Corrigendum to &quot;Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin&quot; published in Hydrol. Earth Syst. Sci., 19, 2577–2586, 2015

Abstract In regions of climatic heterogeneity, finescale assessment of drought risk is needed for policy making and drought management, mitigation, and adaptation. The relationship between drought relief payments (a proxy for drought risk) and meteorological drought indicators is examined through a retrospective analysis for Sri Lanka (1960–2000) based on records of district-level drought relief payments and a dense network of 284 rainfall stations. The standardized precipitation index and a percent-of-annual-average index for rainfall accumulated over 3, 6, 9, and 12 months were used, gridded to a spatial resolution of 10 km. An encouraging correspondence was identified between the spatial distribution of meteorological drought occurrence and historical drought relief payments at the district scale. Time series of drought indices averaged roughly over the four main climatic zones of Sri Lanka showed statistically significant (p < 0.01) relationships with the occurrence of drought relief. The 9-month cumulative drought index provided the strongest relationships overall, although 6- and 12-month indicators provided generally similar results. Some cases of appreciable drought without corresponding relief payments could be attributed to fiscal pressures, as during the 1970s. Statistically significant relationships between drought indicators and relief payments point to the potential utility of meteorological drought assessments for disaster risk management. In addition, the study provides an empirical approach to testing which meteorological drought indicators bear a statistically significant relationship to drought relief across a wide range of tropical climates.

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Comparison of Meteorological and Agricultural Drought Indicators across Ethiopia

10.20944/preprints201908.0020.v1 ◽

2019 ◽

Author(s):

Dawit Teweldebirhan Tsige ◽

Venkatesh Uddameri ◽

Farhang Forghanparast ◽

Elma Hernandez ◽

Stephen Ekwaro-Osire

Keyword(s):

Soil Moisture ◽

Meteorological Drought ◽

Evaluation Framework ◽

Agricultural Drought ◽

Hydrologic Processes ◽

Preparedness Planning ◽

Drought Indicators ◽

Drought Preparedness ◽

Seasonal Droughts ◽

Meteorological Droughts

Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist even after the cessation of meteorological droughts due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorological and agricultural droughts using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2 and Palmer Z-index to assess intra-seasonal droughts and between SPI-6, SPEI-6 and PDSI for full-season evaluations. SPI was seen to correlate well with selected agricultural drought indicators but did not explain all the variability noted in agricultural droughts. The relationships between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states more so than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for better drought-preparedness planning.

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Comparison of Meteorological- and Agriculture-Related Drought Indicators across Ethiopia

Water ◽

10.3390/w11112218 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2218 ◽

Cited By ~ 4

Author(s):

Dawit Teweldebirhan Tsige ◽

Venkatesh Uddameri ◽

Farhang. Forghanparast ◽

Elma Annette. Hernandez ◽

Stephen. Ekwaro-Osire

Keyword(s):

Soil Moisture ◽

Meteorological Drought ◽

Evaluation Framework ◽

Agricultural Drought ◽

Hydrologic Processes ◽

Preparedness Planning ◽

Drought Indicators ◽

Drought Preparedness ◽

The Relationship ◽

Meteorological Droughts

Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist, even after the cessation of meteorological droughts, due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorological- and agriculture-related drought indicators using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2, and Palmer Z-index to assess intraseasonal droughts, and between SPI-6, SPEI-6, and PDSI for full-season evaluations. SPI was seen to correlate well with selected agriculture-related drought indicators, but did not explain all the variability noted in them. The correlation between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for in order to devise better drought-preparedness planning.

