Soil Moisture Drought Indices Research Articles

Development of Drought Vulnerability Index for Upland Crops using Soil Moisture Data

Recently, with changing rainfall patterns worldwide due to climate change, the effects and damages of upland crops are increasing. A water supply system from irrigation facilities such as agricultural reservoirs and pumping stations has been established in paddy fields, whereas irrigation uplands equipped with water supply facilities account for only 18.5% of the upland area. Therefore, it is necessary to analyze vulnerable areas of upland droughts because they depend on natural rainfall and lack the ability to respond to drought damage. Currently, there is limited drought information to respond to an upland drought because the characteristics of crops and actual cultivation period are not analyzed. Soil moisture directly resulting from rainfall shortage is highly related to drought, especially for upland crops and vegetation. Therefore, upland crops drought can be evaluated using soil moisture and weather data. In this study, an upland drought vulnerability index based on runs theory and entropy method was developed using effective soil moisture, soil moisture drought index, and groundwater level. Soil moisture and meteorological data were obtained from the soil moisture observation network (Rural Development Administration), and weather observation network (Korea Meteorological Administration). The results showed that Upland Drought Vulnerability Index (UDVI) can be an effective tool for quantitatively evaluating upland drought vulnerability.

Monitoring of Extreme Agricultural Drought of the Past 20 Years in Southwest China Using GLDAS Soil Moisture

Drought can cause severe agricultural economic losses and hinder social and economic development. To manage drought, the process of drought events needs to be described with the help of an effective drought indicator. As a comprehensive variable, soil moisture is an essential indicator for describing agricultural drought. In this work, the extreme drought events in southwest China were analysed by the Global Land Data Assimilation System (GLDAS) root zone soil moisture data set. To define the drought quantitatively, a Standardized Soil Moisture Drought Index (SSMI) was calculated using the soil moisture data, then used to get the duration, frequency, and severity of drought events in southwest China. The results showed that the frequency and intensity of drought in southwest China had an apparent upward trend before 2014 and an apparent downward trend since 2014. Moreover, there are apparent differences in the frequency and intensity of drought in various regions of southwest China. Yunnan Province is prone to spring drought events. Guangxi Province and Guizhou Province are prone to spring, autumn and winter droughts, and the intensity of autumn and winter droughts is significantly higher than that of spring droughts. The Sichuan-Chongqing border area is prone to summer drought. We found that the monthly variation of soil moisture in different provinces in southwest China is consistent, but the seasonal variation of drought is different. Meanwhile, the performance of the SSMI was compared to the commonly used drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI). The results showed that the SSMI is more sensitive to drought than both SPEI and PDSI in southwest China. The results also demonstrate that GLDAS soil moisture data can be used to study drought at a small regional scale.

Global spatiotemporal consistency between meteorological and soil moisture drought indices

In this study, the consistency of a set of meteorological and soil moisture drought indices has been analyzed using the linear relationship between them. Commonly used Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are compared to soil moisture drought index (SSMI) between the years 1981 and 2019. The most consistent meteorological drought index with SSMI has been selected as the best representative of it for 175,840 pixels, globally. Later, different classification methods (i.e., Decision Tree, K-Nearest Neighbors, Naïve Bayesian, Support Vector Machine, and Random Forest) are trained to identify the best representative of SSMI by considering the most consistent meteorological drought index with SSMI as target, and five ancillary datasets of average precipitation, average temperature, vegetation cover (Normalized difference vegetation index; NDVI), climate class, and land cover class as input variables of them, respectively. Results show that over 48% of pixels SPI, and over 32% of pixels SPEI show significantly better consistency with SSMI (at p-value: 0.05). Overall, in regions with cooler temperatures and low and high vegetation cover densities, the SPI, and over warmer areas with mid-range of vegetation density, the SPEI provides a better correlation with SSMI. The performance of different classification methods over validation pixels showed that the K-Nearest Neighbor method can identify the best correlated meteorological drought index with SSMI better than other methods. Overall results highlight the impact of climate and land use on interactions of meteorological and soil moisture drought indices, particularly that the classification efforts showed that, on average, monitoring of drought events by combine use of SPI and SPEI (each one over the area where it is identified as the best indicator of SSMI) improves the correlation between meteorological and soil moisture drought indices by 4 and 10%, compared to the uniform use of SPI and SPEI, respectively.

