A conceptual data model coupling with physically-based distributed hydrological models based on catchment discretization schemas

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

“…The distributed hydrological model ESSI-3 was set up to capture the spatial-temporal variability of hydrological processes of the SJP. Briefly, The ESSI-3 model, which is based on energy balance and water balance and constituted with various basic modules (e.g., a remote sensing-based ET module, a three-layer soil water balance module, and a groundwater module) and specific hydrological process modules (e.g., permafrost hydrology, snow melting, and wetland hydrology), can accurately characterize various hydrological fluxes (e.g., runoff and ET) and states (e.g., SWE, CWS, SMS, and groundwater storage) (Zhang and Zhang, 2006;Liu et al, 2015;Chen and Zhang, 2019;Wang et al, 2022). Especially for this study, based on a remote sensing-based two-leaf Jarvis-type canopy conductance model (RST-Gc), the actual ET is partitioned into four parts, including wet canopy evaporation (E wet c ), saturated soil surface evaporation (E sat c ), wet soil evaporation (E moi c ), and dry canopy transpiration (E dry c ).…”

“…As a physically-based distributed hydrological model that exploits spatially and temporally varying climate forcing and modeldriven parameters on maximum, ESSI-3 (the third version of the infiltration Excess and Saturation excess Soil-water Integration model for hydrology) has demonstrated excellent performances in hydrological simulations of various watersheds with different catchment sizes under various climatic conditions (Zhang and Zhang, 2006;Xu et al, 2009;Chen et al, 2014;Liu et al, 2015;Chen and Zhang, 2019;Wang et al, 2022). It is worth emphasizing that preparation of reliable input model parameters is the essential step toward a dependable estimation of regional hydrology.…”

Key Technologies for an Urban Overland Flow Simulation System to Support What-If Analysis

“…Many pre-processing tools and conceptual data models have been proposed to support discretization at different research scales (e.g., watersheds, urban areas, and sub-catchments in urban areas). These pre-processing tools have different design ideas, which can be workflow and object oriented, and most of these tools are based on a procedural method to enhance the capability of discretizing urban spatial data [47] [48]. However, previous studies have not considered the main objects in urban areas independently, which seriously hinders the hydrologic simulation of complex and heterogeneous urban areas because buildings, road networks, surfaces, and ponds have different impacts on overland flow processes.…”

Discretizing environmental data for learning Bayesian-network classifiers

“…According to a recent review (Aguilera et al, 2011), in fact, more than 80% of the papers addressing Bayesian networks in environmental science involve discretized data, where the discretization is done using the so-called Equal Frequency method or is based on expert knowledge. While more tailored discretization methods have been designed for specific types of model, such as hydrological models (Pradhanang and Briggs, 2014), models of air quality (Davison and Ramesh, 1996), and models of spatial distributions of the data (Liu et al, 2015), discretization methods specifically designed for environmental modeling through Bayesian networks do not abound. To bring the discretization methods in use with Bayesian networks in general to the attention of environmental modelers, further efforts as well as more tailored insights are called for (Nash et al, 2013).…”