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Drought impact links to meteorological drought indicators and predictability in Spain

10.5194/hess-2021-209 ◽

2021 ◽

Author(s):

Herminia Torelló-Sentelles ◽

Christian Franzke

Keyword(s):

Random Forest ◽

Meteorological Drought ◽

Hydrological Drought ◽

Random Forest Model ◽

The Arctic ◽

Drought Impact ◽

Forest Model ◽

Drought Impacts ◽

Drought Indicators ◽

Impact Data

Abstract. Drought affects many regions worldwide and future climate projections imply that drought severity and frequency will increase. Hence, the impacts of drought on the environment and society will also increase considerably. Monitoring and early warning systems for drought rely on several indicators; however, assessments on how these indicators are linked to impacts are still lacking. Here, we explore the links between different drought indicators and drought impacts within six sub- regions in Spain. We used impact data from the European Drought Impact Report Inventory database, and provide a new case study to evaluate these links. We provide evidence that a region with a small sample size of impact data can still provide useful insights regarding indicator-impact links. As meteorological drought indicators, we use the Standardised Precipitation Index and the Standardised Precipitation-Evapotranspiration Index, as agricultural and hydrological drought indicators we use a Standardised Soil Water Index and, a Standardised Streamflow Index and a Standardised Reservoir Storage Index. We also explore the links between drought impacts and teleconnection patterns and surface temperature by conducting a correlation analysis and then test the predictability of drought impacts using a Random Forest model. Our results show meteorological indices are best linked to impact occurrences overall, and at long time scales between 15 and 33 months. However, we also find robust links for agricultural and hydrological drought indices, depending on the sub-region. The Arctic Oscillation, Western Mediterranean Oscillation and the North Atlantic Oscillation at long accumulation periods (15 to 48 months), are top predictors of impacts in the northwest and northeast regions, the Community of Madrid, and the south regions of Spain respectively. We also find links between temperature and drought impacts. The Random Forest model produces skilful models for most sub- regions. When assessed using a cross-validation analysis, the models in all regions show precision, recall, or R2 values higher than 0.97, 0.62 and 0.68 respectively. Since we find the models to be skilful, we encourage other types of impact data to be used to investigate these links and to predict drought impacts.

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Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

Meteorological Applications ◽

10.1002/met.1856 ◽

2019 ◽

Vol 27 (1) ◽

Author(s):

Tayyebeh Mesbahzadeh ◽

Maryam Mirakbari ◽

Mohsen Mohseni Saravi ◽

Farshad Soleimani Sardoo ◽

Mario M. Miglietta

Keyword(s):

Climate Change ◽

Meteorological Drought ◽

Climate Change Scenarios ◽

Drought Analysis ◽

Copula Theory ◽

Drought Indicators

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The Temporal Variability of Rainfall and Streamflow into Lake Nakuru, Kenya, Assessed Using SWAT and Hydrometeorological Indices

Hydrology ◽

10.3390/hydrology6040088 ◽

2019 ◽

Vol 6 (4) ◽

pp. 88

Author(s):

Alice Nyawira Kimaru ◽

John Mwangi Gathenya ◽

Charles K. Cheruiyot

Keyword(s):

Temporal Variability ◽

Swat Model ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Hydrological Drought ◽

Water Yield ◽

Time Step ◽

Lake Nakuru ◽

Equal Distribution ◽

Drought Indicators

Temporal variability analysis of rainfall and river discharges is useful in determining the likelihood of the occurrence of extreme events such as drought or flooding for the purposes of developing policies to mitigate their effects. This study investigated the temporal variability of rainfall and discharges into Lake Nakuru, Kenya using meteorological drought indicators and hydrological drought indicators from 1981 to 2018. The standardized precipitation index (SPI) and standardized precipitation evaporation index (SPEI) were used to characterize meteorological drought, while the streamflow drought index (SDI) was used to characterize hydrological drought. A SWAT model was applied for the prediction of streamflow on five tributaries of Lake Nakuru (Njoro, Ngosur, Nderit, Larmudiac, and Makalia Rivers). The model was successfully calibrated on Njoro River at the upstream of river gauging station 2FCO5 from 1984 to 1996, and the parameters were validated from 1997 to 2007. The SUFI-2 algorithm was applied in SWATCup to perform the calibration of the model. The model performance was considered satisfactory in daily time step (NSE = 0.58, R2 = 0.58 during calibration and NSE = 0.52, R2 = 0.68 during validation). The average annual water balance revealed that out of 823 mm received annual precipitation, 154 mm was surface runoff and 178 mm was the annual average water yield. The average annual actual evapotranspiration (ET) was 607 mm. The results for the temporal variation of the SPI and SDI for the five subcatchments indicated that the drought events identified by the 12-month SPI/SPEI were almost all identified by the 12-month SDI. At the catchment scale, SPI showed an equal distribution of wet and dry periods, with 50.00% of positive anomalies and 50.00% of negative anomalies being observed from 1981 to 2018, while SDI observes a high frequency of dry periods (52.63%) and a lower frequency of wet periods (47.37%). There is a higher frequency of wet periods compared to dry periods for both indices from 2009 to 2010 at 60.00% and 40.00% for SPI and 90.00% and 10.00% for SDI, respectively. Both indices observed that 1984 and 2000 were severely dry years (SPI/SPEI < −2.00), while 2018 was severely wet (SPI/SPEI > 2.00). The results for the variability in rainfall and streamflow indices revealed that the last 10 years (2009–2018) were wetter than the period from 1981 to 2008.

