scholarly journals Estimating extreme river discharges in Europe through a Bayesian network

2017 ◽  
Vol 21 (6) ◽  
pp. 2615-2636 ◽  
Author(s):  
Dominik Paprotny ◽  
Oswaldo Morales-Nápoles
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Graphical Model ◽  
Model Performance ◽  
Physical Models ◽  
Flood Hazards ◽  
Discharge Data ◽  
Local Climate ◽  
Spatial Coverage ◽  
Catchment Areas

Abstract. Large-scale hydrological modelling of flood hazards requires adequate extreme discharge data. In practise, models based on physics are applied alongside those utilizing only statistical analysis. The former require enormous computational power, while the latter are mostly limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian networks (BNs), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables representing the geographical characteristics of their catchments. Annual maxima of daily discharges from more than 1800 river gauges (stations with catchment areas ranging from 1.4 to 807 000 km2) were collected, together with information on terrain, land use and local climate. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe and better than a comparable global statistical model. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and remains the same in a split-sample validation. Though discharge prediction under climate change is not the main scope of this paper, the BN was applied to a large domain covering all sizes of rivers in the continent both for present and future climate, as an example. Results show substantial variation in the influence of climate change on river discharges. The model can be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

scholarly journals Estimating extreme river discharges in Europe through a Bayesian Network

2016 ◽  
Author(s):  
Dominik Paprotny ◽  
Oswaldo Morales Nápoles
Keyword(s):  
Large Scale ◽  
Graphical Model ◽  
Flood Hazard ◽  
Physical Models ◽  
Method Performance ◽  
Discharge Data ◽  
Dependency Structure ◽  
Spatial Coverage ◽  
Geographical Characteristics ◽  
Better Than

Abstract. Large-scale hydrological modelling of flood hazard requires adequate extreme discharge data. Models based on physics are applied alongside those utilizing only statistical analysis. The former requires enormous computation power, while the latter are most limited in accuracy and spatial coverage. In this paper we introduce an alternate, statistical approach based on Bayesian Networks (BN), a graphical model for dependent random variables. We use a non-parametric BN to describe the joint distribution of extreme discharges in European rivers and variables describing the geographical characteristics of their catchments. Data on annual maxima of daily discharges from more than 1800 river gauge stations were collected, together with information on terrain, land use and climate of catchments that drain to those locations. The (conditional) correlations between the variables are modelled through copulas, with the dependency structure defined in the network. The results show that using this method, mean annual maxima and return periods of discharges could be estimated with an accuracy similar to existing studies using physical models for Europe, and better than a comparable global statistical method. Performance of the model varies slightly between regions of Europe, but is consistent between different time periods, and is not affected by a split-sample validation. The BN was applied to a large domain covering all sizes of rivers in the continent, both for present and future climate, showing large variation in influence of climate change on river discharges, as well as large differences between emission scenarios. The method could be used to provide quick estimates of extreme discharges at any location for the purpose of obtaining input information for hydraulic modelling.

scholarly journals Variability of the water availability in a river lake system – A case study of Lake Symsar

2016 ◽  
Vol 31 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Angela B. Kuriata-Potasznik ◽  
Sławomir Szymczyk
Keyword(s):  
Climate Change ◽  
Water Availability ◽  
Water Retention ◽  
Water Exchange ◽  
Water Losses ◽  
Local Conditions ◽  
Local Climate ◽  
Lake System ◽  
Catchment Areas ◽  
The Impact

