Soil Climate Research Articles

Agronomic Performance of Sweet Orange Genotypes under the Brazilian Humid Subtropical Climate

The diversification of Citrus spp. orchards, for both scion and rootstock genotypes, is essential to prevent outbreaks of insects and diseases, improve yield and fruit quality, and extend harvesting and industrial juice processing. Furthermore, this enables growers to obtain higher off-season profits. Citrus plantings were prohibited in most regions of the state of Paraná in the past due to the spread of citrus canker disease. Therefore, this study aimed to evaluate the agronomic performance of distinct early- and mid-season sweet orange cultivars (C. sinensis (L.) Osbeck) regarding vegetative growth, fruit quality, and yield under the Brazilian humid subtropical climate in order to select new alternatives of sweet orange for the industrial and fresh fruit markets. The experimental orchard was planted in 2012 with 15 sweet orange cultivars (early-maturing: Bahia Cabula, Diva, Cadenera, Marrs, Midsweet, Paulista, Rubi, and Westin; mid-season maturing: Berna Peret, Jaffa, Khalily White, Fukuhara, Seleta do Rio, Seleta Tardia, and Shamouti) grafted on Rangpur lime (C. limonia (L.) Osbeck). The experimental design was randomized blocks with three replicates and five trees per plot, analyzed between each maturation group. Data were submitted to analysis of variance followed by Tukey’s test (p ≤ 0.05). Regarding the early-season cultivars, Diva had the tallest trees with largest canopy diameter and volume, differing from Marrs, which had the smallest trees. Shamouti and Khalily White trees were greatly different from all other mid-season cultivars and produced low fruit load over the evaluated period. The early-season Midsweet scored the highest yield and technological index, similar to the mid-season Berna Peret, producing fruits of high juice quality. These genotypes are more effective under the current situation faced by the citrus industry, as the economic life of orchards has been reduced due huanglongbing (HLB). Altogether, Midsweet and Berna Peret genotypes, previously reported as being less susceptible to citrus canker under the same soil–climate condition, are precocious and exhibit higher agronomic potential to be planted in humid subtropical climates, including Brazil and other similar areas around the world.

Climatic influences on active layer depth between 2000 and 2018 in the McMurdo Dry Valleys, Ross Sea Region, Antarctica

The maximum seasonal depth of thaw (the active layer depth) in permafrost soils is influenced by a range of soil and atmospheric variables. The active layer depth and the temperature at the top of the permafrost have potential to provide a clear signal of a changing climate, damping out noise more effectively than atmospheric variables. A monitoring network of nine soil climate stations was established in the McMurdo Dry Valleys between 1999 and 2012. Each station monitors soil temperature to a depth of up to 1.2 m, along with a range of atmospheric variables including air temperature, solar radiation, and wind speed.Over the period of record included in this study (2000–2018): the mean active layer depth ranged from 7.6 cm at Mt. Fleming to >90 cm at Granite Harbour; the mean annual shallow (15 cm) soil temperature ranged from −24 °C at Mt. Fleming to −14 °C at Granite Harbour; and the mean summer air temperature ranged from −11 °C at Mt. Fleming to −1 °C at Granite Harbour. There was marked between-year variation but no significant trends of increase or decrease in active layer depth, or temperature at the top of the permafrost, between 2000 and 2018.Wavelet analysis showed that both global and regional climate systems correlated with de-seasonalised temperature at the top of the permafrost. The Southern Annular Mode had relationships at both annual and biannual timescales (p < 0.05); the Southern Oscillation Index had a relationship at a 2–3 year timescale (p < 0.05); and the Amundsen Sea Low had an annual signal and some between season signals (p < 0.05).The data set is too short to draw conclusions on potential longer-term changes in the soil climate. Nevertheless, the database provides a baseline against which future change can be assessed and will increase in value the longer it is maintained.

