Conditional Copula-Based Spatial–Temporal Drought Characteristics Analysis—A Case Study over Turkey

Afshar, Mahdi; Şorman, A. Ünal; Yılmaz, M. Tuğrul

doi:10.3390/w8100426

Cited by 28 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous works using copulas in hydro-climatology studies have tended to focus on the joint distribution of dif-ferent characteristics of the hazardous events, such as frequency, intensity, severity, and duration, among others (Li et al, 2015;Chen et al, 2013;Mirabbasi et al, 2012). Moreover, the restriction to the bivariate case allowed for a simpler interpretation of the results, in contrast to higher-dimension copulas (Afshar et al, 2016;Ganguli and Reddy, 2013), for instance by adding other factors influencing crop yield beyond drought as copula variables.…”

Section: Discussionmentioning

confidence: 99%

“…This study adopts a bivariate modelling approach such that for each pair (X, Y ) of cereal and drought indicators over each cluster we considered bivariate copula functions to estimate the joint probability distributions. Trivariate copulas have been proposed in the analysis of hydrological extremes (Afshar et al, 2016;Bezak and Brilly, 2014;Saghafian and Mehdikhani, 2014), but the development of higher-dimensional copulas exhibits very complex structures and further studies and evaluations are required. In comparison to high-dimensional copulas, the two-dimensional copulas involve much less computational cost and allow for more easily interpretable and illustratable relationships between the interval margins.…”

Section: The Concept Of Copulamentioning

confidence: 99%

“…3, bottom panels), mainly due to the correlations between the yield and selected drought indicators (Ribeiro et al, 2019a). Most of the work based on copulas has estimates of the marginal distribution functions (Afshar et al, 2016;Bokusheva et al, 2016;Mirabbasi et al, 2012), whereas this procedure has no requirement for prior knowledge of the marginal distributions, therefore incurring less significant assumptions.…”

Section: Fitting Copula Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Ribeiro

Russo

Gouveia

et al. 2019

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Extreme weather events, such as droughts, have been increasingly affecting the agricultural sector, causing several socio-economic consequences. The growing economy requires improved assessments of drought-related impacts in agriculture, particularly under a climate that is getting drier and warmer. This work proposes a probabilistic model that is intended to contribute to the agricultural drought risk management in rainfed cropping systems. Our methodology is based on a bivariate copula approach using elliptical and Archimedean copulas, the application of which is quite recent in agrometeorological studies. In this work we use copulas to model joint probability distributions describing the amount of dependence between drought conditions and crop yield anomalies. Afterwards, we use the established copula models to simulate pairs of yield anomalies and drought hazard, preserving their dependence structure to further estimate the probability of crop loss. In the first step, we analyse the probability of crop loss without distinguishing the class of drought, and in the second step we compare the probability of crop loss under drought and non-drought conditions. The results indicate that, in general, Archimedean copulas provide the best statistical fits of the joint probability distributions, suggesting a dependence among extreme values of rainfed cereal yield anomalies and drought indicators. Moreover, the estimated conditional probabilities suggest that when drought conditions are below moderate thresholds, the risk of crop loss increases between 32.53 % (cluster 1) and 32.6 % (cluster 2) in the case of wheat and between 31.63 % (cluster 2) and 55.55 % (cluster 2) in the case of barley. From an operational point of view, the results aim to contribute to the decision-making process in agricultural practices.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Concept Of Copulamentioning

confidence: 99%

Section: Fitting Copula Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Ribeiro

Russo

Gouveia

et al. 2019

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…For example, when analyzing flood characteristics, the joint distribution of flood peak and flood volume is established for joint probability estimation [31,32]. When analyzing drought characteristics, the joint distribution of duration and severity is established for joint probability estimation [33,34]. Conditional probability is the percentage of something happen under a certain condition.…”

Section: Conditional Probability Distributionmentioning

confidence: 99%

Quantifying the Relationship between Drought and Water Scarcity Using Copulas: Case Study of Beijing–Tianjin–Hebei Metropolitan Areas in China

