Dataset Information

A new approach to predict soil temperature under vegetated surfaces.

ABSTRACT: In this article, the setup and the application of an empirical model, based on Newton's law of cooling, capable to predict daily mean soil temperature (T_soil) under vegetated surfaces, is described. The only input variable, necessary to run the model, is a time series of daily mean air temperature. The simulator employs 9 empirical parameters, which were estimated by inverse modeling. The model, which primarily addresses forested sites, incorporates the effect of snow cover and soil freezing on soil temperature. The model was applied to several temperate forest sites, managing the split between Central Europe (Austria) and the United States (Harvard Forest, Massachusetts; Hubbard Brook, New Hampshire), aiming to cover a broad range of site characteristics. Investigated stands differ fundamentally in stand composition, elevation, exposition, annual mean temperature, precipitation regime, as well as in the duration of winter snow cover. At last, to explore the limits of the formulation, the simulator was applied to non-forest sites (Illinois), where soil temperature was recorded under short cut grass. The model was parameterized, specifically to site and measurement depth. After calibration of the model, an evaluation was performed, using ~50 % of the available data. In each case, the simulator was capable to deliver a feasible prediction of soil temperature in the validation time interval. To evaluate the practical suitability of the simulator, the minimum amount of soil temperature point measurements, necessary to yield expedient model performance was determined. In the investigated case 13-20 point observations, uniformly distributed within an 11-year timeframe, have been proven sufficient to yield sound model performance (root mean square error <0.9 °C, Nash-Sutcliffe efficiency >0.97). This makes the model suitable for the application on sites, where the information on soil temperature is discontinuous or scarce.

SUBMITTER: Dolschak K

PROVIDER: S-EPMC4746742 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The accurate and timely monitoring of the soil salt content (SSC) at different depths is the prerequisite for the solution to salinization in the arid and semiarid areas. Sentinel-2 has demonstrated significant superiority in SSC inversion for its higher temporal, spatial and spectral resolution, but previous research on SSC inversion with Sentinel-2 mainly focused on the unvegetated surface soil. Based on Sentinel-2 data, this study aimed to build four machine learning models at five depths (0∼20 cm, 20∼40 cm, 40∼60 cm, 0∼40 cm, and 0∼60 cm) in the vegetated area, and evaluate the sensitivity of Sentinel-2 to SSC at different depths and the inversion capability of the models. Firstly, 117 soil samples were collected from Jiefangzha Irrigation Area (JIA) in Hetao Irrigation District (HID), Inner Mongolia, China during August, 2019. Then a set of independent variables (IVs, including 12 bands and 32 spectral indices) were obtained based on the Sentinel-2 data (released by the European Space Agency), and the full subset selection was used to select the optimal combination of IVs at five depths. Finally, four machine learning algorithms, back propagation neural network (BPNN), support vector machine (SVM), extreme learning machine (ELM) and random forest (RF), were used to build inversion models at each depth. The model performance was assessed using adjusted coefficient of determination (R2 adj ), root mean square error (RMSE) and mean absolute error (MAE). The results indicated that 20∼40 cm was the optimal depth for SSC inversion. All the models at this depth demonstrated a good fitting (R2 adj ≈ 0.6) and a good control of the inversion errors (RMSE < 0.16%, MAE < 0.12%). At the depths of 40∼60 cm and 0∼20 cm the inversion performance showed a slight and a great decrease respectively. The sensitivity of Sentinel-2 to SSC at different depths was as follows: 20∼40 cm > 40∼60 cm > 0∼40 cm > 0∼60 cm > 0∼20 cm. All four machine learning models demonstrated good inversion performance (R2 adj > 0.46). RF was the best model with high fitting and inversion accuracy. Its R2 adj at five depths were between 0.5 to 0.68. The SSC inversion capabilities of all the four models were as follows: RF model > ELM model > SVM model > BPNN model. This study can provide a reference for soil salinization monitoring in large vegetated area.

Project description:Terrestrial biogeochemical feedbacks to the climate are strongly modulated by the temperature response of soil microorganisms. Tropical forests, in particular, exert a major influence on global climate because they are the most productive terrestrial ecosystem. We used an elevation gradient across tropical forest in the Andes (a gradient of 20°C mean annual temperature, MAT), to test whether soil bacterial and fungal community growth responses are adapted to long-term temperature differences. We evaluated the temperature dependency of soil bacterial and fungal growth using the leucine- and acetate-incorporation methods, respectively, and determined indices for the temperature response of growth: Q10 (temperature sensitivity over a given 10oC range) and Tmin (the minimum temperature for growth). For both bacterial and fungal communities, increased MAT (decreased elevation) resulted in increases in Q10 and Tmin of growth. Across a MAT range from 6°C to 26°C, the Q10 and Tmin varied for bacterial growth (Q10-20 = 2.4 to 3.5; Tmin = -8°C to -1.5°C) and fungal growth (Q10-20 = 2.6 to 3.6; Tmin = -6°C to -1°C). Thus, bacteria and fungi did not differ significantly in their growth temperature responses with changes in MAT. Our findings indicate that across natural temperature gradients, each increase in MAT by 1°C results in increases in Tmin of microbial growth by approximately 0.3°C and Q10-20 by 0.05, consistent with long-term temperature adaptation of soil microbial communities. A 2°C warming would increase microbial activity across a MAT gradient of 6°C to 26°C by 28% to 15%, respectively, and temperature adaptation of microbial communities would further increase activity by 1.2% to 0.3%. The impact of warming on microbial activity, and the related impact on soil carbon cycling, is thus greater in regions with lower MAT. These results can be used to predict future changes in the temperature response of microbial activity over different levels of warming and over large temperature ranges, extending to tropical regions.

Dataset Information

A new approach to predict soil temperature under vegetated surfaces.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets