Large Spatial Variability Research Articles

Agricultural biomethane production in France: A spatially-explicit estimate

Biomethane production by anaerobic digestion can significantly contribute to the decarbonation and defossilization of the European energy mix. Although previous estimates have been provided about biomethane potential production in several European countries, they suffer from a coarse evaluation of substrate availability and a lack of spatial resolution. This paper provides a spatially-explicit assessment of the potential for biomethane production in France at a fine spatial resolution (NUTS 4 region) from agricultural biomass (livestock manure, crop residues and cover crops) and sewage sludge. This assessment is based on (i) a fine estimation of substrate availability, particularly for cover crop production, by combining a crop model with spatially-explicit soil and climate databases; (ii) the use of an anaerobic digestion model that predicts biomethane production from agricultural biomass; and (iii) a procedure for optimizing biomethane production while respecting the stoichiometric constraints of the substrate mixture. We found a significant potential for annual biomethane production of 64.1 TWh, mainly from cattle manure, a value that could reach 108.7 TWh through the generalization of cover crops within arable cropping systems. Our results also highlighted the importance of detailed substrate availability data to account for their large spatial variations. Accordingly, we showed that biomethane production was highly variable across the French regions and that crop residues could only partly be used for anaerobic digestion – especially in arable regions – because they lead to excessively dry substrate mixtures. This study helped to identify the most suitable regions for biomethane production and to provide accurate estimates for biomethane development in France.

Spatial modeling via geostatistics of the bed in a compost barn system: thermal performance assessments.

For all the benefits of a compost barn system to be achieved, the management, especially with the bedding, must be done correctly. One should consider the requirements regarding the revolving of the material, use of good quality organic matter, suitable ventilation, and an ideal animal stocking rate, so that the composting occurs efficiently. Given this, this work aimed to evaluate the spatial variability of thermal characteristics of rice straw bedding, using values of temperature (surface and internal) and humidity. The temperature and moisture values of the litter were recorded at intervals during the morning shift, from 6:00 am to 7:00 am and from 8:00 am to 9:00 am, and during the afternoon shift, from 3:00 pm to 4:00 pm and from 5:00 pm to 6:00 pm, considering the management of the turn-over, that is, before and after the movement of the litter. Litter variables were collected at points distributed on a 39-point grid. There were no differences between litter surface temperatures before and after turning over in the morning shift, but in the afternoon shift, the surface temperature was lower (P < 0.05) before, when compared to after turning. The internal temperature and litter moisture were higher (P < 0.05) before revolving in both shifts. Spatial dependence was verified (IDE) for the variables studied. Based on the geostatistical analysis, it is concluded that the IDE was rated as strong for surface and internal temperatures, which demonstrates heterogeneity among the points evaluated and a large spatial variability regarding bed temperatures. In the case of litter moisture, the greatest variability occurred in the morning period. The litter surface temperature values did not exceed the ambient air temperature values.

Pastures under dry climate can be cold spots for soil nitrous oxide emissions from grazing cattle excreta

Animal excreta is an important contributor to agricultural nitrous oxide (N2O) emissions – a potent greenhouse gas. Soil N2O emissions are episodic in nature and commonly subjected to large inter-annual and spatial variation. Thus, edaphic-climatic specific studies are needed to further our knowledge on global terrestrial N2O losses and to more accurately estimate national N2O emissions. For the Canadian Prairies, multi-year studies are needed for the determination of beef cattle dung and urine derived N2O emissions under semiarid and sub-humid climate and different soil types, a knowledge gap explored by the present study. We evaluated N2O emissions in dung, urine, and unfertilized control plots following six animal excreta applications in 2009/10 and 2010/11. The experiment was conducted in two locations: (i) Swift Current, Saskatchewan (semiarid prairies/brown Chernozem); and, ii) Lacombe, Alberta (sub-humid prairies/black Chernozem). Our results indicated that beef cattle dung-N derived N2O emission was negligible for all excreta applications on both sites as cumulative N2O emissions were not significantly different from the control plots (p > 0.05). Overall, cattle urine derived soil N2O emission was greater than emissions from dung and control plots, and subjected to large inter-annual variation. For the Swift Current site, 2010 experienced record-high precipitation that stimulated N2O emissions of up to 0.47% of the applied urine-N. Except for the applications exposed to record-high precipitation, the percentage of the urine-N lost as N2O at the Swift Current site was small, ranging from 0.00% to 0.01%. Similarly, for the Lacombe site, urine-N derived N2O emissions ranged from 0.00% to 0.12%, always lower than the 0.32% default IPCC (2019) emission factor used for cattle urine-N. Our results suggest that dry climate locations under near-normal or below-normal precipitation levels are natural “cold spots” for N2O emissions and could experience negligible cattle excreta derived soil N2O emissions.

