scholarly journals Distributed-Framework Basin Modeling System: Ⅲ. Hydraulic Modeling System

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 649
Author(s):  
Xiaoning Li ◽  
Chuanhai Wang ◽  
Gang Chen ◽  
Xing Fang ◽  
Pingnan Zhang ◽  
...  
Keyword(s):  
Hydraulic Modeling ◽  
River Network ◽  
Hydraulic Calculation ◽  
River Networks ◽  
Basin Modeling ◽  
Runoff Generation ◽  
Distributed Framework ◽  
Basin Scale ◽  
Huai River ◽  
River Modeling

A distributed-framework basin modeling system (DFBMS) was developed to simulate the runoff generation and movement on a basin scale. This study is part of a series of papers on DFBMS that focuses on the hydraulic calculation methods in runoff concentration on underlying surfaces and flow movement in river networks and lakes. This paper introduces the distributed-framework river modeling system (DF-RMS) that is a professional modeling system for hydraulic modeling. The DF-RMS contains different hydrological feature units (HFUs) to simulate the runoff movement through a system of rivers, storage units, lakes, and hydraulic structures. The river network simulations were categorized into different types, including one-dimensional river branch, dendritic river network, loop river network, and intersecting river network. The DF-RMS was applied to the middle and downstream portions of the Huai River Plain in China using different HFUs for river networks and lakes. The simulation results showed great consistency with the observed data, which proves that DF-RMS is a reliable system to simulate the flow movement in river networks and lakes.

scholarly journals Distributed-Framework Basin Modeling System: II. Hydrologic Modeling System

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 744
Author(s):  
Gang Chen ◽  
Wenjuan Hua ◽  
Xing Fang ◽  
Chuanhai Wang ◽  
Xiaoning Li
Keyword(s):  
Hydrologic Modeling ◽  
Overland Flow ◽  
Hydrological Processes ◽  
Generation Model ◽  
Basin Modeling ◽  
Hydraulic Simulation ◽  
Essential Information ◽  
Distributed Framework ◽  
Basin Scale ◽  
Hydrological Feature

A distributed-framework hydrologic modeling system (DF-HMS) is a primary and significant component of a distributed-framework basin modeling system (DFBMS), which simulates the hydrological processes and responses after rainfall at the basin scale, especially for non-homogenous basins. The DFBMS consists of 11 hydrological feature units (HFUs) involving vertical and horizontal geographic areas in a basin. Appropriate hydrologic or hydraulic methods are adopted for different HFUs to simulate corresponding hydrological processes. The digital basin generation model is first developed to determine the essential information for hydrologic and hydraulic simulation. This paper mainly describes two significant HFUs contained in the DF-HMS for hydrologic modeling: Hilly sub-watershed and plain overland flow HFUs. A typical hilly area application case study in the Three Gorges area is introduced, which demonstrates DF-HMS’s good performance in comparison with the observed streamflow at catchment outlets.

scholarly journals Distributed-Framework Basin Modeling System: I. Overview and Model Coupling

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 678 ◽  
Author(s):  
Chuanhai Wang ◽  
Wenjuan Hua ◽  
Gang Chen ◽  
Xing Fang ◽  
Xiaoning Li
Keyword(s):  
Hydrological Model ◽  
Hydrological Cycle ◽  
Database Management System ◽  
Integrated System ◽  
Geographical Information ◽  
Hydrological Process ◽  
Basin Modeling ◽  
Runoff Generation ◽  
Double Object ◽  
Distributed Framework

To better simulate the river basin hydrological cycle and to solve practical engineering application issues, this paper describes the distributed-framework basin modeling system (DFBMS), which concatenate a professional hydrological model system, a geographical integrated system, and a database management system. DFBMS has two cores, which are the distributed-frame professional modeling system (DF-PMS) and the double-object sharing structure (DOSS). An area/region that has the same mechanism of runoff generation and/or movement is defined as one type of hydrological feature unit (HFU). DF-PMS adopts different kinds of HFUs to simulate the whole watershed hydrological cycle. The HFUs concept is the most important component of DF-PMS, enabling the model to simulate the hydrological process with empirical equations or physical-based submodules. Based on the underlying source code, the sharing uniform data structure, named DOSS, is proposed to accomplish the integration of a hydrological model and geographical information system (GIS), which is a new way of exploring temporal GIS. DFBMS has different numerical schemes including conceptual and distributed models. The feasibility and practicability of DFBMS are proven through its application in different study areas.

