Wildfires promoted by contrasting soil moisture anomalies in humid versus arid regions

2020 ◽  
Author(s):  
Sungmin Oh ◽  
Xinyuan Hou ◽  
Rene Orth
Keyword(s):  
Soil Moisture ◽  
Arid Regions ◽  
Large Scale ◽  
Natural Soil ◽  
Fire Occurrence ◽  
Human Influence ◽  
Fire Modelling ◽  
Dry Soil ◽  
Spatio Temporal ◽  
Global Carbon

<p>Wildfires are essential for ecosystem development, thereby affecting the global carbon cycle. Soil moisture is a major driver of wildfires, however, due to a lack of large-scale observations it remains unclear which spatio-temporal soil moisture patterns promote wildfires. Using satellite-based soil moisture data, we show contrasting soil moisture anomalies preceding the locally largest wildfires in space and time. In arid regions wetter-than-average soils enable sufficient biomass growth required to fuel fires. By contrast, in humid regions fires are typically preceded by dry soil moisture anomalies inducing suitable ignition conditions and flammability in an otherwise too wet environment. In both regions, soil moisture anomalies are continuously decreasing over the months before the fire occurrence, often from above-normal to below-normal. These signals are most pronounced for larger fires in sparsely populated areas with low human influence. Resolving natural soil moisture-fire interactions supports fire modelling and enables improved fire forecasts and early warning.</p>

scholarly journals Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Nitu Ojha ◽  
Olivier Merlin ◽  
Christophe Suere ◽  
Maria José Escorihuela
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arid Regions ◽  
Data Sets ◽  
Sparse Vegetation ◽  
Thermal Data ◽  
Spatio Temporal ◽  
Temporal Coverage ◽  
Semi Arid

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

Spatio-temporal comparison of different approaches to derive land use and land cover change emissions by models

2021 ◽  
Author(s):  
Wolfgang Obermeier ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Agricultural Land ◽  
Land Cover Changes ◽  
Legacy Effects ◽  
Equatorial Africa ◽  
The Usa ◽  
Spatio Temporal ◽  
Global Carbon

<p>The quantification of the net carbon flux from land use and land cover changes (f<sub>LULCC</sub>) is essential to understand the global carbon cycle, and consequently, to support climate change mitigation. However, large-scale f<sub>LULCC</sub> is not directly measurable, and can only be inferred by models, such as semi-empirical bookkeeping models, and process-based dynamic global vegetation models (DGVMs). By definition, f<sub>LULCC</sub> estimates between these two model types are not directly comparable. For example, transient DGVM-based f<sub>LULCC</sub> of the annual global carbon budget includes the so-called Loss of Additional Sink Capacity (LASC). The latter accounts for environmental impacts on the land carbon storage capacities of managed land compared to potential vegetation which is not included in bookkeeping models. Additionally, estimates of transient DGVM-based f<sub>LULCC</sub> differ from bookkeeping model estimates, since they depend on arbitrarily chosen simulation time periods and the timing of land use and land cover changes within the historic period (which includes different accumulation periods for legacy effects). However, DGVMs enable a f<sub>LULCC</sub> approximation independent of the timing of land use and land cover changes and their legacy effects by simulations run under constant pre-industrial or present-day environmental forcings.</p><p>In this study, we analyze these different DGVM-derived f<sub>LULCC</sub> definitions, under transiently changing environmental conditions and fixed pre-industrial and fixed present-day conditions, within 18 regions for twelve DGVMs and quantify their differences as well as climate- and CO<sub>2</sub>-induced components. The multi model mean under transient conditions reveals a global f<sub>LULCC</sub> of 2.0±0.6 PgC yr<sup>-1</sup> for 2009-2018, with ~40% stemming from the LASC (0.8±0.3 PgC yr<sup>-1</sup>). Within the industrial period (1850 onward), cumulative f<sub>LULCC</sub> reached 189±56 PgC with 40±15 PgC from the LASC.</p><p>Regional hotspots of high LASC values exist in the USA, China, Brazil, Equatorial Africa and Southeast Asia, which we mainly relate to deforestation for cropland. Distinct negative LASC estimates were observed in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land). Negative LASC estimates indicate that fLULCC estimates in these regions are lower in transient DGVM simulations compared to bookkeeping-approaches. By unraveling the spatio-temporal variability of the different DGVM-derived f<sub>LULCC</sub> estimates, our study calls for a harmonized attribution of model-derived f<sub>LULCC</sub>. We propose an approach that bridges bookkeeping and DGVM approaches for f<sub>LULCC</sub> estimation by adopting a mean DGVM-ensemble LASC for a defined reference period.</p>

scholarly journals Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin

2013 ◽  
Vol 17 (4) ◽  
pp. 1401-1414 ◽  
Author(s):  
M. Nied ◽  
Y. Hundecha ◽  
B. Merz
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Large Scale ◽  
Principal Component ◽  
Temporal Analysis ◽  
Elbe River ◽  
High Soil Moisture ◽  
Elbe River Basin ◽  
Spatio Temporal ◽  
The Elbe River