AbstractIt is predicted that climate change will result in the diminution of water resources available both on global and regional scales. Local climate change is harder to observe and therefore, while counteracting its effects, it seems advisable to undertake studies on pertinent regional and local conditions. In this research, our aim was to assess the impact of a river and its catchment on fluctuations in the water availability in a natural lake which belongs to a post-glacial river and lake system. River and lake systems behave most often like a single interacting hydrological unit, and the intensity of water exchange in these systems is quite high, which may cause temporary water losses. This study showed that water in the analyzed river and lake system was exchanged approx. every 66 days, which resulted from the total (horizontal and vertical) water exchange. Also, the management of a catchment area seems to play a crucial role in the local water availability, as demonstrated by this research, where water retention was favoured by wooded and marshy areas. More intensive water retention was observed in a catchment dominated by forests, pastures and wetlands. Wasteland and large differences in the land elevation in the tested catchment are unfavourable to water retention because they intensify soil evaporation and accelerate the water run-off outside of the catchment. Among the actions which should be undertaken in order to counteract water deficiencies in catchment areas, rational use and management of the land resources in the catchment are most often mentioned.

Hydrological modelling and anthropogenic water use

2021 ◽  
Author(s):  
Ponnambalam Rameshwaran ◽  
Ali Rudd ◽  
Vicky Bell ◽  
Matt Brown ◽  
Helen Davies ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Hydrological Model ◽  
Model Performance ◽  
River Flows ◽  
Discharge Data ◽  
New Approach ◽  
Water Demands ◽  
The Impact ◽  
Grid Based

<p>Despite Britain’s often-rainy maritime climate, anthropogenic water demands have a significant impact on river flows, particularly during dry summers. In future years, projected population growth and climate change are likely to increase the demand for water and lead to greater pressures on available freshwater resources.</p><p>Across England, abstraction (from groundwater, surface water or tidal sources) and discharge data along with ‘Hands off Flow’ conditions are available for thousands of individual locations; each with a licence for use, an amount, an indication of when abstraction can take place, and the actual amount of water abstracted (generally less than the licence amount). Here we demonstrate how these data can be used in combination to incorporate anthropogenic artificial influences into a grid-based hydrological model. Model simulations of both high and low river flows are generally improved when abstractions and discharges are included, though for some catchments model performance decreases. The new approach provides a methodological baseline for further work investigating the impact of anthropogenic water use and projected climate change on future river flows.</p>

Downscaling Climate Information

2016 ◽  
Author(s):  
Rasmus Benestad
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Local Climate ◽  
Skill Scores ◽  
The Right

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

scholarly journals BAYWRF: a high-resolution present-day climatological atmospheric dataset for Bavaria

2020 ◽  
Vol 12 (4) ◽  
pp. 3097-3112
Author(s):  
Emily Collier ◽  
Thomas Mölg
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Large Scale ◽  
Wrf Model ◽  
Atmospheric Model ◽  
Climate Impact ◽  
Reanalysis Data ◽  
Near Surface ◽  
Local Climate ◽  
The Impact

Abstract. Climate impact assessments require information about climate change at regional and ideally also local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for this region over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, the Weather Research and Forecasting (WRF) model, was configured with two nested domains of 7.5 and 1.5 km grid spacing centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Using an extensive network of observational data, we evaluate (i) the impact of using grid analysis nudging for a single-year simulation of the period of September 2017 to August 2018 and (ii) the full BAYWRF dataset generated using nudging. The evaluation shows that the model represents variability in near-surface meteorological conditions generally well, although there are both seasonal and spatial biases in the dataset that interested users should take into account. BAYWRF provides a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest-resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://doi.org/10.17605/OSF.IO/AQ58B).

Convection-permitting present-day climatological simulation with WRF over Bavaria

2020 ◽  
Author(s):  
Emily Collier ◽  
Thomas Mölg
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Atmospheric Model ◽  
Open Science ◽  
Climate Impact ◽  
Reanalysis Data ◽  
Grid Spacing ◽  
Local Climate ◽  
Full Dataset ◽  
The Impact

