scholarly journals The Effect of Snow Depth on Spring Wildfires on the Hulunbuir from 2001–2018 Based on MODIS

2019 ◽  
Vol 11 (3) ◽  
pp. 321 ◽  
Author(s):  
Hong Ying ◽  
Yu Shan ◽  
Hongyan Zhang ◽  
Tao Yuan ◽  
Wu Rihan ◽  
...  
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Hurst Exponent ◽  
Linear Regression Analysis ◽  
Sustainable Use ◽  
Estimation Method ◽  
Burned Area ◽  
Natural Ecosystems ◽  
Wildfire Occurrence ◽  
The Impact

Wildfires are one of the important disturbance factors in natural ecosystems and occur frequently around the world. Detailed research on the impact of wildfires is crucial not only for the development of livestock husbandry but also for the sustainable use of natural resources. In this study, based on the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MC464A1 and site snow depth measurements, the kernel density estimation method (KDE), unary linear regression analysis, Sen + Mann-Kendall trend analysis, correlation analysis, and R/S analysis were used to evaluate the relationship between snow and spring wildfires (SWFs) in Hulunbuir. Our results indicated that SWFs decreased during the period of 2001–2018, were mainly distributed in the eastern portion of the study area, and that the highest SWF density was 7 events/km2. In contrast, the maximum snow depth increased during the period of 2001–2018 and the snow depth was deeper in the middle but shallower in the east and west. The SWFs and snow depth have significant negative correlations over space and time. The snow depth mainly affects the occurrence of SWFs indirectly by affecting the land surface temperature (LST) and Land Surface Water Index (LSWI) in spring. The snow depth was positively correlated with the LSWI in most of Hulunbuir and strongly negatively correlated with the LST, and this correlation was stronger in the eastern and western regions of Hulunbuir. The results of the Hurst exponent indicated that in the future, the snow depth trend will be opposite that of the current state, meaning that the trend of decreasing snow depth will increase dramatically in most of the study area, and SWFs may become more prominent. According to the validation results, the Hurst exponent is a reliable method for predicting the snow depth tendency. This research can be based on the snow conditions of the previous year to identify areas where fires are most likely to occur, enabling an improved and more targeted preparation for spring fire prevention. Additionally, the present study expands the theory and methods of wildfire occurrence research and promotes research on disasters and disaster chains.

scholarly journals Spatio-Temporal Variations of Carbon Use Efficiency in Natural Terrestrial Ecosystems and the Relationship with Climatic Factors in the Songnen Plain, China

2019 ◽  
Vol 11 (21) ◽  
pp. 2513 ◽  
Author(s):  
Bo Li ◽  
Fang Huang ◽  
Lijie Qin ◽  
Hang Qi ◽  
Ning Sun
Keyword(s):  
Primary Productivity ◽  
Land Surface ◽  
Climatic Factors ◽  
Transitional Region ◽  
Songnen Plain ◽  
Natural Ecosystems ◽  
Land Surface Phenology ◽  
Carbon Use Efficiency ◽  
Use Efficiency ◽  
The Impact

The Songnen Plain (SNP) is an important grain production base, and is designated as an ecological red-line as a protected area in China. Natural ecosystems such as the ecological protection barrier play an important role in maintaining the productivity and sustainability of farmland. Carbon use efficiency (CUE), defined as the ratio of net primary productivity (NPP) to gross primary productivity (GPP), represents the ecosystem capacity of transferring carbon from the atmosphere to terrestrial biomass. The understanding of the CUE of natural ecosystems in protected farmland areas is vital to predicting the impact of global change and human disturbances on carbon budgets and evaluating ecosystem functions. To date, the changes in CUE at different time scales and their relationships with climatic factors have yet to be fully understood. CUE and the response to land surface phenology are also deserving attention. In this study, variations in ecosystem CUE in the SNP during 2001–2015 were investigated using Moderate-Resolution Imaging Spectroradiometer (MODIS) GPP and NPP data products estimated using the Carnegie-Ames-Stanford approach (CASA) model. The relationships between CUE and phenological and climate factors were explored. The results showed that ecosystem CUE fluctuated over time in the SNP. The lowest and highest CUE values mainly occurred in May and October, respectively. At seasonal scale, average CUE followed a descending order of Autumn > Summer > Spring. The CUE of mixed forest was greater than that of other ecosystems at both monthly and seasonal scales. Land surface phenology plays an important role in the regulation of CUE. The earlier start (SOS), the later end (EOS) and longer length (LOS) of the growing season would contribute increasing of CUE. Precipitation and temperature affected CUE positively in most areas of the SNP. These findings help explain the CUE of natural ecosystems in the protected farmland areas and improve our understanding of ecosystem carbon allocation dynamics in temperate semi-humid to semi-arid transitional region under climate and phenological fluctuations.

