scholarly journals Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2592 ◽  
Author(s):  
María del Pilar Jiménez-Donaire ◽  
Juan Vicente Giráldez ◽  
Tom Vanwalleghem
Keyword(s):  
Soil Moisture ◽  
Soil Depth ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Agricultural Drought ◽  
Annual Precipitation ◽  
Drought Indices ◽  
Climate Data ◽  
Management Tools ◽  
The Standardized Precipitation Index

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools).

scholarly journals EVALUATION OF THE SOIL MOISTURE AGRICULTURAL DROUGHT INDEX (SMADI) AND PRECIPITATION-BASED DROUGHT INDICES IN ARGENTINA

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 53-58
Author(s):  
M. M. Salvia ◽  
N. Sánchez ◽  
M. Piles ◽  
A. Gonzalez-Zamora ◽  
J. Martínez-Fernández
Keyword(s):  
Soil Moisture ◽  
Gross Domestic Product ◽  
Surface Temperature ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Agricultural Exports ◽  
Hydric Stress ◽  
The Standardized Precipitation Index

Abstract. Agricultural drought is one of the most critical hazards with regard to intensity, severity, frequency, spatial extension and impact on livelihoods. This is especially true for Argentina, where agricultural exports can represent up to 10% of gross domestic product (GDP), and where drought events for 2018 led to a decrease of nearly 0.5% of GDP. In this work, we investigate the applicability of the Soil Moisture Agricultural Drought Index (SMADI) for detection of droughts in Argentina, and compare its performance with the use of two well-known precipitation-based indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation- Evaporation Index (SPEI). SMADI includes satellite-based information of soil moisture, surface temperature and vegetation greenness, and was designed to capture the hydric stress on the soil-vegetation ensemble. Results indicate that SMADI has greater capabilities for agricultural drought detection than SPI and SPEI: it was able to recognize more than 83% of the registered emergencies, correctly classifying 75% of them as extreme droughts, and outperforming SPI and SPEI in all the analyzed metrics.

scholarly journals A Multilayer Soil Moisture Dataset Based on the Gravimetric Method in China and Its Characteristics

2019 ◽  
Vol 20 (8) ◽  
pp. 1721-1736 ◽  
Author(s):  
Aihui Wang ◽  
Xueli Shi
Keyword(s):  
Soil Moisture ◽  
Soil Depth ◽  
Linear Trend ◽  
Standardized Precipitation Index ◽  
Gravimetric Method ◽  
Northwest China ◽  
Soil Layers ◽  
Significance Level ◽  
The Mean ◽  
The Standardized Precipitation Index

Abstract Based on the gravimetric-technique-measured soil relative wetness and the observed soil characteristic parameters from 1992 to 2013 in China, this study derives a user-convenient monthly volumetric soil moisture (SM) dataset from 732 stations for five soil layers (10, 20, 50, 70, and 100 cm). The temporal–spatial variations in SM and its relationship with precipitation (Pr) in different subregions are then explored. The magnitude of SM is relatively large in south China and is low in northwest China, and it generally increases with soil depth in each region. The maximum SM appears in spring and/or autumn and the minimum in summer, and the SM seasonality does not vary as distinctly as that of Pr. For the top three soil layers (10-, 20-, and 50-cm levels), the linear trend analysis indicates an overall increasing SM tendency, and the mean trends (averaged across stations with trends passing a 95% significance level test) are 9.35 × 10−7, 7.37 × 10−3, and 2.45 × 10−3 cm3 cm−3 yr−1, respectively. SM memory depends on the soil depth and regions, and it has longer retention time in the deeper layers. Furthermore, the correlation between SM and antecedent Pr varies with soil depth and lag time. The antecedent Pr anomaly (1 or 2 months in advance) can be used to some extent as a surrogate SM anomaly in most regions except for in arid regions. This result is further demonstrated by the relationships between the SM anomaly and the standardized precipitation index. The current SM dataset can be used in various applications, such as validating satellite-retrieved products and model outputs.

scholarly journals Commonly Used Drought Indices as Indicators of Soil Moisture in China

2015 ◽  
Vol 16 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Hongshuo Wang ◽  
Jeffrey C. Rogers ◽  
Darla K. Munroe
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Severity Index ◽  
Agricultural Drought ◽  
Weather Data ◽  
Drought Indices ◽  
Drought Severity ◽  
Better Than

Abstract Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.

