Search

Neyman–Scott rectangular pulse is a stochastic rainfall model with five parameters. The impacts of initial values and optimization methods on the parameter estimation of the Neyman–Scott rectangular pulse model were investigated using both the method of moments and the method of maximum likelihood. The estimates using the method of moments were influenced by the optimization method and were sensitive to the initial values and the aggregation scale of the data. Thus, by using frequentist estimation methods, we cannot guarantee the unique values as estimates. The aim of this study is to find more reliable unique values as estimates using a Bayesian approach. In this approach, parameters are estimated from the posterior distribution, and model performance is assessed by comparing observed values with fitted values. Slice sampling within the Gibbs sampler algorithm demonstrates superior convergence and model fitting, yielding unique estimates for the model parameters. The main conclusion of this study is that Bayesian estimation methods outperform other estimation methods in terms of providing reliable and stable estimates that improve rainfall generation accuracy. [ABSTRACT FROM AUTHOR]

This study analyzed the changes and trends in twelve extreme precipitation-based climate indices obtained using daily data from 10 synoptic stations in the Chungcheong region of South Korea during the 1973–2020 period. The climate indices were used to assess the trends in the extreme precipitation characteristics of duration, frequency, and intensity using the innovative trend analysis (ITA) method. The results of the ITA were further compared with two other non-parametric test methods such as Mann–Kendall (MK) and Spearman's rho (SR). The results showed that most stations exhibited significant increasing trends in all the investigated climate indices at a 95% confidence level as indicated by the ITA method, with only a few stations indicating significant decreasing trends in R95p, R99p, Rx3day, and Rx5day. The sub-trend analysis further revealed the dominance of neutral behavior around the low-value cluster, especially for the extreme precipitation duration. At the same time, increasing trends dominate the high-value cluster at most stations. Meanwhile, only R10mm, R99p, and R95p exhibited monotonic trends in the Boeun and Seosan stations, respectively. Further, the ITA exhibited superior performance over the MK and SR methods by indicating the presence of more significant trends in the climate indices at most stations. The distribution of the extreme precipitation indices for duration, frequency, and intensity indicate the pronounced risk of flood conditions around the north–central and some parts of southern regions, while the western region indicates a potential drought risk, which could greatly impact the water resources and consequently agricultural activities in the study area. The results of this study provide essential information for addressing the climate-related problems of water resource management and agriculture in the study area and other related climatic regions. [ABSTRACT FROM AUTHOR]

Precipitation time series exhibit complex fluctuations and statistical changes. Existing research stops short of a simple and feasible model for precipitation forecasting. In this article, the authors investigate and forecast precipitation variations in South Korea from 1973 to 2021 using cyclostationary empirical orthogonal function (CSEOF) and regression methods. First, empirical orthogonal function (EOF) and CSEOF analyses are used to examine the periodic changes in the precipitation data. Then, the autoregressive integrated moving average (ARIMA) method is applied to the principal component (PC) time series derived from the EOF and CSEOF precipitation analyses. The fifteen leading EOF and CSEOF modes and their corresponding PC time series clearly reflect the spatial distribution and temporal evolution characteristics of the precipitation data. Based on the PC forecasts of the EOF and CSEOF models, the EOF–ARIMA composite model and CSEOF–ARIMA composite model are used to obtain quantitative precipitation forecasts. The comparison results show that both composite models have good performance and similar accuracy. However, the performance of the CSEOF–ARIMA model is better than that of the EOF–ARIMA model under various measurements. Therefore, the CSEOF–ARIMA composite forecast model can be considered an efficient and feasible technology representing an analytical approach for precipitation forecasting in South Korea. [ABSTRACT FROM AUTHOR]

This study aimed to calculate and analyze total overflows that accumulate in urban manholes in the target drainage basin of Samsung-dong, Seoul in heavy rainfall events with different temporal distribution characteristics, using the EPA's Storm Water Management Model (EPA-SWMM model). Inundation behaviors were analyzed using the two-dimensional flood model (FLO-2D). The extreme rainfall events were produced using different exceedance probability Huff distributions for different durations and return periods, such as from 1 to 3 h and 10 years, 50 years, 80 years, 100 years, respectively. The inundation model was validated using the actual flood observations on 21 September 2010 in the Samsung-dong drainage basin. The total overflow amount showed considerable differences according to the different time distribution characteristics, such as the temporal location of the storm peak and the concentration level of the storm. Furthermore, the inundation behaviors were also related to the temporal characteristics of storms. The results illustrated that the consideration of the temporal distribution characteristics of extreme rainfall events is essential for an accurate understanding of the rainfall–runoff response and inundation behavior in urban drainage basins. [ABSTRACT FROM AUTHOR]

