Improvement of Subsurface Flow Predictability Using Land Surface Model in the Unsaturated Zone at Various Spatial Scales

Subsurface flow in the unsaturated zone is an important component of the hydrologic cycle and plays a significant role in the water and energy balance through affecting various hydrological processes. Land surface models (LSMs) have been developed and extended during the past decades with various enhanced processes to understand and quantify the complex interaction between atmosphere and land surface systems. However, there are still critical deficiencies (e.g., simplified processes and parameterization) remaining in simulating land surface hydrology for land surface modeling. Thus, this dissertation focuses on understanding land surface processes from various land surface models and improving land surface processes and parameterization in land surface modeling in the unsaturated zone at various spatial scales. Two main approaches (Bayesian Model Averaging (BMA) based multi-model simulation and physically based hydrologic connectivity approach) to improve the land surface modeling predictability are presented in this dissertation. The BMA-based multi-model simulation approach was developed to reflect the strengths of the models under various land surface wetness conditions and to quantify the model parameter and structural uncertainties. The physically-based hydrologic connectivity concept was proposed to characterize the subsurface flow variability based on spatially distributed patterns of wetness condition or physical controls (e.g., soil type, vegetation, topography). Hydrologic connectivity is an important concept for understanding local processes in the context of catchment hydrology and defining flow path continuity in surface and subsurface flows. These approaches were applied in land surface modeling and tested in various hydro-climate regions and spatial scales showing significant improvement of modeling predictability. Based on the knowledge and experience gained from this dissertation, the proposed concepts will be useful to improve the hydrological model performance and better understand the subsurface flow variability in the unsaturated zone at various scales

DESIGNING AND APPLICATION OF WEB-BASED GEOGRAPHICAL INFORMATION SYSTEM FOR VISUAL ASSESSMENT OF LAND LEVELS

This paper deals with the way to design and apply a web-based Geographical Information System which will help the users see spatial data like land levels through web Visualization tool. The developed application shows that by using solely Open Source software it was possible to develop a customizable web-based GIS application that provides functions necessary to convey environmental data to experts and non-experts alike without the requirement of proprietary software

Development of Field Pollutant Load Estimation Module and Linkage of QUAL2E with Watershed-Scale L-THIA ACN Model

The Long Term Hydrologic Impact Assessment (L-THIA) model was previously improved by incorporating direct runoff lag time and baseflow. However, the improved model, called the L-THIA asymptotic curve number (ACN) model cannot simulate pollutant loads from a watershed or instream water quality. In this study, a module for calculating pollutant loads from fields and through stream networks was developed, and the L-THIA ACN model was combined with the QUAL2E model (The enhanced stream water quality model) to predict instream water quality at a watershed scale. The new model (L-THIA ACN-WQ) was applied to two watersheds within the Korean total maximum daily loads management system. To evaluate the model, simulated results of total nitrogen (TN) and total phosphorus (TP) were compared with observed water quality data collected at eight-day intervals. Between simulated and observed data for TN pollutant loads in Dalcheon A watershed, the R2 and Nash–Sutcliffe efficiency (NSE) were 0.81 and 0.79, respectively, and those for TP were 0.79 and 0.78, respectively. In the Pyungchang A watershed, the R2 and NSE were 0.66 and 0.64, respectively, for TN and both statistics were 0.66 for TP, indicating that model performed satisfactorily for both watersheds. Thus, the L-THIA ACN-WQ model can accurately simulate streamflow, instream pollutant loads, and water quality

Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

In this study, 52 asymptotic Curve Number (CN) regression equations were developed for combinations of representative land covers and hydrologic soil groups. In addition, to overcome the limitations of the original Long-term Hydrologic Impact Assessment (L-THIA) model when it is applied to larger watersheds, a watershed-scale L-THIA Asymptotic CN (ACN) regression equation model (watershed-scale L-THIA ACN model) was developed by integrating the asymptotic CN regressions and various modules for direct runoff/baseflow/channel routing. The watershed-scale L-THIA ACN model was applied to four watersheds in South Korea to evaluate the accuracy of its streamflow prediction. The coefficient of determination (R2) and Nash–Sutcliffe Efficiency (NSE) values for observed versus simulated streamflows over intervals of eight days were greater than 0.6 for all four of the watersheds. The watershed-scale L-THIA ACN model, including the asymptotic CN regression equation method, can simulate long-term streamflow sufficiently well with the ten parameters that have been added for the characterization of streamflow

Evaluation of Sediment Trapping Efficiency of Vegetative Filter Strips Using Machine Learning Models

The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process.</p

Indonesian tweet data of commodity price quote

Indonesian tweet data of commodity price quote between June 2012 and September 2013. for PeerJ reviewer

Analysis of Water Balance Changes and Parameterization Reflecting Soil Characteristics in a Hydrological Simulation Program&mdash;FORTRAN Model

