Yeonjoo Kim

Researchers have attempted to use machine learning algorithms to replace physically based models for streamflow prediction. Although existing studies have contributed to improving machine learning methods, they still have weaknesses, such as large dataset requirements and overfitting. Therefore, we propose an approach that combines the Weather Research and Forecasting hydrological modeling system (WRF-Hydro) and the Long Short-Term Memory (LSTM) network, i.e., WRF-Hydro-LSTM, to improve streamfl

Abstract. Earth system models (ESMs) have been developed to represent the role of terrestrial ecosystems on the energy, water, and carbon cycles. However, many ESMs still lack representation of within-ecosystem heterogeneity and diversity. In this paper, we present the Ecosystem Demography model version 2.2 (ED-2.2). In ED-2.2, the biophysical and physiological processes account for the horizontal and vertical heterogeneity of the ecosystem: the energy, water, and carbon cycles are solved separa

Abstract Satellite‐based observations indicate that seasonal patterns in canopy greenness and productivity in the A mazon are negatively correlated with precipitation, with increased greenness occurring during the dry months. Flux tower measurements indicate that the canopy greening that occurs during the dry season is associated with increases in net ecosystem productivity (NEP) and evapotranspiration (ET). Land surface and terrestrial biosphere model simulations for the region have predicted t

Seasonal variability of vegetation, determined by plant phenology, impacts the seasonality of surface and atmospheric water cycles as well as the seasonality of surface energy budget. At the same time, leaf seasonal variations respond to both cumulative and concurrent hydrometeorological conditions. In order to account for this vegetation feedback at the seasonal timescale, a predictive phenology scheme for various plant functional types is developed on the basis of previous studies, and a metho

Abstract Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). Th

This study developed a multi-criteria approach to spatially assess the robust water resource vulnerability in sub-basins and applied it to the Han River basin. The Intergovernmental Panel on Climate Change (IPCC) suggested three factors of vulnerability; namely, exposure, sensitivity and adaptive capacity were used in this study with respect to water quantity and quality. In this study, 16 water quantity indicators and 13 water quality indicators were selected to identify the vulnerability using

Abstract. This study aims to understand how different reference periods (i.e., calibration periods) of climate data used to estimate drought indices influence regional drought assessments. Specifically, we investigate the influences of different reference periods on historical drought characteristics, such as the trend, frequency, intensity and spatial extent, using the standardized precipitation evapotranspiration index (SPEI) with a 12-month lag (SPEI-12), which was estimated from the datasets

Abstract To investigate the impact of anomalous soil moisture conditions on subsequent precipitation over North America, a series of numerical experiments is performed using a modified version of the Community Atmosphere Model version 3 and the Community Land Model version 3 (CAM3–CLM3). First, the mechanisms underlying the impact of spring and summer soil moisture on subsequent precipitation are examined based on simulations starting on 1 April and 1 June, respectively. How the response of prec

Abstract. The Ent Terrestrial Biosphere Model (Ent TBM) is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models (GCMs). This study describes the leaf phenology submodel implemented in the Ent TBM version 1.0.1.0.0 coupled to the carbon allocation scheme of the Ecosystem Demography (ED) model. The phenology submodel adopts a combination of responses to temperature (growing degree days and frost hardening), so