Simulated distribution of Eragrostis lehmanniana (Lehmann lovegrass): Soil–climate interactions complicate predictions

AbstractThe invasive perennial grass Eragrostis lehmanniana has expanded rapidly throughout the Sonoran Desert (SD) while remaining sparse and patchily distributed in the neighboring Chihuahuan Desert (CD). As temperatures and patterns in precipitation change, identifying the drivers limiting spread in the CD is needed. Our objectives were (1) to identify the climatic and edaphic factors limiting recruitment of E. lehmanniana throughout the CD and (2) to predict the edaphic and climatic locations in the CD where this species is expected to have higher probabilities of recruitment under future climatic conditions. Recruitment was simulated at 57 locations throughout the CD with a daily, multilayer soil water model (SOILWAT) that was parameterized and tested to develop recruitment parameters for E. lehmanniana using long‐term data from research stations in the SD and CD. Logistic regression was used to predict recruitment across the CD using climate and soil factors to create a map of simulated recruitment under current and alternative climate scenarios. Simulated recruitment under current climate was low for most of the CD. However, localized areas with high probabilities (&gt;0.8) occurred along the western transition between the CD and the SD, and in the southern extent of the CD. In general, the CD climate is too cool and dry for rapid and widespread invasion. However, increases to temperature and precipitation are likely to increase recruitment success. Interactions among soil, temperature, and precipitation were important to increases in recruitment of E. lehmanniana and are expected to lead to heterogeneous increases in abundance as climate continues to change. This patchy increase in abundance will result in changes in its geographic distribution that will make predictions in the future challenging.

Response of bacterial community structure to different ecological niches and their functions in Korean pine forests.

A healthy plant microbiome is diverse, taxonomically-structured, and gives its plant host moderate advantages in growth, development, stress tolerance, and disease resistance. The plant microbiome varies with ecological niches and is influenced by variables that are complex and difficult to separate from each other, such as the plant species, soil, and environmental factors. To explore the composition, diversity, and functions of the bacterial community of Korean pine forests, we used high-throughput sequencing to study five areas with different forest ages from June to October 2017 in northeast China. We obtained 3,247 operational taxonomic units (OTUs) based on 16S rRNA gene sequencing via an Illumina Hi-seq platform. A total of 36 phyla and 159 known genera were classified. The Shannon index of the bacterial community from the rhizospheric soil was significantly higher (p < 0.01, n = 10) than that of the root tips. Beta-diversity analysis confirmed that the bacterial community of the rhizospheric soil was significantly different (p < 0.001) from the root tips. Nine bacterial phyla were dominant (relative richness > 1%) in the rhizospheric soil, but there were six dominant phyla in the root tips. Proteobacteria was the core flora in the root tips with a relative abundance of more than 50%. It is known that the formation of bacterial communities in the rhizospheric soil or the root is mainly caused by the processes of selection, and we found a relatively high abundance of a few dominant species. We further analyzed the correlations between the bacterial community from the rhizospheric soil with that of the root tips, as well as the correlations of the bacterial community with soil physicochemical properties and climate factors. We used Functional Annotation of the Prokaryotic Tax (FAPROTAX) to predict the functions of the bacterial community in the rhizospheric soil and root tips. Five related phototrophic functions, nine nitrogen cycle functions, two related chemoheterotrophic functions, and two others were predicted. The abundance of the bacteria phyla performing relevant functions was different in the rhizospheric soil than in the root tips. These functions were significantly influenced by the contents of nitrogen, phosphorus, and potassium in the soil habitat. The bacterial composition and functions in the rhizospheric soil and root tips of Korean pine were analyzed, and the results demonstrated the importance of soil and plant species on the bacterial community in the below ground plant microbiome.

A Digital Advisor Twin for Crop Nitrogen Management

Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a digital knowledge base with an integrated decision support system (DSS), using the example of nutrient supply, specifically nitrogen (N), fertilization. Therefore, the requirements of farmers and crop consultants for DSS to inform fertilization decisions for winter wheat (Triticum aestivum L.) were elaborated using surveys, expert interviews, and a prototype test. Semantic knowledge was enriched by expert knowledge and combined in a web application, the Crop Portal. To map regional and personal decision making patterns and experiences, the tacit knowledge on the complex advisory problem of N fertilization is made digitally usable. For this purpose, 16 fuzzy variables were specified and formalized. Individual decision trees and their interactions with an integrative knowledge base were used to multiply the consulting reach of experts. Using three consultants and nine model farms from different soil–climate areas in Germany, the Crop Portal was tested under practical conditions and the perceived pragmatic and hedonic quality of the system was evaluated using a standardized questionnaire. The field test showed that the variation in fertilizer recommendations from the ‘digital advisor twin’ ranged from 5 kg N ha−1 to 16 kg N ha−1 when compared with the decisions of the experts in the field. The study presents the participatory development and evaluation of a rule-based DSS prototype in agricultural practice.

