scholarly journals Evaluation of five hydrological models across Europe and their suitability for making projections under climate change

2015 ◽  
Vol 12 (10) ◽  
pp. 10289-10330 ◽  
Author(s):  
W. Greuell ◽  
J. C. M. Andersson ◽  
C. Donnelly ◽  
L. Feyen ◽  
D. Gerten ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Large Scale ◽  
Interannual Variation ◽  
Hydrological Models ◽  
High Discharge ◽  
Annual Cycles ◽  
Model Ranking ◽  
The Mean

Abstract. The main aims of this paper are the evaluation of five large-scale hydrological models across Europe and the assessment of the suitability of the models for making projections under climate change. For the evaluation, 22 years of discharge measurements from 46 large catchments were exploited. In the reference simulations forcing was taken from the E-OBS dataset for precipitation and temperature, and from the WFDEI dataset for other variables. On average across all catchments, biases were small for four of the models, ranging between −29 and +23 mm yr−1 (−9 and +8 %), while one model produced a large negative bias (−117 mm yr−1; −38 %). Despite large differences in e.g. the evapotranspiration schemes, the skill to simulate interannual variability did not differ much between the models, which can be ascribed to the dominant effect of interannual variation in precipitation on interannual variation in discharge. Assuming that the skill of a model to simulate interannual variability provides a measure for the model's ability to make projections under climate change, the skill of future discharge projections will not differ much between models. The quality of the simulation of the mean annual cycles, and low and high discharge was found to be related to the degree of calibration of the models, with the more calibrated models outperforming the crudely and non-calibrated models. The sensitivity to forcing was investigated by carrying out alternative simulations with all forcing variables from WFDEI, which increased biases by between +66 and +85 mm yr−1 (21–28 %), significantly changed the inter-model ranking of the skill to simulate the mean and increased the magnitude of interannual variability by 28 %, on average.

Are large scale models useful? A case of nested model domains for assessing current and future stream runoff and sediments

2021 ◽  
Author(s):  
Alena Bartosova ◽  
Berit Arheimer ◽  
Alban de Lavenne ◽  
René Capell ◽  
Johan Strömqvist
Keyword(s):  
Climate Change ◽  
Spatial Patterns ◽  
Large Scale ◽  
Aridity Index ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Nested Model ◽  
Nested Models ◽  
Changing Climate ◽  
Scale Models

<p>Continental and global dynamic hydrological models have emerged recently as tools for e.g. flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. One such tool is a dynamic process-based rainfall-runoff and water quality model Hydrological Predictions for Environment (HYPE). We present and compare historical simulations of runoff, soil moisture, aridity, and sediment concentrations for three nested model domains using global, continental (Europe), and national (Sweden) catchment-based HYPE applications. Future impacts on hydrological variables from changing climate were then assessed using the global and continental HYPE applications with ensembles based on 3 CMIP5 global climate models (GCMs).</p><p>Simulated historical sediment concentrations varied considerably among the nested models in spatial patterns while runoff values were more similar. Regardless of the variation, the global model was able to provide information on climate change impacts comparable to those from the continental and national models for hydrological indicators. Output variables that were calibrated, e.g. runoff, were shown to result in more reliable and consistent projected changes among the different model scales than derived variables such as the actual aridity index. The comparison was carried out for ensemble averages as well as individual GCMs to illustrate the variability and the need for robust assessments.</p><p>Global hydrological models are shown to be valuable tools for e.g. first screenings of climate change effects and detection of spatial patterns and can be useful to provide information on current and future hydrological states at various domains. The challenge is (1) in deciding when we should use the large-scale models and (2) in interpreting the results, considering the uncertainty of the model results and quality of data especially at the global scale. Comparison across nested domains demonstrates the significance of scale which needs to be considered when interpreting the impacts alongside with model performance.</p><p>Bartosova et al, 2021: Large-scale hydrological and sediment modeling in nested domains under current and changing climate. Accepted to Special Issue Journal of Hydraulic Engineering.</p>

scholarly journals Assessing watershed hydrological response to climate change based on signature indices

2021 ◽  
Author(s):  
Atiyeh Fatehifar ◽  
Mohammad Reza Goodarzi ◽  
Seyedeh Sima Montazeri Hedesh ◽  
Parnian Siahvashi Dastjerdi
Keyword(s):  
Climate Change ◽  
Discharge Rate ◽  
Baseline Period ◽  
Future Climate Change ◽  
Hydrological Response ◽  
High Discharge ◽  
Discharge Rates ◽  
Segment Volume ◽  
The Mean ◽  
Hydrological Signatures

Abstract Due to the fact that one of the important ways of describing the performance of basins is to use the hydrological signatures, the present study is to investigate the effects of climate change using the hydrological signatures in Azarshahr Chay basin, Iran. To this end, Canadian Earth system model (CanESM2) is first used to predict future climate change (2030–2059) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Six signature indices were extracted from flow duration curve (FDC) as follows: runoff ratio (RR), high-segment volume (FHV), low-segment volume (FLV), mid-segment slope (FMS), mid-range flow (FMM), and maximum peak discharge (DiffMaxPeak). These signature indices act as sorts of fingerprints representing differences in the hydrological behavior of the basin. The results indicate that the most significant changes in the future hydrological response are related to the FHV and FLV and FMS indices. The BiasFHV index indicates an increase in high discharge rates under RCP8.5 scenario, compared to the baseline period and the RCP2.6 scenario, as well. The mean annual discharge rate, however, is lower than the discharge rate under this scenario. Generally, for the RCP8.5 scenario, the changes in the signature indices in both high discharges and low discharges are significant.