Satellite-Based Assessment of Meteorological and Agricultural Drought in Mainland Southeast Asia

Satellite-based soil moisture products allow direct monitoring of agricultural drought, especially in regions with sparse ground-based observations. In this study, a soil moisture drought index only based on satellite soil moisture is developed and adopted to assess drought in Mainland Southeast Asia. We report here on an exceptionally severe Mainland Southeast Asia drought in 2016, which is believed to be strongly linked to the 2015-2016 super El Niño strongly. The event began in February 2016 and lasted until May 2016, with more than 50% of the study areas suffering from moderate drought or worse at peak period. We assess the evolution of agricultural droughts (defined as prolonged deficit in soil moisture) by placing it in the context of the forcing meteorological drought (prolonged deficit in precipitation). The drought assessment using satellite soil moisture is temporally consistent with precipitation-based metrics, but allows for better mapping of the spatial and temporal patterns of how a precipitation deficit may or may not lead to a soil moisture deficit. The specific advantages of a remote-sensing approach—wide-coverage without need for spatially-dense, ground-based climatological records, and sensitivity to otherwise unmeasured irrigation inputs—suggest further opportunity for drought monitoring in ungauged regions, particularly in agricultural contexts.

Using soil-moisture drought indices to evaluate key indicators of agricultural drought in semi-arid Mediterranean Southern Africa

Droughts are natural disasters that globally affect large numbers of people each year. While different forms of drought exist, their severity and extent are dependent on critical points of onset. Understanding these onsets is crucial for water, food and energy security, as well as to develop climate change adaptation strategies. This study used the JAMS/J2000 hydrological model to detect agricultural drought using the Soil Moisture Deficit Index (SMDI). The Berg River catchment in Mediterranean South Africa was used as the pilot study area, which experienced a severe drought between 2015 and 2018 and where meteorological drought progressed into agricultural drought that resulted in significant crop reductions and job losses within the agricultural sector. To combat the effects of meteorological shortfalls, water resource management opted to curb agricultural reservoir releases, forcing farmers to rely on groundwater. Modelling results illustrated the importance of detecting headwater stress within the catchment, where in 2015/2017 headwater areas were affected for the first time over the 35-year simulation. Furthermore, regional changes to the groundwater system, during which severe to extremely severe SMDI values (−3 to −4) were simulated, is postulated to be caused by wide-spread groundwater overuse resulting in a 47% reduction in winter (JJA) and a 68% reduction in spring (SON) streamflow. Immediate streamflow reductions were observed, illustrating the low resilience of these systems to meteorological and agricultural droughts, as well as the impacts of water use behavioural changes. By using SMDI in conjunction with a well constrained hydrological model, crucial drought onset triggers can be detected as well as tipping points regarding water use behaviour. As climate change drives an increase in the occurrence of meteorological droughts in many parts of the world, understanding the advance of severe long-term effects is important for the development of effective adaption strategies to promote water, food and energy security.

Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series

Severe droughts caused unprecedented impacts on grasslands in Central Europe in 2018 and 2019. Yet, spatially varying drought impacts on grasslands remain poorly understood as they are driven by complex interactions of environmental conditions and land management. Sentinel-2 time series offer untapped potential for improving grassland monitoring during droughts with the required spatial and temporal detail. In this study, we quantified drought effects in a major Central European grassland region from 2017 to 2020 using a regression-based unmixing framework. The Sentinel-2-based intra-annual time series of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil fractional cover provide easily interpretable quantities relevant for understanding drought effects on grasslands. Fractional cover estimates from Sentinel-2 matched in-situ conditions observed during field visits. The comparison to a multitemporal reference dataset showed the best agreement for PV cover (MAE = 7.2%). Agreement was lower for soil and NPV, but we observed positive relationships between fractional cover from Sentinel-2 and the reference data with MAE = 10.1% and MAE = 15.4% for soil and NPV, respectively. Based on the fractional cover estimates, we derived a Normalized Difference Fraction Index (NDFI) time series contrasting NPV and soil cover relative to PV. In line with meteorological and soil moisture drought indices, and with the Normalized Difference Vegetation Index (NDVI), NDFI time series showed the most severe drought impacts in 2018, followed by less severe, but persisting effects in 2019. Drought-specific metrics from NDFI time series revealed a high spatial variability of onset, duration, impact, and end of drought effects on grasslands. Evaluating drought metrics on different soil types, we found that grasslands on less productive, sandy Cambisols were strongly affected by the drought in 2018 and 2019. In comparison, grasslands on Gleysols and Histosols were less severely impacted suggesting a higher drought resistance of these grasslands. Our study emphasizes that the high temporal and spatial detail of Sentinel-2 time series is mandatory for capturing relevant vegetation dynamics in Central European lowland grasslands under drought.

Assessing the soil moisture drought index for agricultural drought monitoring based on green vegetation fraction retrieval methods

Soil moisture in root zone soil layers is one of the most important indicators of agricultural drought. Thus, monitoring agricultural drought requires not only knowledge of rainfall anomaly but also quantification of soil moisture. In this study, the effects of various methods of quantifying the green vegetation fraction green vegetation fraction (GVF) on the land-surface-model (LSM)-based soil moisture drought index (SMDI) were assessed using the harvest area data of the World Meteorological Organization together with the widely used vegetation health index and drought severity index. GVF data used in this study include monthly climatological GVF, weekly advanced very high-resolution radiometer (AVHRR)-normalized difference vegetation index-based and 8-daily moderate-resolution imaging spectroradiometer (MODIS) leaf area index (LAI)-based GVF. The results show that SMDI is optimized when using the near-real-time GVF and that LAI-based GVF increases the accuracy of SMDI when monitoring early agricultural drought. The study shows that we can be confident in the accuracy of signals of emerging drought, particularly during the rapid onset of drought.

Development of an integrated weighted drought index and its application for agricultural drought monitoring

Comprehensive consideration of physical forms of meteorological and agricultural drought is necessary for the development of robust monitoring and assessment of droughts. This consideration facilitated the development and analyze an integrated weighted drought index (IWDI) by using the AHP (analytical hierarchy process) technique that takes into account all possible variables relevant to different types of drought such as meteorological, agriculture, and soil moisture drought indices. Heilongjiang northeastern province of China had suffered frequent droughts due to the changing climate. Droughts affected agricultural production and caused a decrease in total yield due to less water availability, particularly in summer which is the only growing season in the area. In this study, an integrated index was developed to examine its applicability and to assess the impact of drought on rice yield using eight different drought indices included meteorological, remote sensing multi-sensor, soil moisture conditions, and climate variables. The results showed that IWDI had a significant correlation with meteorological and agricultural drought indices. Detailed analysis revealed that, compared to a single meteorological or an agricultural drought index, IWDI achieved good results for agriculture drought monitoring. Results showed that IWDI captured a significant impact of drought on rice yield and its variation in the area which was from 17 to 75% at different stations. This study also concluded that integration of different indices may be a better option for policymakers and economists in understanding and monitoring agricultural droughts losses.

