside.

Assessment Vertical and Spatial Distribution of SO2 Concentration over Baghdad City (Case Study at 2022)

Abstract One of the most important issues at the present time in developed and developing countries alike is air pollution. Sulfur dioxides consider one of important gases pollutant that product from fossil fuel combustion such as oil and coal. It is considered one of the principal air pollutants that has contributed to premature deaths and respiratory illnesses. Globally SO2 also has a high spatial and temporal variability because of its short atmospheric residence time. In this study reanalysis data of SO2 concentration values is download from ERA5 website (fifth generation ECMWF reanalysis for global climate and weather) for months (Jan., April, July and Oct.) from year 2022, and for 8-height levels from ground surface to 5000 meter, for all area of Baghdad province, other atmospheric parameter also download such as boundary layer height BLH and wind and direction. Results show high values of SO2 in January relative to other months and the smallest values is in July at all 8-levels nearly. Spatial Speed and direction given opposite relation with SO2 concentration, while most high concentration of SO2 in located in Baghdad city center and area around because its represent the sources of pollutant.

Ecological differences among hydrothermal vent symbioses may drive contrasting patterns of symbiont population differentiation.

The intra-host composition of horizontally transmitted microbial symbionts can vary across host populations due to interactive effects of host genetics, environmental, and geographic factors. While adaptation to local habitat conditions can drive geographic subdivision of symbiont strains, it is unknown how differences in ecological characteristics among host-symbiont associations influence the genomic structure of symbiont populations. To address this question, we sequenced metagenomes of different populations of the deep-sea mussel Bathymodiolus septemdierum, which are common at Western Pacific deep-sea hydrothermal vents and show characteristic patterns of niche partitioning with sympatric gastropod symbioses. Bathymodiolus septemdierum lives in close symbiotic relationship with sulfur-oxidizing chemosynthetic bacteria but supplements its symbiotrophic diet through filter-feeding, enabling it to occupy ecological niches with little exposure to geochemical reductants. Our analyses indicate that symbiont populations associated with B. septemdierum show structuring by geographic location, but that the dominant symbiont strain is uncorrelated with vent site. These patterns are in contrast to co-occurring Alviniconcha and Ifremeria gastropod symbioses that exhibit greater symbiont nutritional dependence and occupy habitats with higher spatial variability in environmental conditions. Our results suggest that relative habitat homogeneity combined with sufficient symbiont dispersal and genomic mixing might promote persistence of similar symbiont strains across geographic locations, while mixotrophy might decrease selective pressures on the host to affiliate with locally adapted symbiont strains. Overall, these data contribute to our understanding of the potential mechanisms influencing symbiont population structure across a spectrum of marine microbial symbioses that occupy contrasting ecological niches. IMPORTANCE Beneficial relationships between animals and microbial organisms (symbionts) are ubiquitous in nature. In the ocean, microbial symbionts are typically acquired from the environment and their composition across geographic locations is often shaped by adaptation to local habitat conditions. However, it is currently unknown how generalizable these patterns are across symbiotic systems that have contrasting ecological characteristics. To address this question, we compared symbiont population structure between deep-sea hydrothermal vent mussels and co-occurring but ecologically distinct snail species. Our analyses show that mussel symbiont populations are less partitioned by geography and do not demonstrate evidence for environmental adaptation. We posit that the mussel's mixotrophic feeding mode may lower its need to affiliate with locally adapted symbiont strains, while microhabitat stability and symbiont genomic mixing likely favors persistence of symbiont strains across geographic locations. Altogether, these findings further our understanding of the mechanisms shaping symbiont population structure in marine environmentally transmitted symbioses.

