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Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, and equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be a persistent challenge due to complex surface and subsurface watershed processes. Therefore, in addition to the fundamental framework, numerous techniques have been used to enhance prediction accuracy and physical consistency. This work provides a well-organized review of more than two decades of efforts to enhance SFP in a physically consistent way using process modeling and flow domain knowledge. This review covers hydrograph analysis, baseflow separation, and process-based modeling (PBM) approaches. This paper provides an in-depth analysis of each technique and a discussion of their applications. Additionally, the existing techniques are categorized, revealing research gaps and promising avenues for future research. Overall, this review paper offers valuable insights into the current state of enhanced SFP within a physically consistent, domain knowledge-informed data-driven modeling framework. [ABSTRACT FROM AUTHOR]

Tidal sand waves form a dynamic bed pattern, widely occurring in shallow shelf seas such as the North Sea. Their importance to coastal engineering has inspired many advances in process-based sand wave modelling, aimed at explaining physical mechanisms in the formation stage ('linear regime') and capturing the finite amplitude evolution to equilibrium states ('nonlinear regime'). However, systematic validation of particularly the nonlinear sand wave models is still lacking. Here, we perform a two-step calibration and validation study of a sand wave model (specifically, their linear and nonlinear model versions) against field data from the North Sea. In the first step, the linear model is calibrated by seeking overall values of two uncertain input parameters (slip parameter, wave period) for which the modeled and observed wavelengths show the best agreement. In the second step, using the calibrated input parameters and preferred wavelengths from the linear model, equilibrium heights from the nonlinear sand wave model are validated against the observed sand wave heights. Our results show satisfactory agreement between observed and modeled sand wave lengths (from the linear sand wave model) and a systematic overprediction of sand wave heights (using the nonlinear model). Regression analysis can be used to rescale the nonlinear model results to obtain realistic predictions of sand wave heights. [ABSTRACT FROM AUTHOR]

Considering hydro-climatic diversity, integrating dynamic dimensions of water security modeling is vital for ensuring environmental sustainability and its associated full range of climate resilience. Improving climate resiliency depends on the attributing uncertainty mechanism. In this study, a conceptual resilience model is presented with the consideration of input uncertainty. The impact of input uncertainty is analyzed through a multi-model hydrological framework. A multi-model hydrological framework is attributed to a possible scenario to help apply it in a decision-making process. This study attributes water security modeling with the considerations of sustainability and climate resilience using a high-speed computer and Internet system. Then, a subsequent key point of this investigation is accounting for water security modeling to ensure food security and model development scenarios. In this context, a four-dimensional dynamic space that maps sources, resource availability, infrastructure, and vibrant economic options is essential in ensuring a climate-resilient sustainable domain. This information can be disseminated to farmers using a central decision support system to ensure sustainable food production with the application of a digital system. [ABSTRACT FROM AUTHOR]

The main goal of this study was to obtain the attribution results of a physical assessment of the modern hydrological consequences of separately natural and anthropogenic components of climate change, based on the synthesis of detailed process-based models of river runoff formation and an ensemble of Earth system models (ESMs) within the large river basins in Eastern Siberia. This approach allows calculating the river flow using ESM-based data over the observation period under two scenarios, considering: (1) the anthropogenic impact of increasing greenhouse gas emissions and (2) only internal fluctuations of the climate system and natural external forcing. According to the results of the numerical experiments, the attributions of anthropogenic components of climate change in the dynamics of the Lena runoff are weak, i.e., during the observation period, the Lena River flow statistically significantly increases, but it occurs mainly due to natural climate variability. The changes in the Selenga runoff are intensely influenced by the anthropogenic component of climate change. Since the 1970s, the Selenga runoff increased under natural climatic conditions, but since the mid-1980s, it decreased under anthropogenic greenhouse gas emissions, due to reduced summer precipitation. This was the main reason for the last low-water period of 1996–2017 in the Selenga basin. [ABSTRACT FROM AUTHOR]

