scholarly journals Large area land surface simulations in heterogeneous terrain driven by global datasets: application to mountain permafrost

2013 ◽  
Vol 7 (6) ◽  
pp. 5853-5887 ◽  
Author(s):  
J. Fiddes ◽  
S. Endrizzi ◽  
S. Gruber
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Ground Surface ◽  
Swiss Alps ◽  
Large Area ◽  
Heterogeneous Terrain ◽  
Ground Surface Temperature ◽  
Mountain Permafrost

Abstract. Numerical simulations of land-surface processes are important in order to perform landscape-scale assessments of earth-systems. This task is problematic in complex terrain due to: (i) high resolution grids required to capture strong lateral variability, (ii) lack of meteorological forcing data where it is required. In this study we test a topography and climate processor, which is designed for use with large area land surface simulation, in complex and remote terrain. The scheme is driven entirely by globally available datasets. We simulate air temperature, ground surface temperature, snow depth and test the model with a large network of measurements in the Swiss Alps. We obtain RMSE values of 0.64 °C for air temperature, 0.67–1.34 °C for non-bedrock ground surface temperature, and 44.5 mm for snow depth, which is likely affected by poor input precipitation field. Due to this we trial a simple winter precipitation correction method based on melt-dates of the snow-pack. We present a test application of the scheme in the context of simulating mountain permafrost. The scheme produces a permafrost estimate of 2000 km2 which compares well to published estimates. We suggest that this scheme represents a good first effort in application of numerical models over large areas in heterogeneous terrain.

scholarly journals Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost

2015 ◽  
Vol 9 (1) ◽  
pp. 411-426 ◽  
Author(s):  
J. Fiddes ◽  
S. Endrizzi ◽  
S. Gruber
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Ground Surface ◽  
Data Sets ◽  
Large Area ◽  
Heterogeneous Terrain ◽  
Ground Surface Temperature ◽  
Mountain Permafrost

Abstract. Numerical simulations of land surface processes are important in order to perform landscape-scale assessments of earth systems. This task is problematic in complex terrain due to (i) high-resolution grids required to capture strong lateral variability, and (ii) lack of meteorological forcing data where they are required. In this study we test a topography and climate processor, which is designed for use with large-area land surface simulation, in complex and remote terrain. The scheme is driven entirely by globally available data sets. We simulate air temperature, ground surface temperature and snow depth and test the model with a large network of measurements in the Swiss Alps. We obtain root-mean-squared error (RMSE) values of 0.64 °C for air temperature, 0.67–1.34 °C for non-bedrock ground surface temperature, and 44.5 mm for snow depth, which is likely affected by poor input precipitation field. Due to this we trial a simple winter precipitation correction method based on melt dates of the snowpack. We present a test application of the scheme in the context of simulating mountain permafrost. The scheme produces a permafrost estimate of 2000 km2, which compares well to published estimates. We suggest that this scheme represents a useful step in application of numerical models over large areas in heterogeneous terrain.

Homogenization of Monthly Ground Surface Temperature in China during 1961–2016 and Performances of GLDAS Reanalysis Products

2019 ◽  
Vol 32 (4) ◽  
pp. 1121-1135 ◽  
Author(s):  
Wenhui Xu ◽  
Chenghu Sun ◽  
Jingqing Zuo ◽  
Zhuguo Ma ◽  
Weijing Li ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Southern China ◽  
Ground Surface ◽  
Warming Trend ◽  
Test Method ◽  
The Other ◽  
Central China ◽  
Surface Model ◽  
Ground Surface Temperature

Maps of observed ground surface temperature (GST) in China generally contain inhomogeneities due to relocation of the observation site, changes in observation method, transition to automatic instruments, and so on. By using the observations of collocated manual and automatic weather stations in China, bias in daily GST caused by the transition to automatic observation systems is corrected for the first time in the present work. Then, the inhomogeneities caused by nonclimatic factors (e.g., relocation of the station and change of observation time) in the historical records of monthly GST are further reduced by using the penalized maximal F-test method. Analysis based on this new homogenized dataset reveals that the trend of annual-mean GST in China is approximately 0.273°C decade−1 during 1961–2016. The warming trend is stronger in winter (0.321°C decade−1) and spring (0.312°C decade−1) and weakest in summer (0.173°C decade−1). Spatially, all the stations in China, except for a few stations in southern China, present warming trends in the annual mean and in spring, fall, and winter seasons. In summer, cooling trends are observed in central and southern China. Moreover, we assess the monthly GST from five reanalysis products of the Global Land Data Assimilation System (GLDAS) during 1980–2016. The warming trends of Noah and the Catchment Land Surface Model (CLSM) from GLDAS-V2.0 are the closest to those of the homogenized observation, while the linear trends in the other three products (Noah, CLM, and MOS) from GLDAS-V1 are obviously different from those of the homogenized observation. Also, it is found that the spatial distribution of the warming trend is substantially overestimated in central China but underestimated in the other regions of China in these five GLDAS reanalysis products.

