Comprehensive flood prevention plans are established in large basins to cope with recent abnormal floods in South Korea. In order to make economically effective plans, appropriate design rainfalls are critically determined from the rainfall depth-frequency curves which take the occurrence of abnormal floods into consideration. Conventional approaches to construct the rainfall depthfrequency curves are based on the stationarity assumption. However, this assumption has a critical weak aspect in that it cannot reflect non-stationarities in rainfall observations. As an alternative, this study suggests the non-stationary Gumbel model (NSGM) which incorporates a linear trend of rainfall observations into rainfall frequency analysis to construct the rainfall depth-frequency curves. A comparison of various schemes employed in the model found that the proposed NSGM permits the estimation of the distribution parameters even when shifted in the future by using linear relationships between rainfall statistics and distribution parameters, and produces more acceptable estimates of design rainfalls in the future than the conventional model. The NSGM was applied at several stations in South Korea and then expected the design rainfalls to increase by up to 15-30% in 2050.
This study proposed a practical approach to estimate design rainfalls that can be applied to design reference year using the relationships among distribution parameters, annual rainfalls estimated from GCMs(Global Circulation Models), and annual maximum rainfalls observed at gauging stations, leading to consider climate change. The highlight of this study is to estimate distribution parameters through regression models with annual rainfalls as a causal factor. The proposed model was applied to estimated ensemble design rainfalls with 24-hr duration in built-up design reference years of 2020 and 2030 at 10 stations considering the uncertainty induced by emission scenarios and GCMs. The overall results indicated that the design rainfalls of Gangneung, Daejeon, Seoul, and Sokcho in 2020 and 2030 are higher than those in 2009(assumed as the current), the design rainfalls of Gwangju, Seosan, and Jeju in 2020 and 2030 are very close to the current, and the design rainfalls of Busan, Mokpo, and Daegu in 2020 and 2030 are lower than the current. It is considered that the trend component of the annual maximum rainfalls is incorporated into the process of estimating design rainfalls. The proposed method can provide design rainfalls in the next 10 more years in which the effect of climate change is reflected, and thus may suggest useful reference or alternatives for the hydrologic plan and management.
Hydrologic frequency analysis practically assumes that a time series of interest is temporally stationary. However, the assumption is sometimes not valid within the context of climate change. Monotonic trend tests, which are widely used in practice to investigate the presence of significant non-stationarity of hydrologic variables, can determine only whether a trend is significant. This study proposed a practical method for characterizing trend pattern embedded in hydrologic observations through the efficient illustration of changes in trend significance over time. This study analyzed the trend patterns of observed rainfalls in the Han River basin using Mann-Kendall test, and developed checker board plots to show the pattern of trend variations. The results indicate that the time and the magnitude of significant trend would differ by station although most of record periods show a certain trend of increasing annual maximum rainfalls.
Our objective is to compose design flood-hydrograph for analysis of unsteady seepage through river levees. Flood data in the study area, whose observations period are sufficiently long, were collected. Design water surface level and design flood of the study area were analyzed. The method that was introduced in design criteria guideline for river levee in Japan was modified and applied in this study for decision of design flood-hydrograph. For examination of applicability of composed flood-hydrograph, the relation of slope of falling limb and duration of flood-hydrograph was analyzed. It was estimated that flood duration in composed flood-hydrograph was longer than one of design flood-hydrograph, that represents duration of design flood-hydrograph can not take duration of real flood into account. Slope of falling limb of composed flood-hydrograph was more gradual than one of design flood-hydrograph in most study area. Although the results showed that the larger area of a basin accompanies the gentler slope, the pronounced correlation, which can be seen between basin area and flood duration, did not exist. The present study suggests that employing duration of composed flood-hydrograph and slope of falling limb of design flood-hydrograph is safer design when analyzing unsteady seepage through river levees. This study proposed a practical approach to estimate design flood-hydrograph using past flood data. This study applied the methodology to the study area in five river basins and identified the validation.
Environmental models typically possess large uncertainty due to contributions from model structure, assumptions, parameterization, and data errors, not to mention lack of consideration of problem framing and the associated choice and justification of objective function. Sensitivity analysis (SA) is a fundamental tools to help identify uncertainties relevant to the modelling objectives. It provides information on the impact on model outputs from inputs and parameters and can contribute to simplifying models to make them more identifiable. However, sensitivity analysis can produce different results in accordance with several sources, such as input forcing, objective function, and the sampling undertaken.The Sobol' method of SA is applied here to the Soil and Water Assessment Tool (SWAT). The method is based on variance decomposition, is categorized as a global sensitivity analysis (GSA) and is known to be model independent. It is able to handle non-linearity and non-monotonic functions and models. This study illustrates its findings using the total sensitivity index which includes the main effect and parameter interactions. Quasi Monte Carlo is invoked as the sampling method.The SWAT model can be regarded as an example of a complex, dynamic, over-parameterized environmental model, albeit in the hydrology domain. It has been used to simulate both water quantity and quality. It represents catchment processes based on spatially distributed soil type, weather variables, topography and land use. Multiple modules in SWAT handle hydrologic processes, weather conditions, erosion and nutrient processes. The SWAT model used here is based on previous studies by Leta et al. (2015) and Zadeh et al. (2015) for the Senne river basin in Belgium. This paper investigates how individual sources affect the results of a Sobol' global sensitivity analysis of the SWAT model. Sensitivity analyses are performed with different weather conditions and multiple objective functions, and the stability of the ranking of parameter sensitivity is discussed.The objective functions used as illustration in this study are: the Nash-Sutcliffe Efficiency (NSE), the modified NSE (NSE*), NSE*Log, and NSE*combined. The study analyses the sensitivity indices and rank of the parameters for different weather conditions, using wet and dry calendar years selected from the five-year observation period. In case of the selected wet year, NSE and NSE* produce the same rank for parameter sensitivity. The objective functions NSE*Log and NSE*combined both return different sensitivity indices and rankings to NSE and NSE*, as they emphasize low flows and mid flows more than high flows. The SWAT parameter Cn2 (runoff curve number) becomes more influential in drier conditions whereas Ch_K2 (effective hydraulic conductivity), for example, yields lower sensitivity indices for the dry year.In addition, the study presents a visual comparison of the stability of relative sensitivities with the different sources using the estimated confidence intervals for different numbers of s...
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