Stormwater control measures (SCMs) are decentralized technical elements, which can prevent the negative effects of uncontrolled stormwater flow while providing co-benefits. Optimal SCMs have to be selected and designed to achieve the desired hydrological response of an urban catchment. In this study, automated modeling and domain-specific knowledge in the fields of modeling rainfall-runoff (RR) and SCMs are applied to automate the process of optimal SCM design. A new knowledge library for modeling RR and SCMs, compliant with the equation discovery tool ProBMoT (Process-Based Modeling Tool), was developed. The proposed approach was used to (a) find the optimal RR model that best fits the available pipe flow measurements, and (b) to find the optimal SCMs design that best fits the target catchment outflow. The approach was applied to an urban catchment in the city of Ljubljana, Slovenia. First, nine RR models were created that generally had »very good« performance according to the Nash–Sutcliffe efficiency criteria. Second, six SCM scenarios (i.e., detention pond, storage tank, bio-retention cell, infiltration trench, rain garden, and green roof) were automatically designed and simulated, enabling the assessment of their ability to achieve the target outflow. The proposed approach enables the effective automation of two complex calibration tasks in the field of urban drainage. [ABSTRACT FROM AUTHOR]

The purpose of the study was to analyze the formation conditions of catastrophic floods in the Iya River basin over the observation period, as well as a long-term forecast of the impacts of future climate change on the characteristics of the high flow in the 21st century. The semi-distributed process-based Ecological Model for Applied Geophysics (ECOMAG) was applied to the Iya River basin. Successful model testing results were obtained for daily discharge, annual peak discharge, and discharges exceeding the critical water level threshold over the multiyear period of 1970–2019. Modeling of the high flow of the Iya River was carried out according to a Kling–Gupta efficiency (KGE) of 0.91, a percent bias (PBIAS) of −1%, and a ratio of the root mean square error to the standard deviation of measured data (RSR) of 0.41. The preflood coefficient of water-saturated soil and the runoff coefficient of flood-forming precipitation in the Iya River basin were calculated in 1980, 1984, 2006, and 2019. Possible changes in the characteristics of high flow over summers in the 21st century were calculated using the atmosphere–ocean general circulation model (AOGCM) and the Hadley Centre Global Environment Model version 2-Earth System (HadGEM2-ES) as the boundary conditions in the runoff generation model. Anomalies in values were estimated for the middle and end of the current century relative to the observed runoff over the period 1990–2019. According to various Representative Concentration Pathways (RCP-scenarios) of the future climate in the Iya River basin, there will be less change in the annual peak discharge or precipitation and more change in the hazardous flow and its duration, exceeding the critical water level threshold, at which residential buildings are flooded. [ABSTRACT FROM AUTHOR]

Ship block assembly planning is very complex due to the various activities and characteristics of ship production. Therefore, competitiveness in the shipbuilding industry depends on how well a company operates its ship block assembly plan. Many shipbuilders are implementing various studies to improve their competitiveness in ship block assembly planning, specifically regarding technology usage, such as modeling and simulation (M&S) and Cyber-Physical Systems (CPS). Although these technologies are successfully applied in some production planning systems, it is difficult to tailor ship production planning systems with flexibility due to unexpected situations. Providing a flexible plan for these production planning systems requires a way to describe and review the organic relationships of ship production processes. In this research, a process-based modeling (PBM) method proposes a novel approach to describing the production process of ship block assembly planning by redefining production information based on changing instructions. The proposed method consists of four modeling steps. The first creates a unit model, which includes the products, processes, and resource information for the block. The second designs an integrated network process for linking unit models according to the bill of materials (BOM). The third creates a process-based model that describes the production processes by combining unit models. The fourth generates a simulation model by applying a Petri-net to the process-based model, which analyzes the productivity of the ship's block assembly processes. PBM identifies the assembly process' interrelationship and shows that productivity can be reviewed to uncover ship production problems. [ABSTRACT FROM AUTHOR]

In process-based numerical models, reducing the amount of input parameters, known as input reduction (IR), is often required to reduce the computational effort of these models and to enable long-term, ensemble predictions. Currently, a comprehensive performance assessment of IR-methods is lacking, which hampers guidance on selecting suitable methods and settings in practice. In this study, we investigated the performance of 10 IR-methods and 36 subvariants for wave climate reduction to model the inter-annual evolution of nearshore bars. The performance of reduced wave climates is evaluated by means of a brute force simulation based on the full climate. Additionally, we tested how the performance is affected by the number of wave conditions, sequencing, and duration of the reduced wave climate. We found that the Sediment Transport Bins method is the most promising method. Furthermore, we found that the resolution in directional space is more important for the performance than the resolution in wave height. The results show that a reduced wave climate with fewer conditions applied on a smaller timescale performs better in terms of morphology than a climate with more conditions applied on a longer timescale. The findings of this study can be applied as initial guidelines for selecting input reduction methods at other locations, in other models, or for other domains. [ABSTRACT FROM AUTHOR]