Evaluation of long-term carbon sequestration of biochar in soil with biogeochemical field model

Return of biomass-derived biochar (BC) into soil has been considered as one of the carbon sequestration (CS) methods. It is important to evaluate the long-term biochar CS potential by integrating the complex physical interferences and biochemical reactions in real soil. This study incorporated biochar into a biogeochemical field model and established a daily-resolution simulator to assess 5-, 50-, 500-year CS potential upon Soil-Biochar-Plant interaction. Through the scenario simulation of burying 7.5–75 t/ha BC-C in a 50 cm-depth rainfed cropland soil with corn planted, we found biochar could retain 483–557 kg C/t BC-C after 500 years' natural decomposition, although soil pedoturbation and plant erosion accelerated its mineralization. Moreover, biochar provided labile-C to compensate microbial decomposition and modified long-term soil climate, resulting in a decrease in soil organic carbon degradation of 44–265 kg C/t BC-C. Furthermore, biochar promoted plant photosynthetic performance by offering exogenous nutrients, equivalent to capturing 66–1039 kg C/t BC-C over 50 years. But biochar limited endogenous nutrient release and inhibited plant growth after exogenous nutrients exhausted, so total CS decreases yearly after reaching an upper limit (1030–1722 kg C/t BC-C). A total of 651–725 kg C/t BC-C could be sequestered after 500 years. And biochar is more potential in infertile and arid soils. Overall, this study indicates the necessity of taking the biogeochemical reactions into consideration to assess biochar long-term CS, and it further demonstrates biochar soil implementation is a prospective carbon-negative strategy.

The other side of tropical forest drought: do shallow water table regions of Amazonia act as large-scale hydrological refugia from drought?

Tropical forest function is of global significance to climate change responses, and critically determined by water availability patterns. Groundwater is tightly related to soil water through the water table depth (WT), but historically neglected in ecological studies. Shallow WT forests (WT < 5 m) are underrepresented in forest research networks and absent in eddy flux measurements, although they represent c. 50% of the Amazon and are expected to respond differently to global-change-related droughts. We review WT patterns and consequences for plants, emerging results, and advance a conceptual model integrating environment and trait distributions to predict climate change effects. Shallow WT forests have a distinct species composition, with more resource-acquisitive and hydrologically vulnerable trees, shorter canopies and lower biomass than deep WT forests. During 'normal' climatic years, shallow WT forests have higher mortality and lower productivity than deep WT forests, but during moderate droughts mortality is buffered and productivity increases. However, during severe drought, shallow WT forests may be more sensitive due to shallow roots and drought-intolerant traits. Our evidence supports the hypothesis of neglected shallow WT forests being resilient to moderate drought, challenging the prevailing view of widespread negative effects of climate change on Amazonian forests that ignores WT gradients, but predicts they could collapse under very strong droughts.

Evaluation and Comparison of the Common Land Model and the Community Land Model by Using In Situ Soil Moisture Observations from the Soil Climate Analysis Network

Soil moisture is a key state variable in land surface processes. Since field measurements of soil moisture are generally sparse and remote sensing is limited in terms of observation depth, land surface model simulations are usually used to continuously obtain soil moisture data in time and space. Therefore, it is crucial to evaluate the performance of models that simulate soil moisture under various land surface conditions. In this work, we evaluated and compared two land surface models, the Common Land Model version 2014 (CoLM2014) and the Community Land Model Version 5 (CLM5), using in situ soil moisture observations from the Soil Climate Analysis Network (SCAN). The meteorological and soil attribute data used to drive the models were obtained from SCAN station observations, as were the soil moisture data used to validate the simulation results. The validation results revealed that the correlation coefficients between the simulations by CLM5 (0.38) and observations are generally higher than those by CoLM2014 (0.11), especially in shallow soil (0–0.1016 m). The simulation results by CoLM2014 have smaller bias than those by CLM5 . Both models could simulate diurnal and seasonal variations of soil moisture at seven sites, but we found a large bias, which may be due to the two models’ representation of infiltration and lateral flow processes. The bias of the simulated infiltration rate can affect the soil moisture simulation, and the lack of a lateral flow scheme can affect the models’ division of saturated and unsaturated areas within the soil column. The parameterization schemes in land surface models still need to be improved, especially for soil simulations at small scales.