Low-Frequency Climate Response of Quasigeostrophic Wind-Driven Ocean Circulation

2012 ◽  
Vol 42 (2) ◽  
pp. 243-260 ◽  
Author(s):  
Rafail V. Abramov ◽  
Andrew J. Majda
Keyword(s):  
Climate Change ◽  
Ocean Circulation ◽  
Linear Response ◽  
Large Scale ◽  
Response Times ◽  
Low Frequency ◽  
External Perturbation ◽  
Climate Dynamics ◽  
The Mean ◽  
General Response

Abstract Linear response to external perturbation through the fluctuation–dissipation theorem has recently become a popular topic in the climate research community. It relates an external perturbation of climate dynamics to climate change in a simple linear fashion, which provides key insight into physics of the climate change phenomenon. Recently, the authors developed a suite of linear response algorithms for low-frequency response of large-scale climate dynamics to external perturbation, including the novel blended response algorithm, which combines the geometrically exact general response formula using integration of a linear tangent model at short response times and the classical quasi-Gaussian response algorithm at longer response times, overcoming numerical instability of the tangent linear model for longer times due to positive Lyapunov exponents. Here, the authors apply the linear response framework to several leading empirical orthogonal functions (EOFs) of a quasigeostrophic model of wind-driven ocean circulation. It is demonstrated that the actual nonlinear response of this system under external perturbation at leading EOFs can be predicted by the linear response algorithms with adequate skill with moderate errors; in particular, the blended response algorithm has a pattern correlation with the ideal response operator on the four leading EOFs of the mean state response of 94% after 5 yr. In addition, interesting properties of the mean flow response to large-scale changes in wind stress at the leading EOFs are observed.

scholarly journals On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments

2020 ◽  
Vol 24 (1) ◽  
pp. 397-416 ◽  
Author(s):  
Thanh Duc Dang ◽  
A. F. M. Kamal Chowdhury ◽  
Stefano Galelli
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Change Impact ◽  
Large Scale ◽  
Water Reservoir ◽  
Hydrological Models ◽  
Water Reservoirs ◽  
Reservoir Storage ◽  
Global Circulation Models ◽  
Change Impact

Abstract. During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human–water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated with potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a multi-objective evolutionary algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing model that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005), a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated with the use of such a model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five global circulation models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human–water interactions in large-scale hydrological models.

scholarly journals High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin

2020 ◽  
Vol 12 (1) ◽  
pp. 629-645 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Mohamed Ezzat Elshamy ◽  
Daniel Princz ◽  
Howard Simon Wheater ◽  
John Willard Pomeroy ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Impact Assessment ◽  
River Basin ◽  
Climate Change Impact ◽  
Large Scale ◽  
Historical Record ◽  
Hydrological Models ◽  
Climate Change Impact Assessment ◽  
Change Impact

Abstract. Cold region hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw, and changing patterns of precipitation, with an increased proportion of winter precipitation falling as rainfall and shorter durations of snow cover, as well as consequent changes in flow regimes. Future warming is expected to continue along these lines. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic, particularly in data-sparse regions. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment. The best available gridded data in Canada are from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long-record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impact assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h ×0.125∘ resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess their added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950 and 2100 under RCP8.5, and an analysis of the datasets shows that the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final historical product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at https://doi.org/10.20383/101.0111 (Asong et al., 2018), while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).

COVID-19 complicates global humanitarian aid response

2020 ◽  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Humanitarian Aid ◽  
Content Type ◽  
High Profile ◽  
Low Profile ◽  
Humanitarian Crises ◽  
Competing Priorities ◽  
Slow Onset

Subject COVID-19's impact on the global humanitarian system. Significance The COVID-19 pandemic is generating immense new humanitarian needs at the same time as negatively affecting the quantity and quality of humanitarian aid. Meanwhile, countries with existing humanitarian crises are highly vulnerable to the effects of the pandemic and ill-equipped to prevent its spread. Impacts Funding for responses to ongoing emergencies, particularly slow-onset and low-profile ones, may suffer amid competing priorities. Even large-scale and high-profile emergencies, such as the conflicts in Yemen or Syria, may struggle for funding. Unmet humanitarian needs will increase vulnerability to the effects of climate change and fuel ongoing conflicts.

scholarly journals Effects of front-of-pack labels on the nutritional quality of supermarket food purchases: evidence from a large-scale randomized controlled trial