A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two-dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L-band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively. [ABSTRACT FROM AUTHOR]

Previous work showed that remotely sensed soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observations and hydrometeorological forcing (Remote Sens. Environ. 81 (2002) 1). Specifically, it was determined that this multiscaling behavior is consistent with the scaling of soil hydraulic properties and vegetation cover, while the multifractal behavior is associated with the temporal evolution of soil moisture fields. Here, we apply these findings by directly incorporating information on the spatial structure of soil texture and vegetation water content to the spatial interpolation of remotely sensed soil moisture data.A downscaling model is presented which consists of a modified fractal interpolation method based on contraction mapping. This methodology is different from other fractal interpolation schemes because it generates unique fractal surfaces. It is different from other contraction mapping models because it includes spatially and temporally varying scaling functions as opposed to single-valued scaling factors. The scaling functions are linear combinations of the spatial distributions of ancillary data. The model is demonstrated by downscaling soil moisture fields from 10 to 1 km resolution using remote-sensing data from the Southern Great Plains 1997 (SGP''97) field experiment. [Copyright &y& Elsevier]

The statistical structure of soil moisture fields was examined using large-scale images (40×250 km) obtained during the Southern Great Plains 1997 (SGP''97) hydrology experiment. In particular, empirical scaling analysis was conducted to investigate the linkages between the spatial and temporal variability of soil moisture, and landscape characteristics including terrain, soils, and vegetation. The results show that the soil moisture fields exhibit multiscaling and multifractal behavior varying with the scales of observation and hydrometeorological forcing. A break in statistical symmetry (multiscaling behavior) was identified, which separates the spatial and temporal evolution of the statistical structure of soil moisture fields for wavelengths below and above 10 km, the α- and β-scale ranges, respectively. Specifically, the multiscaling behavior is consistent with the scaling behavior of soil hydraulic properties as described by soil texture parameters such as sand and clay content. The multifractal behavior is associated with the temporal evolution of drying and wetting regimes, reflecting the nonlinear character of soil moisture dynamics. Finally, Empirical Orthogonal Function (EOF) analysis was conducted to explain the relationship between the spatial structure of estimated soil moisture and that of ancillary data including topography, soil texture, and vegetation cover. Topography appears to dominate the spatial structure of soil moisture only during and immediately after rainfall. In interstorm periods, the spatial evolution of soil moisture is closely associated with the spatial variability of soil hydraulic properties when the soil is above field capacity, while vegetation dominates the evolution of soil moisture fields through evapotranspiration as the landscape dries down. [Copyright &y& Elsevier]

Non-point source (NPS) pollution is a primary cause of water pollution in the Saemangeum watershed in South Korea. The changes in NPS pollutant loads in the Saemangeum watershed for an 81-year period (2019–2099) were simulated and analyzed by applying the soil and water assessment tool. Six climate model (BCC-CSM1–1, CanESM2, GFDL-ESM2G, HadGEM2-CC, INM-CM4, and MIROC-ESM) outputs using representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) were obtained from the South Korean Asia-Pacific Economic Cooperation (APEC) Climate Center. Simulated streamflow and water quality were evaluated using the Nash–Sutcliffe efficiency (NSE) index and coefficient of determination (R2). The model satisfactorily simulated streamflow with positive NSE values and R2 > 0.5. Based on two climate change scenarios (RCP 4.5 and RCP 8.5), gradual increases of 70.9 to 233.8 mm and 1.7 to 5.7 °C in annual precipitation and temperature, respectively, are likely for two time periods (2019–2059 and 2060–2099). Additionally, the expected future average annual and monthly streamflow, sediment, and total phosphorus showed changes of 5% to 43%, 3% to 40%, and −55% to 15%, respectively, whereas the expected future average annual and monthly total nitrogen showed decreases of −5% to −27%. Future NPS pollutant loads in the Saemangeum watershed should be managed according to different climate change scenarios. [ABSTRACT FROM AUTHOR]