scholarly journals Competing roles of rising CO<sub>2</sub> and climate change in the contemporary European carbon balance

2007 ◽  
Vol 4 (4) ◽  
pp. 2385-2405 ◽  
Author(s):  
R. Harrison ◽  
C. Jones
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Carbon Balance ◽  
Atmospheric Co2 ◽  
Natural Ecosystems ◽  
Climate Effect ◽  
Carbon Cycle Model ◽  
Land Use And Management ◽  
The Impact ◽  
And Storage

Abstract. Natural ecosystems respond to, and may affect climate change through uptake and storage of atmospheric CO2. Here we use the land-surface and carbon cycle model JULES to simulate the contemporary European carbon balance and its sensitivity to rising CO2 and changes in climate. We find that the impact of climate change is to decrease the ability of Europe to store carbon by about 175 TgC yr−1. In contrast, the effect of rising atmospheric CO2 has been to stimulate increased uptake and storage. The CO2 effect is currently dominant leading to a net increase of around 150 TgC yr−1. Our simulations do not at present include other important factors such as land use and management, the effects of forest age classes and nitrogen deposition. There seems to be an emerging consensus that changes in climate will weaken the European land-surface's ability to take up and store carbon. It is likely that this effect is happening at the present and will continue even more strongly in the future as climate continues to change. Although CO2 enhanced growth currently exceeds the climate effect, this may not continue indefinitely. Understanding this balance and its implications for mitigation policies is becoming increasingly important.

scholarly journals THE IMPACT OF TECHNOLOGICAL CAPACITY DEVELOPMENT ON THE LABOR MARKET CONDITION. THE CASE OF THE DOLNOŚLĄSKIE AND LUBUSKIE VOIVODSHIPS

2020 ◽  
Vol 50 (50) ◽  
pp. 13-20
Author(s):  
Łukasz Grzęda ◽  
Sylwester Kozak
Keyword(s):  
Labour Market ◽  
Positive Impact ◽  
Linear Regression Analysis ◽  
Estimation Method ◽  
Data Bank ◽  
Market Condition ◽  
University Graduates ◽  
Local Data ◽  
Technological Capacity ◽  
The Impact

The aim of the article is to assess the impact of the development of technological capacity on the labour market in Poland, in voivodships with significantly different GDP levels: the Dolnośląskie and nearly four times smaller the Lubuskie. Data for the years 2002-2017 were obtained from the CSO Local Data Bank. The research used linear regression analysis and the OLS estimation method. The results have shown that the expenditure on R&D are not a positive factor of employment growth in both voivodships, which may be due to their low values in relation to GDP (on average around 0.5%). The number of students and universities had a positive impact on the labour market in both regions. The catch-up effect and accelerated development recorded in the second part of the examined period in the Lubuskie could have had an impact on better absorption of university graduates and registered patents by businesses and their positive impact on employment levels, in contrast to the Dolnośląskie. Improvement in the economic situation in both voivodships and in the whole country also had a positive impact on the improvement of conditions on the labour market.

scholarly journals Impact of Surface Albedo Assimilation on Snow Estimation

2020 ◽  
Vol 12 (4) ◽  
pp. 645 ◽  
Author(s):  
Sujay Kumar ◽  
David Mocko ◽  
Carrie Vuyovich ◽  
Christa Peters-Lidard
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Snow Accumulation ◽  
High Plains ◽  
Surface Model ◽  
Fractional Snow Cover ◽  
The Impact

Surface albedo has a significant impact in determining the amount of available net radiation at the surface and the evolution of surface water and energy budget components. The snow accumulation and timing of melt, in particular, are directly impacted by the changes in land surface albedo. This study presents an evaluation of the impact of assimilating Moderate Resolution Imaging Spectroradiometer (MODIS)-based surface albedo estimates in the Noah multi-parameterization (Noah-MP) land surface model, over the continental US during the time period from 2000 to 2017. The evaluation of simulated snow depth and snow cover fields show that significant improvements from data assimilation (DA) are obtained over the High Plains and parts of the Rocky Mountains. Earlier snowmelt and reduced agreements with reference snow depth measurements, primarily over the Northeast US, are also observed due to albedo DA. Most improvements from assimilation are observed over locations with moderate vegetation and lower elevation. The aggregate impact on evapotranspiration and runoff from assimilation is found to be marginal. This study also evaluates the relative and joint utility of assimilating fractional snow cover and surface albedo measurements. Relative to surface albedo assimilation, fractional snow cover assimilation is found to provide smaller improvements in the simulated snow depth fields. The configuration that jointly assimilates surface albedo and fractional snow cover measurements is found to provide the most beneficial improvements compared to the univariate DA configurations for surface albedo or fractional snow cover. Overall, the study also points to the need for improving the albedo formulations in land surface models and the incorporation of observational uncertainties within albedo DA configurations.

