Long-term trends in agricultural droughts over Netherlands and Germany: how extreme was the year 2018?

Abstract. Droughts can have important impacts on environment and economy like in the year 2018 in parts of Europe. Droughts can be analyzed in terms of meteorological drought, agricultural drought, hydrological drought and social-economic drought. In this paper, we focus on meteorological and agricultural drought and analyzed drought trends for the period 1965–2019 and assessed how extreme the drought year 2018 was in Germany and the Netherlands. The analysis was made on the basis of the following drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), potential precipitation deficit (PPD) and ET deficit. SPI and SSI were computed at two time scales, the period April-September and a 12-months period. In order to analyze drought trends and the ranking of the year 2018, HYDRUS 1-D simulations were carried out for 31 sites with long-term meteorological observations and soil moisture, potential evapotranspiration (ET) and actual ET were determined for five soil types (clay, silt, loam, sandy loam and loamy sand). The results show that the year 2018 was severely dry, which was especially related to the highest potential ET in the time series 1965–2019, for most of the sites. For around half of the 31 sites the year 2018 had the lowest SSI, and largest PPD and ET-deficit in the 1965–2019 time series, followed by 1976 and 2003. The trend analysis reveals that meteorological drought (SPI) hardly shows significant trends over 1965–2019 over the studied domain, but agricultural droughts (SSI) are increasing, at several sites significantly, and at even more sites PPD and ET deficit show significant trends. The increasing droughts over Germany and Netherlands are mainly driven by increasing potential ET and increasing vegetation water demand.

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Commonly Used Drought Indices as Indicators of Soil Moisture in China

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0076.1 ◽

2015 ◽

Vol 16 (3) ◽

pp. 1397-1408 ◽

Cited By ~ 43

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.

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Model-Based Drought Indices over the United States

Journal of Hydrometeorology ◽

10.1175/2008jhm1002.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1212-1230 ◽

Cited By ~ 110

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.

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Elucidating Diverse Drought Characteristics from Two Meteorological Drought Indices (SPI and SPEI) in China

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0290.1 ◽

2020 ◽

Vol 21 (7) ◽

pp. 1513-1530 ◽

Cited By ~ 3

Author(s):

Lingcheng Li ◽

Dunxian She ◽

Hui Zheng ◽

Peirong Lin ◽

Zong-Liang Yang

Keyword(s):

Clustering Algorithm ◽

Potential Evapotranspiration ◽

Standardized Precipitation Index ◽

Three Dimensional ◽

Meteorological Drought ◽

Drought Indices ◽

Drought Severity ◽

Severe Drought ◽

Small Areas ◽

Drought Characteristics

AbstractThis study elucidates drought characteristics in China during 1980–2015 using two commonly used meteorological drought indices: standardized precipitation index (SPI) and standardized precipitation–evapotranspiration index (SPEI). The results show that SPEI characterizes an overall increase in drought severity, area, and frequency during 1998–2015 compared with those during 1980–97, mainly due to the increasing potential evapotranspiration. By contrast, SPI does not reveal this phenomenon since precipitation does not exhibit a significant change overall. We further identify individual drought events using the three-dimensional (i.e., longitude, latitude, and time) clustering algorithm and apply the severity–area–duration (SAD) method to examine the drought spatiotemporal dynamics. Compared to SPI, SPEI identifies a lower drought frequency but with larger total drought areas overall. Additionally, SPEI identifies a greater number of severe drought events but a smaller number of slight drought events than the SPI. Approximately 30% of SPI-detected drought grids are not identified as drought by SPEI, and 40% of SPEI-detected drought grids are not recognized as drought by SPI. Both indices can roughly capture the major drought events, but SPEI-detected drought events are overall more severe than SPI. From the SAD analysis, SPI tends to identify drought as more severe over small areas within 1 million km2 and short durations less than 2 months, whereas SPEI tends to delineate drought as more severe across expansive areas larger than 3 million km2 and periods longer than 3 months. Given the fact that potential evapotranspiration increases in a warming climate, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change.