UAV thermal images to support the study of the expansion and contraction dynamics of river networks: a preliminary methodological approach

2020 ◽  
Author(s):  
Massimo Micieli ◽  
Gianluca Botter ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore
Keyword(s):  
Quantitative Description ◽  
Weather Conditions ◽  
Control Area ◽  
River Network ◽  
River Networks ◽  
Flowing Water ◽  
Climatic Fluctuations ◽  
Thermal Images ◽  
Basin Scale ◽  
Contraction Dynamics

<p>River networks are dynamic entities, periodically subject to expansion and contraction processes due to natural hydrological and climatic fluctuations. The ERC project "DyNET: Dynamical River Networks" aims at providing a systematic and quantitative description of such processes. The experimental activities are focused on the mapping at the basin scale of the active (i.e., characterized by flowing water) portion of the river network with the aid of drones, satellite images and field surveys, for the collection of data useful to the modelling of evolutionary processes and the development of theories to be extended on a regional scale. The use of UAVs (Unmanned Air Vehicles) specifically concerns the observation of the space-time evolution of processes, allowing to monitor wide areas and identify the presence/absence of flowing water in the river network with the help of infrared (IR) thermal imaging cameras.</p><p>The contribution discusses the effectiveness of UAVs for river networks dynamics monitoring in the Turbolo creek network (Calabria, southern Italy). Specifically, an experimental method is described that identifies and extrapolates from thermal images the pixels representing the active river network. The method is defined based on multiple acquisitions of thermal IR images on some channelized sites in different periods of the year, weather conditions, daytimes and flight altitudes. Several surveys were carried out in autumn, winter and spring seasons, with variable cloud conditions, always repeating the same flight plan, at three different altitudes and at three different times for each day of analysis. During the experiments, air temperature data were recorded by a weather station near the test area, as well as the water temperature values ​​in a small control area in the river bed, with the ascertained presence of water, monitored by the UAV. The thermal images were analyzed on GIS software, extrapolating the pixels falling within a range of values defined from the control area. The "water pixels" thus obtained allowed, through appropriate post-processing, to reconstruct the active river network even in areas not accessible by land. The methodology developed allows defining, for different periods of the year and weather conditions, optimal altitudes and flight times to accurately identify the expansion/contraction dynamics of river networks.</p>

scholarly journals Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

2021 ◽  
Vol 10 (3) ◽  
pp. 186
Author(s):  
HuiHui Zhang ◽  
Hugo A. Loáiciga ◽  
LuWei Feng ◽  
Jing He ◽  
QingYun Du
Keyword(s):  
Spatial Scales ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Drainage Density ◽  
River Network ◽  
Landscape Patterns ◽  
Estimation Methods ◽  
River Networks ◽  
Flow Accumulation ◽  
Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

scholarly journals A new vector-based global river network dataset accounting for variable drainage density

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peirong Lin ◽  
Ming Pan ◽  
Eric F. Wood ◽  
Dai Yamazaki ◽  
George H. Allen
Keyword(s):  
Spatial Variability ◽  
Flow Direction ◽  
River System ◽  
Drainage Density ◽  
River Network ◽  
River Networks ◽  
Digital Elevation ◽  
Machine Learning Approach ◽  
Elevation Model ◽  
National Hydrography Dataset

AbstractSpatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

scholarly journals An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

2021 ◽  
Author(s):  
Mehdi Mazaheri ◽  
J. M. V. Samani ◽  
Fulvio Boano
Keyword(s):  
Real Time ◽  
Transport Model ◽  
River Network ◽  
Point Sources ◽  
Release Time ◽  
River Networks ◽  
Observation Data ◽  
Geostatistical Approach ◽  
Pollutant Sources ◽  
Simultaneous Identification

Abstract The simultaneous identification of location and source release history in complex river networks is a very complicated ill-posed problem, particularly in a case of multiple unknown pollutant sources with time-varying release pattern. This study presents an innovative method for simultaneous identification of the number, locations and release histories of multiple pollutant point sources in a river network using minimum observation data. Considering two different type of monitoring stations with an adaptive arrangement as well as real-time data collection at those stations and using a reliable numerical flow and transport model, at first the number and suspected reach of presence of pollutant sources are determined. Then the source location and its intensity function is calculated by solving inverse source problem using a geostatistical approach. A case study with three different scenarios in terms of the number, release time and location of pollutant sources are discussed, concerning a river network with unsteady and non-uniform flow. Results showed the capability of the proposed method in identifying of sought source characteristics even in complicated cases with simultaneous activity of multiple pollutant sources.

scholarly journals On the propagation of diel signals in river networks using analytic solutions of flow equations

2015 ◽  
Vol 12 (8) ◽  
pp. 8175-8220 ◽  
Author(s):  
M. Fonley ◽  
R. Mantilla ◽  
S. J. Small ◽  
R. Curtu
Keyword(s):  
Analytic Solutions ◽  
River Network ◽  
Low Flow ◽  
River Networks ◽  
Signal Delay ◽  
Flow Conditions ◽  
Flow Equations ◽  
Linear Transport Equation ◽  
Daily Evapotranspiration ◽  
Self Similar

