Informing Environmental Flow Planning through Landscape Evolution Modeling in Heavily Modified Urban Rivers in China

Worldwide, urban rivers suffer various degrees of ecological degradation. Rehabilitating heavily modified urban rivers requires holistic approaches, including environmental flow management. We examine the case of Lower Yongding River, Beijing’s mother river, which had dried up since the 1980s and is undergoing a flow replenishment experiment, receiving 342 million m3 of water during 2019–2020 for ecosystem enhancement. Considering the massive cost of replenishment, we address the urgent need to evaluate its outcomes and inform future management through an interdisciplinary modeling approach under the circumstance of severe data shortage. We simulated the study reach’s landscape evolution under five flow scenarios and assessed their ecological effects using the CAESAR-Lisflood model and habitat suitability index method. Despite overall minor morphological differences across scenarios, individual reaches presented pronounced physical changes. Higher-flow scenarios shaped a distinct channel in certain reaches, but historic channel modifications by mining and farming caused minimal responses in others. Additionally, higher-flow scenarios generally created larger and more evenly distributed habitat areas but showed a low payback given the higher flow volumes needed. Targeted channel-floodplain geomorphological restoration is essential for flows to generate desired ecological outcomes. The demonstrated modeling framework offers great promise, informing future rehabilitation actions for heavily modified urban streams.

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CLIMATIC CONTROLS ON GLACIAL EROSION – INSIGHTS FROM NUMERICAL GLACIAL LANDSCAPE EVOLUTION MODELING

10.1130/abs/2018am-323595 ◽

2018 ◽

Author(s):

Jingtao Lai ◽

◽

Alison Anders

Keyword(s):

Landscape Evolution ◽

Glacial Erosion ◽

Evolution Modeling

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Constraining plateau uplift in southern Africa by combining thermochronology, sediment flux, topography, and landscape evolution modeling

10.1002/essoar.10504712.1 ◽

2020 ◽

Author(s):

Jessica R. Stanley ◽

Jean Braun ◽

Guillaume Baby ◽

François Guillocheau ◽

Cecile Robin ◽

...

Keyword(s):

Southern Africa ◽

Landscape Evolution ◽

Sediment Flux ◽

Plateau Uplift ◽

Evolution Modeling

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Environmental Flow Assessment of a Tropical River System Using Hydrological Index Methods

Water ◽

10.3390/w13182477 ◽

2021 ◽

Vol 13 (18) ◽

pp. 2477

Author(s):

Mohammad Haroon Hairan ◽

Nor Rohaizah Jamil ◽

Mohammad Noor Amal Azmai ◽

Ley Juen Looi ◽

Moriken Camara

Keyword(s):

River Flow ◽

Maximum Flow ◽

River System ◽

Environmental Flow ◽

Low Flow ◽

Index Method ◽

Flood Pulses ◽

Post Impact ◽

High Flood ◽

High Level

Tropical rivers and wetlands are recognized as one of the greatest and most abundant ecosystems in terms of ecological and social benefits. However, climate change, damming, overfishing, water pollution, and the introduction of exotic species threaten these ecosystems, which puts about 65% of river flow and aquatic ecosystems under a moderate to high level of threat. This paper aims to assess the environmental flow of the Selangor River based on the hydrological index method using the Global Environmental Flow Calculator (GEFC) and Indicators of Hydrological Alterations (IHA) software. The daily flow data collected by the Department of Irrigation and Drainage (DID), Malaysia, over a 60-year period (1960–2020) was used in this study to assess the Selangor River flow alterations. As per the results, the river flow has had two distinct periods over the last 60 years. In the first period, the river flows without any alteration and has a natural flow with high flood pulses and low flow pulses. While in the second, or post-impact, period, the flow of the river has a steady condition throughout the year with very little fluctuations between the dry and wet seasons of the year. From the overall comparison of the pre- and post-impact periods, it can be concluded that the minimum flow in the dry seasons of the year has increased, while the maximum flow has decreased in the monsoon seasons during the post-impact period. As a result, the Flow Duration Curve (FDC) and Environmental Management Class (EMC) analysis of the river flow recommends that the Selangor River be managed under EMC “C” to provide sufficient water for both human use and ecosystem conservation, which would also help to avoid a water level drop in the reservoirs. However, further holistic studies are suggested for a detailed analysis of the effects of the dams on aquatic biodiversity and ecosystem services in the Selangor River Basin.

