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Monitoring and Predicting Soil Moisture and Drought Conditions
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Monitoring and Predicting Soil Moisture and Drought Conditions

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (12 January 2019) | Viewed by 23275

Special Issue Editors

Dr. Christoph Rüdiger

E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Engineering, 23 College Walk, Monash University, VIC 3800, Australia (Clayton campus)
Interests: soil moisture; remote sensing; hydrology; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Lionel Jarlan

E-Mail Website
Guest Editor
French Research Institute for Development/Spatial Study Centre of the Biosphere (CESBIO), 18 Avenue Edouard Belin, CEDEX 4, BPI 2801 Toulouse, France
Interests: multi-spectral remote sensing; soil-vegetation-atmosphere transfer; water resources and use; semi-arid areas
Dr. Clement Albergel

E-Mail Website
Guest Editor
ESA - European Space Agency, Climate Office, Oxfordshire, UK
Interests: land surface modelling; climate change; hydrology; data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Ming Pan

E-Mail Website
Guest Editor
Princeton University
Interests: land surface fluxes; remote sensing; modelling; data assimilation

Special Issue Information

Dear Colleagues,

Droughts come in various forms and can be defined as an ecosystem response or socio-economic impacts. While various definitions do exist, measuring and quantifying droughts through observable means is still difficult, as droughts across different ecosystems take different pathways to manifest themselves, and the same quantity of water deficit may not result in drought conditions in two different locations. A further complication is the abundant range of drought indices, that range from simple precipitation deficits to more complex systems of equations, incorporating temperature, evapotranspiration and other variables.

One of the simplest ways to quantify hydrological droughts is through the quantification of the soil moisture deficit using observations, models or a combination of both (e.g., data assimilation). Soil moisture memory in the soil has been shown to have a significant effect on the development of heatwaves and droughts, however, its estimates come with uncertainties.

In this Special Issue, contributions are invited to address either the quality and error assessment of soil moisture information from modelling or remote sensing techniques, or its application in the assessment of drought conditions. Papers presenting novel ways to merge often conflicting drought indices are equally welcome, as are field validation studies of novel indices.

Dr. Christoph Rüdiger
Dr. Lionel Jarlan
Dr. Clément Albergel
Dr. Ming Pan
Guest Editors

 

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land surface hydrology
  • modelling
  • remote sensing
  • droughts
  • climate impact

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Published Papers (4 papers)

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Research

21 pages, 6650 KiB  
Article
Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone
by Melanie Oertel, Francisco Javier Meza, Jorge Gironás, Christopher A. Scott, Facundo Rojas and Nicolás Pineda-Pablos
Water 2018, 10(11), 1564; https://doi.org/10.3390/w10111564 - 2 Nov 2018
Cited by 29 | Viewed by 6347
Abstract
Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation [...] Read more.
Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries. Full article
(This article belongs to the Special Issue Monitoring and Predicting Soil Moisture and Drought Conditions)
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15 pages, 6524 KiB  
Communication
Tools for Communicating Agricultural Drought over the Brazilian Semiarid Using the Soil Moisture Index
by Marcelo Zeri, Regina Célia S. Alvalá, Rogério Carneiro, Gisleine Cunha-Zeri, José Maria Costa, Luciana Rossato Spatafora, Domingos Urbano, Mercè Vall-Llossera and José Marengo
Water 2018, 10(10), 1421; https://doi.org/10.3390/w10101421 - 11 Oct 2018
Cited by 37 | Viewed by 7045
Abstract
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Monitoring and Predicting Soil Moisture and Drought Conditions)
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19 pages, 994 KiB  
Article
Sensitivity of Soil Moisture Analyses to Contrasting Background and Observation Error Scenarios
by Joaquín Muñoz-Sabater, Patricia De Rosnay, Clément Albergel and Lars Isaksen
Water 2018, 10(7), 890; https://doi.org/10.3390/w10070890 - 4 Jul 2018
Cited by 9 | Viewed by 4003
Abstract
Soil moisture is a crucial variable for numerical weather prediction. Accurate, global initialization of soil moisture is obtained through data assimilation systems. However, analyses depend largely on the way observation and background errors are defined. In this study, a wide range of short [...] Read more.
Soil moisture is a crucial variable for numerical weather prediction. Accurate, global initialization of soil moisture is obtained through data assimilation systems. However, analyses depend largely on the way observation and background errors are defined. In this study, a wide range of short experiments with contrasted specifications of the observation error and soil moisture background were conducted. As observations, screen-level variables and brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission were used. The region of interest is North America, given the good availability of in situ observations and mixture of different climates, making it a good test for global applications. The impact of these experiments on soil moisture and the atmospheric layer near the surface were evaluated. The results highlighted the importance of assimilating observations sensitive to soil moisture for air temperature and humidity forecasts. The benefits on predicting the soil water content were more noticeable with increasing the SMOS observation error, and with the introduction of soil texture dependency in the soil moisture background error. Full article
(This article belongs to the Special Issue Monitoring and Predicting Soil Moisture and Drought Conditions)
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16 pages, 2668 KiB  
Article
Simulation of Soil Water Content in Mediterranean Ecosystems by Biogeochemical and Remote Sensing Models
by Piero Battista, Marta Chiesi, Luca Fibbi, Lorenzo Gardin, Bernardo Rapi, Stefano Romanelli, Maurizio Romani, Francesco Sabatini, Elena Salerni, Claudia Perini and Fabio Maselli
Water 2018, 10(5), 665; https://doi.org/10.3390/w10050665 - 19 May 2018
Cited by 9 | Viewed by 4725
Abstract
The current study assesses the potential of two modeling approaches to simulate the daily site water budget in Mediterranean ecosystems. Both models utilize a simplified one-bucket approach but are fed with different drivers. The first model, BIOME-BGC, simulates all main biogeochemical fluxes based [...] Read more.
The current study assesses the potential of two modeling approaches to simulate the daily site water budget in Mediterranean ecosystems. Both models utilize a simplified one-bucket approach but are fed with different drivers. The first model, BIOME-BGC, simulates all main biogeochemical fluxes based on conventional meteorological and ancillary data, while the second uses evapotranspiration estimates derived from the combination of meteorological data and satellite normalized difference vegetation index (NDVI) images. The two models were tested for three Italian sites which are characterized by different vegetation types and ecoclimatic conditions: (i) low mountain coniferous forest; (ii) hilly deciduous forest; (iii) urban grassland. The soil water balance simulated by the two models was evaluated through comparison with daily measurements of soil water content (SWC) taken during a growing season. Satisfactory results were obtained in all cases by both approaches; the SWC estimates are significantly correlated with the measurements (correlation coefficient, r, higher than 0.74), and the mean errors are lower than 0.079 cm3 cm−3. The second model, however, generally shows a higher accuracy, which is dependent on the quality of the NDVI data utilized (r higher than 0.79 and errors lower than 0.059 cm3 cm−3). The study therefore provides useful indications for the application of these and similar simulation methods in different environmental situations. Full article
(This article belongs to the Special Issue Monitoring and Predicting Soil Moisture and Drought Conditions)
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