Assessment of drought frequency, severity, and duration and its impacts on vegetation greenness and agriculture production in Mediterranean dryland: A case study in Tunisia

Drought is among the highest-impact natural hazards affecting drylands around the world in a warming climate. The Mediterranean region, including Tunisia, is projected to experience the most predominant drying trends worldwide. However, a detailed regional scale study of drought for Tunisia has been limited, hampering an assessment of drought impact on the ecosystem and society. This study explored drought characteristics and its effect on vegetation greenness and agricultural productivity in three vegetation zones of Tunisia during 1982–2011, taking advantage of both meteorological and soil moisture drought indices and identifying the most appropriate index for each zone. The results revealed that meteorological droughts were short and frequent, triggering soil moisture droughts that were long-lasting and intense. The standardized precipitation index was identified as the best indicator of vegetation and agricultural droughts in the Northern forest, while the Palmer Drought Severity Index was best in the Central steppe and Southern desert (no crop data in the Southern desert). The lag-correlation analysis revealed that the response of both vegetation greenness and wheat productivity to droughts was more pronounced and had a longer significant lag in the Central steppe than in the other regions. These results suggest that arable land species (Central steppe and Southern desert), characterized by shallow roots, have a rapid response to rainfall variability when compared with forest species (Northern forest), which have deep roots allowing them an adequate supply of moisture. The region-specific indices identified here will provide a useful measure for drought monitoring and mitigation in Tunisia.

Evaluation of Model-Based Soil Moisture Drought Monitoring over Three Key Regions in China

AbstractLand surface models (LSMs) have been widely used to provide objective monitoring of soil moisture during drought, but large uncertainties exist because of the different parameterizations in LSMs. This study aims to evaluate the ability to monitor soil moisture drought over three key regions in China by using the Noah LSM from the Global Land Data Assimilation System, version 2 (GLDASv2), and the Community Atmosphere Biosphere Land Exchange (CABLE) model that is currently used at the China Meteorological Administration. The modeled soil moisture drought indices were verified against the standardized precipitation evapotranspiration index (SPEI), which served as a reference drought indicator over northern China (NC), northwestern China (NWC), and southwestern China (SWC) from 1961 to 2010. The results show that the precipitation forcing data that drive both LSMs have high accuracy when compared with local observational data. GLDASv2/Noah outperforms CABLE in capturing soil moisture anomalies and variability, especially in SWC, but both show good correlations with the 3-month SPEI (SPEI3) in NC, NWC, and SWC. The autumn drought of 2002 and spring drought of 2010 were selected for the comparison of the modeled drought categories with the SPEI3 drought category, where GLDASv2/Noah performed slightly better than CABLE. This work demonstrates that the choice of LSM is crucial for monitoring soil moisture drought and that the GLDASv2/Noah LSM can be a good candidate for the development of a new operational drought-monitoring system in China.

Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula

Droughts, typically caused by the deficiencies of precipitation and soil moisture, affect water resources and agriculture. As soil moisture is of key importance in understanding the interaction between the atmosphere and Earth’s surface, it can be used to monitor droughts. In this study, a High resolution Soil Moisture Drought Index (HSMDI) was proposed and evaluated for meteorological, agricultural, and hydrological droughts. HSMDI was developed using the 1km downscaled soil moisture data produced from the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) from 2003 to 2011 (March to November) over the Korean peninsula. Seven products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellite sensors were used to downscale AMSR-E soil moisture based on random forest machine learning. The downscaled 1km soil moisture was correlated well with both in situ and AMSR-E soil moisture with the mean coefficient of determination (R2) of 0.29 and 0.59, respectively. The Standardized Precipitation Index (SPI) with time scales from 1 to 12 months, crop yields (for sesame, highland radish, and highland napa cabbage) and streamflow data were used to validate HSMDI for various types of droughts. The results showed that HSMDI depicted meteorological drought well, especially during the dry season, with a similar pattern with the 3-month SPI. However, the performance fluctuated a bit during the wet season possibly due to the limited availability of optical sensor data and heterogeneous land covers around the stations. HSMDI also showed high correlation with crop yield data, in particular the highland radish and napa cabbage cultivated in non-irrigated regions with a mean R2 of 0.77. However, HSMDI did not monitor streamflow well for hydrological drought presenting a various range of correlations with streamflow data (from 0.03 to 0.83).