scholarly journals Assessing the Impact of Climate Variability on Wildfires in the N’Zi River Watershed in Central Côte d’Ivoire

Fire ◽  
2018 ◽  
Vol 1 (3) ◽  
pp. 36 ◽  
Author(s):  
Jean-Luc Kouassi ◽  
Narcisse Wandan ◽  
Cheikh Mbow
Keyword(s):  
Climate Variability ◽  
Cote D'ivoire ◽  
Côte D’Ivoire ◽  
Additive Models ◽  
Burned Area ◽  
Cote D’Ivoire ◽  
River Watershed ◽  
Wildfire Occurrence ◽  
Côte D'ivoire ◽  
The Impact

This study evaluates the impact of climate variability on wildfire regime in the N’Zi River Watershed (NRW) in central Côte d’Ivoire. For that purpose, MODIS active fire and monthly burned area data are used to evaluate wildfire occurrence, impacts and trends. Wildfire data are compared to past trends of different climatic parameters extracted from long-term meteorological records. Generalized additive models and Spearman correlations are used to evaluate the relationships between climate variables and wildfire occurrence. Seasonal Kendall and Sen’s slope methods were used for trend analysis. Results showed that from 2001 to 2016, 19,156 wildfire occurrences are recorded in the NRW, of which 4443 wildfire events are observed in forest, 9536 in pre-forest, and 5177 in Sudanian zones. The burned areas are evaluated at 71,979.7 km2, of which 10,488.41 km2 were registered in forest, 33,211.96 km2 in pre-forest, and 28,279.33 km2 in Sudanian zones. A downward trend is observed in fire records. The results indicates a strong correlation between some climatic variables and wildfire regime in this ecoregion. These correlations can be used to develop models that could be used as prediction tools for better management of fire regimes and support decision-making in the NRW.

Technology Lecture. Land restoration: now and in the future

1984 ◽  
Vol 223 (1230) ◽  
pp. 1-23 ◽  
Keyword(s):  
Land Surface ◽  
Ecological Knowledge ◽  
Land Restoration ◽  
Natural Ecosystems ◽  
Degraded Land ◽  
Ecological Processes ◽  
Proper Understanding ◽  
Productive Land ◽  
The Right ◽  
The Impact

Derelict and degraded land destroys amenity, causes pollution and is a waste of productive land surface. Despite the worldwide activity to restore it there is an enormous backlog, which in England has increased since 1974. In the past much of this restoration was empirically based and not always successful. But natural ecosystems develop unaided on raw starting materials by natural ecological processes. A proper understanding of these has led to more reliable and inexpensive restoration techniques. At the same time we have come to realize that, because, at the start, the slate has been wiped clean, many different end points are possible. Derelict land is a challenge and opportunity for creative manipulation of our landscape. Yet what is achieved in practice is often pedestrian, unscientific and uneconomic. Often the simple treatments that would minimize the impact of industrial activity, and would set the restoration off early and in the right direction, are not carried out. Yet there are plenty of good examples of what can be done. It appears that once more we may be victims of the British failure in technology and imagination transfer. For this the fault seems to lie broadly, not only with planners, industrialists and government, for not always making sure something is done, but also with scientists, for not applying their ecological knowledge sufficiently to problems of hard practice.

scholarly journals Quantifying the Added Value of Snow Cover Area Observations in Passive Microwave Snow Depth Data Assimilation

2015 ◽  
Vol 16 (4) ◽  
pp. 1736-1741 ◽  
Author(s):  
Sujay V. Kumar ◽  
Christa D. Peters-Lidard ◽  
Kristi R. Arsenault ◽  
Augusto Getirana ◽  
David Mocko ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Accurate Determination ◽  
Passive Microwave ◽  
Added Value ◽  
Surface Model ◽  
Snow Cover Area ◽  
The Impact

