scholarly journals Projection of Droughts as Multivariate Phenomenon in the Rhine River

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2288
Author(s):  
Alejandro Chamorro ◽  
Tobias Houska ◽  
Shailesh Singh ◽  
Lutz Breuer
Keyword(s):  
General Circulation ◽  
Critical Role ◽  
General Circulation Models ◽  
Historical Period ◽  
Change Scenario ◽  
Climate Projections ◽  
Rhine River ◽  
Return Periods ◽  
Drought Duration ◽  
Future Projections

Drought is a complex phenomenon whose characterization is best achieved from a multivariate perspective. It is well known that it can generate adverse consequences in society. In this regard, drought duration, severity, and their interrelationship play a critical role. In a climate change scenario, drought characterization and the assessment of the changes in its pattern are essential for a proper quantification of water availability and managing strategies. The purpose of this study is to characterize hydrological droughts in the Rhine River in a multivariate perspective for the historical period and estimate the expected multivariate drought patterns for the next decades. Further, a comparison of bivariate drought patterns between historical and future projections is performed for different return periods. This will, first, indicate if changes can be expected and, second, what the magnitudes of these possible changes could be. Finally, the underlying uncertainty due to climate projections is estimated. Four Representative Concentration Pathways (RCP) are used along with five General Circulation Models (GCM). The HBV hydrological model is used to simulate discharge in both periods. Characterization of droughts is accomplished by the Standardized Runoff Index and the interdependence between drought severity and duration is modelled by a two-dimensional copula. Projections from different climate models show important differences in the estimation of the number of drought events for different return periods. This study reveals that duration and severity present a clear interrelationship, suggesting strongly the appropriateness of a bivariate model. Further, projections show that the bivariate interdependencies between drought duration and severity show clearly differences depending on GCMs and RCPs. Apart from the influence of GCMs and RCMs, it is found that return periods also play an important role in these relationships and uncertainties. Finally, important changes in the bivariate drought patterns between the historical period and future projections are estimated constituting important information for water management purposes.

scholarly journals Uncertainties in runoff projection and hydrological drought assessment over Gharesu basin under CMIP5 RCP scenarios

2020 ◽  
Vol 11 (S1) ◽  
pp. 145-163 ◽  
Author(s):  
S. M. Ashrafi ◽  
H. Gholami ◽  
M. R. Najafi
Keyword(s):  
River Basin ◽  
General Circulation ◽  
General Circulation Models ◽  
Hydrological Drought ◽  
Drought Severity ◽  
Historical Period ◽  
Drought Duration ◽  
Rcp Scenarios ◽  
Drought Assessment ◽  
Drought Characteristics

Abstract Hydrological drought plays an important role in planning and managing water resources systems to meet increasing water demands due to population growth. In this study, the effects of climate change on the hydrological drought characteristics of the Gharasu basin, as one of the major sub-basins of the Karkheh river basin, are investigated. This river basin has experienced severe droughts, and floods, in recent years. The uncertainties in projected drought conditions are characterized based on a suite of 34 general circulation models (GCMs). Based on hydrological simulations over the historical period, 12 GCMs are selected to estimate projected runoff values and the corresponding streamflow drought index (SDI) in the future period. The ‘run theory’ is applied to evaluate the drought characteristics under Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. Results show that uncertainties of drought projection under RCP8.5 are higher than under RCP4.5, where among different drought characteristics, the maximum uncertainty is detected for drought severity and maximum drought duration. Moreover, the uncertainty of drought projection in wet periods is greater than that in dry periods.

Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

2021 ◽  
Vol 64 (1) ◽  
pp. 203-220
Author(s):  
Aleksey Y. Sheshukov ◽  
Jungang Gao ◽  
Kyle R. Douglas-Mankin ◽  
Haw Yen
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Historical Data ◽  
Representative Concentration Pathway ◽  
Data Sources ◽  
Historical Period ◽  
Climate Projections ◽  
Weather Station ◽  
Future Projections ◽  
Data Source

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

scholarly journals Propagation of biases in humidity in the estimation of global irrigational water

2015 ◽  
Vol 6 (1) ◽  
pp. 81-132 ◽  
Author(s):  
Y. Masaki ◽  
N. Hanasaki ◽  
K. Takahashi ◽  
Y. Hijioka
Keyword(s):  
Relative Humidity ◽  
Bias Correction ◽  
General Circulation ◽  
Meteorological Data ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Climate Projections ◽  
Future Projections ◽  
Water Abstraction ◽  
The Impact

