scholarly journals Tools for Communicating Agricultural Drought over the Brazilian Semiarid Using the Soil Moisture Index

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1421 ◽  
Author(s):  
Marcelo Zeri ◽  
Regina S. Alvalá ◽  
Rogério Carneiro ◽  
Gisleine Cunha-Zeri ◽  
José Costa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Real Time ◽  
Agricultural Drought ◽  
Drought Risk ◽  
Continuous Exposure ◽  
Short Term ◽  
Moisture Index ◽  
Brazilian Semiarid ◽  
Soil Moisture Index

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.

scholarly journals Development of the Soil Moisture Index to Quantify Agricultural Drought and Its “User Friendliness” in Severity-Area-Duration Assessment

2008 ◽  
Vol 9 (4) ◽  
pp. 660-676 ◽  
Author(s):  
Venkataramana Sridhar ◽  
Kenneth G. Hubbard ◽  
Jinsheng You ◽  
Eric D. Hunt
Keyword(s):  
Soil Moisture ◽  
Agricultural Drought ◽  
Extreme Drought ◽  
Weather Data ◽  
Moisture Index ◽  
Drought Duration ◽  
Available Water ◽  
Soil Moisture Index ◽  
The Difference ◽  
Wilting Point

Abstract This paper examines the role of soil moisture in quantifying drought through the development of a drought index using observed and modeled soil moisture. In Nebraska, rainfall is received primarily during the crop-growing season and the supply of moisture from the Gulf of Mexico determines if the impending crop year is either normal or anomalous and any deficit of rain leads to a lack of soil moisture storage. Using observed soil moisture from the Automated Weather Data Network (AWDN), the actual available water content for plants is calculated as the difference between observed or modeled soil moisture and wilting point, which is subsequently normalized with the site-specific, soil property–based, idealistic available water for plants that is calculated as the difference between field capacity and wilting point to derive the soil moisture index (SMI). This index is categorized into five classes from no drought to extreme drought to quantitatively assess drought in both space and time. Additionally, with the aid of an in-house hydrology model, soil moisture was simulated in order to compute model-based SMI and to compare the drought duration and severity for various sites. The results suggest that the soil moisture influence, a positive feedback process reported in many earlier studies, is unquestionably a quantitative indicator of drought. Also, the severity and duration of drought across Nebraska has a clear gradient from west to east, with the Panhandle region experiencing severe to extreme drought in the deeper soil layers for longer periods (>200 days), than the central and southwestern regions (125–150 days) or the eastern regions about 100 days or less. The anomalous rainfall years can eliminate the distinction among these regions with regard to their drought extent, severity, and persistence, thus making drought a more ubiquitous phenomenon, but the recovery from drought can be subject to similar gradations. The spatial SMI maps presented in this paper can be used with the Drought Monitor maps to assess the local drought conditions more effectively.

A real-time online drought broadcast system for monitoring soil moisture index

2012 ◽  
Vol 16 (3) ◽  
pp. 357-365 ◽  
Author(s):  
Won-Ho Nam ◽  
Jin-Yong Choi ◽  
Seung-Hwan Yoo ◽  
B. A. Engel
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Moisture Index ◽  
Soil Moisture Index

Mechanism and Characteristics of Flash Droughts in India and their Evaluation Using Evaporative Soil Moisture Index (ESMI)

2020 ◽  
Author(s):  
Shanti Shwarup Mahto ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Positive Relationship ◽  
Monsoon Season ◽  
Vapour Pressure Deficit ◽  
Stress Index ◽  
Driving Mechanism ◽  
Short Term ◽  
Moisture Index ◽  
Strong Negative Correlation ◽  
Soil Moisture Index

<p>Flash droughts can cause a short-term but severe devastating impacts to agriculture and the ecosystem. However, the mechanism and characteristics of flash droughts remain unexplored in the monsoon dominating climate over India. Here, we use the hydro-meteorological variables from ERA-5 reanalysis to derive surface vapour pressure deficit (VPD), and soil moisture (SM) from GLEAM to construct a copula based SM-VPD index [named as Evaporative Soil Moisture Index (ESMI)], which is used to identify flash droughts in India. First, we evaluate the land-atmospheric coupling, which suggests that SM-VPD has a strong negative correlation in both monsoon and non-monsoon seasons. Soil Moisture and evapotranspiration (ET) show a strong negative and positive relationship in the monsoon and non-monsoon season, respectively. Our results show that unlike ET based indices (e.g. evaporative stress index), ESMI captures flash droughts in both monsoon and non-monsoon seasons over India. We identified and evaluated six major flash drought that occurred during the 1980-2018 period using ESMI along with their driving mechanism.</p>

scholarly journals Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2813
Author(s):  
Marangely Gonzalez Cruz ◽  
E. Annette Hernandez ◽  
Venkatesh Uddameri
Keyword(s):  
Soil Moisture ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Potential Evapotranspiration ◽  
Kernel Density ◽  
Agricultural Drought ◽  
High Plains ◽  
Moisture Index ◽  
The Us ◽  
Soil Moisture Index

