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Rivers are among the most biodiverse and endangered ecosystems on earth. In Europe, concern over their conservation promoted the development of legal instruments for habitat and species conservation, the Habitats Directive, and water resource management, the Water Framework Directive. This legal protection demanded the estimate of river ecosystem surface for different purposes. Different approaches allow river surface to be measured at a low cost. Some accurate techniques like satellite images or LiDAR (Light Detection and Ranging) do not always work at a large scale or for streams and small rivers. We discuss here the use of the traditional hydraulics relationship between drainage area and bankfull width as a good approach to river surface estimation. We confirm that the use of this cheap and simple method could be a good approach to estimate river surface. However, we also proved that the development of regional curves, i.e., to establish the empirical relationship based on study area data, constitutes an essential improvement to estimation. This research was funded by the Ministry of Ecological Transition and Demographic Challenge, project number 3081147. This research was supported by the Government of Catalonia (CERCA Program).

Sediment control structures such as check dams, groundsills, and revetments are commonly used to balance sediment transport. In this study, we investigated the visual quality of sediment control structures that have been installed to manage mountain streams by analyzing images from the Soil and Water Conservation Bureau (SWCB) of Taiwan. We used visual preference (P) as an indicator in the evaluation of visual quality and considered two softscape elements and four cognitive factors associated with P. The two softscape elements were the visible body of water and vegetation, which were represented by the percentage of visible water (WR) and the percentage of visible greenery (GR). We considered four cognitive factors: naturalness, harmony, vividness, and closeness. Using a questionnaire-based survey, we asked 212 experts and laypeople to indicate their visual preferences (P) for the images. We examined the associations of the P ratings with cognitive factors and softscape elements and then established an empirical relationship between P and the cognitive factors using multiple regression analysis. The results showed that the subjects&rsquo, visual preferences were strongly affected by the harmony factor, the subjects preferred the proportion of softscape elements to be 30% WR and 40% GR for optimal harmony, naturalness, and visual quality of the sediment control structures. We discuss the visual indicators, visual aesthetic experiences, and applications of the empirical relationship, and offer insights into the study&rsquo, s implications.

Leaf wetness duration (LWD) models have been proposed as an alternative to in situ LWD measurement, as they can predict leaf wetness using physical mechanism and empirical relationship with meteorological conditions. Applications of advanced machine learning (ML) algorithms in the development of empirical LWD model can lead to improvements in the LWD prediction. The current study developed LWD model using extreme learning machine, random forest method, and a deep neural network. Additionally, performances of these ML-based LWD models are evaluated and compared with existing models. Observed LWD and meteorological variable data are obtained from nine farms in South Korea. Temporal and geographical information were also used. Additionally, the priorities of the employed variables in the development of the ML-based LWD models were analyzed. As a result, the ML-based LWD models outperformed the existing models, the random forest led to the best performance for LWD prediction among the tested LWD models. Strengths of associations between input variables and leaf wetness were relative humidity, short wave radiation, air temperature, hour, latitude, longitude, and wind speed in descending order. Uses of the geographical and time information in development of LWD model can improve the performance of LWD model.

Water resource management is critical for the economic development of the Democratic People&rsquo, s Republic of Korea (DPRK), where runoff plays a central role. However, long and continuous runoff data at required spatial and temporal scales are generally not available in many regions in DPRK, the same as in many countries around the world. A common practice to fill the gaps is to use some kind of interpolation or data-infilling methods. In this study, the gaps in annual runoff data were filled using a distributed runoff map. A novel statistical&ndash, distributed model of average annual runoff was derived from 50 years&rsquo, observation on 200 meteorological observation stations in DPRK, considering the influence of climatic factors. Using principal component analysis, correlation analysis and residual error analysis, average annual precipitation, average annual precipitation intensity, average annual air temperature, and hot seasonal air temperature were selected as major factors affecting average annual runoff formation. Based on the water balance equation and assumptions, the empirical relationship for runoff depth and impact factors was established and calibrated. The proposed empirical model was successfully verified by 93 gauged stations. The cartography of the average annual runoff map was automatically implemented in ArcGIS. A case study on the Tumen River Basin illustrated the applicability of the proposed model. This model has been widely used for the development and management of water resources by water-related institutes and design agencies in DPRK. The limitation of the proposed model and future works are also discussed, especially the impacts of climate changes and topology changes and the combination with the physical process of runoff formation.

Based on the results of experimental studies concerning the filtration coefficient, the Darcianity of the observed flows for eight cohesive soils at four hydraulic gradients was analyzed. It is observed that linear dependence of flow velocity on hydraulic gradient is an approximation only, and it is the worse the more cohesive a given soil is. Despite this, Darcy&rsquo, s law can be a correct approximation of the empirical relationship between hydraulic gradient and the flow velocity, also in very cohesive soils. A statistical analysis was carried out to identify correlation between soil properties and permeability coefficient. For each soil, 109 parameters were analyzed, among others applying mercury intrusion porosimetry, scanning electron microscopy, dynamic image analysis, and laser diffraction. Ultimately, three single-variable models best fitted to the experimental data were found, using the plasticity index IP as the independent variable, the average pore diameter DP, and the convexity of silt fraction particles. All model parameters are statistically significant at p &lt, 0.05. Comparison with reference multi-variable models showed that the best fit for experimental data is observed by the model with the plasticity index, while the results suggest low usability of single-variable models with structural parameters.