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This paper considers an output-based event-triggered control approach for discrete-time systems and proposes three new types of performance measures under unknown disturbances. These measures are motivated by the fact that signals in practical systems are often associated with bounded energy or bounded magnitude, and they should be described in the $ \ell_{2} $ and $ \ell_\infty $ spaces, respectively. More precisely, three performance measures from $ \ell_{q} $ to $ \ell_{p} $, denoted by the $ \ell_{p/q} $ performances with $ (p, q) = (2, 2), \ (\infty, 2) $ and $ (\infty, \infty) $, are considered for event-triggered systems (ETSs) in which the corresponding event-trigger mechanism is defined as a function from the measured output of the plant to the input of the dynamic output-feedback controller with the triggering parameter $ \sigma (>0) $. Such a selection of the pair $ (p, q) $ represents the $ \ell_{p/q} $ performances to be bounded and well-defined, and the three measures are natural extensions of those in the conventional feedback control, such as the $ H_\infty $, generalized $ H_2 $ and $ \ell_1 $ norms. We first derive the corresponding closed-form representation with respect to the relevant ETSs in terms of a piecewise linear difference equation. The asymptotic stability condition for the ETSs is then derived through the linear matrix inequality approach by developing an adequate piecewise quadratic Lyapunov function. This stability criterion is further extended to compute the $ \ell_{p/q} $ performances. Finally, a numerical example is given to verify the effectiveness of the overall arguments in both the theoretical and practical aspects, especially for the trade-off relation between the communication costs and $ \ell_{p/q} $ performances.

The severity and incidence of flash floods are increasing in forested regions, causing significant harm to residents and the environment. Consequently, accurate estimation of flood peaks is crucial. As conventional physically based prediction models reflect the traits of only a small number of areas, applying them in ungauged catchments is challenging. The interrelationship between catchment characteristics and flood features to estimate flood peaks in ungauged areas remains underexplored, and evaluation standards for the appropriate number of flood events to include during data collection to ensure effective flood peak prediction have not been established. Therefore, we developed a machine-learning predictive model for flood peaks in ungauged areas and determined the minimum number of flood events required for effective prediction. We employed rainfall-runoff data and catchment characteristics for estimating flood peaks. The applicability of the machine learning model for ungauged areas was confirmed by the high predictive performance. Even with the addition of rainfall-runoff data from ungauged areas, the predictive performance did not significantly improve when sufficient flood data were used as input data. This criterion could facilitate the determination of the minimum number of flood events for developing adequate flood peak predictive models.

Continuously increasing demand for freshwater makes many scientists study water yield prediction. In South Korea, of which two-third is covered by forests, understanding the water cycle especially in forests is even more important. Throughfall is penetrated rainfall through the tree canopy and it is the basic source for groundwater recharge which is directly related to the water yield on the catchment scale. Therefore, understanding the throughfall characteristics is essential for sustainable water management. This study is conducted to develop simple throughfall simulation models and estimate prediction uncertainty from developed models by different forest stand characteristics. National Institute of Forest Service (NIFoS) has collected throughfall data for 2 years from 7 different forest stand sites. Rutter model was used for the structure of the simple throughfall simulation model and it had several parameters for simulating. And over a million Monte Carlo experiments and generalized likelihood uncertainty estimation (GLUE) methodology were used for selecting parameters sets of behavioural models from comparing simulated throughfall and observed throughfall. From the range of behaviours in a period, we successfully estimated the prediction uncertainty. We also compared the features of behavioural parameter sets by different forest stand characteristics. We expect developed models can be applied for several forest stands in South Korea with various physical-based hydrological models.

Machine learning models are now capable of delivering coveted digital soil mapping (DSM) benefits (e.g., field capacity (FC) prediction); therefore, determining the optimal sample sites and sample size is essential to maximize the training efficacy. We solve this with a novel optimal sampling algorithm that allows the authentic augmentation of insufficient soil features using machine learning predictive uncertainty. Nine hundred and fifty-three forest soil samples and geographically referenced forest information were used to develop predictive models, and FCs in South Korea were estimated with six predictor set hierarchies. Random forest and gradient boosting models were used for estimation since tree-based models had better predictive performance than other machine learning algorithms. There was a significant relationship between model predictive uncertainties and training data distribution, where higher uncertainties were distributed in the data scarcity area. Further, we confirmed that the predictive uncertainties decreased when additional sample sites were added to the training data. Environmental covariate information of each grid cell in South Korea was then used to select the sampling sites. Optimal sites were coordinated at the cell having the highest predictive uncertainty, and the sample size was determined using the predictable rate. This intuitive method can be generalized to improve global DSM.

This paper is concerend with tackling the L 1 performance analysis problem of continuous and piecewise continuous nonlinear systems with non-unique solutions by using the involved arguments of set-invariance principles. More precisely, this paper derives a sufficient condition for the L 1 performance of continuous nonlinear systems in terms of the invariant set. However, because this sufficient condition intrinsically involves analytical representations of solutions of the differential equations corresponding to the nonlinear systems, this paper also establishes another sufficient condition for the L 1 performance by introducing the so-called extended invariance domain, in which it is not required to directly solving the nonlinear differential equations. These arguments associated with the L 1 performance analysis is further extended to the case of piecewise continuous nonlinear systems, and we obtain parallel results based on the set-invariance principles used for the continuous nonolinear systems. Finally, numerical examples are provided to demonstrate the effectiveness as well as the applicability of the overall results derived in this paper.

