scholarly journals The impact of drought on soil moisture trends across Brazilian biomes

2021 ◽  
Vol 21 (3) ◽  
pp. 879-892
Author(s):  
Flavio Lopes Ribeiro ◽  
Mario Guevara ◽  
Alma Vázquez-Lule ◽  
Ana Paula Cunha ◽  
Marcelo Zeri ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Atlantic Forest ◽  
National Level ◽  
European Space Agency ◽  
Warning System ◽  
Water Security ◽  
Drought Risk ◽  
Positive Trend ◽  
Adaptation To Climate Change ◽  
The Impact

Abstract. Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes responds to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa and Pantanal. We found an overall soil moisture decline of −0.5 % yr−1 (p<0.01) at the national level. At the biome level, Caatinga presented the most severe soil moisture decline (−4.4 % yr−1), whereas the Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but had a sharp reduction of soil moisture from 2013 to 2015. In contrast, the Pampa and Pantanal biomes presented a positive trend (1.6 % yr−1 and 4.3 % yr−1, respectively). These trends are consistent with vegetation productivity trends across each biome. This information provides insights into drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome.

scholarly journals The Impact of Drought on Soil Moisture Trends across Brazilian Biomes

2020 ◽  
Author(s):  
Flavio Lopes Ribeiro ◽  
Mario Guevara ◽  
Alma Vázquez-Lule ◽  
Ana Paula Cunha ◽  
Marcelo Zeri ◽  
...  
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Water Security ◽  
Direct Impact ◽  
The Past ◽  
Space Agency ◽  
The Impact ◽  
Soil Moisture Variability ◽  
Moisture Dynamics ◽  
Agriculture Water

Abstract. Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on agriculture, water security, and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes respond to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: The Amazon, Atlantic Forest, Caatinga, Cerrado, Pampas and Pantanal. We found an overall soil moisture decline of −0.5 %/year (p 

scholarly journals Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

2020 ◽  
Author(s):  
Mario Guevara ◽  
Michela Taufer ◽  
Rodrigo Vargas
Keyword(s):  
Pattern Recognition ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Soil Type ◽  
European Space Agency ◽  
Terrain Parameters ◽  
Precipitation Records ◽  
The Impact ◽  
Type Information

Abstract. Soil moisture is key for quantifying soil-atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency-Climate Change Initiative v4.5) soil moisture dataset, which contains more than 40 years of satellite soil moisture global grids with a spatial resolution of ~27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict the finer-grained satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating the pattern recognition of soil moisture with and without the support of bioclimatic and soil type information. The outcome is a new dataset of gap-free global mean annual soil moisture and uncertainty for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, n = 13376) and in situ precipitation records (n = 4909) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r = 0.69 to r = 0.87 with root mean squared errors (RMSE, m3/m3) around 0.03 and 0.04. Our soil moisture predictions improve: (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r = 0.30 (RMSE = 0.09, ubRMSE = 0.37) to r = 0.66 (RMSE = 0.05, ubRMSE = 0.18); and (b) the correlation with local precipitation records across boreal (from r = 

scholarly journals Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1900
Author(s):  
Cong Yin ◽  
Ernesto Lopez-Baeza ◽  
Manuel Martin-Neira ◽  
Roberto Fernandez-Moran ◽  
Lei Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Circular Polarization ◽  
European Space Agency ◽  
In Situ Measurements ◽  
Anchor Station ◽  
L Band ◽  
The Impact ◽  
Band Radiometer ◽  
Soil Moisture Monitoring

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.

scholarly journals Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records

2020 ◽  
Vol 12 (20) ◽  
pp. 3439
Author(s):  
Mendy van der Vliet ◽  
Robin van der Schalie ◽  
Nemesio Rodriguez-Fernandez ◽  
Andreas Colliander ◽  
Richard de Jeu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Data Availability ◽  
Climate Data ◽  
Mountainous Regions ◽  
Soil Moisture Data ◽  
Climate Records ◽  
The Individual ◽  
The Impact

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.