Empirical insights on the use of sun-induced chlorophyll fluorescence to estimate short-term changes in crop transpiration under controlled water limitation

Knowledge of actual crop transpiration (T) is important for advanced crop management but challenging to obtain due to the large spatial and temporal variation of T. Remote sensing offers various possibilities to assess T dynamics, while particularly sun-induced chlorophyll fluorescence (SIF) has been demonstrated as a sensitive empirical proxy for T. Despite this success, the advancement of the mechanistic understanding of how SIF relates to T dynamics is key for the future development and implementation of robust and reliable SIF-based T products. This study aims to contribute insights by experimentally assessing the sensitivity of several SIF-based T estimation strategies for evolving soil water limitation. We investigated extensive in situ and airborne data acquired during a water limitation experiment in a maize canopy in northern Italy. We evaluated five empirical strategies to integrate SIF in a T modelling framework based on the Penman-Monteith (PM) and the Ball-Berry-Leuning (BBL) concepts. Our results indicate that replacing model parameters sensitive to canopy conductance with SIF results in the best agreement between modelled and measured T under evolving water limitation. Our study contributes expanding existing knowledge with empirical insights on the sensitivity of SIF based T approaches under increasing soil water limitation at short time scales.

Changes in Water-Use Efficiency of Eucalyptus Plantations and Its Driving Factors in a Small County in South China

Ecosystem water-use efficiency (WUE) has been central in revealing the variability in terrestrial carbon and water cycles. Short-rotation plantations such as Eucalyptus plantations can simultaneously impact net primary production (NPP) and actual evapotranspiration (ETa), components of WUE, resulting in changes in terrestrial carbon and water cycles. However, there are few detailed studies on the changes in the WUE of Eucalyptus plantations at the catchment scale with high spatial remote sensing imagery. Here, we present the changes in the WUE of Eucalyptus plantations and its driving factors (i.e., NPP and ETa) using satellite-based models combined with 5-m spatial resolution RapidEye imagery in a small county in South China. The increases in ETa of Eucalyptus plantations are primarily the result of climate warming and result in low WUE of Eucalyptus plantations. The management practice used (short rotation in this study) can enhance the effect of climate warming on WUE by varying the NPP of Eucalyptus plantations. A high value of NPP leads to a high WUE of Eucalyptus plantations at the end of a short rotation, while a low value of NPP results in a low WUE at the beginning of another short rotation. Changes in the WUE of Eucalyptus plantations indicated large spatial and temporal variability, associated with climate warming and short-rotation practices.

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents

AbstractAimTheoretically, woody biomass turnover time () quantified using outflux (i.e. tree mortality) predicts biomass dynamics better than using influx (i.e. productivity). This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody in mature forests. We further compared woody estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes—CARDAMOM.LocationContinents.Time PeriodHistoric from 1951 to 2018.Major Taxa StudiedTrees and forests.MethodsWe compared the approaches of using outflux versus influx for estimating woody and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody and generated a spatially explicit map of woody at a 0.25‐degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody .ResultsWoody quantified by the outflux approach better (with R2 0.4–0.5) predicted the biomass accumulation rates than the influx approach (with R2 0.1–0.4) across continents. We found large spatial variations of woody for mature forests, with highest values in temperate forests (98.8 ± 2.6 y) followed by boreal forests (73.9 ± 3.6 y) and tropical forests. The map of woody extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody across continents. The highest woody in temperate forests was not captured by either DGVMs or CARDAMOM.Main ConclusionsOur study empirically demonstrated the preference of using outflux over influx to estimate woody for predicting biomass accumulation rates. The spatially explicit map of woody and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs.