Evapotranspiration estimation using Surface Energy Balance Model and medium resolution satellite data: An operational approach for continuous monitoring

Monitoring spatial and temporal trends of water use is of utmost importance to ensure water and food security in river basins that are challenged by water scarcity and climate change induced abnormal weather patterns. To quantify water consumption by the agriculture sector, continuous monitoring is required over different spatial scales ranging from field (< 1 ha) to basin. The demand driven requirement of covering large areas yet providing spatially distributed information makes the use of in-situ measurement devices unfeasible. Earth observation satellites and remote sensing techniques offer an effective alternative in estimating the consumptive use of water (Actual EvapoTranspiration (ETa) fluxes) by using periodic observations from the visible and infrared spectral region. Optical satellite data, however, is often hindered by noises due to cloud cover, cloud shadow, aerosols and other satellite related issues such as Scan Line Corrector (SLC) failure in Landsat 7 breaking the continuity of temporal observations. These gaps have to be statistically filled in order to compute aggregated seasonal and annual estimates of ETa. In this paper, we introduce an approach to develop a gap-filled multi-year monthly ETa maps at medium spatial resolution of 30 m. The method includes two major steps: (i) estimation of ETa using the python based implementation of surface energy balance model called PySEBAL and (ii) temporal interpolation using Locally Weighted Regression (LWR) model followed by spline based spatial interpolation to fill the gaps over time and space. The approach is applied to a large endorheic Lake Urmia Basin (LUB) basin with a surface area of ~ 52,970 km2 in Iran for the years 2013–2015 using Landsat 7 and 8 satellite data. The results show that the implemented gap filling approach could reconstruct the monthly ETa dynamics over different agriculture land use types, while retaining the high spatial variability. A comparison with a similar dataset from FAO WaPOR reported a very high correlation with R2 of 0.93. The study demonstrates the applicability of this approach to a larger basin which is extendible and reproducible to other geographical areas.

Mixing behavior of dissolved organic matter at the Yukon and Kolyma land ocean interface

Unprecedented rates of climate change in the Arctic are causing altered land to ocean transport of dissolved organic matter (DOM) and subsequent processing in the Arctic Ocean. Low salinity waters have been suggested as hotspots for DOM dynamics. Although a wide range of biogeochemical processes have been observed in temperate and tropical estuaries, very little is known about DOM behavior at the Arctic land-ocean interface. Here, we use dissolved organic carbon (DOC) concentration, DOM absorption properties and ultrahigh resolution mass spectrometry to assess DOM mixing behavior at the Yukon and Kolyma land-ocean interface. Mixing behavior varied seasonally in the Yukon River. During freshet, despite high spatial variability, DOC concentration was depleted ~10% compared to conservative mixing, however aromatic DOM was enriched through mid-salinity (≤15). In late summer, DOC concentration was ~20% depleted at mid-salinity, yet DOM composition reflected enhanced in situ production compared to conservative mixing. In the Kolyma, DOC concentration suggested non-conservative loss at salinity <1 (~7%) with concurrent enrichment of aliphatics and heteroatoms before DOC enrichment of ~50% at mid-salinity. This relatively large addition of DOC at mid-salinity in the Kolyma, likely due to in situ primary production or sediment resuspension, deviates from the linear relationship previously observed between colored DOM and DOC concentration in large Arctic rivers. Ultimately, land-ocean transects from the Yukon and Kolyma Rivers represent a variety of DOM mixing behaviors in the near-shore coastal environment and highlight that land-ocean mixing in the Arctic is highly complex, with readily apparent spatial and temporal variability. Furthering our understanding of seasonal and system-specific controls of DOC concentration and DOM composition in Arctic River-Ocean mixing is critical in constraining riverine carbon fluxes on a global scale.

Occupant thermal comfort and indoor environmental conditions in Mies van der Rohe’s S. R. Crown Hall

ABSTRACT There is little empirical data in the published literature on occupant thermal comfort in buildings from the Modern Movement in architecture. We present a field survey of occupant thermal comfort and indoor environmental conditions in Mies van der Rohe’s modern S. R. Crown Hall (1956) on the campus of Illinois Institute of Technology in Chicago, IL. Surveys were deployed to 557 student participants on four separate days, including two days in the cooling season and two days in the heating season, to assess their perceptions of thermal comfort throughout the space. Indoor air temperature, relative humidity and mean radiant temperature were measured concurrently in several locations throughout the space. Occupants reported high levels of dissatisfaction with comfort in the space (percent dissatisfied ranging 37%–54%), with somewhat counterintuitive results for this building type. Overcooling was apparent during warm weather and a combination of both over- and under-heating occurred during cold weather, contrary to what was expected in this building with a high thermal transmittance enclosure. There was also high spatial variability in comfort responses and measured indoor environmental conditions. Findings highlight the need to develop contextual approaches to meeting occupant comfort needs in this building while preserving architectural aesthetics/intent.