Abstract. Floods are the result of a complex interaction between meteorological event characteristics and pre-event catchment conditions. While the large-scale meteorological conditions have been classified and successfully linked to floods, this is lacking for the large-scale pre-event catchment conditions. Therefore, we propose classifying soil moisture as a key variable of pre-event catchment conditions and investigating the link between soil moisture patterns and flood occurrence in the Elbe River basin. Soil moisture is simulated using a semi-distributed conceptual rainfall-runoff model over the period 1951–2003. Principal component analysis (PCA) and cluster analysis are applied successively to identify days of similar soil moisture patterns. The results show that PCA considerably reduced the dimensionality of the soil moisture data. The first principal component (PC) explains 75.71% of the soil moisture variability and represents the large-scale seasonal wetting and drying. The successive PCs express spatially heterogeneous catchment processes. By clustering the leading PCs, we identify large-scale soil moisture patterns which frequently occur before the onset of floods. In winter, floods are initiated by overall high soil moisture content, whereas in summer the flood-initiating soil moisture patterns are diverse and less stable in time.

scholarly journals Large-scale biospheric drought response intensifies linearly with drought duration in arid regions

2020 ◽  
Vol 17 (9) ◽  
pp. 2647-2656 ◽  
Author(s):  
René Orth ◽  
Georgia Destouni ◽  
Martin Jung ◽  
Markus Reichstein
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Arid Regions ◽  
Large Scale ◽  
Crop Yields ◽  
Terrestrial Ecosystems ◽  
Drought Response ◽  
Drought Duration ◽  
Vegetation Models ◽  
Model Benchmark

Abstract. Soil moisture droughts have comprehensive implications for terrestrial ecosystems. Here we study time-accumulated impacts of the strongest observed droughts on vegetation. The results show that drought duration, the time during which surface soil moisture is below seasonal average, is a key diagnostic variable for predicting drought-integrated changes in (i) gross primary productivity, (ii) evapotranspiration, (iii) vegetation greenness, and (iv) crop yields. Drought-integrated anomalies in these vegetation-related variables scale linearly with drought duration with a slope depending on climate. In arid regions, the slope is steep such that vegetation drought response intensifies with drought duration, whereas in humid regions, it is small such that drought impacts on vegetation are weak even for long droughts. These emergent large-scale linearities are not well captured by state-of-the-art hydrological, land surface, and vegetation models. Overall, the linear relationship of drought duration versus vegetation response and crop yield reductions can serve as a model benchmark and support drought impact interpretation and prediction.

scholarly journals Simulation of soil moisture for typical plain region using the Variable Infiltration Capacity model

2015 ◽  
Vol 368 ◽  
pp. 215-220 ◽  
Author(s):  
Z. Wu ◽  
Y. Mao ◽  
G. Lu ◽  
J. Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Social Economy ◽  
Moisture Distribution ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Variable Infiltration Capacity Model ◽  
Capacity Model ◽  
Spatio Temporal ◽  
River Plain

Abstract. Droughts have a severe impact on the development of the social economy in developed plain areas. Soil moisture is a good index, it can reasonably reflect changes in drought. In this study, Jiangsu province in the Yangtze River Plain was selected as the research region, and the VIC (Variable Infiltration Capacity) large-scale hydrological model was selected to simulate the daily soil moisture with a resolution of 0.125 × 0.125 degree from 1956 to 2009. The simulated soil moisture was verified by measured soil moisture. The results indicate that the simulated soil moisture distribution is relatively consistent for the three soil layers (0–20, 20−100 and 0–100 cm), showing an increasing trend from northwest to southeast. The simulated soil moisture anomalies agreed well with in situ observations. The simulated soil moisture data thus can be used to analyze the spatio-temporal variation of the regional water content and to provide support for drought monitoring and forecasting.

scholarly journals Soil solarization based on natural soil moisture: a practical approach for reducing the seed bank of invasive plants in wetlands

NeoBiota ◽  
2019 ◽  
Vol 51 ◽  
pp. 1-18
Author(s):  
Oded Cohen ◽  
Abraham Gamliel ◽  
Jaacov Katan ◽  
Iris Shubert ◽  
Aviv Guy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Seed Bank ◽  
Large Scale ◽  
Invasive Plant ◽  
Early Stage ◽  
Natural Vegetation ◽  
Soil Solarization ◽  
Natural Soil ◽  
Natural Ecosystems ◽  
Natural Habitats