On the Significance of Divje babe I Cave for the Stratigraphy, Sedimentology, and Chronology of Palaeolithic Cave Sites in Slovenia

&#x0D; &#x0D; &#x0D; The article reviews the development and interpretation of procedures used in the study of the sedimentary sequence of the Divje babe I Palaeolithic cave site. The data were obtained from publications, archives, and personal information provided by Ivan Turk, who directed the excavations and directed the laboratory investigations. The profile of Divje babe I is currently the best researched and chronologically defined Late Pleistocene cave profile in Slovenia. For the first time, various sedimentological and other data from the standard profile were upgraded with the same data obtained on standardized ground plan surfaces and depths. A large spatial (lateral) variability was discovered, associated with generally contemporaneous but different cave microenvironments, which were crucially influenced by different amounts of water in all aggregate states. Without taking into account temporal and spatial dimensions, it would not be possible to combine all statistical sets of collected data on sediments, their contents, and climatic parameters into a unified system.&#x0D; &#x0D; &#x0D;

Physics-based modeling of climate change impact on hurricane-induced coastal erosion hazards

Coastal erosion is an adverse impact of extreme water levels during major hurricanes. A warmer climate is expected to increase storm surge and wave hazards due to hurricane climatology change (HCC) and sea level rise (SLR). We conduct physics-based morphodynamic modeling to quantify the regional impacts of HCC and SLR on erosion hazards to sandy beaches and dunes along the barrier islands of New Jersey in the United States. Under the RCP8.5 scenario, we find a substantial increase in erosion hazards from the late-20th-century to late-21st-century. The regionally averaged 100-year eroded volume of beach-dune systems would increase by 58 and 84%, respectively, under the HCC and HCC + SLR scenarios. Our projections show a large spatial variability in future changes to erosion hazards, suggesting that, in addition to HCC and SLR, the morphological characteristics of beach-dune systems play an important role in the impacts of climate change on coastal erosion.

Evaluation of remotely sensed imagery to monitor temporal changes in soil organic carbon at a long-term grazed pasture trial

Temporal variation of soil organic carbon (SOC) is driven by land use/management practice, ecosystem conditions and climatic variation. Robust quantification of changes in SOC that is cost-effective and provides a statistical assessment of uncertainty is challenging, particularly in the face of large spatial variability and slow soil SOC changes. Remote-sensing indicators of above-ground vegetation provide some indication of the amount of fresh organic material being supplied to the soil. Although, because of the time taken for this organic material to decay and become incorporated into the soil, there will be a lag between the changes in the indicator of vegetation growth and the resulting changes in SOC. In this work, we investigate how a remotely sensed indicator of vegetation cover can be used with a lag period to predict or indicate changes in SOC for grazed pasture sites at a long-term monitoring study, which has been monitoring soil under different land uses for over forty years. We assessed how well this worked for indicating the SOC changes for different depths in the soil profile. Results suggested that a lagged remotely sensed vegetation cover—the average cover of the two preceding years—provides some indication of SOC changes for the 0–10 cm soil depth, but changes for deeper soil depths were not well predicted. Further, we investigated the potential of using soil data from a point-in-time spatial dataset (e.g. data from a baseline sampling round) to calibrate a relationship between the remotely sensed cover and SOC, which can then be applied to predict or indicate the temporal variation of SOC. Results showed this approach gave large prediction errors, likely because the temporal variation (at a fixed point in space) and spatial variation (for a fixed point in time) of SOC that is predictable by cover differences are not interchangeable.

Quantitative Mechanisms of the Responses of Abrupt Seasonal Temperature Changes and Warming Hiatuses in China to Their Influencing Factors

Abrupt temperature changes and warming hiatuses have a great impact on socioeconomic systems; however, their mechanisms remain unclear. In this study, the quantitative mechanisms of the responses of abrupt seasonal temperature changes and warming hiatuses in China to their influencing factors were analysed using the monthly mean temperature (Tav), mean minimum temperature (Tnav), and mean maximum temperature (Txav) from 622 meteorological stations in China covering 1951–2018, the CMIP6 model data, and data at large spatial scales, including Atlantic multidecadal oscillation (AMO) data. The results showed that the contributions of the influencing factors to the abrupt changes in Tav, Tnav, and Txav showed large spatial variability and peaked in the spring and summer and bottomed out in the autumn. The Pacific decadal oscillation (PDO) greatly impacted the abrupt temperature changes in Northeast China and North China at a contribution rate of approximately 12%, strongly influenced the abrupt temperature changes south of the Yangtze River, and markedly influenced the abrupt temperature changes in Northwest China. The AMO had a large impact on temperature in most regions of China in all seasons except for the summer. The MEI mainly affected the abrupt seasonal temperature changes in the region between 25° N and 35° N. The Arctic oscillation (AO) substantially impacted the warming hiatuses in Northeast China in the winter at a contribution rate of approximately 12%. These influencing factors contributed less to warming hiatuses than to abrupt temperature changes. Among the regional influencing factors, AP and WS greatly impacted warming hiatuses, more so than abrupt temperature changes, while relative humidity (RH) and solar radiation (SR) contributed little to warming hiatuses.

