Yongchul Shin

Effective soil moisture estimate and its uncertainty using multimodel simulation based on Bayesian Model Averaging

Various hydrological models have been developed for estimating root zone soil moisture dynamics. These models, however, incorporated their own parameterization approaches indicating that the output from the different model inherent structures might include uncertainties because we do not know which model structure is correct for describing the real system. More recently, multimodel approaches using a Bayesian Model Averaging (BMA) scheme can improve the overall predictive skill while individual models retain their own uncertainties for simulated soil moisture based on a single set of weights in modeling under different land surface wetness conditions (e.g., wet, moderately wet, and dry conditions). In order to overcome their limitations, we developed a BMA-based multimodel simulation approach based on various soil wetness conditions for estimating effective surface soil moisture dynamics (0–5 cm) and quantifying uncertainties efficiently based on the land surface wetness conditions. The newly developed approach adapts three different hydrological models (i.e., Noah Land Surface Model, Noah LSM; Soil-Water-Atmosphere-Plant, SWAP; and Community Land Model, CLM) for simulating soil moisture. These models were integrated with a modified-microGA (advanced version of original Genetic Algorithm (GA)) to search for optimized soil parameters for each model. Soil moisture was simulated from the estimated soil parameters using the hydrological models in a forward mode. It was found that SWAP performed better than others during wet condition, while Noah LSM and CLM showed a good agreement with measurements during dry condition. Thus, we inferred that performance of individual models with different model structures can be different with land surface wetness. Taking into account the effects of soil wetness on different model performances, we categorized soil moisture measurements and estimated different weights for each category using the BMA scheme. Effective surface soil moisture dynamics were obtained by aggregating multiple weighted soil moisture. Our findings demonstrated that the effective soil moisture estimates derived by this approach showed a better match with the measurements compared to the original models and single-weighted outputs. Multimodel simulation approach based on land surface wetness enhances the ability to predict reliable soil moisture dynamics and reflects the strengths of different hydrological models under various soil wetness conditions.

Assessment of soil loss in South Korea based on land-cover type

Soil loss poses a significant threat to the long-term sustainability of hydrological systems, the environment, and agriculture. In this regard, efficient soil management relies on accurate quantification of soil loss. To this end, the Organization for Economic Cooperation and Development (OECD) standard of soil erosion, developed for agricultural areas, has been applied in many countries, including South Korea. Due to the lack of standard methods for assessing soil erosion in South Korea, the OECD standard has been applied to non-agricultural regions of Korea despite the possibility that local soil erosion characteristics may differ from those in agricultural areas. Such an approach might give erroneous information on soil loss to policy and decision makers. This study estimated soil loss for eight different land cover-types in Korea using the universal soil loss equation, and compared the results with those from the unmodified OECD soil erosion standard. Estimated soil loss differed considerably among land-cover types. The results have implications on the limitations in applying the OECD soil erosion standard to soil management in Korea. Thus, this study suggests a modified soil erosion standard for efficient soil management.

Pollutant Load Characteristics from a Small Mountainous Agricultural Watershed in the North Han River Basin

Natural environment of the Wolgokri stream watershed, located in Chuncheon, Gangwon province, Korea, has been well preserved as a traditional agricultural watershed. To analyze characteristics of NPS pollution generated from an mountainous agricultural watershed, the flow and water qualities of the study watershed were monitored and were analyzed to estimate pollution loads. Annual runoff volume ratio was . Concentrations of T-N, T-p, COD, and TOC were higher when monthly rainfall was between than those when monthly rainfall was between . However, the concentrations varied considerably when monthly rainfall was higher than 100mm. The flow weighted mean concentrations(mg/L) of BOD, COD, TOC, , T-N, T-P and SS were 1.96, 2.72, 3.32, 1.41, 4.70, 0.187 and 13.36, respectively. The BOD, SS, T-N and T-P loads of July, 2004 were of annual load, respectively. The BOD, COD, TOC, , T-N, T-p, and SS loads (kg/ha) from Mar. 2004 to Apr. 2005 were 19.09, 26.55, 32.39, 13.85, 45.92, 1.887 and 130.18, respectively. The highest concentrations of BOD, NO3-N, T-N, T-p, SS, COD and TOC were found before the flow reached the peak runoff, possibly due to the first flushing effect. Generally, pollution loads of the Wolgokri watershed were not that significant. Phosphorus load, however, was higher enough to cause eutrophication in the receiving water body It was recommended that best management practices need to be implemented to reduce phosphorus sources.