<p>Climate impact assessments require information about climate change at regional and ideally local scales. Traditionally, this information has been obtained using statistical methods, precluding the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, within the BayTreeNet project, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for the region of Bavaria over the thirty-year period of September 1987 to August 2018. The open-source community-developed atmospheric model employed in this study, WRF, was configured with two nested domains of 7.5- and 1.5-km grid spacing centered over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Based on a shorter evaluation period of September 2017 to August 2018, we evaluate two aspects of the simulations: (i) we investigate the influence of using grid-analysis nudging; and (ii) we assess model biases compared with an extensive observational data at both two-hourly and daily mean temporal resolutions. Then, we present a brief overview of the full dataset, which will provide a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Minimally subsetted data from the finest resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Foundation.</p>

Underestimation of temperature variability in weather generators and implications for the representation of extreme temperatures in downscaled climate change scenarios

2020 ◽  
Author(s):  
Pierluigi Calanca
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Extreme Values ◽  
Climatic Variability ◽  
Model Performance ◽  
Maximum Temperature ◽  
Climate Change Scenarios ◽  
Modes Of Variability ◽  
Weather Generators ◽  
Total Variability

<p>Stochastic weather generators are still widely used for downscaling climate change scenarios, in particular in the context of agricultural and hydrological impact assessments. Their performance is in many respects satisfactory, except perhaps for the fact that they fail to represent climatic variability in an adequate way. This has implications for the representation of extreme values and their statistics. Concerning precipitation, different approaches for amending this situation have proposed in the past, including using more sophisticated models to better simulate the persistence of wet and dry spells, conditioning rainfall-generating parameters on indices of the large-scale atmospheric circulation, or employing autoregressive models to represent year-to-year variations in annual precipitation amounts. With regard to (minimum and maximum) temperature, efforts to address the question of why weather generators underestimate total variability have been less systematic. Based on results obtained with a well-known weather generator (LARS-WG), this contribution aims to discuss which modes of variability are missing and why, elaborate on the implications of underrepresenting temperature variance for the simulation of temperature extremes in downscaled climate change scenarios, and suggest options to tackle the problem and improve the model performance.</p>

Investigating the reasons for poor model performance in a changing climate

2020 ◽  
Author(s):  
Doris Duethmann ◽  
Günter Blöschl ◽  
Juraj Parajka
Keyword(s):  
Climate Change ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Model Performance ◽  
Model Structure ◽  
Precipitation Data ◽  
Hydrological Models ◽  
Discharge Data ◽  
Climate Conditions ◽  
Changing Climate

<p>Hydrological models are often applied to estimate climate change impacts on hydrology. However, several studies demonstrated that hydrological models do not perform well when applied under changing climate conditions. In order to decide on the way forward for improving hydrological modelling in climate change contexts, it is important to understand the reasons for poor performance in a changing climate, but there are only a few studies on this topic.</p><p>Here we revisit a study in Austria that demonstrated the inability of a conceptual model to simulate the discharge response to increases in precipitation and air temperature. We set up hypotheses for the differences between the observed and simulated changes in discharge and test these using simulations with various modifications of the model (including modifications of the input data, model calibration, and model structure).</p><p>The baseline model overestimates discharge trends over 1978−2013, on average over all 156 catchments, by 93 ± 50 mm yr<sup>−1</sup> per 35 years. Accounting for vegetation dynamics in the calculation of reference evaporation based on a satellite-derived vegetation index, reduces the difference between simulated and observed discharge by 35 ± 9 mm yr<sup>−1</sup> per 35 years. Inhomogeneities in the precipitation data, caused by a variable number of stations and, to a lesser degree, climate variability effects on the undercatch error, can explain 44 ± 28 mm yr<sup>−1</sup> per 35 years of this difference. Extending the calibration period from 5 to 25 years, varying the objective function by including annually aggregated discharge data, or estimating evaporation with the Penman-Monteith instead of the Blaney-Criddle approach has little influence on the simulated discharge trends. The model structure problem with respect to vegetation dynamics has important implications for studies in a climate change context. Our results furthermore highlight the importance of using precipitation data based on a stationary input station network for studying observed hydrologic changes.</p>

Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling

2018 ◽  
Author(s):  
Aristita Busuioc ◽  
Alexandru Dumitrescu
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Local Climate

This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art global climate models, in terms of spatial resolution and their performance in simulation of climate characteristics, they are still skillful only in reproducing large-scale feature of climate variability, such as global mean temperature or various circulation patterns (e.g., the North Atlantic Oscillation). However, these models are not able to provide reliable information on local climate characteristics (mean temperature, total precipitation), especially on extreme weather and climate events. The main reason for this failure is the influence of local geographical features on the local climate, as well as other factors related to surrounding large-scale conditions, the influence of which cannot be correctly taken into consideration by the current dynamical global models.Impact models, such as hydrological and crop models, need high resolution information on various climate parameters on the scale of a river basin or a farm, scales that are not available from the usual global climate models. Downscaling techniques produce regional climate information on finer scale, from global climate change scenarios, based on the assumption that there is a systematic link between the large-scale and local climate. Two types of downscaling approaches are known: a) dynamical downscaling is based on regional climate models nested in a global climate model; and b) statistical downscaling is based on developing statistical relationships between large-scale atmospheric variables (predictors), available from global climate models, and observed local-scale variables of interest (predictands).Various types of empirical-statistical downscaling approaches can be placed approximately in linear and nonlinear groupings. The empirical-statistical downscaling techniques focus more on details related to the nonlinear models—their validation, strengths, and weaknesses—in comparison to linear models or the mixed models combining the linear and nonlinear approaches. Stochastic models can be applied to daily and sub-daily precipitation in Romania, with a comparison to dynamical downscaling. Conditional stochastic models are generally specific for daily or sub-daily precipitation as predictand.A complex validation of the nonlinear statistical downscaling models, selection of the large-scale predictors, model ability to reproduce historical trends, extreme events, and the uncertainty related to future downscaled changes are important issues. A better estimation of the uncertainty related to downscaled climate change projections can be achieved by using ensembles of more global climate models as drivers, including their ability to simulate the input in downscaling models. Comparison between future statistical downscaled climate signals and those derived from dynamical downscaling driven by the same global model, including a complex validation of the regional climate models, gives a measure of the reliability of downscaled regional climate changes.

scholarly journals The future of the Amazon: new perspectives from climate, ecosystem and social sciences

2008 ◽  
Vol 363 (1498) ◽  
pp. 1729-1735 ◽  
Author(s):  
Richard A Betts ◽  
Yadvinder Malhi ◽  
J. Timmons Roberts
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Global Climate ◽  
Amazon Region ◽  
Economic Science ◽  
Adaptation To Climate Change ◽  
Local Climate ◽  
Climate Stability ◽  
Multidisciplinary Analysis ◽  
Iconic Images

The potential loss or large-scale degradation of the tropical rainforests has become one of the iconic images of the impacts of twenty-first century environmental change and may be one of our century's most profound legacies. In the Amazon region, the direct threat of deforestation and degradation is now strongly intertwined with an indirect challenge we are just beginning to understand: the possibility of substantial regional drought driven by global climate change. The Amazon region hosts more than half of the world's remaining tropical forests, and some parts have among the greatest concentrations of biodiversity found anywhere on Earth. Overall, the region is estimated to host about a quarter of all global biodiversity. It acts as one of the major ‘flywheels’ of global climate, transpiring water and generating clouds, affecting atmospheric circulation across continents and hemispheres, and storing substantial reserves of biomass and soil carbon. Hence, the ongoing degradation of Amazonia is a threat to local climate stability and a contributor to the global atmospheric climate change crisis. Conversely, the stabilization of Amazonian deforestation and degradation would be an opportunity for local adaptation to climate change, as well as a potential global contributor towards mitigation of climate change. However, addressing deforestation in the Amazon raises substantial challenges in policy, governance, sustainability and economic science. This paper introduces a theme issue dedicated to a multidisciplinary analysis of these challenges.

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