scholarly journals Spatio-Temporal Variations of Soil Active Layer Thickness in Chinese Boreal Forests from 2000 to 2015

2018 ◽  
Vol 10 (8) ◽  
pp. 1225 ◽  
Author(s):  
Xiongxiong Bai ◽  
Jian Yang ◽  
Bo Tao ◽  
Wei Ren
Keyword(s):  
Surface Temperature ◽  
Layer Thickness ◽  
Active Layer ◽  
Air Temperature ◽  
Climate Warming ◽  
Boreal Forests ◽  
Ground Surface ◽  
Active Layer Thickness ◽  
Ground Surface Temperature ◽  
Spatio Temporal

The soil active layer in boreal forests is sensitive to climate warming. Climate-induced changes in the active layer may greatly affect the global carbon budget and planetary climatic system by releasing large quantities of greenhouse gases that currently are stored in permafrost. Ground surface temperature is an immediate driver of active layer thickness (ALT) dynamics. In this study, we mapped ALT distribution in Chinese boreal larch forests from 2000 to 2015 by integrating remote sensing data with the Stefan equation. We then examined the changes of the ALT in response to changes in ground surface temperature and identified drivers of the spatio-temporal patterns of ALT. Active layer thickness varied from 1.18 to 1.3 m in the study area. Areas of nonforested land and low elevation or with increased air temperature had a relatively high ALT, whereas ALT was lower at relatively high elevation and with decreased air temperatures. Interannual variations of ALT had no obvious trend, however, and the ALT changed at a rate of only −0.01 and 0.01 m year−1. In a mega-fire patch of 79,000 ha burned in 2003, ΔALT (ALTi − ALT2002, where 2003 ≤ i ≤ 2015) was significantly higher than in the unburned area, with the influence of the wildfire persisting 10 years. Under the high emission scenario (RCP8.5), an increase of 2.6–4.8 °C in mean air temperature would increase ALT into 1.46–1.55 m by 2100, which in turn would produce a significant positive feedback to climate warming.

scholarly journals Scale-dependent measurement and analysis of ground surface temperature variability in alpine terrain

2011 ◽  
Vol 5 (2) ◽  
pp. 431-443 ◽  
Author(s):  
S. Gubler ◽  
J. Fiddes ◽  
M. Keller ◽  
S. Gruber
Keyword(s):  
Surface Temperature ◽  
Environmental Variables ◽  
Ground Surface ◽  
Multiple Linear Regression Model ◽  
Ground Cover ◽  
Environmental Variable ◽  
Swiss Alps ◽  
Slope Aspect ◽  
Ground Surface Temperature ◽  
Surface Temperatures

Abstract. Measurements of environmental variables are often used to validate and calibrate physically-based models. Depending on their application, the models are used at different scales, ranging from few meters to tens of kilometers. Environmental variables can vary strongly within the grid cells of these models. Validating a model with a single measurement is therefore delicate and susceptible to induce bias in further model applications. To address the question of uncertainty associated with scale in permafrost models, we present data of 390 spatially-distributed ground surface temperature measurements recorded in terrain of high topographic variability in the Swiss Alps. We illustrate a way to program, deploy and refind a large number of measurement devices efficiently, and present a strategy to reduce data loss reported in earlier studies. Data after the first year of deployment is presented. The measurements represent the variability of ground surface temperatures at two different scales ranging from few meters to some kilometers. On the coarser scale, the dependence of mean annual ground surface temperature on elevation, slope, aspect and ground cover type is modelled with a multiple linear regression model. Sampled mean annual ground surface temperatures vary from −4 °C to 5 °C within an area of approximately 16 km2 subject to elevational differences of approximately 1000 m. The measurements also indicate that mean annual ground surface temperatures vary up to 6 °C (i.e., from −2 °C to 4 °C) even within an elevational band of 300 m. Furthermore, fine-scale variations can be high (up to 2.5 °C) at distances of less than 14 m in homogeneous terrain. The effect of this high variability of an environmental variable on model validation and applications in alpine regions is discussed.

scholarly journals Scale-dependent measurement and analysis of ground surface temperature variability in alpine terrain

2011 ◽  
Vol 5 (1) ◽  
pp. 307-338 ◽  
Author(s):  
S. Gubler ◽  
J. Fiddes ◽  
S. Gruber ◽  
M. Keller
Keyword(s):  
Surface Temperature ◽  
Environmental Variables ◽  
Ground Surface ◽  
Ground Cover ◽  
Environmental Variable ◽  
Swiss Alps ◽  
Slope Aspect ◽  
Ground Surface Temperature ◽  
Alpine Regions ◽  
Surface Temperatures