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On the role of hydrologic processes in soil and landscape evolution modeling: concepts, complications and partial solutions

Earth-Science Reviews ◽

10.1016/j.earscirev.2018.09.001 ◽

2018 ◽

Vol 185 ◽

pp. 1088-1106 ◽

Cited By ~ 12

Author(s):

W.M. van der Meij ◽

A.J.A.M. Temme ◽

H.S. Lin ◽

H.H. Gerke ◽

M. Sommer

Keyword(s):

Landscape Evolution ◽

Hydrologic Processes ◽

Evolution Modeling

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Introduction to Soil Erosion and Landscape Evolution Modeling

Landscape Erosion and Evolution Modeling ◽

10.1007/978-1-4615-0575-4_1 ◽

2001 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

William W. Doe ◽

Russell S. Harmon

Keyword(s):

Soil Erosion ◽

Landscape Evolution ◽

Evolution Modeling

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A novel deep learning scheme for morphology-based classification of mycobacterial infection in unstained macrophages

10.1101/611434 ◽

2019 ◽

Author(s):

Xinzhuo Zhao ◽

Yanqing Bao ◽

Lin Wang ◽

Wei Qian ◽

Jianjun Sun

Keyword(s):

Deep Learning ◽

Bacterial Infections ◽

Morphological Changes ◽

Model Organism ◽

Bacterial Pathogen ◽

Mycobacterial Infection ◽

Fine Tuning ◽

Great Promise ◽

Morphological Differences ◽

Guided Propagation

AbstractObjectiveMycobacterium tuberculosis (Mtb) is an airborne, contagious bacterial pathogen that causes widespread infections in humans. Using Mycobacterium marinum (Mm), a surrogate model organism for Mtb research, the present study develops a deep learning-based scheme that can classify the Mm-infected and uninfected macrophages in tissue culture solely based on morphological changes.MethodsA novel weak-and semi-supervised learning method is developed to detect and extract the cells, firstly. Then, transfer learning and fine-tuning from the CNN is built to classify the infected and uninfected cells.ResultsThe performance is evaluated by accuracy (ACC), sensitivity (SENS) and specificity (SPEC) with 10-fold cross-validation. It demonstrates that the scheme can classify the infected cells accurately and efficiently at the early infection stage. At 2 hour post infection (hpi), we achieve the ACC of 0.923 ± 0.005, SENS of 0.938 ± 0.020, and SPEC of 0.905 ± 0.019, indicating that the scheme has detected significant morphological differences between the infected and uninfected macrophages, although these differences are hardly visible to naked eyes. Interestingly, the ACC at 12 and 24 hpi are 0.749 ± 0.010 and 0.824 ± 0.009, respectively, suggesting that the infection-induced morphological changes are dynamic throughout the infection. Finally, deconvolution with guided propagation maps the key morphological features contributing to the classification.SignificanceThis proof-of-concept study provides a novel venue to investigate bacterial pathogenesis in a macroscopic level and has a great promise in diagnosis of bacterial infections.

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The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds

Geoscientific Model Development ◽

10.5194/gmd-10-1645-2017 ◽

2017 ◽

Vol 10 (4) ◽

pp. 1645-1663 ◽

Cited By ~ 16

Author(s):

Jordan M. Adams ◽

Nicole M. Gasparini ◽

Daniel E. J. Hobley ◽

Gregory E. Tucker ◽

Eric W. H. Hutton ◽

...

Keyword(s):

Shallow Water ◽

Land Surface ◽

Landscape Evolution ◽

Spatial Scales ◽

Water Discharge ◽

Flowing Water ◽

Modeling Framework ◽

Flow Routing ◽

Creek Watershed ◽

Runoff Model

Abstract. Representation of flowing water in landscape evolution models (LEMs) is often simplified compared to hydrodynamic models, as LEMs make assumptions reducing physical complexity in favor of computational efficiency. The Landlab modeling framework can be used to bridge the divide between complex runoff models and more traditional LEMs, creating a new type of framework not commonly used in the geomorphology or hydrology communities. Landlab is a Python-language library that includes tools and process components that can be used to create models of Earth-surface dynamics over a range of temporal and spatial scales. The Landlab OverlandFlow component is based on a simplified inertial approximation of the shallow water equations, following the solution of de Almeida et al.(2012). This explicit two-dimensional hydrodynamic algorithm simulates a flood wave across a model domain, where water discharge and flow depth are calculated at all locations within a structured (raster) grid. Here, we illustrate how the OverlandFlow component contained within Landlab can be applied as a simplified event-based runoff model and how to couple the runoff model with an incision model operating on decadal timescales. Examples of flow routing on both real and synthetic landscapes are shown. Hydrographs from a single storm at multiple locations in the Spring Creek watershed, Colorado, USA, are illustrated, along with a map of shear stress applied on the land surface by flowing water. The OverlandFlow component can also be coupled with the Landlab DetachmentLtdErosion component to illustrate how the non-steady flow routing regime impacts incision across a watershed. The hydrograph and incision results are compared to simulations driven by steady-state runoff. Results from the coupled runoff and incision model indicate that runoff dynamics can impact landscape relief and channel concavity, suggesting that, on landscape evolution timescales, the OverlandFlow model may lead to differences in simulated topography in comparison with traditional methods. The exploratory test cases described within demonstrate how the OverlandFlow component can be used in both hydrologic and geomorphic applications.

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