Drought monitoring in the Ebro basin: comparison of Soil Moisture and Vegetation anomalies

2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Ebro Basin ◽  
Soil Moisture Data

<p>Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.</p><p>Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 
</p><p>A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.</p><p>Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.</p><p>A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.</p><p>The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).</p><p>In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.</p>

scholarly journals A Nonparametric Multivariate Multi-Index Drought Monitoring Framework

2014 ◽  
Vol 15 (1) ◽  
pp. 89-101 ◽  
Author(s):  
Zengchao Hao ◽  
Amir AghaKouchak
Keyword(s):  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Multi Index ◽  
Additional Information ◽  
Indicator Variables ◽  
Monitoring Framework ◽  
Drought Mitigation ◽  
The Standardized Precipitation Index

Abstract Accurate and reliable drought monitoring is essential to drought mitigation efforts and reduction of social vulnerability. A variety of indices, such as the standardized precipitation index (SPI), are used for drought monitoring based on different indicator variables. Because of the complexity of drought phenomena in their causation and impact, drought monitoring based on a single variable may be insufficient for detecting drought conditions in a prompt and reliable manner. This study outlines a multivariate, multi-index drought monitoring framework, namely, the multivariate standardized drought index (MSDI), for describing droughts based on the states of precipitation and soil moisture. In this study, the MSDI is evaluated against U.S. Drought Monitor (USDM) data as well as the commonly used standardized indices for drought monitoring, including detecting drought onset, persistence, and spatial extent across the continental United States. The results indicate that MSDI includes attractive properties, such as higher probability of drought detection, compared to individual precipitation and soil moisture–based drought indices. This study shows that the MSDI leads to drought information generally consistent with the USDM and provides additional information and insights into drought monitoring.

scholarly journals A joint probabilistic index for objective drought identification: the case study of Haiti

2020 ◽  
Vol 20 (2) ◽  
pp. 471-487
Author(s):  
Beatrice Monteleone ◽  
Brunella Bonaccorso ◽  
Mario Martina
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Extreme Event ◽  
Risk Mitigation ◽  
Characteristic Curve ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Drought Indices

Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such an extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programmes or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions by combining in a probabilistic framework two consolidated drought indices: the standardized precipitation index (SPI) and the vegetation health index (VHI). The new index, called the probabilistic precipitation vegetation index (PPVI), is scalable, transferable all over the globe and can be updated in near real time. Furthermore, it is a remote-sensing product, since precipitation is retrieved from satellite data and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of the PPVI has been evaluated by means of a receiver operating characteristic curve and compared to that of the SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early-warning systems.

scholarly journals A joint probabilistic index for objective drought identification: the case study of Haiti

2019 ◽  
Author(s):  
Beatrice Monteleone ◽  
Brunella Bonaccorso ◽  
Mario Martina
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Extreme Event ◽  
Risk Mitigation ◽  
Standardized Precipitation Index ◽  
Early Warning Systems ◽  
Mitigation Strategies ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Operating Characteristics

Abstract. Since drought is a multifaceted phenomenon, more than one variable should be considered for a proper understanding of such extreme event in order to implement adequate risk mitigation strategies such as weather or agricultural indices insurance programs, or disaster risk financing tools. This paper proposes a new composite drought index that accounts for both meteorological and agricultural drought conditions, by combining in a probabilistic framework two consolidated drought indices: the Standardized Precipitation Index (SPI) and the Vegetation Health Index (VHI). The new index, called Probabilistic Precipitation Vegetation Index (PPVI), is scalable, transferable all over the globe and can be updated in near-real time. Furthermore, it is a remote-sensing product, since precipitation are retrieved from satellite and the VHI is a remote-sensing index. In addition, a set of rules to objectively identify drought events is developed and implemented. Both the index and the set of rules have been applied to Haiti. The performance of PPVI has been evaluated by means of the Receiver Operating Characteristics curve and compared to the ones of SPI and VHI considered separately. The new index outperformed SPI and VHI both in drought identification and characterization, thus revealing potential for an effective implementation within drought early warning systems.

scholarly journals Discovering Trends of Agricultural Drought in Tihama Plain, Yemen : A Preliminary Assessment

2017 ◽  
Vol 49 (1) ◽  
pp. 17 ◽  
Author(s):  
Noorazuan Md hashim ◽  
Ali Ahmed Dhaif Allah ◽  
Azahan Awang
Keyword(s):  
Soil Moisture ◽  
Agricultural Production ◽  
Surface Soil ◽  
Standardized Precipitation Index ◽  
Water Security ◽  
Agricultural Drought ◽  
Preliminary Assessment ◽  
Soil Layers ◽  
The Standardized Precipitation Index ◽  
Agricultural Areas

Agricultural drought is characterized by lack of sufficient moisture in the surface soil layers to support crop and forage growth. Indicators of agricultural drought often are precipitation, temperature and soil moisture to measure soil moisture and crop yield.  This study aims to assess spatiotemporal of drought in the Tihama Plain, which is one of the most important agricultural areas in Yemen, where contributes about 42% of the total agricultural production in the country. In recent years, the Tihama Plain faced changes in the rainy season, which reflect negatively on agriculture production and water security in the area. In this study the Standardized Precipitation Index (SPI) was used to temporal evaluation of the situation of drought, also it has been used Geographic Information Systems (GIS) in order to show the spatial variability distribution of drought in the study area. The analysis results by SPI-6 showed that the years 1984,1991,2002, 2003,2004,2005,2006 and 2008 were the most affected by drought during the study period 30 years (1980-2010), also show that the year 1991 was the worst years of drought experienced by the study area. Based on the fact that the study area is the most important agricultural areas in Yemen, it is recommended a study the drought and its impact on agricultural crops in the area.

scholarly journals Model-Based Drought Indices over the United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1212-1230 ◽  
Author(s):  
Kingtse C. Mo
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
The United States ◽  
Horizontal Resolution ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Eastern United States ◽  
Infiltration Capacity

Abstract Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian. Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events. The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap.

Evaluating the Utility of Drought Indices as Soil Moisture Proxies for Drought Monitoring and Land–Atmosphere Interactions

2020 ◽  
Vol 21 (9) ◽  
pp. 2157-2175
Author(s):  
Shanshui Yuan ◽  
Steven M. Quiring ◽  
Chen Zhao
Keyword(s):  
Soil Moisture ◽  
Great Plains ◽  
Soil Layer ◽  
Standardized Precipitation Index ◽  
Drought Indices ◽  
Moisture Index ◽  
Drought Conditions ◽  
Crop Moisture Index ◽  
The Standardized Precipitation Index ◽  
The Relationship

AbstractThere are a variety of metrics that are used to monitor drought conditions, including soil moisture and drought indices. This study examines the relationship between in situ soil moisture, NLDAS-2 soil moisture, and four drought indices: the standardized precipitation index, the standardized precipitation evapotranspiration index, the crop moisture index, and the Palmer Z index. We evaluate how well drought indices and the modeled soil moisture represent the intensity, variability, and persistence of the observed soil moisture in the southern Great Plains. We also apply the drought indices to evaluate land–atmosphere interactions and compare the results with soil moisture. The results show that the SPI, SPEI, and Z index have higher correlations with 0–10-cm soil moisture, while the CMI is more strongly correlated with 0–100-cm soil moisture. All the drought indices tend to overestimate the area affected by moderate to extreme drought conditions. Significant drying trends from 2003 to 2017 are evident in SPEI, Z index, and CMI, and they agree with those in the observed soil moisture. The CMI captures the intra- and interannual variability of 0–100-cm soil moisture better than the other drought indices. The persistence of CMI is longer than that of 0–10-cm soil moisture and shorter than that of 0–100-cm soil moisture. Model-derived soil moisture does not outperform the CMI in the 0–100-cm soil layer. The Z index and CMI are better drought indices to use as a proxy for soil moisture when examining land–atmosphere interactions while the SPI is not recommended. Soil type and climate affect the relationship between drought indices and observed soil moisture.

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