A bivariate kernel density estimation (KDE) method was utilized to develop a stochastic framework to assess how agricultural droughts are related to unfavorable meteorological conditions. KDE allows direct estimation of the bivariate cumulative density function which can be used to extract the marginal distributions with minimal subjectivity. The approach provided excellent fits to bivariate relationships between the standardized soil moisture index (SSMI) computed at three- and six-month accumulations and standardized measures of precipitation (P), potential evapotranspiration (PET), and atmospheric water deficit (AWD = P − PET) at 187 stations in the High Plains region of the US overlying the Ogallala Aquifer. The likelihood of an agricultural drought given a precipitation deficit could be as high as 40–65% within the study area during summer months and between 20–55% during winter months. The relationship between agricultural drought risks and precipitation deficits is strongest in the agriculturally intensive central portions of the study area. The conditional risks of agricultural droughts given unfavorable PET conditions are higher in the eastern humid portions than the western arid portions. Unfavorable PET had a higher impact on the six-month standardized soil moisture index (SSMI6) but was also seen to influence three-month SSMI (SSMI3). Dry states as defined by AWD produced higher risks than either P or PET, suggesting that both of these variables influence agricultural droughts. Agricultural drought risks under favorable conditions of AWD were much lower than when AWD was unfavorable. The agricultural drought risks were higher during the winter when AWD was favorable and point to the role of soil characteristics on agricultural droughts. The information provides a drought atlas for an agriculturally important region in the US and, as such, is of practical use to decision makers. The methodology developed here is also generic and can be extended to other regions with considerable ease as the global datasets required are readily available.

Suitability of assimilated data-based standardized soil moisture index for agricultural drought monitoring

2019 ◽  
Vol 39 (6) ◽  
Author(s):  
周洪奎 ZHOU Hongkui ◽  
武建军 WU Jianjun ◽  
李小涵 LI Xiaohan ◽  
刘雷震 LIU Leizhen ◽  
杨建华 YANG Jianhua ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Moisture Index ◽  
Soil Moisture Index

scholarly journals A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

2020 ◽  
Vol 246 ◽  
pp. 111864 ◽  
Author(s):  
Sara Sadri ◽  
Ming Pan ◽  
Yoshihide Wada ◽  
Noemi Vergopolan ◽  
Justin Sheffield ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Food Security ◽  
Real Time ◽  
Moisture Index ◽  
Soil Moisture Index

Real Time WWW Broadcasting of Soil Moisture Index for Drought Response

2007 ◽  
Author(s):  
Won Ho Nam ◽  
Jin Yong Choi ◽  
Min Won Jang ◽  
Seung Hwan Yoo ◽  
Bernard A Engel
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Drought Response ◽  
Moisture Index ◽  
Soil Moisture Index

A real-time drought monitoring method: cropland soil moisture index (CSMI) and application

2009 ◽  
Author(s):  
Huailiang Chen ◽  
Hongwei Zhang ◽  
Shuang-he Shen ◽  
Weidong Yu ◽  
Chunhui Zou
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Drought Monitoring ◽  
Moisture Index ◽  
Monitoring Method ◽  
Soil Moisture Index

A Real-time Ensemble Hydrological Forecasting System over Germany at Sub-seasonal to Seasonal Time Range

2020 ◽  
Author(s):  
Husain Najafi ◽  
Stephan Thober ◽  
Friedrich Boeing ◽  
Oldrich Rakovec ◽  
Matthias Kelbling ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Time Scale ◽  
Hydrologic Model ◽  
Ensemble Prediction ◽  
Time Range ◽  
Moisture Index ◽  
Hydrological Forecasting ◽  
Soil Moisture Index ◽  
Forecasting System

<p>Real-time hydrological forecasting provides valuable information to mitigate the impact of extreme hydrological events such as flood and drought. An ensemble hydrological forecasting system is developed to investigate the hydrological predictability at sub-seasonal to seasonal (S2S) time scale over Germany. The ensemble hydrological simulations are performed with the mesoscale hydrologic model (mHM) which benefits from a multiscale parameter regionalization module (MPR). The model is forced by the operational ensemble prediction System from the European Center for Medium-range Weather Forecast (ECMWF). 51 hydrological ensemble forecasts are generated in real-time (twice a week) for up to 45 days in advance. We used the initial condition records from the German Drought Monitor (GDM, www.ufz.de/duerremonitor) which provides daily up-to-date high resolution drought information at a spatial resolution of 4 km. The performance of the system is evaluated for three consecutive years started from 2016 for Soil Moisture Index (SMI) and real-time streamflow records (222 based in Zink et al 2017). Comparison between forecasted Soil Moisture Index (SMI) and the one derived by the GDM suggested promising results for certain areas over the study area at S2S time scale. The predictability of the ensemble forecasting system is evaluated against that generated with the Ensemble Streamflow Prediction (ESP) method. This research is one of the first attempts to investigate the hydrological forecasting skill at S2S time scale in Europe. The study is supported as a part of the Modular Observation Solutions for Earth System (MOSES) project.</p>

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