Dissolution rate variability at carbonate surfaces: 4D X-ray micro-tomography and stochastic modeling investigations

We provide a detailed 3D characterization of the geometry evolution and dissolution rate mapping at the surface of four carbonate samples, namely a calcite spar crystal, two limestone rock fragments, and an aragonite ooid, using time-lapse X-ray micro-tomography during dissolution experiments at pH 4.0. Evaluation of the retreat and mapping of the reaction rates at the whole surface of the samples reveals a large spatial variability in the dissolution rates, reflecting the composition and the specific contributions of the different regions of the samples. While crystal edges and convex topographies record the highest dissolution rates, the retreat is slower for flat surfaces and in topographic lows (i.e., concave areas), suggesting surface-energy related and/or diffusion-limited reactions. Microcrystalline aragonite has the highest rate of dissolution compared to calcite. Surprisingly, rough microcrystalline calcite surface dissolves globally more slowly than the {101̄4} faces of the calcite spar crystal. The presence of mineral impurities in rocks, through the development of a rough interface that may affect the transport of species across the surface, may explain the slight decrease in reactivity with time. Finally, a macroscopic stochastic model using the set of detachment probabilities at corner, edge, and face (terrace) sites obtained from kinetic Monte Carlo simulations is applied at the spar crystal scale to account for the effect of site coordination onto reactivity. Application of the model to the three other carbonate samples is discussed regarding their geometry and composition. The results suggest that the global dissolution process of carbonate rocks does not reflect only the individual behavior of their forming minerals, but also the geometry of the crystals and the shape of the fluid-mineral interface.

Unevenness of temporal distribution of precipitation over the Yangtze River basin and its main influencing factors

AbstractUnderstanding the unevenness of precipitation distribution within a year is important for water resources management and agricultural production. In this study, spatiotemporal variations of precipitation unevenness in the Yangtze River basin (YRB) during 1961–2020 and its main influencing factors are investigated by the relative entropy (RE) and rotated empirical orthogonal function. The timings of different percentiles of annual total precipitation show that the temporal distribution of precipitation in the YRB is uneven. The unevenness of precipitation distribution exhibits a large spatial variability with a high‐value centre in the northeast and a low value centre in the northwest of the YRB, and an upward trend in almost the entire YRB. Precipitation becomes more uneven with the decrease of annual number of wet days and low‐intensity precipitation amount and the increase of high‐intensity precipitation amount during the study period. Precipitation unevenness of different regions is modulated by the different combinations of different seasonal climate indices with specific time lags. For example, in the south‐central YRB, precipitation unevenness generally tends to be enhanced by the positive Tropical Northern Atlantic Index in current summer and sea surface temperature anomalies over South China Sea (SCS) in previous summer, and negative North Atlantic Oscillation in current winter, while in the northeast YRB, be weakened by negative Quasi‐Biennial Oscillation in current spring, Atlantic Meridional Mode in current summer and positive SCS in current autumn. Besides, the identified significant climate indices can partly explain the temporal changes of precipitation unevenness, and their explanatory ability is more powerful in the south‐central, southeast and northwest YRB than in the north‐central and northeast YRB, which reveals a strong negative correlation with the degree of precipitation unevenness.

Isotopic and chemical signatures of high mountain rivers in catchments with contrasting glacier and rock glacier cover

Glaciers and rock glaciers are key elements of mountain hydrological systems, but their relative influence on the chemical and isotopic conditions of streams within the river continuum is still overlooked. During three consecutive years (2019–2021), we studied 24 stream sections in two catchments (Plima and Schnals, Eastern Italian Alps) with varying cover of glaciers and rock glaciers. End-member mixing models based on δ2H and d-excess revealed a large spatial and temporal variability in the contribution of different water sources to stream runoff. Overall, snowmelt (77 ± 17 %) and rainwater (5 ± 5 %) were the largest and the smallest runoff components, respectively. The ice melt contribution was high in streams fed by glaciers (23 ± 15 %) and rock glaciers (16 ± 16 %). In the highly-glacierised Plima basin, the tracer-based estimation of annual ice melt fraction matched reasonably well (90–167%) the mean annual glacial ice loss estimated by geodetic mass balance. In contrast, we found a large overestimation of the ice melt component derived from mixing models in the poorly glacierised (but rock glacier-rich) Schnals catchment. In streams influenced by rock glaciers, at both catchments the particular temporal patterns of electrical conductivity resulted in unreliable estimates of the meltwater/groundwater fractions of runoff. Depending on the local lithology, concentrations of trace elements (Sr, Ni, Ba, Mn, Zn, Al) were high in streams fed by rock glaciers and glaciers, and close/below the limits of quantification in non-glacial streams. In alpine areas, the abundance of rock glaciers can confound the isotopic and chemical signature imparted by glaciers, thus hindering the use of tracer-based methods for hydrograph separation. Under the combined influence from glaciers and rock glaciers, concentrations of trace elements can surpass the limits for drinking water quality even in downstream areas, as we observed at the Schnals catchment for nickel.