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Assessment of spatiotemporal characteristics of agro-meteorological drought events based on comparing Standardized Soil Moisture Index, Standardized Precipitation Index and Multivariate Standardized Drought Index

Journal of Water and Climate Change ◽

10.2166/wcc.2020.280 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Muhammad Imran Khan ◽

Xingye Zhu ◽

Muhammad Arshad ◽

Muhammad Zaman ◽

Yasir Niaz ◽

...

Keyword(s):

Soil Moisture ◽

Drought Index ◽

Standardized Precipitation Index ◽

Precipitation Index ◽

Drought Indices ◽

Moisture Index ◽

Drought Characteristics ◽

Soil Moisture Index ◽

The Impact ◽

Standardized Drought Index

Abstract Drought indices that compute drought events by their statistical properties are essential stratagems for the estimation of the impact of drought events on a region. This research presents a quantitative investigation of drought events by analyzing drought characteristics, considering agro-meteorological aspects in the Heilongjiang Province of China during 1980 to 2015. To examine these aspects, the Standardized Soil Moisture Index (SSI), Standardized Precipitation Index (SPI), and Multivariate Standardized Drought Index (MSDI) were used to evaluate the drought characteristics. The results showed that almost half of the extreme and exceptional drought events occurred during 1990–92 and 2004–05. The spatiotemporal analysis of drought characteristics assisted in the estimation of the annual drought frequency (ADF, 1.20–2.70), long-term mean drought duration (MDD, 5–11 months), mean drought severity (MDS, −0.9 to −2.9), and mild conditions of mean drought intensity (MDI, −0.2 to −0.80) over the study area. The results obtained by MSDI reveal the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. The results of this study provide valuable information and can prove to be a reference framework to guide agricultural production in the region.

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Comparison of Meteorological Drought using SPI and SPEI

Civil Engineering Journal ◽

10.28991/cej-2021-03091783 ◽

2021 ◽

Vol 7 (12) ◽

pp. 2130-2149

Author(s):

Shashi Shankar Ojha ◽

Vivekanand Singh ◽

Thendiyath Roshni

Keyword(s):

River Basin ◽

Potential Evapotranspiration ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Drought Indices ◽

Resources Management ◽

Drought Assessment ◽

Time Period ◽

Almost All ◽

Effective Water

Drought assessment is crucial for effective water resources management in a river basin. Drought frequency has increased worldwide in recent years due to global warming. In this paper, an attempt is made to assess the meteorological drought in the Punpun river basin, India using two globally accepted drought indices namely, Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The SPI and SPEI at 1-, 3-, 6-, 9-, and 12-month timescale were obtained to analyze the temporal variability of different drought levels. Correlation analysis of available observed data and gridded data has been carried out and the correlation coefficient was found to be 0.956. Hence gridded rainfall data from the year 1991 to 2020 is used for further analysis. Potential evapotranspiration (PET) used in the calculation of SPEI was computed by the Thornthwaite method. Water deficit was observed throughout as there is a decrease in rainfall and an increase in PET during the selected period. The results show that the period 2004 to 2006 and 2009 to 2010 years are observed as drought periods by both indices for almost all timescale. The intensity and duration of drought have increased after 2004. A negative trend of both the indices have been observed in all seasons on all timescale, which clearly shows a transition from near normal to moderately dry during the selected time period. The highest correlation between both the indices is for the 12-month scale with R² value 0.92 and the RMSE value 0.28. The main outcome of this study is that both SPI and SPEI show a strong correlation on same time scales adopted in this study. The dependency of SPEI on temperature is also observed in this study. Doi: 10.28991/cej-2021-03091783 Full Text: PDF

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Performance of Drought Indices in Trichy Region, Tamil Nadu

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v38i530385 ◽

2019 ◽

pp. 1-10

Author(s):

L. Sathya ◽

R. Lalitha

Keyword(s):