Estimation of flood risk index considering the regional flood characteristics: a case of South Korea

Global warming is increasing the variability of climate change and intensifying hydrologic cycle components including precipitation, infiltration, evapotranspiration, and runoff. These changes increase the chance of more severe and frequent natural conditions, and limit ecosystem function and human activities. Adaptation to climate change requires assessment of the potential disaster risk. The objectives of this study were to estimate the flood risk index (FRI) considering regional flood characteristics at the national level and to prioritize the factors affecting flood risk through principal component analysis. FRI was estimated based on the Delphi survey results from 50 water resources experts in South Korea. The potential risk analysis was conducted for 229 local governments in South Korea. The results showed that natural and social factors were more influential flood risk factors to South Korea than administrative and economic and facility factors. Specifically, natural, social, administrative and economic, and facility factors were, respectively, highest at Jindo-Gun in Jennam-Do, Gumi-Si in Kyongsanbuk-Do, Dong-Gu in Incheon-Si, and Suwon-Si, Kyonggi-Do. Overall, the highest FRI is shown in Anyang-Si, Kyongggi-Do. The spatial distribution of the FRI was high in the southeastern coastal region and basins of the two biggest rivers in South Korea, and normalized flood frequency followed spatial patterns similar to FRIs. This study provided information on the relative flood risk index among administrative units for investment prioritization in flood risk management. In this regard, the suggested FRI is expected to significantly contribute to methodical and economic improvements in budget allocations for flood risk management.

Development of Irrigation Water Management Model for Reducing Drought Severity Using Remotely Sensed Soil Moisture Footprints

AbstractWith an increase of population, agriculture, and industry, the demand for water has increased gradually across the world. Currently, agricultural crops have been damaged by drought severity due to climate changes that contribute to water scarcity. Policy/decision makers need to be prepared for reducing damages to crops due to severe droughts. For this reason, a genetic algorithm (GA)-based irrigation water management model (IWMM) adapting a hydrological model [soil water atmosphere plant (SWAP)] was developed. This approach is linked with a noisy Monte Carlo genetic algorithm (NMCGA) that can estimate effective soil hydraulic properties from in situ/remotely sensed (RS) soil moisture data. Based on the estimated soil parameters, vegetation information, and historical weather forcings, long-term root zone soil moisture (SM) and evapotranspiration (ET) dynamics were reproduced at fields using SWAP in a forward mode. This approach incorporates a soil moisture deficit index (SMDI) that can estimate th...

Analysis of Flow-Weighted Mean Concentration(FWMC) Characteristics from Rural Watersheds

Stream flow and water quality were measured and analyzed with respect to flow-weighted mean concentrations (FWMCs) of 21 rainfall events from a forested watershed (Forest Research Watershed: FRW) and two mixed watersheds of agriculture and forest (YuPo-Ri Watershed: YPW and WolGog-ri Watershed: WGW) located in the middle of the North Han River basin. The monitoring of each watershed was one year and conducted between 2004 and 2006. YPW showed more intensive agricultural practices than WGW where traditional practices were common. The average of the 21 FWMCs were in the order of YPF>WGW>FRW and were significantly different from each other at the level of 0.05. It was shown that the land use with intensive agricultural practices produced and discharged more NPS pollutants than that with traditional practices and forest. Specially, SS concentrations from the mixed watersheds were significantly higher than those from FRW. Influencing factors on runoff were analyzed rainfall and watershed area. And rainfall intensity was greater impact on runoff than daily rainfall. Measured water quality indices were shown positive correlations among them in general. However, no significant correlation was shown between COD and nutrients(T-N and T-P).

A Framework for Assessing Soil Moisture Deficit and Crop Water Stress at Multiple Space and Time Scales Under Climate Change Scenarios Using Model Platform, Satellite Remote Sensing, and Decision Support System

Better understanding of water cycle at different space–time scales would be a key for sustainable water resources, agricultural production, and ecosystems health in the twenty-first century. Efficient agricultural water management is necessary for sustainability of the growing global population. This warrants better predictive tools for aridity (based on precipitation, temperature, land use, and land cover), root zone (~top 1 m) soil moisture deficit, and crop water stress at farm, county, state, region, and national level, where decisions are made to allocate and manage the water resources. It will provide useful strategies for not only efficient water use but also for reducing potential risk of crop failure due to agricultural drought. Leveraging heavily on ongoing multiscale hydrologic modeling, data assimilation, soil moisture dynamics, and inverse model development research activities, and ongoing Land Data Assimilation (LDAS) and National Climate Assessment (NCA) indexing efforts we are developing a drought assessment framework. The drought assessment platform includes: (1) developing disaggregation methods for extracting various field-scale (1-km or less) climate indicators from the (SMOS, VIIRS, SMAP, AMSR-2) satellite / LDAS-based soil moisture in conjunction with a multimodel simulation–optimization approach using ensemble of Soil Vegetation Atmosphere Transfer, SVAT (Noah, CLM, VIC, Mosaic in LIS) models; (2) predicting farm/field-scale long-term root zone soil moisture status under various land management and climate scenarios for the past decades in hindcast mode and for the next decades in forecast mode across the USA using effective land surface parameters and meteorological input from Global Circulation Model (GCM) outputs; (3) assessing the potential risk of agricultural drought at different space–time scales across the USA based on predicted root zone soil moisture; and (4) evaluating various water management and cropping practices (e.g., crop rotation, soil modification, irrigation scheduling, better irrigation method/efficiency, water allocation, etc.) for risk reduction at field, county, state, region, and national scale using a web-based Decision Support System. This ongoing research provides a unifying global platform for forecasting several lagging indices for root zone soil moisture status as aridity index (AI), soil moisture deficit index (SMDI), and crop water stress index (CWSI) at the field, county, state, and regional scale on weekly, biweekly, monthly, and seasonal time scales by using various satellite and LDAS simulated data. Using available historical data, our approach is tested in various hydroclimatic regions (Great Plains, Midwest, West, Northeast, Southeast, and Southwest) across the USA. These indices form the basis for developing efficient management Decision Support Systems (DSS) for agricultural drought risk reduction and mitigation/adaption under the evolving climatic scenarios.