Soil solarization is a well-established method to disinfect soil for efficient weed control. However, the feasibility of applying this method in the restoration of invaded natural habitats is unclear. This is because soil moisture is necessary for the success of solarization, but pre-irrigation in natural ecosystems is often not applicable, or demands high labor investment, making it unsuitable for use in restoration. The present study was based on the idea that the relatively high soil moisture in wetlands might obviate the need for pre-irrigation, rendering this method much more applicable in natural habitats. We examined the efficacy of soil solarization using natural soil moisture to control the seed bank of the invasive plant, Acacia saligna, in a wetland, using large-scale experimental plots (0.38 ha each). An old, dense A. saligna grove was cut down and the roots were removed by a bulldozer. The plot was mulched with a transparent polyethylene sheet in early July and left on the soil for 14 weeks. Soil solarization significantly reduced the viability of seeds of A. saligna that had been experimentally buried. Additionally, viability of seeds in the natural seed bank was reduced, and seedling emergence was close to zero. Exposing seeds to soil temperature and soil moisture levels equivalent to those obtained during field soil solarization under controlled conditions significantly increased the release from dormancy of the seeds, suggesting that release from dormancy during the early stage of solarization is a critical stage leading to seed weakening or mortality in the soil. Soil solarization also decreased the cover and abundance of the natural vegetation; therefore, active revegetation is required to restore the natural vegetation and to conserve endangered and endemic species.

scholarly journals Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe river basin

2012 ◽  
Vol 9 (9) ◽  
pp. 10053-10094
Author(s):  
M. Nied ◽  
Y. Hundecha ◽  
B. Merz
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Large Scale ◽  
Principal Component ◽  
Temporal Analysis ◽  
Elbe River ◽  
High Soil Moisture ◽  
Elbe River Basin ◽  
Spatio Temporal ◽  
The Elbe River

Abstract. Floods are the result of a complex interaction between meteorological event characteristics and pre-event catchment conditions. While the large-scale meteorological conditions have been classified and successfully linked to floods, this is lacking for the large-scale pre-event catchment conditions. Therefore, we propose to classify soil moisture as a key variable of pre-event catchment conditions and to investigate the link between soil moisture patterns and flood occurrence in the Elbe river basin. Soil moisture is simulated using a semi-distributed conceptual rainfall-runoff model over the period 1951–2003. Principal component analysis (PCA) and cluster analysis are applied successively to identify days of similar soil moisture patterns. The results show that PCA considerably reduced the dimensionality of the soil moisture data. The first principal component (PC) explains 75.71% of the soil moisture variability and represents the large-scale seasonal wetting and drying. The successive PCs express the spatial heterogeneous antecedent catchment conditions. By clustering the leading PCs, we detected large-scale soil moisture patterns which frequently occur before the onset of floods. In winter floods are initiated by overall high soil moisture content whereas in summer the flood initiating soil moisture patterns are diverse and less stable in time. The results underline the importance of large-scale pre-event catchment conditions in flood initiation.

PROTOTIPE APLIKASI PENYIRAMAN TANAMAN MENGGUNAKAN SENSOR KELEMBABAN TANAH BERBASIS MICRO CONTOLLER ATMEGA 328

2019 ◽  
Vol 5 (1) ◽  
pp. 97-106
Author(s):  
Rudi Budi Agung ◽  
Muhammad Nur ◽  
Didi Sukayadi
Keyword(s):  
Soil Moisture ◽  
Growth And Development ◽  
Large Scale ◽  
Simple Method ◽  
Moisture Sensor ◽  
Sensor Development ◽  
Soil Moisture Sensor ◽  
Application Systems ◽  
Harvesting Systems ◽  
Working Principle

The Indonesian country which is famous for its tropical climate has now experienced a shift in two seasons (dry season and rainy season). This has an impact on cropping and harvesting systems among farmers. In large scale this is very influential considering that farmers in Indonesia are stilldependent on rainfall which results in soil moisture. Some types of plants that are very dependent on soil moisture will greatly require rainfall or water for growth and development. Through this research, researchers tried to make a prototype application for watering plants using ATMEGA328 microcontroller based soil moisture sensor. Development of application systems using the prototype method as a simple method which is the first step and can be developed again for large scale. The working principle of this prototype is simply that when soil moisture reaches a certainthreshold (above 56%) then the system will work by activating the watering system, if it is below 56% the system does not work or in other words soil moisture is considered sufficient for certain plant needs.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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