Closure to “Development of Soil Moisture Drought Index to Characterize Droughts” by Mohammad M. Sohrabi, Jae H. Ryu, John Abatzoglou, and John Tracy

Closure to “Development of Soil Moisture Drought Index to Characterize Droughts” by Mohammad M. Sohrabi, Jae H. Ryu, John Abatzoglou, and John Tracy

Discussion of “Development of Soil Moisture Drought Index to Characterize Droughts” by Mohammad M. Sohrabi, Jae H. Ryu, John Abatzoglou, and John Tracy

In the discussed paper, Sohrabi et al. presented a new drought index, termed soil moisture drought index (SODI), which is based on the difference between water supply and water demand [in other words, the moisture departure from the field capacity (FC) of soil] at a given site. The moisture departure [Eq. (1) in the discussed paper] used in SODI is the improved form of the moisture departure in Plamer Drought Severity Index (PDSI) [Eq. (17) in the discussed paper]. In the following, some cases presented in the discussed paper about SODI’s moisture departure are discussed: 1. There are some ambiguities in methodology of SODI’s moisture departure. Although the SODI is a two-layer soil-water balance model, the discussed paper has not clearly explained the relations of soil-water components between the first and second soil layers. Are these relations considered just like the Palmer approach when calculating the soil-water components of the moisture departure? 2. According to Eq. (1) of the discussed paper, when Pi > ðPEi þ SMDi−1Þ, the equation can simply be rewritten as Di 1⁄4 ROi þ ROi−1 ð1Þ where ROi (surface runoff in the current month) = Pi − ðPEi þ SMDi−1Þ; Pi = total precipitation; PEi = potential evapotranspiration; SMDi−1 = soil moisture deficiency; Di = moisture departure; and i = month number in the calendar year. All variables are expressed in terms of millimeters. Under this condition, the term Li (moisture loss from soil column in Eq. (1) of the discussed paper) is zero. Therefore, for the some successive high-precipitation months, the moisture departure may be calculated only based on sum of the surface runoffs in the current and previous months. There are two issues with Eq. (1) of this discussion paper: (1) the surface runoff in the previous month (ROi−1) is considered as important as the surface runoff in the current month (ROi), and (2) there is no upper limit for surface runoff and it is assumed that any value of surface runoff can be retained in soil column. The latter is unlike the PDSI, where the potential surface runoff is assumed to be the available water balance (AWC). The state where Pi < ðPEi þ SMDi−1Þ can be envisaged and anticipated to occur in a dry month, but the surface runoff in the previous month (ROi−1) is high enough to compensate the moisture deficiency in the current month. Such a state may be occurred due to the two previously mentioned issues. 3. In addition to the surface runoff, the soil moisture deficiency in the previous month (SMDi−1) is accounted for calculating the moisture departure in the current month. While the maximum value of SMDi−1 is considered AWC, there is no maximum value for ROi−1 in SODI’s moisture departure equation. This lead to the moisture departure series be strongly skewed, which in turn needs to be normalized by a proper transformation method such as that used in the discussed paper (i.e., Box-Cox transformation method). Box-Cox transformation should be used with caution in some cases because the transformed data may not be satisfactory even when the best value of transformation parameter had been chosen. There are some alternatives for BoxCox transformation such as Manly (1976) and Yeo and Johnson (2000), which can be used where the Box-Cox method is unable to satisfactorily convert nonnormal data to normal ones. The Manly transformation function is of the form

Commonly Used Drought Indices as Indicators of Soil Moisture in China

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.

Development of Soil Moisture Drought Index to Characterize Droughts

AbstractA new drought index termed the “soil moisture drought index (SODI)” is developed to characterize droughts. The premise of the index is based on how much water is required to attain soil moisture at field capacity. SODI captures variations of precipitation, temperature, and soil moisture over time. Three widely used drought indices, including the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrated palmer drought index (sc-PDSI) are compared with SODI along with local hydrological variables such as streamflow, reservoir storage, and groundwater level for cross-validation. The result indicates that SODI reacts more evidently to relate changes in precipitation and temperature than SPI and SPEI by characterizing soil moisture over time. Results also show that SODI outperforms the existing drought indices in the sense that SODI can detect and quantify the extended severe droughts associated with climate variability and change. S...