Toward a theoretical framework for integrated modeling of hydrological change

Ceola, Serena; Montanari, Alberto; Koutsoyiannis, Demetris

doi:10.1002/wat2.1038

Cited by 15 publications

(12 citation statements)

References 76 publications

(139 reference statements)

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“…178 Ceola et al developed a theoretical framework to deal with hydrological nonstationarity, which may help to alleviate this problem. 179 In addition, the forecasting of extreme events is of great importance for applications such as flood warnings. However, as there are far fewer extreme events than normal events in the historical archive, it is difficult to train a statistical postprocessing model for extreme events.…”

Section: Discussionmentioning

confidence: 99%

A review on statistical postprocessing methods for hydrometeorological ensemble forecasting

et al. 2017

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Computer simulation models have been widely used to generate hydrometeorological forecasts. As the raw forecasts contain uncertainties arising from various sources, including model inputs and outputs, model initial and boundary conditions, model structure, and model parameters, it is necessary to apply statistical postprocessing methods to quantify and reduce those uncertainties. Different postprocessing methods have been developed for meteorological forecasts (e.g., precipitation) and for hydrological forecasts (e.g., streamflow) due to their different statistical properties. In this paper, we conduct a comprehensive review of the commonly used statistical postprocessing methods for both meteorological and hydrological forecasts. Moreover, methods to generate ensemble members that maintain the observed spatiotemporal and intervariable dependency are reviewed. Finally, some perspectives on the further development of statistical postprocessing methods for hydrometeorological ensemble forecasting are provided.

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Section: Discussionmentioning

confidence: 99%

A review on statistical postprocessing methods for hydrometeorological ensemble forecasting

et al. 2017

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“…Runoff is the result of complex interactions between atmospheric and soil processes, which are normally described and synthesized in the rainfall-runoff transformation (Cannarozzo, Noto, Viola, & La Loggia, 2009). In the last decades, climate change and land-use change due to urban growth have been widely recognized as the two main altering factors of the hydrological regimes (Ceola, Montanari, & Koutsoyannis, 2014;Ward, Renssen, Aerts, van Balen, & Vandenberghe, 2008). Specifically, the increase of the impervious areas due to an urban growth may determine an acceleration of the rainfall-runoff transformation.…”

Section: Introductionmentioning

confidence: 99%

The role of urban growth, climate change, and their interplay in altering runoff extremes

Arnone

Pumo

Francipane

et al. 2018

Hydrological Processes

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Changes in climate and urban growth are the most influential factors affecting hydrological characteristics in urban and extra‐urban contexts. The assessment of the impacts of these changes on the extreme rainfall–runoff events may have important implications on urban and extra‐urban management policies against severe events, such as floods, and on the design of hydraulic infrastructures. Understanding the effects of the interaction between climate change and urban growth on the generation of runoff extremes is the main aim of this paper. We carried out a synthetic experiment on a river catchment of 64 km2 to generate hourly runoff time series under different hypothetical scenarios. We imposed a growth of the percentage of urban coverage within the basin (from 1.5% to 25%), a rise in mean temperature of 2.6 °C, and an alternatively increase/decrease in mean annual precipitation of 25%; changes in mean annual precipitation were imposed following different schemes, either changing rainstorm frequency or rainstorm intensity. The modelling framework consists of a physically based distributed hydrological model, which simulates fast and slow mechanisms of runoff generation directly connected with the impervious areas, a land‐use change model, and a weather generator. The results indicate that the peaks over threshold and the hourly annual peaks, used as hydrological indicators, are very sensitive to the rainstorm intensity. Moreover, the effects of climate changes dominate on those of urban growth determining an exacerbation of the fast runoff component in extreme events and a reduction of the slow and deep runoff component, thus limiting changes in the overall runoff.

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“…In the future, the post-processing could be used to: (1) directly post-process global runoff or streamflow predictions from CGCMs for local hydrologic applications, which needs less computation efforts using CGCM forecasts that are publically available (e.g., CFSv2 real-time forecasts); (2) merge hydrologic predictions from different hydrologic models within the CM-SHF framework; and (3) downscale soil moisture and streamflow from CM-SHF for applications that need hyper-resolution forecasts, such as reservoir modeling. However, the non-stationary hydrology 111 under a changing environment (e.g., climate change, catchment modification) makes the hydrologic post-processing more challenging, and perhaps an integrated modeling framework 112 is necessary.…”

Section: Hydrologic Post-processingmentioning

confidence: 99%

A review on climate‐model‐based seasonal hydrologic forecasting: physical understanding and system development

2015

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Climate-model-based seasonal hydrologic forecasting (CM-SHF) is an emerging area in recent decade because of the development of coupled atmosphere-ocean-land general circulation models (CGCMs) and land surface hydrologic models, and increasing needs for transferring the advances in climate research into hydrologic applications within the framework of climate services. In order to forecast terrestrial hydrology from monthly to seasonal time scales, a CM-SHF system should take advantage of important information from initial land surface conditions (ICs) as well as skillful seasonal predictions of atmospheric boundary conditions that mostly rely on the predictability of large-scale climate precursors such as the El Niño Southern Oscillation (ENSO). The progresses in the understanding of seasonal hydrologic predictability in terms of ICs and climate precursors are reviewed, and future emphases are discussed. Both the achievements and challenges of the CM-SHF system development, including multimodel ensemble prediction, seamless hydrologic forecasting, dynamical downscaling, hydrologic post-processing, and seasonal forecasting of hydrologic extremes with the hyper-resolution modeling framework that is able to address both the climate change and water resources management impacts on terrestrial hydrology, are presented. Regardless of great strides in CM-SHF, a grand challenge is the effective dissemination of the information provided by the seasonal hydrologic forecasting system to the decision-makers, which cannot be resolved without cross-disciplinary dialog and collaboration. © 2015 The Authors. WIREs Water published by Wiley Periodicals, Inc. How to cite this article:WIREs Water 2015Water , 2:523-536. doi: 10.1002Water /wat2.1088 INTRODUCTION G lobal change is influencing the frequency and severity of hydrologic extremes, including floods and droughts, 1 resulting in a number of issues with * Correspondence to: yuanxing@tea.ac.cn respect to food and water security. While decadal plans for infrastructure adaptation and capacity building are important for managing water resources under a changing climate, timely early (seasonal) warning, or so-called seasonal hydrologic forecasting (SHF), is essential for hydrologic hazard mitigation by increasing preparedness. Basically, the aim of SHF is to predict the land surface hydrologic variables (e.g., streamflow, soil moisture) at monthly to seasonal time scales. It is also named as long-term hydrologic forecasting in the hydrologic community because it is targeted for the forecasting of persistent land surface hydrologic This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. anomalies (e.g., drought), 2 which is different from short-term flood forecasting. 3 A successful SHF not only requires accurate initial land surface conditions (ICs) from upstream river flow, 4 snow c...

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Toward a theoretical framework for integrated modeling of hydrological change

Cited by 15 publications

References 76 publications

A review on statistical postprocessing methods for hydrometeorological ensemble forecasting

A review on statistical postprocessing methods for hydrometeorological ensemble forecasting

The role of urban growth, climate change, and their interplay in altering runoff extremes

A review on climate‐model‐based seasonal hydrologic forecasting: physical understanding and system development

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