Modification of SWAT auto-calibration for accurate flow estimation at all flow regimes

To secure accuracy in the Soil and Water Assessment Tool (SWAT) simulation for various hydrology and water quality studies, calibration and validation should be performed. When calibrating and validating the SWAT model with measured data, the Nash–Sutcliffe efficiency (NSE) is widely used, and is also used as a goal function of auto-calibration in the current SWAT model (SWAT ver. 2009). However, the NSE value has been known to be influenced by high values within a given dataset, at the cost of the accuracy in estimated lower flow values. Furthermore, the NSE is unable to consider direct runoff and baseflow separately. In this study, the existing SWAT auto-calibration was modified with direct runoff separation and flow clustering calibration, and current and modified SWAT auto-calibration were applied to the Soyanggang-dam watershed in South Korea. As a result, the NSE values for total streamflow, high flow, and low flow groups in direct runoff, and baseflow estimated through modified SWAT auto-calibration were 0.84, 0.34, 0.09, and 0.90, respectively. The NSE values of current SWAT auto-calibration were 0.83, 0.47, −0.14, and 0.90, respectively. As shown in this study, the modified SWAT auto-calibration shows better calibration results than current SWAT auto-calibration. With these capabilities, the SWAT-estimated flow matched the measured flow data well for the entire flow regime. The modified SWAT auto-calibration module developed in this study will provide a very efficient tool for the accurate simulation of hydrology, sediment transport, and water quality with no additional input datasets.

Soil Moisture Estimation and Drought Assessment at the Spatio-Temporal Scales using Remotely Sensed Data: (I) Soil Moisture

In this study, we estimated root zone soil moisture dynamics using remotely sensed (RS) data. A soil moisture data assimilation scheme was used to derive the soil and root parameters from MODerate resolution Imaging Spectroradiometer (MODIS) data. Based on the estimated soil/root parameters and weather forcings, soil moisture dynamics were simulated at spatio-temporal scales based on a hydrological model. For calibration/validation, the Little Washita (LW13) in Oklahoma and Chungmi-cheon/Seolma-cheon sites were selected. The derived water retention curves matched the observations at LW 13. Also, the simulated soil moisture dynamics at these sites was in agreement with the Time Domain Reflectrometry (TDR)-based measurements. To test the applicability of this approach at ungauged regions, the soil/root parameters at the pixel where the Seolma-cheon site is located were derived from the calibrated MODIS-based (Chungmi-cheon) soil moisture data. Then, the simulated soil moisture was validated using the measurements at the Seolma-cheon site. The results were slightly overestimated compared to the measurements, but these findings support the applicability of this proposed approach in ungauged regions with predictable uncertainties. These findings showed the potential of this approach in Korea. Thus, this proposed approach can be used to assess root zone soil moisture dynamics at spatio-temporal scales across Korea, which comprises mountainous regions with dense forest.

Enhancement of the SATEEC Soil Erosion and Sediment Modeling System with Better Sediment Delivery Ratio Module

The soil erosion has been one of the biggest concerns in Korea as well as in other countries because of its impacts on ecology and water quality in the receiving water bodies. Among many computer models simulating soil erosion at field and watershed scale has been integrated with the Geographic Information System (GIS) because its nature is essentially spatial. The prototype and enhanced version of the Sediment Assessment Tool for Effective Erosion Control (SATEEC) system utilizes the area-based sediment delivery ratio methods. However the sediment delivery mechanism is not solely dependent on the watershed area. Thus new sediment delivery ratio estimation module was automated in the SATEEC system by considering watershed area, watershed relief ratio, and average Curve Number for all sub watersheds in the watershed having each cell as an outlet. The estimated sediment delivery ratio shows the spatial variability and could provide soil erosion decision makers with better information regarding soil erosion and sediment yield behaviors. This result can be used to identify the most critical areas and assess the effects on sediment yield of soil erosion best management practices implemented in the critical areas. However, new sediment delivery ratio method needs to be extended for its application for the bigger watershed because of limitations in ArcView GRID library in the number of processes. Thus the works are undergoing to port the ArcView 3.x based SATEEC system into the ArcGIS platform.