Tamil Nadu ◽

Standardized Precipitation Index ◽

Synthetic Data ◽

Meteorological Drought ◽

Severity Index ◽

Rainfall Anomaly ◽

Drought Indices ◽

Drought Severity ◽

Data Series

Droughts are regional phenomena, which are considered as one of the major natural environmental hazards and severely affect the water resources. Climate variability may result in harmful drought periods in semiarid regions. Meteorological drought indices are considered as important tools for drought monitoring, they are embedded with different theoretical and experimental structures. This study compares the performance of three indices of Standardized Precipitation Index (SPI), Rainfall Anomaly Index (RAI) End Palmer Drought Severity Index (PNPI) to predict long-term drought events using the Thomas-Feiring Model and historical data. For studies of areal drought extent, the 61 years (1951-2011) historical rainfall data of Trichy District were utilized to generate 58 years (2012-2070) synthetic data series so that the characteristics of long-term drought might be determined and the performance of those three indices might be analyzed and compared. The results show that SPI and PNPI perform similarly with regard to drought identification and detailed analysis to determine the characteristics of long-term drought. Finally, the RAI indicated significant deviations from normalized natural processes.

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Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation

Water ◽

10.3390/w12102813 ◽

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.

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

10.5194/egusphere-egu2020-11120 ◽

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

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.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. &#8232;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.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&#8217;s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Mu&#241;oz-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.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).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.

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Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

Remote Sensing ◽

10.3390/rs11232742 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2742 ◽

Cited By ~ 3

Author(s):

Tran ◽

Myint ◽

Latorre-Carmona ◽

Ho ◽

...

Keyword(s):

Time Series ◽

Land Surface ◽

Drought Index ◽

Negative Impact ◽

Statistical Significance ◽

Standardized Precipitation Index ◽

Agricultural Drought ◽

Series Data ◽

Drought Indices ◽

Drought Severity

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

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Smart method of agricultural drought regionalization: A winter wheat case study

Contributions to Geophysics and Geodesy ◽

10.2478/congeo-2019-0003 ◽

2019 ◽

Vol 49 (1) ◽

pp. 25-36 ◽

Cited By ~ 1

Author(s):

Tomáš Středa ◽

Hana Středová ◽

Filip Chuchma ◽

Josef Kučera ◽

Jaroslav Rožnovský

Keyword(s):

Time Series ◽

Czech Republic ◽

Winter Wheat ◽

Negative Impact ◽

Potential Evapotranspiration ◽

Agricultural Drought ◽

Soil Conditions ◽

Significant Deterioration ◽

The Czech Republic

Abstract The occurrence of drought during flowering (usually from the end of May to the beginning of June) is the most hazardous timing in terms of the possible negative impact of agricultural drought on winter wheat, which is the most cultivated crop in the Czech Republic (about 800000 ha). Lack of water, often accompanied by high temperatures, negatively affects the number of grains in the wheat ear and the tissue development of the developing grain, with consequent impacts on yield and quality of product. With the use of a) long-term time series of agrometeorological data (1961–2010), b) long-term phenological time series of winter wheat (1981–2010), and c) soil conditions data (available water capacity of soils of the Czech Republic) for the arable soil, the ratio of actual evapotranspiration and potential evapotranspiration for the period of 1961–2010, used as an indicator of agricultural drought (lack of water) for wheat, was calculated. The innovative aspect of this categorization of the territory of the Czech Republic according to the risk of occurrence of agricultural drought for winter wheat is considering drought from the aspect of the plant, i.e., evaluation based on the actual consumption of water by the vegetation. This is a very sophisticated procedure. Frequently, water content in soils data, presented as an output of some models, do not fully indicate the possible negative impacts on yield generation because the plants themselves are typically not considered. The method used in this study is universally applicable and allows comparisons of regions at the local, regional, and supra-regional levels. For estimation of the development of agronomic drought in the future, the basic water balances in the growing seasons of 1961–2010 and 2071–2100 were compared using a climate scenario. The forecast indicates a significant deterioration of agricultural drought in the region with probable direct impacts on agricultural production.

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