This study aims to contribute to better understanding the effects of the changes in the parameter values of GroundFilter algorithm(GF), which performs filtering process, and of GridsurfaceCreate algorithm(GC), which creates regular grid, provided in Fusion software on the accuracy of elevation of the final LiDAR-DEM products through comparative analysis. In order to test whether there are significant effects on the accuracy of the final LiDAR-DEM products due to the changes of GF(1, 3, 5, 7, 9) parameter levels and GC(1, 3, 5, 7, 9) parameter levels, two-way ANOVA is conducted based on residuals. The residuals are calculated using the differences between each sample plot`s paired field-measured and DEM-derived elevation values given each individual GF and GC level. After that, Tukey HSD test is conducted as a post hoc test for grouping the levels. As a result of two-way ANOVA test, it is found that the change in the GF levels significantly affects the accuracy of LiDAR-DEM elevations(F-value : 27.340, p

This study was carried out to investigate coefficient and exponent of runoff energy in MUSLE for small forest watershed, Hwachoen watershed in Gangwon-do. For 15 rainfall events, runoff volume, peak discharge and sediment yield were measured and these data were used to calculate coefficient and exponent of runoff energy. The results of this study showed that factors of MUSLE were affected by slope steepness. The coefficient and exponent of runoff energy were validated with coefficient of efficiency of 0.92 and these values were suggested to 0.002 and 0.81 respectively. The comparison of the coefficients and exponents between Hwacheon and other forest watersheds showed that these values would reflect the effect of forest management within watershed.

This study was carried to figure out appropriate method for time of concentration used in the Rational method at design flood estimation. For 6 small forest watershed, time of concentration was obtained from time of concentration formulas including Kirpich Rziha, Kraven (I), Kraven (II) and SCS lag method and the results from these were compared to the results from cross correlation function based on real rainfall runoff events. The results of cross correlation function ranged from 12.9 to 28.8 minutes and the average difference between SCS lag method and cross correlation function was 2.5 minutes. SCS lag method would be feasible to estimate time of concentration in small forest watershed.

This study was carried out to investigate the characteristics of suspended solid concentration in small forest watershed, Hwacheon, Gangwondo. For five rainfall events from July 2013 to August 2013, rainfall, discharge, and suspended solid load has been measured. The results showed that the fist flush effect was observed for suspended solid in each rainfall event, sediment rating curve was obtained with at rising limb and at falling limb, and EMC (event mean concentration) of suspended solid was calculated to 9.4 mg/L. EMC was compared to the values from the watershed that has various land use types and EMC from forest watershed was much lower that from the crop, paddy or low covered forest watershed.

In South Korea, since small forest catchments are located upstream of most river basins, the baseflow from these catchments is important for a clean water supply to downstream areas. Baseflow recession analysis is widely recognized as a valuable tool for estimating the baseflow component of the stream hydrograph. However, few studies have applied this tool to small forest catchments. So, this study was conducted to assess the applicability of the recession analysis methods proposed in previous studies. The data used were long-term rainfall-runoff data from 1982 to 2011 in the Gwangneung coniferous (GC) and deciduous (GD) forest catchment in Gyeonggi-do, South Korea. For the applicability assessment, six recession constant estimation methods, which were used by previous studies, were selected. The recession constants of the GC and GD catchments were calculated, and applicability assessments were conducted by comparing the recession predictions and baseflow separations. As a result, the recession constants for GC and GD were 0.8480 and 0.9235, respectively. This clear difference may be due to the different forest cover in each area. The correlation regression line, AR(1) model, and the Vogel and Kroll method showed lower error rates and appropriate baseflow indexes compared with other methods.

A new approach to multi-criteria model evaluation is presented. The approach is consistent with the equifinality thesis and is developed within the Generalised Likelihood Uncertainty Estimation (GLUE) framework. The predictions of Monte Carlo realisations of TOPMODEL parameter sets are evaluated using a number of performance measures calibrated for both global (annual) and seasonal (30 day) periods. The seasonal periods were clustered using a Fuzzy C-means algorithm, into 15 types representing different hydrological conditions. The model shows good performance on a classical efficiency measure at the global level, but no model realizations were found that were behavioural over all multi-period clusters and all performance measures, raising questions about what should be considered as an acceptable model performance. Prediction uncertainties can still be calculated by allowing that different clusters require different parameter sets. Variations in parameter distributions between clusters, as well as examination of where observed discharges depart from model prediction bounds, give some indication of model structure deficiencies.

The assimilated carbon stored in terrestrial ecosystems is exported with water movement in both organic and inorganic forms, which are defined as particulate organic carbon (POC), dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC). The transport of terrestrial carbon into streams, rivers and eventually the oceans is an important link in the global carbon cycle (Ludwig et al., 1996; Warnken and Santschi, 2004). The Committee on Flux of Carbon to the Ocean estimated that of the organic carbon entering rivers globally, around 50% is transported to the ocean, 25% is oxidized within the system and 25% stored as POC in the system as sediment (Hope et al. 1994). As compared to the terrestrial carbon sinks (1.9 Gt-C/yr; Prentice et al., 2001), the organic carbon transport from terrestrial ecosystems to oceans is 0.4–0.9 Gt-C/yr (Meybeck, 1982; Hope et al., 1994; Prentice et al., 2001), representing a substantial component of the ecosystem carbon balance.