Abstract Accurate determination of snow conditions is important for several water management applications, partly because of the significant influence of snowmelt on seasonal streamflow prediction. This article examines an approach using snow cover area (SCA) observations as snow detection constraints during the assimilation of snow depth retrievals from passive microwave sensors. Two different SCA products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS)] are employed jointly with the snow depth retrievals from a variety of sensors for data assimilation in the Noah land surface model. The results indicate that the use of MODIS data is effective in obtaining added improvements (up to 6% improvement in aggregate RMSE) in snow depth fields compared to assimilating passive microwave data alone, whereas the impact of IMS data is small. The improvements in snow depth fields are also found to translate to small yet systematic improvements in streamflow estimates, especially over the western United States, the upper Missouri River, and parts of the Northeast and upper Mississippi River. This study thus demonstrates a simple approach for exploiting the information from SCA observations in data assimilation.

scholarly journals Droughts and heatwaves in the Western Mediterranean: impact on vegetation and wildfires using the coupled WRF-ORCHIDEE regional model (RegIPSL)

2021 ◽  
Author(s):  
Antoine Guion ◽  
Solène Turquety ◽  
Jan Polcher ◽  
Romain Pennel ◽  
Sophie Bastin ◽  
...  
Keyword(s):  
Extreme Events ◽  
Land Surface ◽  
Synergistic Effects ◽  
Regional Model ◽  
Western Mediterranean ◽  
Extreme Weather Events ◽  
Fuel Load ◽  
Burned Area ◽  
Area Index ◽  
The Impact

AbstractDroughts and heatwaves in the Mediterranean can induce plant activity decline and severe wildfires leading to considerable economic, social and environmental damages. This study aims at statistically quantifying the isolated and combined impacts of these extreme events based on a combination of regional land surface-atmosphere modeling and satellite observations of surface properties (MODIS). A simulation by the RegIPSL coupled regional model (ORCHIDEE-WRF) over the 1979–2016 period in the Western Mediterranean is used to identify heatwaves and droughts. After an evaluation of the model performance against surface observations of temperature and precipitation, a spatio-temporal analysis is conducted using specific indicators of extreme events: Percentile Limit Anomalies (PLA) and the Standardized Precipitation Evapotranspiration Index (SPEI). The impact on vegetation and wildfires is assessed using the MODIS observations of Leaf Area Index (LAI), burned area (BA) and fire radiative power (FRP), clustered by simulated extreme weather events. Due to water stress, droughts lead to significant biomass decrease (− 10$$\%$$ % LAI on average and reaching − 23$$\%$$ % in some areas). The isolated effect of heatwaves is smaller ($$\sim$$ ∼  − 3$$\%$$ % LAI) so that the combined effect is dominated by the impact of droughts. Heatwaves and droughts significantly exacerbate wildfire regimes. Through synergistic effects, simultaneous droughts and heatwaves increase BA and FRP by 2.1 and 2.9 times, respectively, compared to normal conditions. By reducing biomass, droughts slightly decrease fuel availability. However, our results show that the inter-annual variation in fire activity is mainly driven by weather conditions rather than fuel load.

scholarly journals Competing roles of rising CO<sub>2</sub> and climate change in the contemporary European carbon balance

2008 ◽  
Vol 5 (1) ◽  
pp. 1-10 ◽  
Author(s):  
R. G. Harrison ◽  
C. D. Jones ◽  
J. K. Hughes
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Carbon Balance ◽  
Atmospheric Co2 ◽  
Natural Ecosystems ◽  
Age Classes ◽  
Carbon Cycle Model ◽  
Land Use And Management ◽  
The Impact ◽  
And Storage

Abstract. Natural ecosystems respond to, and may affect climate change through uptake and storage of atmospheric CO2. Here we use the land-surface and carbon cycle model JULES to simulate the contemporary European carbon balance and its sensitivity to rising CO2 and changes in climate. We find that the impact of climate change is to decrease the ability of Europe to store carbon by 97 TgC yr−1. In contrast, the effect of rising atmospheric CO2 has been to stimulate increased uptake and storage. The CO2 effect is currently dominant leading to a net increase of 114 TgC yr−1. Our simulations do not at present include other important factors such as land use and management, the effects of forest age classes and nitrogen deposition. Understanding this balance and its implications for mitigation policies is becoming increasingly important.

The Impact of Snow Model Complexity at Three CLPX Sites

2008 ◽  
Vol 9 (6) ◽  
pp. 1464-1481 ◽  
Author(s):  
Xia Feng ◽  
Alok Sahoo ◽  
Kristi Arsenault ◽  
Paul Houser ◽  
Yan Luo ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Model Complexity ◽  
Snow Albedo ◽  
Community Land Model ◽  
Snow Model ◽  
The Impact

Abstract Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters. All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics. A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.

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