Exploring a variable-resolution approach for simulating the regional climate in Southwest China using VR-CESM

The complex terrain of Southwest China (SWC) leads to large spatial variations in its climate, which are challenging for climate models to accurately simulate. In this study, we perform a high-resolution simulation (∼0.125°) using the Variable-Resolution Community Earth System Model (VR-CESM) for SWC and evaluate the model's ability to simulate the spatiotemporal variations of precipitation and temperature in SWC. The VR-CESM results are compared with observations, as well as the simulation of the standard version of CESM at a quasi-uniform ∼1° resolution. We find that the high-resolution VR-CESM model better portrays the fine distribution of precipitation and temperature than the coarse resolution CESM model (∼1°). VR-CESM also shows some improvements in the seasonal variations of precipitation in SWC. VR-CESM reduces the overestimulation of precipitation in the Hengduan Mountains and surrounding areas. For heavy precipitation (daily precipitation >25 mm), VR-CESM better simulates its frequency than CESM (∼1°). Overall, VR-CESM has good simulation capabilities for the spatiotemporal evolution of precipitation and the occurrence frequency of extreme precipitation in SWC. Therefore, the VR-CESM model is a useful tool for understanding the mechanism driving the climate changes in SWC in the past and for projecting future climate changes in the region.

On the Global Decrease in the Deep and Abyssal Density Stratification Along the Spreading Pathways of Antarctic Bottom Water Since the 1990s

AbstractThe density stratification in the ocean is directly related to the diapycnal mixing, which drives the abyssal cell of the Meridional Overturning Circulation (MOC). It is important to understand how stratification has been changing in the world's deep and abyssal oceans under climate change. Using repeat hydrographic data obtained since the 1990s, we find a decreasing stratification associated with changes in the source Antarctica Bottom Water (AABW) properties in its formation basins as well as in basins along its dispersal pathways. Averaged south of 60°S, the squared buoyancy frequency N2 shows a negative trend of −6% per decade in waters deeper than 4,000 m. The observed decadal reduction in stratification is associated with large spatial variability, especially in the Southern Ocean basins with multiple AABW sources. Additionally, there are also significant differences between neighboring basins that are related to the blocking effect of topography.

N-Map: High-resolution groundwater N-retention mapping and modelling by integration of geophysical, geological, geochemical, and hydrological data

A key aspect of protecting aquatic ecosystems from agricultural nitrogen (N) is to locate (i) farmlands where nitrate leaches from the bottom of the root zone and (ii) denitrifying zones in the aquifers where nitrate is removed before entering the surface water (N-retention). N-retention affects the choice of field mitigation measures to reduce delivered N to surface water. Farmland parcels associated with high N-retention gives the lowest impact of the targeted field measures and vice versa. In Denmark, a targeted N-regulation approach is currently implemented on small catchment scale (approx. 15 km2). Although this regulatory scale is much more detailed than what has been used previously, it is still so large that regulation for most individual fields will be either over- or under-regulated due to large spatial variation in the N-retention. The potential cost reduction for farmers is of up to 20–30% from detailed retention mapping at the field scale compared to the current small catchment scale.In this study, we present a mapping framework (N-Map) for differentiating farmland according to their N-retention, which can be used for improving the effectiveness of targeted N-regulation. The framework currently only includes N-retention in the groundwater. The framework benefits from the incorporation of innovative geophysics in hydrogeological and geochemical mapping and modelling. To capture and describe relevant uncertainties a large number of equally probable realizations are created through Multiple Point Statistical (MPS) methods. This allows relevant descriptions of uncertainties of parts of the model structure and includes other relevant uncertainty measures that affects the obtained N-retention. The output is data-driven high-resolution groundwater N-retention maps, to be used by the individual farmers to manage their cropping systems due to the given regulatory boundary conditions.The detailed mapping allows farmers to use this information in the farm planning in order to optimize the use of field measures to reduce delivered agricultural N to the surface water and thereby lower the costs of the field measures. From farmer interviews, however, it is clear that not all farms will have an economic gain from the detailed mapping as the mapping costs will exceed the potential economic gains for the farmers. The costs of N-Map is here estimated to 5–7 €/ha/year plus implementation costs at the farm. At the society level, the N-retention maps allow authorities to point out opportunities for a more targeted implementation of field measures to efficiently reduce the delivered N-load to surface waters.

