An Index-Flood Statistical Model for Hydrological Drought Assessment
Abstract
:1. Introduction
2. Study Area—Czech Republic
3. Data and Methods
3.1. Data
Drought Definition
- event severity (deficit volume), D [mm or m3];
- event length, L [months];
- event intensity, [mm/month or m3/month];
- relative severity (i.e., deficit volume to monthly runoff ratio), [-];
- relative event intensity, [t−1].
3.2. Statistical Model
3.3. Model Assessment
3.3.1. Ratio Diagrams and Gumbel Plot
3.3.2. Discordance
3.3.3. Anderson–Darling Test
- Fit the statistical model to the original sample.
- Calculate standard normal residuals with the parameter estimates from step using quantile mapping.
- Calculate the average correlation of the standard normal residuals.
- Generate a sample of S equicorrelated standard normal variables with correlation .
- Transform the sample from step 4 back to the original scale using the parameter estimates from step 1.
- Fit the statistical model again.
- Calculate the A2 statistics.
- Repeat steps 4–7 until the desired number of bootstrap samples is obtained.
4. Results and Discussion
4.1. Spatial Pooling
4.2. Choice of the At-Site Distribution
4.3. Drought Definition
4.4. Reduction of Uncertainty
4.5. Identification of Homogeneous Regions
5. Summary and Concluding Remarks
- Regional frequency analysis reduces uncertainty of estimated drought characteristics and parameters of its distribution.
- Use of Generalized Pareto Distribution is appropriate to describe the deficit volumes on majority of catchments, which is not the case for Generalized Extreme Value distribution. However, it is not clear to what extent this result depends on characteristics of the area under study and other parameters of the analysis like the threshold defining drought.
- The most subjective part of the regional frequency analysis is the definition of homogeneous regions—methods such as region of influence or Self Organizing maps could be considered to minimize the subjective decisions within the regional frequency analysis.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Period | D | I | L | rD | rI |
---|---|---|---|---|---|
1901–1930 | 4.46 | 1.70 | 2.34 | 0.24 | 0.09 |
1931–1960 | 6.01 | 1.97 | 2.76 | 0.36 | 0.11 |
1961–1990 | 6.68 | 2.19 | 2.95 | 0.44 | 0.12 |
1991–2015 | 4.74 | 1.79 | 2.38 | 0.29 | 0.10 |
D | I | L | rD | rI | |
---|---|---|---|---|---|
Observed runoff | 5.25 | 1.94 | 2.29 | 0.24 | 0.09 |
Simulated runoff | 6.15 | 2.35 | 2.36 | 0.28 | 0.10 |
P [mm] | DV [mm] | p0 | |
---|---|---|---|
Cluster 1 | 993.87 | 21.65 | 0.30 |
Cluster 2 | 699.80 | 10.73 | 0.36 |
Cluster 3 | 574.50 | 6.43 | 0.49 |
ξ | α | κ | A2 Critical Value | |
---|---|---|---|---|
Cluster 1 | −0.01 | 0.86 | −0.15 | 2.42 |
Cluster 2 | −0.02 | 0.83 | −0.19 | 2.64 |
Cluster 3 | −0.04 | 0.71 | −0.32 | 2.79 |
α | κ | 2yr | 50yr | |
---|---|---|---|---|
Cluster 1 | 99.86 | 69.97 | 67.99 | 66.44 |
Cluster 2 | 99.84 | 75.03 | 74.95 | 72.82 |
Cluster 3 | 99.40 | 55.94 | 56.28 | 52.04 |
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Strnad, F.; Moravec, V.; Markonis, Y.; Máca, P.; Masner, J.; Stočes, M.; Hanel, M. An Index-Flood Statistical Model for Hydrological Drought Assessment. Water 2020, 12, 1213. https://doi.org/10.3390/w12041213
Strnad F, Moravec V, Markonis Y, Máca P, Masner J, Stočes M, Hanel M. An Index-Flood Statistical Model for Hydrological Drought Assessment. Water. 2020; 12(4):1213. https://doi.org/10.3390/w12041213
Chicago/Turabian StyleStrnad, Filip, Vojtěch Moravec, Yannis Markonis, Petr Máca, Jan Masner, Michal Stočes, and Martin Hanel. 2020. "An Index-Flood Statistical Model for Hydrological Drought Assessment" Water 12, no. 4: 1213. https://doi.org/10.3390/w12041213
APA StyleStrnad, F., Moravec, V., Markonis, Y., Máca, P., Masner, J., Stočes, M., & Hanel, M. (2020). An Index-Flood Statistical Model for Hydrological Drought Assessment. Water, 12(4), 1213. https://doi.org/10.3390/w12041213