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 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 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.

Impact of meteorological drought on agriculture in the Tensift watershed of Morocco

2019 ◽  
Vol 11 (4) ◽  
pp. 1323-1338 ◽  
Author(s):  
Modeste Meliho ◽  
Abdellatif Khattabi ◽  
Guy Jobbins ◽  
Fathallah Sghir
Keyword(s):  
Agricultural Production ◽  
Temporal Evolution ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Water Shortage ◽  
Water Volume ◽  
Annual Crops ◽  
Irrigated Area ◽  
The Standardized Precipitation Index ◽  
The Impact

Abstract Located in the mid-west of Morocco, the Tensift watershed shelters the Takerkoust dam, which provides a part of the water used for irrigation of the N'fis agricultural area, which is an important irrigated area of the Tensift watershed. This study deals with the impact of droughts on water inflows to the Takerkoust dam and how the water shortage caused by droughts affects agricultural production in the N'Fis area. The standardized precipitation index (SPI) was used to illustrate the temporal evolution of drought periods. The trend observed on data showed that the Tensift watershed experienced a succession of droughts and humid periods of varying intensities. Periods of drought have negatively affected water inflows to the Takerkoust dam, and therefore the amount of water allocated to agricultural irrigation. Years that experienced droughts showed a restriction of more than 50% of water volume planned for irrigation. During periods of water scarcity, farmers reduce or completely avoid irrigation of annual crops to save water for irrigation of perennial crops. The water shortage for irrigation has led in some cases to a drop of up to 100% of the surface allocated to the production of annual crops.

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.

scholarly journals Analysis of meteorological and agricultural droughts in Serbia

2014 ◽  
Vol 12 (3) ◽  
pp. 253-264 ◽  
Author(s):  
Mladen Milanovic ◽  
Milan Gocic ◽  
Slavisa Trajkovic
Keyword(s):  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Precipitation Index ◽  
Precipitation Extreme ◽  
Agricultural Drought ◽  
Drought Intensity ◽  
The North ◽  
Rainfall Index ◽  
The Standardized Precipitation Index ◽  
Year 2000

Drought represents a combined heat-precipitation extreme and has become an increasingly frequent phenomenon in recent years. In order to access the entire analysis of drought, it is necessary to include the analysis of several types of drought. In this paper, impacts of meteorological and agricultural drought were analyzed across the Standardized Precipitation Index (SPI) and Agricultural Rainfall Index (ARI) on the territory of Serbia for the period from 1980 to 2010. For both types of drought, year 2000 is notable as the year when most of the observed stations had the highest drought intensity. It was found that meteorological drought for year 2000 has a higher intensity in the central and southeastern parts of the country, as well as in the north. Of all the stations, the highest intensity of meteorological drought was observed at Loznica station in 1989. Agricultural drought in 2000 had the lowest intensity in western Serbia.

scholarly journals DROUGHT HAZARD CHARACTERISTIC USING SOIL MOISTURE DEFICIT INDEX MODELLING

2018 ◽  
Vol 5 (1) ◽  
pp. 91
Author(s):  
Lulu Mari Fitria ◽  
Septiana Fathurrohmah
Keyword(s):  
Soil Moisture ◽  
Standardized Precipitation Index ◽  
Hydrological Drought ◽  
Agricultural Drought ◽  
Extreme Condition ◽  
Hazard Mapping ◽  
Soil Moisture Deficit ◽  
Positive Correlation ◽  
Drought Hazard ◽  
Moisture Deficit

Drought happen when the rainfall decreases in the extreme condition for long period of  time (above normal). Drought hazard mapping can be analyzed by various approaches, like environmental approach, ecological approach, hydrological approach, meteorological approach, geological approach, agricultural approach, and many other. Meteorological, Climatological, and Geophysical Agency (in Indonesia a.k.a BMKG) measures the drought hazard by utilizing Standardized Precipitation Index (SPI)The comparison of rainfall rate through SPI has positive correlation with  drought type, for example SPI 3 indicates agricultural drought; while SPI 6, SPI 9 and SPI 12 indicate  hydrological drought. The analysis of drought hazard level also can be done using soil moisture level measurement. Soil moisture is the result of water shortages in the hydroclimatological concept. Soil moisture analysis utilizes several influenced variables, such as soil water, precipitation, evapotranspiration, and percolation. Each of variables was analyzed using GIS as a method of soil moisture modeling. Drought index level analysis is using soil moisture deficit index, which indicates that drought occurs if the index score less than (-0.5). Some assumptions used in this modeling are both SMDI modeling using WHC (Water Holding Capacity) and  without using WHC. This modeling used medium term analysis during 2007-2012 to prove the occurrence of extreme drought on 2009 and 2012 for measurement of drought level in agriculture area. Based on SMDI, it is known that the dangers of SMDI drought have positive correlation to SPI 3, SPI 6, SPI 9, and SPI 12, where SPI is in accordance with the interpretation of meteorolgy, agriculture, and hydrological drought indices.

scholarly journals Assimilation of surface soil moisture into a multilayer soil model: design and evaluation at local scale

2014 ◽  
Vol 18 (2) ◽  
pp. 673-689 ◽  
Author(s):  
M. Parrens ◽  
J.-F. Mahfouf ◽  
A. L. Barbu ◽  
J.-C. Calvet
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Soil Column ◽  
Cumulative Distribution ◽  
Surface Soil Moisture ◽  
Deep Soil ◽  
Background Error ◽  
Soil Layers ◽  
Diffusion Scheme

Abstract. Land surface models (LSM) have improved considerably in the last two decades. In this study, the Interactions between Surface, Biosphere, and Atmosphere (ISBA) LSM soil diffusion scheme is used (with 11 soil layers represented). A simplified extended Kalman filter (SEKF) allows ground observations of surface soil moisture (SSM) to be assimilated in the multilayer LSM in order to constrain deep soil moisture. In parallel, the same simulations are performed using the ISBA LSM with 2 soil layers (a thin surface layer and a bulk reservoir). Simulations are performed over a 3 yr period (2003–2005) for a bare soil field in southwestern France, at the SMOSREX (Surface Monitoring Of the Soil Reservoir Experiment) site. Analyzed soil moisture values correlate better with soil moisture observations when the ISBA LSM soil diffusion scheme is used. The Kalman gain is greater from the surface to 45 cm than below this limit. For dry periods, corrections introduced by the assimilation scheme mainly affect the first 15 cm of soil whereas weaker corrections impact the total soil column for wet periods. Such seasonal corrections cannot be described by the two-layer ISBA LSM. Sensitivity studies performed with the multilayer LSM show improved results when SSM (0–6 cm) is assimilated into the second layer (1–5 cm) than into the first layer (0–1 cm). The introduction of vertical correlations in the background error covariance matrix is also encouraging. Using a yearly cumulative distribution function (CDF)-matching scheme for bias correction instead of matching over the three years permits the seasonal variability of the soil moisture content to be better transcribed. An assimilation experiment has also been performed by forcing ISBA-DF (diffusion scheme) with a local forcing, setting precipitation to zero. This experiment shows the benefit of the SSM assimilation for correcting inaccurate atmospheric forcing.

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 Drought Monitoring for Washington State: Indicators and Applications

2011 ◽  
Vol 12 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Shraddhanand Shukla ◽  
Anne C. Steinemann ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Standardized Precipitation Index ◽  
Land Surface Model ◽  
Washington State ◽  
Drought Monitoring ◽  
Surface Model ◽  
Drought Conditions ◽  
The Standardized Precipitation Index

Abstract A drought monitoring system (DMS) can help to detect and characterize drought conditions and reduce adverse drought impacts. The authors evaluate how a DMS for Washington State, based on a land surface model (LSM), would perform. The LSM represents current soil moisture (SM), snow water equivalent (SWE), and runoff over the state. The DMS incorporates the standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture percentile (SMP) taken from the LSM. Four historical drought events (1976–77, 1987–89, 2000–01, and 2004–05) are constructed using DMS indicators of SPI/SRI-3, SPI/SRI-6, SPI/SRI-12, SPI/SRI-24, SPI/SRI-36, and SMP, with monthly updates, in each of the state’s 62 Water Resource Inventory Areas (WRIAs). The authors also compare drought triggers based on DMS indicators with the evolution of drought conditions and management decisions during the four droughts. The results show that the DMS would have detected the onset and recovery of drought conditions, in many cases, up to four months before state declarations.

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