scholarly journals DERIVATION OF POPULATION DISTRIBUTION BY COMBINING CENSUS AND LANDUSE DATA: AS AN INPUT FOR TSUNAMI RISK AND VULNERABILITY ASSESSMENT

2010 ◽  
Vol 6 (1) ◽  
Author(s):  
Muhamad Rokhis Khomarudin ◽  
Gunter Strunz ◽  
Joachim Post ◽  
Kai Zobeder ◽  
Ralf Ludwig
Keyword(s):  
Statistical Data ◽  
Population Distribution ◽  
National Level ◽  
Warning System ◽  
Accurate Information ◽  
Tsunami Early Warning ◽  
Hazard Zone ◽  
Tsunami Risk ◽  
Available Information ◽  
The Impact

Information on population distribution is crucial in distater risk management. Every disaster such as flood, drought, volcanic eruption, storm, earthquake and tsunamis implies theats to people with respect to loss of live, injury, and misery. Therefore, the information on detailed population distribution in the disaster or hazard zone is important in order to mitigate the impact of natural disasters. Moreover, accurate information on people exposure will help the goverment to improve the evacuation planning and to decrease the amount of people at risk. The available information on population distribution is mostly based on statistical data related to administrative boundaries, e.g. village, municipal, district, province, or national level. Within the border of adsministrative boundaries, the population is assumed to be distributed homogeneously within each unit area, even in the part of uninhabited areas e.g. lakes, forest, swamps, and areas with high slopes. Hence, this research focuses on the improvement of the available data on population distribution for the area along the west coast of Sumatera, south coast of Java and Bali. The results were used as an input for the tsunami risk assessment in the framework of the German-Indonesia Tsunami Early Warning System (GITEWS) project. Keyword: People distribution, spatial improvement, tsunami, remote sensing and GIS.

scholarly journals Gap-free global annual soil moisture: 15 km grids for 1991–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 1711-1735
Author(s):  
Mario Guevara ◽  
Michela Taufer ◽  
Rodrigo Vargas
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Soil Type ◽  
Temporal Trends ◽  
European Space Agency ◽  
Terrain Parameters ◽  
Precipitation Records ◽  
The Impact ◽  
Type Information

Abstract. Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains > 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= < 0.3 up to r= 0.49) or tropical areas (from r= < 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).

Probing the relationship between formaldehyde column concentrations and soil moisture using mixed models and attribution analysis

2020 ◽  
Author(s):  
Susanna Strada ◽  
Josep Penuelas ◽  
Marcos Fernández Martinez ◽  
Iolanda Filella ◽  
Ana Maria Yanez-Serrano ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Drought Index ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Numerical Models ◽  
Positive Trend ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

&lt;p align=&quot;justify&quot;&gt;In response to changes in environmental factors (e.g., temperature, radiation, soil moisture), plants emit biogenic volatile organic compounds (BVOCs). Once released in the atmosphere, BVOCs influence levels of greenhouse gases and air pollutants (e.g., methane, ozone and aerosols), thus affecting both climate and air quality. In turn, climate change may alter BVOC emissions by modifying the driving environmental conditions and by increasing the occurrence and intensity of severe stresses that alter plant functioning. To understand and better constrain the evolution of BVOC emissions under future climates, it is important to reduce the uncertainties in global and regional estimates of BVOC emissions under present climate. Part of the uncertainty in the estimates of BVOC emissions is related to the impact that water stress might have on BVOC emissions. Field campaign, in-situ and laboratory experiments investigated the effect of different regimes of water stress (short- vs. long-term) on BVOC emissions. However, these studies provide geographically scattered and uneven results. To explore the relationship between BVOC emissions and water stress globally, we use remotely sensed soil moisture and formaldehyde, a proxy of BVOC emissions. As BVOCs include a multitude of gas tracers with lifetime ranging from few hours to days, a fully characterisation of these components is virtually impossible. Nevertheless, in the continental boundary layer, formaldehyde is an intermediate by-product of the oxidation of BVOCs, it thus provides a proxy for probing local BVOC emissions, and in particular isoprene, which accounts for about 50% of the total BVOC emissions.&lt;/p&gt;&lt;p align=&quot;justify&quot;&gt;In the present study, retrievals of formaldehyde from the Ozone Monitoring Instrument (OMI) are combined with observations of soil moisture, biomass, aerosols, evapotranspiration, drought index, temperature and precipitation. Firstly, we look into the linear annual trend of the selected fields. Secondly, assuming formaldehyde as the dependent variable, we apply a linear mixed model analysis that extends the application of a simple linear regression model by accounting for both fixed (i.e., explained by the independent variables) and random (i.e., due to dependence in the data) effects. The analysis of the linear trend of formaldehyde concentrations shows a positive trend over the Amazon and Central Africa and a negative trend over South Africa and Australia. Over the Amazon, formaldehyde is negatively correlated with the Standardised Precipitation-Evapotranspiration Index (SPEI), a drought index that accounts for both changes in temperature and precipitation, with positive and negative values identifying wet and dry events, respectively. The outcomes of this analysis might provide new insights in the relationship between BVOC emissions and water stress and might help in improving parameterizations that link soil moisture to BVOC emissions in numerical models.&lt;/p&gt;