Fan

Wang

Liu

et al. 2018

Water

View full text Add to dashboard Cite

Making the distinction between drought and water scarcity is not trivial, because they often occur simultaneously. In this study, we used Copulas to quantify the relationship between drought and water scarcity. Beijing–Tianjin–Hebei Metropolitan Areas (BTHMA) was chosen as the study area. Standard Precipitation and Evapotranspiration Index (SPEI) and water exploitation index plus (WEI+) was chosen to represent metrological drought and water scarcity. Inverse Distance Weighted method was used for spatial analysis of SPEI and WEI+, and Archimedean Copula was used to establish two-dimensional joint probability distribution of SPEI and WEI+. The results are as follows: (1) The southern part of the study area was wetter. The middle part was drier, with moderate drought happened for most times. (2) WEI+ of Beijing and Tianjin showed significant decreasing trends from 2000 to 2015, while WEI+ of Hebei Province did not, which indicated that Hebei Province is facing much severer water scarcity situation than Beijing and Tianjin. (3) Gumbel copula was the best-fitting model to establish the joint probability distribution of SPEI and WEI+. The condition probability provided a probability distribution of water scarcity under different drought conditions, which can provide technical support for government managers during policy making.

show abstract

“…Even though such observations are critical for the satellite mission validation efforts, their use is often impractical for studies focusing on large spatial areas (Bulut et al, 2019). Instead, hydrological model-or satellite remote sensing-based soil moisture data sets are commonly used for coarsescale applications related such as drought monitoring (Afshar et al, 2016), crop yield monitoring (Anderson et al, 2015;Anderson et al, 2016;Mladenova et al, 2017), and improvement of hydrological models via data assimilation (Crow et al, 2003;Houser et al, 1998;Lievens et al, 2015;Yilmaz et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products

Afshar

Yılmaz

Crow

2019

Water Resources Research

View full text Add to dashboard Cite

In this study, the impact of various rescaling approaches in the framework of data fusion is explored. Four different soil moisture products (Advanced Scatterometer; Advanced Microwave Scanning Radiometer for EOS, AMSR‐E; Antecedent Precipitation Index; and Global Land Data Assimilation System‐NOAH) are fused. The systematic differences between products are removed before the fusion utilizing various rescaling approaches focusing on different methods (regression, variance/cumulative distribution function (CDF) matching, multivariate adaptive regression splines, and support vector machines based), stationarity assumptions (constant or time‐varying rescaling coefficients), and time‐frequency techniques (periodic or nonperiodic high‐ and low‐frequency components). Given that statistical descriptions (e.g., standard deviation and correlation coefficient) of reference data sets are utilized in rescaling approaches, the precision of the selected reference data set also impacts the final fused product precision. Experiments are validated over 542 soil moisture monitoring sites selected from the International Soil Moisture Network data sets between 2007 and 2011. Overall, results highlight the importance of reference data set selection—particularly that a more precise reference product yields a higher precision fused soil moisture product. This conclusion is sensitive neither to the number of fused products nor the rescaling procedure. Among rescaling approaches, the precision of fused products is most affected by the choice of rescaling stationary assumption and time‐frequency decomposition technique. Variations in rescaling methods have only a small impact on the precision of pair fused products. In contrast, utilizing a time‐varying stationary assumption and nonperiodic decomposition technique produces correlation improvements of 0.07 [−] and 0.02 [−], respectively, versus the other widely implemented rescaling approaches.

show abstract

Conditional Copula-Based Spatial–Temporal Drought Characteristics Analysis—A Case Study over Turkey

Cited by 28 publications

References 27 publications

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Probabilistic modelling of the dependence between rainfed crops and drought hazard

Quantifying the Relationship between Drought and Water Scarcity Using Copulas: Case Study of Beijing–Tianjin–Hebei Metropolitan Areas in China

Impact of Rescaling Approaches in Simple Fusion of Soil Moisture Products

Contact Info

Product

Resources

About