Estimating Land Cover-Based Soil Organic Carbon Values to Support Soil Health, Decarbonization, and Climate Resilience Planning in Massachusetts

Estimating Land Cover-Based Soil Organic Carbon Values to Support Soil Health, Decarbonization, and Climate Resilience Planning in Massachusetts

Cooler Equatorial Climate in the Late Lopingian Estimated from Paleosols Developed on Emeishan Basalts

Paleosols that developed on large igneous province flood basalts provide a valuable opportunity to quantitatively reconstruct the terrestrial paleoclimate during a given period. This study uses a newly constructed basaltic soil–climate function to obtain terrestrial paleoclimatic data following the termination of the Emeishan basalt eruption in the late Lopingian (∼253 Ma). The constructed relationships are mean annual temperature of 32.81e−0.4166X (R2=0.55, standard error [SE]=4.6°C) and mean annual precipitation of −875.5ln(X)+1792 (R2=0.69, SE=328 mm), where X=SiO2/(Al2O3+Fe2O3) (the Saf index). We collected samples from nine paleosol profiles developed on the last-phase Emeishan basalts and conducted field observations as well as micromorphological, mineral composition, and geochemical analyses. On the basis of these paleosols, we reconstructed the regional mean temperature and precipitation to be 18.9°C±4.6°C and 1546±328 mm, respectively. The regional climate was cooler than those in modern equatorial regions of similar latitudes. The reduced CO2 concentration in the atmosphere resulting from rapid weathering of Emeishan basalts in a tropical climate could have played a dominant role in causing the regional cool terrestrial paleoclimate following the termination of the Emeishan basalt eruption.

Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States

A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SM measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network, and the National Oceanic and Atmospheric Administration Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. The accuracy of THySM is 0.048 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01–0.02 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The ubRMSE of THySM 1 km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

Contribution of land use practices to GHGs in the Canadian Prairies crop sector.

The global crop sector is estimated to contribute about 10.4% of global GHGs annually. The Canadian crop sector is assessed as adding about 6.5% to total national emissions. These estimates over report the impact of farming as they ignore the complex interaction of cropping with the environment and the role land use, land use change and forestry (LULUCF) play in sequestering carbon. This study quantifies the contribution of land use to GHG emissions and removals in the Canadian Prairies crop sector between 1985 and 2016. The modeling effort explores how different farming practices (i.e., conventional tillage (CT), minimum tillage (MT), zero tillage (ZT), summerfallow, crop rotations, and residue retention) and input usage rates (i.e., fertilizer and fuel) affect GHG emissions in different soil climate zones and provinces in the Prairies region. The adoption of sustainable practices led to an 80% decline in GHG emissions in the crop sector between 1985 and 2016. Since 2005, the baseline for Canada’s Paris commitment, sectoral emissions dropped 53%, more than is required to meet the 2030 target. Most promising, the crop sector was a net GHG sink between 2013 and 2016 in Alberta and between 2006 and 2016 in Saskatchewan. As positive as these developments have been, more can be done by directing research to identify options for reducing GHGs in Manitoba (which made only minimal improvements as farmers there faced conditions requiring continuous use of conventional tillage practices), to explore better nitrogen management (a major continuing source of GHG from cropping) and by searching for low carbon transport options.

