Abstract:Soil moisture is a main factor for the study of drought impacts on vegetation. Drought is a regional phenomenon and affects the food security more than any other natural disaster. Currently, the monitoring of different types of drought is based on indexes that standardize in temporal and regional level allowing, thus, comparison of water conditions in different areas. Therefore, in order to assess the impact of soil moisture during periods of drought, drought Palmer Severity Index was estimated for the entire … Show more
“…A new soil map was elaborated using the available soil profile information from Brazil, Paraguay, Uruguay, and Argentina, and it depicts 18 different soil types. Results indicate that the use of more accurate initial soil moisture conditions and incorporating a new soil map with hydraulic parameters, more representative of South American soils, improve the daily total precipitation forecasts both in quantitative and spatial representations [26]. The spatial resolution of the MUSA is 25 km, with daily information.…”
The potential of Global Navigation Satellite Systems-Reflectometry (GNSS-R) techniques to estimate land surface parameters such as soil moisture (SM) is experimentally studied using 2014–2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The approach is based on the analysis of the sensitivity to SM of different observables extracted from the Delay Doppler Maps (DDM) computed by the Space GNSS Receiver–Remote Sensing Instrument (SGR-ReSI) instrument using the L1 (1575.42 MHz) left-hand circularly-polarized (LHCP) reflected signals emitted by the Global Positioning System (GPS) navigation satellites. The sensitivity of different GNSS-R observables to SM and its dependence on the incidence angle is analyzed. It is found that the sensitivity of the calibrated GNSS-R reflectivity to surface soil moisture is ~0.09 dB/% up to 30° incidence angle, and it decreases with increasing incidence angles, although differences are found depending on the spatial scale used for the ground-truth, and the region. The sensitivity to subsurface soil moisture has been also analyzed using a network of subsurface probes and hydrological models, apparently showing some dependence, but so far results are not conclusive.
“…A new soil map was elaborated using the available soil profile information from Brazil, Paraguay, Uruguay, and Argentina, and it depicts 18 different soil types. Results indicate that the use of more accurate initial soil moisture conditions and incorporating a new soil map with hydraulic parameters, more representative of South American soils, improve the daily total precipitation forecasts both in quantitative and spatial representations [26]. The spatial resolution of the MUSA is 25 km, with daily information.…”
The potential of Global Navigation Satellite Systems-Reflectometry (GNSS-R) techniques to estimate land surface parameters such as soil moisture (SM) is experimentally studied using 2014–2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The approach is based on the analysis of the sensitivity to SM of different observables extracted from the Delay Doppler Maps (DDM) computed by the Space GNSS Receiver–Remote Sensing Instrument (SGR-ReSI) instrument using the L1 (1575.42 MHz) left-hand circularly-polarized (LHCP) reflected signals emitted by the Global Positioning System (GPS) navigation satellites. The sensitivity of different GNSS-R observables to SM and its dependence on the incidence angle is analyzed. It is found that the sensitivity of the calibrated GNSS-R reflectivity to surface soil moisture is ~0.09 dB/% up to 30° incidence angle, and it decreases with increasing incidence angles, although differences are found depending on the spatial scale used for the ground-truth, and the region. The sensitivity to subsurface soil moisture has been also analyzed using a network of subsurface probes and hydrological models, apparently showing some dependence, but so far results are not conclusive.
“…Also, this SDF framework could couple interdisciplinary studies, with better relationships towards the nexus of water security, energy security and food security. Thus, we recommend future research of SDF framework linked to: Palmer's drought indices (Rossato et al, 2017), modelbased framework to disaster management (Horita et al, 2017), ecosystem-based assessment for water security modeling (Taffarello et al, 2017), effectiveness of drought securitization under climate change scenarios (Mohor and Mendiondo, 2017). Moreover, SDF framework is capable of integrating actions towards: dynamic price incentive programs related to wise human-water co-evolution patterns, water-sensitive programs under deep cultural features, socio-hydrological observatories for water security, feasibility analysis of the economic impacts of implementing new technologies for water economy and flow measurement, leakage control, detecting and legalizing illegal connections and water reuse, among others.…”
10Climate variability and increasing water demands prioritize the need to implement planning 11 strategies for urban water security in the long and medium term. However, actions to manage 12 the drought risk impacts entail great complexity, such as the calculation of economic losses The model framework is applied to the Cantareira Water Supply System for Sao Paulo
27Metropolitan Region, Brazil, with severe vulnerability to droughts. By using hydrological
“…Assimilation of soil moisture into land surface models has resulted in increased understanding of processes controlling the energy exchange at the land-atmosphere interface. The spatial distribution and temporal evolution of SM is of central importance to different societal sectors and activities, such as weather forecasting, management of water reservoirs, food security, watershed management, development of climate models, drought monitoring, land slide prediction, flood forecasting and others [1][2][3][4][5][6][7][8][9]. Of particular importance is the use of SM to monitor drought conditions over forest and agricultural sites, either using soil moisture only or in combination with other parameters, such as The objective of this study was to describe soil moisture products developed using the network of sensors established by Cemaden.…”
Soil moisture over the Brazilian semiarid region is presented in different visualizations that highlight spatial, temporal and short-term agricultural risk. The analysis used the Soil Moisture Index (SMI), which is based on a normalization of soil moisture by field capacity and wilting point. The index was used to characterize the actual soil moisture conditions into categories from severe drought to very wet. In addition, the temporal evolution of SMI was implemented to visualize recent trends in short-term drought and response to rainfall events at daily time steps, as new data are available. Finally, a visualization of drought risk was developed by considering a critical value of SMI (assumed as 0.4), below which water stress is expected to be triggered in plants. A novel index based on continuous exposure to critical SMI was developed to help bring awareness of real time risk of water stress over the region: the Index of Stress in Agriculture (ISA). The index was tested during a drought over the region and successfully identified locations under water stress for periods of three days or more. The monitoring tools presented here help to describe the real time conditions of drought over the region using daily observations. The information from those tools support decisions on agricultural management such as planting dates, triggering of irrigation, or harvesting.
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