Abstract. Two hypotheses have been put forth to explain the magnitude and timing of diel streamflow oscillations during low flow conditions. The first suggests that delays between the peaks and troughs of streamflow and daily evapotranspiration are due to processes occurring in the soil as water moves toward the channels in the river network. The second posits that they are due to the propagation of the signal through the channels as water makes its way to the outlet of the basin. In this paper, we design and implement a theoretical experiment to test these hypotheses. We impose a baseflow signal entering the river network and use a linear transport equation to represent flow along the network. We develop analytic streamflow solutions for two cases: uniform and nonuniform velocities in space over all river links. We then use our analytic solutions to simulate streamflows along a self-similar river network for different flow velocities. Our results show that the amplitude and time delay of the streamflow solution are heavily influenced by transport in the river network. Moreover, our equations show that the geomorphology and topology of the river network play important roles in determining how amplitude and signal delay are reflected in streamflow signals. Finally, our results are consistent with empirical observations that delays are more significant as low flow decreases.

Identifying global hotspots of plastic waste accumulation along river networks

2021 ◽  
Author(s):  
Jesus Gomez-Velez ◽  
Stefan Krause
Keyword(s):  
Multiple Scales ◽  
Transport Model ◽  
Freshwater Ecosystems ◽  
River Network ◽  
Plastic Waste ◽  
Trapping Efficiency ◽  
River Networks ◽  
Clear Understanding ◽  
Relative Location ◽  
Plastic Pollution

<p>Global plastic pollution is affecting ecosystems and human health globally. Proposing solutions and coping strategies for this threat requires a clear understanding of the processes controlling the fate and transport of mismanaged plastics at multiple scales, going from watersheds to regions and even continents. River corridors are the primary conveyor and trap for mismanaged plastic produced within the landscape and eventually released to the ocean. New approaches that apply technological sensing innovations for monitoring plastic waste in aquatic environments can improve observations and plastic waste datasets globally. However, our understanding of when, where, and how to target monitoring is limited, reducing the benefit gained. There is therefore a critical demand for predictions of hotspots (as well as hot moments) of plastic accumulation along river networks globally, in order to optimize observational capacity.     </p><p>Here, we present a new global flow and transport model for plastic waste in riverine environments. Our model predicts that only a small fraction (roughly 2.5%) of the global mismanaged plastic that entered rivers since the 1950s has been delivered to the ocean by 2020, with an overwhelming majority sequestered in freshwater ecosystems. Furthermore, we predict the patterns of mismanaged plastic accumulation and its residence time depend on (i) the topology and geometry of the river network, (ii) the relative location of plastic sources, and (ii) the relative location and trapping efficiency of flow regulation structures, primarily large dams. Our results highlight the role of rivers as major sinks for plastic waste and the need for targeted remedial strategies that consider the structure of the river network and anthropogenic regulation when proposing intervention measures and sampling efforts.</p>

scholarly journals Quantification of drainable water storage volumes on landmasses and in river networks based on GRACE and river runoff using a cascaded storage approach – first application on the Amazon

2020 ◽  
Vol 24 (3) ◽  
pp. 1447-1465 ◽  
Author(s):  
Johannes Riegger
Keyword(s):  
Phase Shift ◽  
River Runoff ◽  
River Discharge ◽  
Time Lag ◽  
Water Storage ◽  
River Network ◽  
River Networks ◽  
Time Constants ◽  
Tropical Conditions

Abstract. The knowledge of water storage volumes in catchments and in river networks leading to river discharge is essential for the description of river ecology, the prediction of floods and specifically for a sustainable management of water resources in the context of climate change. Measurements of mass variations by the GRACE gravity satellite or by ground-based observations of river or groundwater level variations do not permit the determination of the respective storage volumes, which could be considerably bigger than the mass variations themselves. For fully humid tropical conditions like the Amazon the relationship between GRACE and river discharge is linear with a phase shift. This permits the hydraulic time constant to be determined and thus the total drainable storage directly from observed runoff can be quantified, if the phase shift can be interpreted as the river time lag. As a time lag can be described by a storage cascade, a lumped conceptual model with cascaded storages for the catchment and river network is set up here with individual hydraulic time constants and mathematically solved by piecewise analytical solutions. Tests of the scheme with synthetic recharge time series show that a parameter optimization either versus mass anomalies or runoff reproduces the time constants for both the catchment and the river network τC and τR in a unique way, and this then permits an individual quantification of the respective storage volumes. The application to the full Amazon basin leads to a very good fitting performance for total mass, river runoff and their phasing (Nash–Sutcliffe for signals 0.96, for monthly residuals 0.72). The calculated river network mass highly correlates (0.96 for signals, 0.76 for monthly residuals) with the observed flood area from GIEMS and corresponds to observed flood volumes. The fitting performance versus GRACE permits river runoff and drainable storage volumes to be determined from recharge and GRACE exclusively, i.e. even for ungauged catchments. An adjustment of the hydraulic time constants (τC, τR) on a training period facilitates a simple determination of drainable storage volumes for other times directly from measured river discharge and/or GRACE and thus a closure of data gaps without the necessity of further model runs.

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