Local Conditions and Topography Control Soil Climate

Local Conditions and Topography Control Soil Climate

Growth stability of four drought resistant plant species in different regions

There are abundant germplasm resources of drought resistant trees in China. It is difficult for foresters to evaluate and screen excellent germplasm that is suitable for various drought adverse circumstances. In this study, four tree species from different provenances, namely Amygdalus davi-diana, Prunus sibirica, Salix gordejevii, and Caryopteris mongolica, were used as test materials. Four soil regions, namely Dalad Banner, Siziwang Banner, Kouhezi town and Liujiazi town of Kulun Banner in the Central and Eastern Inner Mongolia Autonomous Region were selected as multi-point experiment sites to analyze the growth and physiological status of different tree species and provenances. The additive main effects and multiplicative interaction model was used to evaluate the regional adaptability and stability of the germplasm. The growth and physiological indices of four tree species differed between provenances and locations. Soil conditions (potassium content, nitrogen content, and pH) and climate conditions (annual average temperature, precipitation, and potential evaportranspiration) in different locations all influenced the growth of different provenance species. Concerning tree species, S. gordejevii and C. mongolica are more adapted to the sandy loam and chestnut soil of Dalad Banner and Siziwang Banner. A. davidiana and P. sibirica L. are more adapted to the loess and aeolian sandy soil of Kouhezi town and Liujiazi town. Concerning tree provenances, A. davidiana of Tuzuo, P. sibirica of Ningcheng and Yuanzhou, S. gordejevii of Lanqi, and C. mongolica of Jingbian displayed higher regional stability and better growth adaptability, indicating their suitability for afforestation in similar areas.

ANALYSIS OF DEFORESTATION MULTISPECTRAL SATELLITE DATA USING REMOTE SENSING TECHNIQUES

The coverage of global forest cover is most essential for soil health, climate, water cycle and air quality. The condition can be improved by reforestation and afforestation to some extent but cannot be restored to full range. The satellite images used for this analysis of forest change detection are of planet scope having 3 m resolution. The target of this research is to detect the forest cover change using remote sensing and GIS. For the identification of forest covers various techniques like segmentation and classification are used. The best classification is by Normalized Difference Vegetation Index (NDVI) which identifies the health of vegetation along with the changes in the different parameters. After using NDVI some values generated will detect the areas of forest changes with the amount of change. And with the help of this research it is observed that out of total forest area i.e. 76740.32 hectares, the negative forest change is reduced to 29.44% due to degradation of forests maybe due to fires, deforestation, leaf shedding etc.

The Effect of Soil-Climate Conditions, Farmyard Manure and Mineral Fertilizers on Potato Yield and Soil Chemical Parameters.

If available to farmers, potatoes represent a crop classically fertilized with farmyard manure in the Czech Republic. At the same time, potatoes are a crop sensitive to soil–climate conditions. We evaluated the effect of cattle manure (FYM), manure and mineral nitrogen (FYM + N1, FYM + N2), manure and mineral fertilizers (FYM + N1PK, FYM + N2PK, FYM + N3PK) application and the effect of three soil-climatic conditions (Caslav—maize production area with degraded Chernozem, Ivanovice—maize production area with Chernozem, Lukavec—potatoes production area with Cambisol) over four years (2016–2019) on potatoes yield and soil chemical properties. Of all the factors, yields were most affected by location. Lukavec provided the highest average yields (37.2 t ha−1), followed by Ivanovice (23.5 t ha−1) and Caslav (15.5 t ha−1). The second most important factor was the climatic conditions of the year. Fertilization was the third most important parameter. FYM significantly increased yields compared to Control, but applied alone cannot cover the needs of potatoes. Similarly, the application of FYM and N increases yields, but for the highest yields, it is best to apply FYM + NPK (80 kg ha−1 N). Co-application of FYM and mineral N fertilizers mitigates the negative impact of mineral N on soil pH.

Dynamics of changes in thawing and freezing depth in soils of Western Transbaikalia in different types of permafrost distribution

This study reveals temperature regimes of the three contrasting ecosystems in the permafrost area of Western Transbaikalia: meadow-forest ecosystem, forest-steppe and dry-steppe. Annual profile temperature data show the functioning of the studied soils in cryogenic and long-term-seasonally-freezing temperature regime. The most explanatory temperature variables are temperature penetration above 5°C at 100 cm depth and the amount of days with temperature above 0.5°C and 10°C at 20 cm, 50 cm, and 100 cm depths. Also, this work presents a comparative analysis of indicators of soil climate change and shows the spatial-temporal distribution of the reaction of the thawing and freezing depth in soils.