Inter‐Annual Variability in Phytoplankton and Nutrients in the Gulf of Elat/Aqaba

AbstractThe Gulf of Elat/Aqaba exhibits high inter‐annual variability in mixed layer depth. Observations from the northern Gulf show differences of hundreds of meters in winter mixing depth, which ranges between 300 m in years with shallow mixing and up to 700 m in years with deep mixing. Deep mixing events can occur in two consecutive years or after four consecutive years of shallow mixing. The mixing depth has an effect on the concentration of nutrients and chlorophyll (and other tracers) in the surface and deep water. Using a 3D coupled physical‐ecological model, we studied the effect of shallow versus deep mixing on the processes controlling the phytoplankton bloom and on nutrient accumulation in the deep water. We found that years with deep mixing are characterized by larger spatial variability in surface and integrated chlorophyll concentration during the mixing season. We found that horizontal advection is a dominant contributor for integrated phytoplankton concentration in years with deep mixing in the northern Gulf. Even when mixing was deep and nutrient limitation decreased, light limitation on phytoplankton integrated growth was enhanced in the north compared with the south. In addition, we showed that the nutrient accumulation in the deep water after a year with deep mixing in the northern Gulf was initially affected mostly by physical processes (such as advection and vertical mixing), and less from ecological regeneration, and switched to be dominated by ecological processes alone during the third year after mixing.

High temporal and spatial resolution characteristics of evaporation, transpiration, and evapotranspiration from a subalpine wetland by an advanced UAV technology

Wetland evapotranspiration (ET), which involves the land–atmosphere exchange of energy and water, is dynamic and affects the spatiotemporal distribution of water resources. However, due to the variability and complexity of wetlands, accurate estimation of patch-scale ET and its spatial variability remain insufficiently characterized. To overcome this challenge, an advanced unmanned aerial vehicle (UAV) technology was developed by combining the three-temperature (3T) model, which is robust to estimate transpiration and its spatial variability with UAV-based thermal infrared remote sensing, and Penman equation, which is commonly used to estimate open water evaporation. The combined approach was verified using the Bowen ratio system over a subalpine wetland. The results show that the proposed method is simple and applicable for estimating wetland ET and its spatial variability, with a determination coefficient (R2) of 0.93, mean absolute percentage error (MAPE) of 7.90%, root mean squared error (RMSE) of 0.05 mm h−1, and Nash-Sutcliffe efficiency (NSE) of 0.93. It depicts a large spatial variability in wetland ET with respect to surface vegetation characteristics, water regimes, meteorological factors, and larger transpiration rates than open water evaporation. With its limited inputs and no calibration requirements, the proposed method is concluded to be simple and to easily reveal the high temporal and spatial resolution characteristics of patch-scale ET and its components.

Soil Moisture Variations From Boreal Forests to the Tundra

AbstractSoil moisture has a profound influence on life on Earth, and this vital water resource varies across space and time. Here, we explored soil moisture variations in boreal forest and tundra environments, where comprehensive soil moisture datasets are scarce. We installed soil moisture sensors up to 14 cm depth at 503 measurement sites within seven study areas across northern Europe. We recorded 6,138,528 measurements to capture soil moisture variations of the snowless season from April to September 2020. We described the spatio‐temporal patterns of soil moisture, and test how these patterns are linked to topography and how these links vary in space and time. We found large spatial variation and often less pronounced temporal variation in soil moisture across the measurement sites within all study areas. We found that throughout the measurement periods both univariate and multivariate models with topographic predictors showed great temporal variation in their performance and in the relative influence of the predictors within and across the areas. We found that the soil moisture‐topography relationships are site‐specific, as the topography‐based models often performed poorly when transferred from one area to another. There was no general solution that would work well in all the study areas when modeling soil moisture variation with topography. This should be carefully considered before applying topographic proxies for soil moisture. Overall, these data and results highlight the strong spatio‐temporal heterogeneity of soil moisture conditions in boreal forest and tundra environments.