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A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models

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TLDR
In this article, the authors proposed an iterative hydrography upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models.
Abstract
. Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream–downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives representative sub-grid river length and slope parameters, which are required for resolution-independent model results. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec ( ∼  1 km), 5 arcmin ( ∼  10 km) and 15 arcmin ( ∼  30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often-applied upscaling methods. Furthermore, we show that MERIT Hydro IHU minimizes the errors made in the timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is open source and fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.

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Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters

TL;DR: In this article , a top-down approach is proposed to estimate how vegetation adapts its root zone storage capacity at the catchment scale in response to changes in the magnitude and seasonality of hydro-climatic variables.
Journal ArticleDOI

Estimating water balance components and their uncertainty bounds in highly groundwater-dependent and data-scarce area: An example for the Upper Citarum basin

TL;DR: In this article, a distributed hydrological model, wflow_sbm, was used to estimate the discharge and actual evaporation in the Upper Citarum basin in West Java, Indonesia.

Global hydrological droughts in the 21st century under a changing hydrological regime

TL;DR: In this article, the authors quantify the impact of climate change on future low flows and associated hydrological drought characteristics on a global scale using an alternative drought identification approach that considers adaptation to future changes in hydrologogical regime.

Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections

TL;DR: In this article, the authors evaluate the ensemble consistency and apply two weighting approaches; the Climate model Weighting by Independence and Performance (ClimWIP) that focuses on meteorological variables and the Reliability Ensemble Averaging (REA) in their study applied to discharge statistics per basin.

Anatomy of simultaneous flood peaks at a lowland confluence

TL;DR: In this paper, a new way of analyzing lowland discharge and water level dynamics was introduced by tracing individual flood waves based on dynamic time warping, and the analysis showed that the exact timing of the arrival of discharge peaks is of little relevance because of the long duration of the average discharge wave compared to typical time lags between peaks.
References
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Journal ArticleDOI

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

TL;DR: SciPy as discussed by the authors is an open source scientific computing library for the Python programming language, which includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics.
Journal ArticleDOI

SciPy 1.0: fundamental algorithms for scientific computing in Python.

TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Book

The hydraulic geometry of stream channels and some physiographic implications

TL;DR: In this paper, the hydraulic characteristics of stream channels are measured quantitatively and vary with discharge as simple power functions at a given river cross section, and similar variations in relation to discharge exist among the cross sections along the length of a river under the condition that discharge at all points is equal in frequency of occurrence.
BookDOI

Rainfall-runoff modelling : the primer

Keith Beven
TL;DR: Rainfall Runoff Modelling: The Primer Second Edition as discussed by the authors provides a comprehensive overview of available techniques based on established practices and recent research and offers a thorough and accessible overview of the area.
Journal ArticleDOI

Global flood risk under climate change

TL;DR: This article used several climate models to estimate the global risk of flooding at the end of the century and showed that vulnerability is dependent on the degree of warming and the interannual variability in precipitation.
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