Detecting the long-term spatiotemporal crop phenology changes in a highly fragmented agricultural landscape

Trends in crop phenometrics reveal the influence of climate variability and change on crop growth and development. However, the trends are less clear in fragmented tropical smallholder landscapes, because there is high spatial and temporal variability in crop phenology. Frequent historical and high spatial resolution (≤30 m) Earth observations are needed to track changes in crop phenology in fragmented landscapes but are often unavailable. The spatial–temporal gap can be closed by integrating infrequent high spatial resolution Earth observations with low spatial/high temporal resolution observations through data fusion. We fused 30 m resolution Landsat and 250 m resolution MODIS imagery to investigate trends in crop phenology from 2000 to 2020 in a fragmented agricultural landscape of Ethiopia. We used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) that had recently been modified for application in fragmented agricultural landscapes. We used the non-parametric Mann–Kendall test for crop phenology trend analysis. Crop phenology based on Landsat–MODIS fusion was compared to MODIS-based crop phenology without fusion. We found data fusion yielded a smaller magnitude of changes in the start of season (SOS: -0.2-day-y−1) and end of season (EOS: -0.50-day-y−1) compared to MODIS SOS (-0.5-day-y−1) and EOS (1.38-day-y−1) due to MODIS-related mixing with the surrounding natural vegetation in the fragmented agricultural landscape. EOS showed a faster rate of change compared to SOS over the 21-years. The Landsat and MODIS fusion captured spatial variation in the timing and magnitude of change specific to crops and their growing environment, which has implications for adaptation strategies. Our results highlight the importance of long-term data fusion to improve the spatial dimension of crop phenology time series analysis. Integrating time series land cover maps into the data fusion processing chain could further improve long-term data fusion for crop phenology trend analysis.

Temporal climate conditions and spatial drought patterns across rangelands in pastoral areas of West Guji and Borana zones, Southern Ethiopia

Climate change and its variability adversely impact pastoral livelihoods, and understanding climate variability and its change is important to deduce policy implications for pastoral communities. Accordingly, the aim of this study was to investigate the temporal climate conditions and spatial patterns of drought across the rangelands in pastoral areas of West Guji and Borana Zones, Southern Ethiopia. Temporal climate conditions were performed at the rangeland level, and spatial drought patterns (distributions) were estimated across the sample Kebeles (Kebele is the smallest administrative unit in Ethiopia which works in the Duda rangeland) and Reeras (Reeras refer to the smallest customary administrative unit in the Borana Zonal administration) structures. Station-satellite temperature and rainfall data (38 years from 1981 to 2018) were obtained from the Ethiopian Meteorological Agency. For data analysis, the Mann–Kendall trend test was employed to test rainfall, temperature and drought patterns, while Sen’s slope was used to test their magnitude of change, and coefficient of variation was employed to estimate rainfall and temperature variability. Standardized precipitation index was used to estimate drought event, while inverse distance-weighted method was used to estimate spatial drought patterns. The study reveals very high temporal rainfall variability with notable disparities in the rangelands mainly attributed to climate change. Besides, the study area exhibits high spatial variability of drought signifying agro-ecological characteristics in the rangelands. In the Duda and Gomole rangelands, annual rainfall increased non-significantly by 0.01 and 0.03 mm per annum, respectively. The annual minimum temperature in both Duda and Gomole increased significantly by 0.008 and 0.007 ℃ per year, respectively, albeit the annual maximum temperature in the rangelands decreased non-significantly by 0.02 and 0.009 ℃ per annum. Both rangelands experienced high annual rainfall variability, increasing annual minimum temperature, drought frequency and severity. Therefore, the outcome of the study is believed to be vital for identifying drought hotspot areas in the rangelands and devising strategies that help to reduce drought impacts on pastoral communities in Southern Ethiopia.

