Kuk-Hyun Ahn

The effect of land cover change on duration and severity of high and low flows

Land cover has been increasingly recognized as an important factor affecting hydrologic processes at the basin and regional level. Therefore, improved understanding of how land cover change affects hydrologic systems is needed for better management of water resources. The objective of this study is to investigate the effects of land cover change on the duration and severity of high and low flows by using the Soil Water Assessment Tool (SWAT) model, Bayesian Model Averaging (BMA) and copulas. Two basins dominated by different land cover in the Ohio River basin are used as study area in this study. Two historic land covers from the 1950s and 1990s are considered as input to the SWAT model, thereby investigating the hydrologic high and low flow response of different land cover conditions of these two basins. The relationships between the duration and severity of both low and high flow are defined by applying the copula method; changes in the frequency of the duration and severity are investigated. The results show that land cover changes affect both the duration and severity of both high and low flows. An increase in forest area leads to a decrease in the duration and severity during both high and low flows, but its impact is highest during extreme flows. The results also show that the land cover changes have had significant influences on changes in the joint return periods of duration and severity of low and high flows. While this study sheds light on the role of land cover change on severity and duration of high and low flow conditions, more studies using various land cover conditions and climate types are required in order to draw more reliable conclusions in future. This article is protected by copyright. All rights reserved.

Role of Watershed Geomorphic Characteristics on Flooding in Indiana, United States

AbstractDespite the documented evidence of geomorphic characteristics on flooding, an understanding of the relative effects of the geomorphic characteristics on flooding remains elusive. The objective of this study is to investigate the relationship between flooding and geomorphic characteristics in Indiana, United States, by using data at 94 streamflow gauging stations. The flood magnitude as determined through the flash flood index is categorized into three groups, including moderate, extreme, and severe. The flood in each group is then related to geomorphic characteristics, including topography, morphometry, slope, land use, soil, channel network, and aspect through stepwise regression. Results show that extreme flooding is most affected by watershed morphometry, particularly watershed length, whereas severe flooding is most affected by watershed slope and land use type. The methodology used in this study also highlights that the stronger the severity of flooding, the more it is explained by geomorphic...

Dynamic linear models to explore time-varying suspended sediment-discharge rating curves

This study presents a new method to examine long-term dynamics in sediment yield using time-varying sediment-discharge rating curves. Dynamic linear models (DLMs) are introduced as a time series filter that can assess how the relationship between streamflow and sediment concentration or load changes over time in response to a wide variety of natural and anthropogenic watershed disturbances or long-term changes. The filter operates by updating parameter values using a recursive Bayesian design that responds to one-day-ahead forecast errors while also accounting for observational noise. The estimated time series of rating curve parameters can then be used to diagnose multi-scale (daily-decadal) variability in sediment yield after accounting for fluctuations in streamflow. The technique is applied in a case study examining changes in turbidity load, a proxy for sediment load, in the Esopus Creek watershed, part of the New York City drinking water supply system. The results show that turbidity load exhibits a complex array of variability across time scales. The DLM highlights flood event-driven positive hysteresis, where turbidity load remained elevated for months after large flood events, as a major component of dynamic behavior in the rating curve relationship. The DLM also produces more accurate one-day-ahead loading forecasts compared to other static and time-varying rating curve methods. The results suggest that DLMs provide a useful tool for diagnosing changes in sediment-discharge relationships over time and may help identify variability in sediment concentrations and loads that can be used to inform dynamic water quality management.

A hierarchical Bayesian model for regionalized seasonal forecasts: Application to low flows in the northeastern United States

This study presents a regional, probabilistic framework for seasonal forecasts of extreme low summer flows in the northeastern United States conditioned on antecedent climate and hydrologic conditions. The model is developed to explore three innovations in hierarchical modeling for seasonal forecasting at ungaged sites: (1) predictive climate teleconnections are inferred directly from ocean fields instead of predefined climate indices, (2) a parsimonious modeling structure is introduced to allow climate teleconnections to vary spatially across streamflow gages, and (3) climate teleconnections and antecedent hydrologic conditions are considered jointly for regional forecast development. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with five simpler nested formulations to test specific hypotheses embedded in the full model structure. Results indicate that each of the three innovations improve out-of-sample summer low-flow forecasts, with the greatest benefits derived from the spatially heterogeneous effect of climate teleconnections. We conclude with a discussion of possible model improvements from a better representation of antecedent hydrologic conditions at ungaged sites.

Manipulating Large-Scale Qualitative Meteorological Information for Drought Outlook

AbstractDespite many strides made in the development of global circulation models as well as the expansive understanding of meteorological phenomena, many countries still lack sufficient meteorological information that can be conveniently utilized for a hydrologic outlook. This paper suggests a technique of processing the meteorological information, which is not only difficult to differentiate by reducing to a specific basin because of extensive data, but is also impossible to be led to a quantitative drought outlook because of its presentation in qualitative forms.To assess the drought conditions, two indices were selected—the standardized precipitation index (SPI), which is a meteorological index, and the soil moisture index (SMI), an agricultural index. The long-range forecasts, provided by the Korea Meteorological Administration (KMA) to target the Korean peninsula, were used to predict these indices. As a means to convert the qualitative interval forecast into a quantitative probability forecast, pre...

Time-varying suspended sediment-discharge rating curves to estimate climate impacts on fluvial sediment transport

This study presents time‐varying suspended sediment‐discharge rating curves to model suspended‐sediment concentrations (SSCs) under alternative climate scenarios. The proposed models account for hysteresis at multiple time scales, with particular attention given to systematic shifts in sediment transport following large floods (long‐term hysteresis). A series of nested formulations are tested to evaluate the elements embedded in the proposed models in a case study watershed that supplies drinking water to New York City. To maximize available data for model development, a dynamic regression model is used to estimate SSC based on denser records of turbidity, where the parameters of this regression are allowed to vary over time to account for potential changes in the turbidity‐SSC relationship. After validating the proposed rating curves, we compare simulations of SSC among a subset of models in a climate change impact assessment using an ensemble of flow simulations generated using a stochastic weather generator and hydrologic model. We also examine SSC estimates under synthetic floods generated using a peaks‐over‐threshold model. Our results indicate that estimates of extreme SSC under new climate and hydrologic scenarios can vary widely depending on the selected model and may be significantly underestimated if long‐term hysteresis is ignored when simulating impacts under sequences of large storm event. Based on the climate change scenarios explored here, average annual maximum SSC could increase by as much as 2.45 times over historical values.

Assessing a Regression-Based Regionalization Approach to Ungauged Sites with Various Hydrologic Models in a Forested Catchment in the Northeastern United States

AbstractAnalysis of daily streamflow is of interest to river restoration and conservation efforts in many regions in the world. However, the paucity of stream-gauging stations presents significant ...