Efficient water resource management requires accurate analyses of hydrological components and water balance. The Hydrological Simulation Program&mdash;FORTRAN (HSPF) model serves this purpose at the watershed scale. It has limited accuracy in calculating runoff and infiltration because the model simulates hydrological processes using one representative parameter for each land use in the watershed. Accuracy requires field-scale analysis of hydrological components. We calculated the lower zone storage nominal parameter, which markedly affects runoff in HSPF, from effective moisture content and depth of each soil layer. Analysis of hydrological components suggested re-calculating the parameters reflecting soil characteristics. We investigated two scenarios through simulations: Scenario 1 used the existing method. Scenario 2 used parameters that reflected soil properties. Total flows for each sub-catchment were identical, but proportions of direct and intermediate runoff were larger in Scenario 1. Ratios of baseflow, evapotranspiration, and infiltration were larger in Scenario 2, reflecting soil characteristics. Comparing the baseflow ratio to total flow, Scenario 2 values were similar to observed values. Comparisons of R2 and Nash&ndash;Sutcliffe Efficiency (NSE) at the end of the watershed were well matched (R2 and NSE are higher than 0.9) in both scenarios, but proportions of each hydrological component differed. It is important to consider soil characteristics when applying water quantity and quality analyses in an HSPF simulation

Analysis of Water Balance Changes and Parameterization Reflecting Soil Characteristics in a Hydrological Simulation Program—FORTRAN Model

Efficient water resource management requires accurate analyses of hydrological components and water balance. The Hydrological Simulation Program—FORTRAN (HSPF) model serves this purpose at the watershed scale. It has limited accuracy in calculating runoff and infiltration because the model simulates hydrological processes using one representative parameter for each land use in the watershed. Accuracy requires field-scale analysis of hydrological components. We calculated the lower zone storage nominal parameter, which markedly affects runoff in HSPF, from effective moisture content and depth of each soil layer. Analysis of hydrological components suggested re-calculating the parameters reflecting soil characteristics. We investigated two scenarios through simulations: Scenario 1 used the existing method. Scenario 2 used parameters that reflected soil properties. Total flows for each sub-catchment were identical, but proportions of direct and intermediate runoff were larger in Scenario 1. Ratios of baseflow, evapotranspiration, and infiltration were larger in Scenario 2, reflecting soil characteristics. Comparing the baseflow ratio to total flow, Scenario 2 values were similar to observed values. Comparisons of R2 and Nash–Sutcliffe Efficiency (NSE) at the end of the watershed were well matched (R2 and NSE are higher than 0.9) in both scenarios, but proportions of each hydrological component differed. It is important to consider soil characteristics when applying water quantity and quality analyses in an HSPF simulation

A Study on the Effect of TMT Characteristics and Vertical Dyad Similarity on Enterprise Achievements

The top management team (TMT) is a key resource for an enterprise&#8217;s sustainability, and the study of TMT characteristics is very important to explain the factors involved in an enterprise&#8217;s development. In order to comprehensively evaluate the impact of TMT characteristics on enterprise performance in China, the effect of average characteristics and vertical dyad characteristics of TMTs on enterprises performance was researched in this paper. This study is based on upper echelon theory, the similarity&#8722;attraction paradigm, and social categorization theory, stemming from the dual perspectives of social psychology and social politics. The concept of chairperson&#8722;TMT vertical dyad similarity is first proposed as a TMT characteristic, and the effect of vertical dyad similarity on enterprise performance is empirically analyzed using panel data from 235 manufacturing small- and medium-sized enterprise (SME) samples. The findings demonstrated that TMT average tenure, TMT age vertical dyad similarity, and tenure vertical dyad similarity have a significant positive effect on enterprise performance, while TMTs&#8217; other average characteristics and chairperson&#8722;TMT vertical dyad differences have no significant effect on enterprise performance

Assessment of Baseflow Estimates Considering Recession Characteristics in SWAT

Baseflow is influenced by incoming groundwater to aquifers and is closely related to watershed characteristics. Understanding baseflow characteristics is of great importance to river ecosystems and water management. Baseflow estimation typically depends on the observed streamflow in gauged watersheds, but accurate predictions of streamflow through modeling can also be useful in estimating baseflow. However, uncertainty occurs in the baseflow estimation process when modeling streamflow. Therefore, the purpose of this study is to compare the method that is proposed by Arnold and Allen (Scenario I) to an improved recession prediction method where the alpha factor (baseflow recession coefficient) is recalibrated and is applied to SWAT (Scenario II). Although the differences between the results (NSE, R2, RMSE, MAE, d) of Scenarios I and II were small regarding streamflow and recession, the Scenario II method more accurately reflected the recession characteristics than the Scenario I method. Furthermore, the Scenario II method was better in baseflow prediction than for the Scenario I method proposed by Arnold and Allen. Therefore, these outputs pave the way and contribute to an efficient method for water management in watersheds