2020 ◽  
Vol 49 (1) ◽  
pp. 119-138 ◽  
Author(s):  
Pierre Dubois ◽  
Paulo Albuquerque ◽  
Olivier Allais ◽  
Céline Bonnet ◽  
Patrice Bertail ◽  
...  
Keyword(s):  
Nutritional Quality ◽  
Large Scale ◽  
Controlled Trial ◽  
Real Life ◽  
Grocery Shopping ◽  
Nutrition Labels ◽  
Nutrition Quality ◽  
Food Purchases ◽  
The Mean

AbstractTo examine whether four pre-selected front-of-pack nutrition labels improve food purchases in real-life grocery shopping settings, we put 1.9 million labels on 1266 food products in four categories in 60 supermarkets and analyzed the nutritional quality of 1,668,301 purchases using the FSA nutrient profiling score. Effect sizes were 17 times smaller on average than those found in comparable laboratory studies. The most effective nutrition label, Nutri-Score, increased the purchases of foods in the top third of their category nutrition-wise by 14%, but had no impact on the purchases of foods with medium, low, or unlabeled nutrition quality. Therefore, Nutri-Score only improved the nutritional quality of the basket of labeled foods purchased by 2.5% (−0.142 FSA points). Nutri-Score’s performance improved with the variance (but not the mean) of the nutritional quality of the category. In-store surveys suggest that Nutri-Score’s ability to attract attention and help shoppers rank products by nutritional quality may explain its performance.

scholarly journals Annual and Interannual Variability in the California Current System: Comparison of an Ocean State Estimate with a Network of Underwater Gliders

2018 ◽  
Vol 48 (12) ◽  
pp. 2965-2988 ◽  
Author(s):  
Katherine D. Zaba ◽  
Daniel L. Rudnick ◽  
Bruce D. Cornuelle ◽  
Ganesh Gopalakrishnan ◽  
Matthew R. Mazloff
Keyword(s):  
Interannual Variability ◽  
Large Scale ◽  
Current System ◽  
Potential Temperature ◽  
California Current ◽  
Late Summer ◽  
Near Surface ◽  
State Estimate ◽  
California Current System ◽  
Annual Cycles

AbstractA data-constrained state estimate of the southern California Current System (CCS) is presented and compared with withheld California Cooperative Oceanic Fisheries Investigations (CalCOFI) data and assimilated glider data over 2007–17. The objective of this comparison is to assess the ability of the California State Estimate (CASE) to reproduce the key physical features of the CCS mean state, annual cycles, and interannual variability along the three sections of the California Underwater Glider Network (CUGN). The assessment focuses on several oceanic metrics deemed most important for characterizing physical variability in the CCS: 50-m potential temperature, 80-m salinity, and 26 kg m−3 isopycnal depth and salinity. In the time mean, the CASE reproduces large-scale thermohaline and circulation structures, including observed temperature gradients, shoaling isopycnals, and the locations and magnitudes of the equatorward California Current and poleward California Undercurrent. With respect to the annual cycle, the CASE captures the phase and, to a lesser extent, the magnitude of upper-ocean warming and stratification from late summer to early fall and of isopycnal heave during springtime upwelling. The CASE also realistically captures near-surface diapycnal mixing during upwelling season and the semiannual cycle of the California Undercurrent. In terms of interannual variability, the most pronounced signals are the persistent warming and downwelling anomalies of 2014–16 and a positive isopycnal salinity anomaly that peaked with the 2015–16 El Niño.

scholarly journals Did Noise Pollution Really Improve during COVID-19? Evidence from Taiwan

2021 ◽  
Vol 13 (11) ◽  
pp. 5946
Author(s):  
Rezzy Eko Caraka ◽  
Yusra Yusra ◽  
Toni Toharudin ◽  
Rung-Ching Chen ◽  
Mohammad Basyuni ◽  
...  
Keyword(s):  
Large Scale ◽  
Noise Pollution ◽  
Time Frame ◽  
Posterior Distributions ◽  
Research Papers ◽  
Climate Conditions ◽  
Random Samples ◽  
The Mean ◽  
Two Sides

Background and objectives: The impacts of COVID-19 are like two sides of one coin. During 2020, there were many research papers that proved our environmental and climate conditions were improving due to lockdown or large-scale restriction regulations. In contrast, the economic conditions deteriorated due to disruption in industry business activities and most people stayed at home and worked from home, which probably reduced the noise pollution. Methods: To assess whether there were differences in noise pollution before and during COVID-19. In this paper, we use various statistical methods following odds ratios, Wilcoxon and Fisher’s tests and Bayesian Markov chain Monte Carlo (MCMC) with various comparisons of prior selection. The outcome of interest for a parameter in Bayesian inference is complete posterior distribution. Roughly, the mean of the posterior will be clear with point approximation. That being said, the median is an available choice. Findings: To make the Bayesian MCMC work, we ran the sampling from the conditional posterior distributions. It is straightforward to draw random samples from these distributions if they have regular shapes using MCMC. The case of over-standard noise per time frame, number of noise petition cases, number of industry petition cases, number of motorcycles, number of cars and density of vehicles are significant at α=5%. In line with this, we prove that there were differences of noise pollution before and during COVID-19 in Taiwan. Meanwhile, the decreased noise pollution in Taiwan can improve quality of life.

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