scholarly journals Forward Simulation of Multi-Frequency Microwave Brightness Temperature over Desert Soils in Kuwait and Comparison with Satellite Observations

2019 ◽  
Vol 11 (14) ◽  
pp. 1647 ◽  
Author(s):  
Hala K. AlJassar ◽  
Marouane Temimi ◽  
Dara Entekhabi ◽  
Peter Petrov ◽  
Hussain AlSarraf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Surface Soil ◽  
Dynamic Range ◽  
European Space Agency ◽  
Mixing Model ◽  
Surface Soil Moisture ◽  
Horizontal Polarization ◽  
Brightness Temperatures ◽  
The Impact

In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of ±0.005 m3·m−3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.

scholarly journals Effects of Climate Finance on Risk Appraisal: A Study in the Southwestern Coast of Bangladesh

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Firdaus Ara Hussain ◽  
Mokbul Morshed Ahmad
Keyword(s):  
Climate Change ◽  
Local Level ◽  
Critical Role ◽  
National Level ◽  
Coastal Region ◽  
Climate Finance ◽  
Adaptation To Climate Change ◽  
Adaptation Measures ◽  
Risk Appraisal ◽  
The Impact

Utilising climate funds properly to reduce the impact of potential risks of climate change at the local level is essential for successful adaptation to climate change. Climate change has been disrupting the lives of millions of households along the coastal region of Bangladesh. The country has allocated support from its national funds and accessed international funds for the implementation of adaptation interventions. With the focus of the scientific community on climate finance mechanisms and governance at the global and the national level, there is a lacuna in empirical evidence of how climate finance affects risk appraisal and engagement in adaptation measures at the local level. This paper aims to examine how the support from climate finance affects risk appraisal in terms of the perceived probability and severity and the factors which influence risk appraisal. A field survey was conducted on 240 climate finance recipient households (CF HHs) and 120 nonclimate finance recipient households (non-CF HHs) in Galachipa Upazila of Patuakhali District in coastal Bangladesh. The results indicate that both CF and non-CF HHs experience a high probability of facing climatic events in the future; however, CF HHs anticipated a higher severity of impacts of climatic events on different dimensions of their households. With higher income and social capital, the overall risk appraisal decreases for CF HHs. CF HHs have higher engagement in adaptation measures and social groups and maintain alternative sources of income. Climate finance played a critical role in supporting households in understanding the risks that they were facing, assisting them in exploring as well as enhancing their engagement in adaptation options.

scholarly journals Calculating Crop Water Requirement Satisfaction in the West Africa Sahel with Remotely Sensed Soil Moisture

2015 ◽  
Vol 16 (1) ◽  
pp. 295-305 ◽  
Author(s):  
Amy McNally ◽  
Gregory J. Husak ◽  
Molly Brown ◽  
Mark Carroll ◽  
Chris Funk ◽  
...  
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
National Level ◽  
European Space Agency ◽  
Early Warning Systems ◽  
Water Requirement ◽  
Agricultural Drought ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index

Abstract The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

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