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Baseline Probabilities for the Seasonal Prediction of Meteorological Drought

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-11-0132.1 ◽

2012 ◽

Vol 51 (7) ◽

pp. 1222-1237 ◽

Cited By ~ 41

Author(s):

Bradfield Lyon ◽

Michael A. Bell ◽

Michael K. Tippett ◽

Arun Kumar ◽

Martin P. Hoerling ◽

...

Keyword(s):

Seasonal Cycle ◽

Serial Correlation ◽

Climate Model ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Seasonal Prediction ◽

Monthly Precipitation ◽

Predictive Skill ◽

Drought Indicators ◽

The Standardized Precipitation Index

AbstractThe inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, moving monthly averages of precipitation) decays linearly with increasing lag. It is shown that seasonality in the variance of accumulated precipitation serves to enhance or diminish the persistence characteristics (AC) of the SPI and related drought indicators, and the seasonal cycle can thereby provide an appreciable source of drought predictability at regional scales. The AC is used to obtain a parametric probability density function of the future state of the SPI that is based solely on its inherent persistence characteristics. In addition, a method is presented for determining the optimal persistence of the SPI for the case of no serial correlation in precipitation (again, the baseline case). The optimized, baseline probabilities are being incorporated into Internet-based tools for the display of current and forecast drought conditions in near–real time.

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Satellite Data Based Soil Water Index as a Tool to Monitor Droughts and Agricultural Crisis -An Example of Yavatmal District, Maharashtra, India

Remote Sensing of Land ◽

10.21523/gcj1.20040102 ◽

2020 ◽

Vol 4 (1-2) ◽

pp. 12-18

Author(s):

Vijendra Boken

Keyword(s):

Soil Water ◽

Satellite Data ◽

Suicide Rate ◽

Crop Yields ◽

Meteorological Drought ◽

Agricultural Drought ◽

Coefficient Of Correlation ◽

Water Index ◽

Farmer Suicides ◽

June July

Yavatmal is one of the drought prone districts in Maharashtra state of India and has witnessed an agricultural crisis to the extent that hundreds of its farmers have committed suicides in recent years. Satellite data based products have previously been used globally for monitoring and predicting of drought, but not for monitoring their extreme impacts that may include farmer-suicides. In this study, the performance of the Soil Water Index (SWI) derived from the surface soil moisture estimated by the European Space Agency’s Advanced Scatterometer (ASCAT) is assessed. Using the 2007-2015 data, it was found that the relationship of the SWI anomaly was bit stronger (coefficient. of correlation = 0.59) with the meteorological drought or precipitation than with the agricultural drought or crop yields of major crops (coefficient. of correlation = 0.50). The farmer-suicide rate was better correlated with the SWI anomaly averaged annually than with the SWI anomaly averaged only for the monsoon months (June, July, August, and September). The correlation between the SWI averaged annually increased to 0.89 when the averages were taken for three years, with the highest correlation occurring between the suicide rate and the SWI anomaly averaged for three years. However, a positive relationship between SWI and the suicide rate indicated that drought was not a major factor responsible for suicide occurrence and other possible factors responsible for suicide occurrence need to examine in detail.

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