A drought is usually represented by duration and severity, and may last several months or years. Multidimensional characteristics of a drought make univariate analysis unable to reveal the significant relationship among drought properties. Furthermore, historical records tend to be too short to fully evaluate drought characteristics. A practical method was proposed in this study to estimate the bivariate return period of droughts based on the use of synthetic data to overcome the above considerations. The bivariate return period of droughts is dependent on the drought interarrival time and the joint distribution of drought properties. A nonparametric method was employed in this study to estimate the joint distribution of drought properties. The historical droughts in the Conchos River Basin, Mexico were evaluated based on their return period estimated by the proposed method. The proposed method allowed a better understanding of the joint probabilistic behavior of droughts beyond the limitation of the univ...

In this study, a hydrological analysis of rainwater harvesting facilities was conducted using a model based on the IHACRES model. Using this model, the rainfall, rainfall loss, inflow to the storage tank, tank storage volume, overflow from the tank, and rainwater consumption data were simulated to evaluate the hydrological characteristics of the rainwater harvesting facilities. This study evaluated three rainwater harvesting facilities in Korea. The results of the study are summarized as follows. (1) The proposed model was found to well simulate the rainfall-runoff processes of rainwater harvesting facilities. (2) As the target amount of rainwater consumption increases, the amount of rainwater consumption also increases, but the number of days with available rainwater decreases. Especially in Korea, the difference in rainwater use when applying different target amounts of rainwater consumption is larger during the rainy compared to the dry season. (3) The effect of increasing the tank capacity was found t...

In this study we compared applications of mixed and continuous distribution functions to the theory of entropy for the evaluation of rain gauge networks. The use of a mixed distribution function to evaluate rain gauge networks has an important advantage of considering rainfall intermittency in both time and space. Parameters of both mixed and continuous distribution functions were estimated using the daily rainfall data collected in the Choongju Dam Basin, Korea. The optimal number of rain gauge stations is, then, decided by using the entropy theory. The optimal number of rain gauge stations estimated by applying the mixed distribution function was found to be much smaller, but still reasonable, than that estimated by applying the continuous distribution function; mostly due to the small wet probability and the high coincidence of daily rainfall between rain gauge stations.

This study evaluated five models of rainfall temporal distribution (i.e., the Yen and Chow model, Mononobe model, alternating block method, Huff model, and Keifer and Chu model), with the annual maximum rainfall events selected from Seoul, Korea, from 1961 to 2016. Three different evaluation measures were considered: the absolute difference between the rainfall peaks of the model and the observed, the root mean square error, and the pattern correlation coefficient. Also, sensitivity analysis was conducted to determine whether the model, or the randomness of the rainfall temporal distribution, had the dominant effect on the runoff peak flow. As a result, the Keifer and Chu model was found to produce the most similar rainfall peak to the observed, the root mean square error was smaller for the Yen and Chow model and the alternating block method, and the pattern correlation was larger for the alternating block method. Overall, the best model to approximate the annual maximum rainfall events observed in Seoul, Korea, was found to be the alternating block method. Finally, the sensitivity of the runoff peak flow to the model of rainfall temporal distribution was found to be much higher than that to the randomness of the rainfall temporal distribution. In particular, in small basins with a high curve number (CN) value, the sensitivity of the runoff peak flow to the randomness of the rainfall temporal distribution was found to be insignificant.