Abstract. Measurements of environmental variables are often used to validate and calibrate physically-based models. Depending on their application, models are used at different scales, ranging from few meters to tens of kilometers. Environmental variables can vary strongly within the grid cells of these models. Validating a model with a single measurement is therefore delicate and susceptible to induce bias in further model applications. To address the question of uncertainty associated with scale in permafrost models, we present data of 390 spatially-distributed ground surface temperature measurements recorded in terrain of high topographic variability in the Swiss Alps. We illustrate a way to program, deploy and refind a large number of measurement devices efficiently, and present a strategy to reduce data loss reported in earlier studies. Data after the first year of deployment is presented. The measurements represent the variability of ground surface temperatures at two different scales ranging from few meters to some kilometers. On the larger scale, the dependence of mean annual ground surface temperature on elevation, slope, aspect and ground cover type is modelled with a linear regression model. Sampled mean annual ground surface temperatures vary from −4 °C to 5 °C within an area of 16 km2 subject to elevational differences of approximately 1000 m. The measurements also indicate that mean annual ground surface temperatures vary up to 6 °C (i.e., from −2 °C to 4 °C) even within an elevational band of 300 m. Furthermore, variations can be high (up to 2.5 °C) at distances of less than 14 m in homogeneous terrain. The effect of this high variability of an environmental variable on model validation and applications in alpine regions is discussed.

scholarly journals Effects of groundwater pumping on ground surface temperature: A regional modeling study in the North China Plain

2020 ◽  
Author(s):  
You-Kuan Zhang ◽  
Chen Yang ◽  
Xiaofan Yang
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
North China Plain ◽  
North China ◽  
Ground Surface ◽  
Temporal Variations ◽  
Heat Fluxes ◽  
Ground Surface Temperature ◽  
The North ◽  
The North China Plain

<p>It is recognized that groundwater (GW) may play an important role in the subsurface–land-surface–atmosphere system and that pumping of GW may affect soil moisture which in turn influences local weather and climate through land-atmosphere interactions. In this study effects of GW pumping on ground surface temperature (GST) in the North China Plain (NCP) were investigated with a coupled ParFlow.CLM model of subsurface and land-surface processes and their interactions. The model was validated using the water and energy fluxes reported in previous studies and from the JRA-55 reanalysis. Numerical experiments were designed to examine the impacts of GW pumping and irrigation on GST. Results show significant effects of GW pumping on GST in the NCP. Generally, the subsurface acts as a buffer to temporal variations in heat fluxes at the land-surface, but long-term pumping can gradually weaken this buffer, resulting in increases in the spatio-temporal variability of GST, as exemplified by hotter summers and colder winters. Considering that changes of water table depth (WTD) can significantly affect land surface heat fluxes when WTD ranges between 1–10 m, the 0.5 m/year increase of WTD simulated by the model due to pumping can continue to raise GST for about 20 years from the pre-pumping WTD in the NCP. The increase of GST is expected to be faster initially and gradually slow down. The findings from this study may implicate similar GST increases may occur in other regions with GW depletion.</p>

scholarly journals Effect of the Silvopastoral System on the Thermal Comfort of Lambs in a Subtropical Climate: A Preliminary Study

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 790
Author(s):  
Frederico Márcio C. Vieira ◽  
Jaqueline Agnes Pilatti ◽  
Zilmara Maria Welfer Czekoski ◽  
Vinicius F. C. Fonsêca ◽  
Piotr Herbut ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Heat Load ◽  
Thermal Environment ◽  
Ground Surface ◽  
Radiant Heat ◽  
Silvopastoral System ◽  
Hair Coat ◽  
Ground Surface Temperature ◽  
Preliminary Study

The silvopastoral system has the potential to alleviate the negative impacts of heat stress on livestock. Through a preliminary study, we assessed the thermal environment experienced by hair coat lambs, as well as the impacts on their bio-thermal and behavioural responses, when either kept in either the silvopastoral system, or exposed to full sun. Twelve hair coat lambs (Dorper × Santa Ines) were randomly assigned to a silvopastoral system or full sun exposure during the summer (from January to February 2017). Parameters, including air temperature, black globe temperature, relative humidity, wind speed, and ground surface temperature, were measured daily for both thermal environments. From 14:00 to 16:00, lambs kept in silvopastoral areas experienced lower levels of air temperature, radiant heat load, and ground surface temperature. Consequently, they had a lower hair coat surface and lower body rectal temperatures. Lambs exposed to a shaded environment spent more time grazing and walking, and less time standing at rest. In conclusion, lambs kept in a silvopastoral system experienced lower levels of radiant heat load and ground surface temperature. In addition, the animals showed a reduced requirement for evaporative cooling and expressed behaviours that indicated a comfortable thermal environment.

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