Soil and Climate Characterization to Define Environments for Summer Crops in Senegal

Investigating soil and climate variability is critical to defining environments for field crops, understanding yield-limiting factors, and contributing to the sustainability and resilience of agro-ecosystems. Following this rationale, the aim of this study was to develop a soil–climate characterization to describe environmental constraints in the Senegal summer-crops region. For the soil database, 825 soil samples were collected characterizing pH, electrical conductivity (EC), phosphorus (P), potassium (K), cation exchange capacity (CEC), and total carbon (C) and nitrogen (N). For the climate, monthly temperature, precipitation, and evapotranspiration layers were retrieved from WorldClim 2.1, CHIRPS and TERRACLIMATE. The same analysis was applied individually to both databases. Briefly, a principal component analysis (PCA) was executed to summarize the spatial variability. The outcomes from the PCA were subjected to a spatial fuzzy c-means algorithm, delineating five soil and three climate homogeneous areas, accounting for 73% of the soil and 88% of the climate variation. To our knowledge, no previous studies were done with large soil databases since availability field data is often limited. The use of soil and climate data allowed the characterization of different areas and their main drivers. The use of this classification will assist in developing strategic planning for future land use and capability classifications.

Slope and aspect controls on soil climate: Field documentation and implications for large‐scale simulation of critical zone processes

AbstractSoil climate, as quantified by soil temperature (TS) and water content (θ), exerts important controls on critical zone processes. It may be sensitive to variations in local slope and aspect (SA), but this attribute remains poorly quantified at the local scale and unresolved in large‐scale models. Estimation of SA effects on soil climate across multiple scales may facilitated using topographically modified, incoming clear‐sky solar radiation (SR,CS,T). We established six paired automated soil climate monitoring stations on opposing north‐facing (NF) and south‐facing (SF) slopes (4 yr) and collected spatialTSand θ data within the hectare surrounding four stations (2 yr) to measure SA effects on soil climate. Results were compared with physically based simulations and evaluated in the context ofSR,CS,T. Spatial θ data were more variable thanTs, and both were consistent with values from continuous monitoring stations. On average, the SFTSwas much greater (4.7 °C) and the annual summer drought longer (36 d) than on the adjacent NF aspect. Seasonal variations ofTSand θ were different from each other and also different fromSR,CS,T. Local conditions, including snow cover, precipitation patterns, and soil properties, largely controlled seasonal variations ofTSand θ, which cannot be predicted fromSR,CS,T. This indicates that realistic simulation of many critical zone processes requires high‐resolution inputs. Simulations captured first‐order SA effects and could be useful for estimating SA effects in lieu of field monitoring.

Modeling the thermic soil temperature regime boundary of the eastern United States

AbstractWithin U.S. soil taxonomy,soil temperature regimes (STRs) are recognized and incorporated in the classification and mapping of soils. Although knowledge of STR can inform crop selection, yield estimates, and nutrient management decisions, detailed and current maps of STRs derived from measured data are not available for most of the United States. Soil temperature measurements at the prescribed depth of 50 cm were summarized for 69 Soil Climate Analysis Network (SCAN) stations in the eastern United States. Mean annual soil temperature (MAST) was calculated for the 2005–2015 period from monthly averages calculated from daily soil temperature observations. A prediction equation for MAST was developed using mean annual air temperature (MAAT) from the PRISM (Parameter‐elevation Regressions on Independent Slopes Model) group as a predictor: MAST = 2.49 + (0.91 × MAAT). The adjusted r2 was .96 with a RMSE of 0.74 °C and mean absolute error of 0.59 °C based on 10‐fold cross‐validation. Orographic influence is incorporated in PRISM MAAT, yielding a simple model. The MAST surfaces were calculated by applying the prediction equation to MAAT grids for the 1895–2015 period. The yearly MAST surfaces allow estimation of STR over time and for various summary periods including the 1895–2015 record, 30‐yr normal, and probabilities based on individual years for the 1895–2015 period. Based on this investigation, the mesic–thermic STR boundary occurs north of the presently recognized location, is dynamic, and is supported by existing native plant communities associated with thermic STR.