Reliability Analyses of Soil Slopes with Multiple Spatially Varying Parameters Using Multi-Input Convolutional Neural Networks

Constructing meta-models and selecting a suitable deterministic analysis method are important to improve the computational efficiency and accuracy of the nonintrusive reliability analysis of a spatially varying soil slope. However, existing meta-models are not applicable to the slopes considering multiple parameters with high spatial variability. Moreover, it is difficult to identify the failure modes when the spatial variability is high by using deterministic analysis methods based on slip surface search. Therefore, a nonintrusive stochastic strength reduction finite-element method (SRFEM) is developed based on the multi-input convolution neural networks (CNNs) and ABAQUS 2016. The SRFEM developed based on ABAQUS is adopted as the deterministic analysis method to avoid the uncertain search for the critical slip surfaces of slopes with high spatial variability. A multi-input CNN is proposed to construct the meta-model to avoid the “curse of dimensionality” and replace the overmuch times of time-consuming finite-element simulations. It can fit the relationships between multiple spatially varying parameters and the factor of safety by processing different parameters with different streams of CNNs. Two illustrative examples show that the proposed method can accurately identify the failure modes of slopes with different degrees of spatial variability. The agreement of the reliability results based on the proposed method and the general random finite-element method (RFEM) shows the high accuracy of the proposed method. The time cost of the proposed method can be reduced to 6.0 × 10−3 times that of the general RFEM, verifying the high computational efficiency of the proposed method. The multi-input CNN also shows higher fitting accuracy and better interpretability than the single-stream CNN and the support vector machines (SVMs). The generalization ability, accuracy, and efficiency of the proposed method show its potential to carry out the reliability analyses of slopes with multiple spatially varying parameters.

Modeling Coastal Water Clarity Using Landsat‐8 and Sentinel‐2

AbstractUnderstanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future.

Contribution of Fresh Submarine Groundwater Discharge to the Gulf of Alaska

AbstractHigh latitude mountain environments are experiencing disproportionately adverse effects from climate change. The Gulf of Alaska (GoA) region is an embodiment of this change, particularly concerning a shifting hydrologic balance. Even so, the magnitude and contribution of fresh submarine groundwater discharge (fresh SGD) remains virtually unexplored within the region, though it has gained increasing attention globally due to its chemical significance and influence on coastal ecosystems. Here we provide the first regional estimates of fresh SGD to the GoA using two established water balance approaches. This is an effective way to distinguish the contribution of terrestrially derived fresh SGD, rather than the more commonly quantified total SGD which includes discharge that is driven by marine forces such as sea‐level oscillations and density gradients. We compare the approaches and assess their capabilities in computing the magnitude of fresh SGD over a large regional scale. Mean annual fresh SGD flux ranges between 26.5 and 86.8 km3 yr−1 to the GoA, equivalent to 3.5%–11.4% of the total freshwater discharge. Contributions are highest in the Southeastern panhandle and lowest in the Cook Inlet basin, with the highest area normalized contribution occurring in the Prince William Sound. Fresh SGD exhibits high spatial and temporal variability throughout the region. Although freshwater discharge to the GoA is investigated considerably, the importance of fresh SGD has, thus far, been overlooked.

Regionalization of an agricultural area by means of multivariate data and their relationship with soybean productivity

Regionalization of an agricultural area by dividing it into different clusters is an important strategy in the precision agriculture scope. Multivariate and spatial data are common in the design of these divisions. This paper sought to characterize regional differences in the area under study through different subsets of variables formed by soil physical-chemical variables and vegetative indices, in an agricultural area for four soybean harvest years in the period from 2013/2014 to 2016/2017. To such end, three subsets were generated comprised by these variables, which presented spatial dependence and were grouped according to their characteristics. By means of decision trees, it was identified which of these variables exerted the most influence on subdivision of the area. The multivariate and non-parametric spatial clustering technique was used to generate the clusters. Finally, by means of maps and boxplots, the spatial relationships between these variables and soybean productivity were evaluated. There was variation across the harvest years in relation to the subset of variables that determined the best design of the different clusters. The regional differences determined by the different variables used in the study showed no relationship with soybean productivity, which presented spatial homogeneity in its data for the harvest years evaluated. This approach is recommended when there is high spatial variability of factors that exert impacts on productivity, advising on using both soil physical-chemical variables and the vegetative indices to explain the causes of soybean productivity spatial variability