Hydraulic Efficiency of Green-Blue Flood Control Scenarios for Vegetated Rivers: 1D and 2D Unsteady Simulations

Lama, Giuseppe Francesco Cesare; Giovannini, Matteo Rillo Migliorini; Errico, Alessandro; Mirzaei, Sajjad; Padulano, Roberta; Chirico, Giovanni Battista; Preti, Federico

doi:10.3390/w13192620

Cited by 43 publications

(22 citation statements)

References 74 publications

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“…Vegetation characteristics were provided in terms of LAI retrieved from freely available remote-sensing products (Mao and Yan, 2019). LAI is used to quantify rainfall interception, the partition of ET p into E p and T p , which are crucial steps for obtaining reliable numerical simulations in HYDRUS-1D (Kool et al, 2014;Anderson et al, 2017;Lama et al, 2021). Vegetation influences evapotranspiration that decreases exponentially with decreasing root depth (Adane et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Analysis of Groundwater Recharge in Mongolian Drylands Using Composite Vadose Zone Modeling

2022

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Knowledge of groundwater recharge (GR) is important for the effective management of water resources under semi-arid continental climates. Unfortunately, studies and data in Mongolia are limited due to the constraints in funding and lack of research infrastructures. Currently, the wide accessibility of freely available global-scale digital datasets of physical and chemical soil properties, weather data, vegetation characteristics, and depths to the water table offers new tools and basic information that can support low-cost physically based and process-oriented models. Estimates of GR over 41 study sites in Mongolia were obtained using HYDRUS-1D in a 2-m-thick soil profile with root depths of either 0.30 or 0.97 m by exploiting the daily precipitation and biome-specific potential evapotranspiration values. The GR simulated by HYDRUS-1D arrives at the water table and becomes the actual GR with a lag time that has been calculated using a simplified form of the Richards equation and a traveling wave model. The mean annual precipitation ranges from 57 to 316 mm year−1, and on average about 95% of it is lost by mean annual actual evapotranspiration. In the steppe region, the vegetation cover induces higher-than-normal actual transpiration losses and consequently lower GR. The mean annual GR rates span between 0.3 and 12.0 mm year−1, while travel times range between 4 and 558 years. Model prediction uncertainty was quantified by comparing actual evapotranspiration and GR with available maps and by a sensitivity assessment of lag time to the soil moisture in the deep vadose zone. The partial least squares regression (PLSR) was used to evaluate the impact of available environmental properties in explaining the 47.1 and 59.1% variability of the spatially averaged mean annual GR and travel time, respectively. The most relevant contributors are clay content, aridity index, and leaf area index for GR, and depth to the water table and silt content for the lag time. In data-poor, arid, and semi-arid regions such as Mongolia, where the mean annual GR rates are low and poorly correlated to precipitation, the ever-increasing availability of world databases and remote sensing products offers promise in estimating GR.

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Section: Discussionmentioning

confidence: 99%

Analysis of Groundwater Recharge in Mongolian Drylands Using Composite Vadose Zone Modeling

2022

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“…These emergent approaches also more explicitly promote the integration of nature-based solutions into climate adaptation, as these offer more flexible long-term solutions with multiple benefits, including flood protection [18]. Nature-based solutions have gained increasing prominence in applications for river systems since the impacts of flood control areas with riverine vegetation stand unchanged [19]. Similar impacts on flood mitigation occur in urban areas populated by trees and green roofs.…”

Section: Barriers To Progressmentioning

confidence: 99%

A Framework for Cloud to Coast Adaptation: Maturity and Experiences from across the North Sea

Sayers¹,

Gersonius²,

Özerol

et al. 2022

Land

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The low-lying coastal areas of the countries around the North Sea are exposed to flooding and the influence of sea level rise. The countries in the North Sea Region need to continue to adapt if the associated risk is to be well-managed into the future. In addition to reducing flood risk, adaptation measures can bring development opportunities for those same places. These opportunities, however, are unlikely to be achieved through a ‘defence only’ paradigm, and instead a new approach is needed that simultaneously reduces risk and promotes liveable places, ecosystem health and social well-being. The building blocks of this new approach are promoted here and are based on an adaptation process that is collaborative and takes a whole-system, long-term perspective. The approach developed through the Interreg funded project, C5a, brings together governments, practitioners and researchers from across the North Sea to share policies, practices and the emerging science of climate change adaptation and enabling sustainable development. The new approach reflects a Cloud to Coast management paradigm and emerged through a combination of knowledge exchange and peer-to-peer learning across seven case studies. Central to the case studies was a maturity analysis of existing capabilities across the North Sea countries and their ability to adopt the new approach. This paper presents the results of this analysis, including the common challenges that emerged and the methods and examples of good practice to overcome them. Building upon these findings, the paper concludes by presenting four priority policy directions to support the uptake of the Cloud to Coast approach.

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“…Also, under the same modeling scheme, the bias of ET estimation was larger in a more clumped canopy [14]. Moreover, the knowledge of experimental LAI is crucial in ecohydraulic research [25,26].…”

Section: Introductionmentioning

confidence: 98%

Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration

Chen

Wang

et al. 2021

Remote Sensing

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In terrestrial ecosystems, leaves are aggregated into different spatial structures and their spatial distribution is non-random. Clumping index (CI) is a key canopy structural parameter, characterizing the extent to which leaf deviates from the random distribution. To assess leaf clumping effects on global terrestrial ET, we used a global leaf area index (LAI) map and the latest version of global CI product derived from MODIS BRDF data as well as the Boreal Ecosystem Productivity Simulator (BEPS) to estimate global terrestrial ET. The results show that global terrestrial ET in 2015 was 511.9 ± 70.1 mm yr−1 for Case I, where the true LAI and CI are used. Compared to this baseline case, (1) global terrestrial ET is overestimated by 4.7% for Case II where true LAI is used ignoring clumping; (2) global terrestrial ET is underestimated by 13.0% for Case III where effective LAI is used ignoring clumping. Among all plant functional types (PFTs), evergreen needleleaf forests were most affected by foliage clumping for ET estimation in Case II, because they are most clumped with the lowest CI. Deciduous broadleaf forests are affected by leaf clumping most in Case III because they have both high LAI and low CI compared to other PFTs. The leaf clumping effects on ET estimation in both Case II and Case III is robust to the errors in major input parameters. Thus, it is necessary to consider clumping effects in the simulation of global terrestrial ET, which has considerable implications for global water cycle research.

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Hydraulic Efficiency of Green-Blue Flood Control Scenarios for Vegetated Rivers: 1D and 2D Unsteady Simulations

Cited by 43 publications

References 74 publications

Analysis of Groundwater Recharge in Mongolian Drylands Using Composite Vadose Zone Modeling

Analysis of Groundwater Recharge in Mongolian Drylands Using Composite Vadose Zone Modeling

A Framework for Cloud to Coast Adaptation: Maturity and Experiences from across the North Sea

Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration

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