Abstract. Future projections on irrigational water under a changing climate are highly dependent on meteorological data derived from general circulation models (GCMs). Since climate projections include biases, bias correction is widely used to adjust meteorological elements, such as the atmospheric temperature and precipitation, but less attention has been paid to biases in humidity. Hence, in many cases, raw GCM outputs have been directly used to analyze the impact of future climate change. In this study, we examined how the biases remaining in the humidity data of five GCMs propagate into the estimation of irrigational water demand and abstraction from rivers using the global hydrological model (GHM) H08. First, to determine the effects of humidity bias across GCMs, we used meteorological data sets to which a state-of-the-art bias correction method was applied except to the humidity. Uncorrected GCM outputs were used for the humidity. We found that differences in the monthly relative humidity of 11.7 to 20.4% RH (percent used as the unit of relative humidity) from observations across the GCMs caused the estimated irrigational water abstraction from rivers to range between 1217.7 and 1341.3 km3 yr−1 for 1971–2000. Differences in humidity also propagate into future projections. Second, sensitivity analysis with hypothetical humidity biases of ±5% RH added homogeneously worldwide revealed the large negative sensitivity of irrigational water abstraction in India and East China, which have high areal fractions of irrigated cropland. Third, we performed another set of simulations with bias-corrected humidity data to examine whether bias correction of the humidity can reduce uncertainties in irrigational water across the GCMs. The results showed that bias correction, even with a primitive methodology that only adjusts the monthly climatological relative humidity, helped reduce uncertainties across the GCMs. Although the GHMs have different sensitivities to atmospheric humidity because of the implementation of different types of potential evapotranspiration formulae, bias correction of the humidity should be included in hydrological analysis, particularly for the evaluation of evapotranspiration and irrigational water.

scholarly journals Projected changes in Rhine River flood seasonality under global warming

2020 ◽  
Author(s):  
Erwin Rottler ◽  
Axel Bronstert ◽  
Gerd Bürger ◽  
Oldrich Rakovec
Keyword(s):  
Global Warming ◽  
General Circulation ◽  
General Circulation Models ◽  
Climate Projections ◽  
Rhine River ◽  
Climate Conditions ◽  
Projected Changes ◽  
Liquid Precipitation ◽  
Flood Characteristics ◽  
Precipitation Events

Abstract. Climatic change alters the frequency and intensity of natural hazards. In order to assess potential future changes in flood seasonality in the Rhine River Basin, we analyse changes in streamflow, snowmelt, precipitation, and evapotranspiration at 1.5, 2.0 and 3.0 °C global warming levels. The mesoscale Hydrological Model (mHM) forced with an ensemble of climate projection scenarios (five general circulation models under three representative concentration pathways) is used to simulate the present and future climate conditions of both, pluvial and nival hydrological regimes. Our results indicate that the interplay between changes in snowmelt- and rainfall-driven runoff is crucial to understand changes in streamflow maxima in the Rhine River. Climate projections suggest that future changes in flood characteristics in the entire Rhine River are controlled by both, more intense precipitation events and diminishing snow packs. The nature of this interplay defines the type of change in runoff peaks. On the sub-basin level (the Moselle River), more intense rainfall during winter is mostly counterbalanced by reduced snowmelt contribution to the streamflow. In the High Rhine (gauge at Basel), the strongest increases in streamflow maxima show up during winter, when strong increases in liquid precipitation intensity encounter almost unchanged snowmelt-driven runoff. The analysis of snowmelt events suggests that at no point in time during the snowmelt season, a warming climate results in an increase in the risk of snowmelt-driven flooding. We do not find indications of a transient merging of pluvial and nival floods due to climate warming.

scholarly journals The Canadian Earth System Model version 5 (CanESM5.0.3)

2019 ◽  
Vol 12 (11) ◽  
pp. 4823-4873 ◽  
Author(s):  
Neil C. Swart ◽  
Jason N. S. Cole ◽  
Viatcheslav V. Kharin ◽  
Mike Lazare ◽  
John F. Scinocca ◽  
...  
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Earth System Model ◽  
System Model ◽  
Historical Period ◽  
Earth System ◽  
Historical Climate ◽  
Model Version

Abstract. The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8∘) and ocean (nominally 1∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.

Hadley Cell Widening: Model Simulations versus Observations

2009 ◽  
Vol 22 (10) ◽  
pp. 2713-2725 ◽  
Author(s):  
Celeste M. Johanson ◽  
Qiang Fu
Keyword(s):  
Twentieth Century ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Hadley Cell ◽  
Climate Projections ◽  
The West ◽  
Model Simulations ◽  
Stratospheric Cooling ◽  
Future Climate Projections

Abstract Observations show that the Hadley cell has widened by about 2°–5° since 1979. This widening and the concomitant poleward displacement of the subtropical dry zones may be accompanied by large-scale drying near 30°N and 30°S. Such drying poses a risk to inhabitants of these regions who are accustomed to established rainfall patterns. Simple and comprehensive general circulation models (GCMs) indicate that the Hadley cell may widen in response to global warming, warming of the west Pacific, or polar stratospheric cooling. The combination of these factors may be responsible for the recent observations. But there is no study so far that has compared the observed widening to GCM simulations of twentieth-century climate integrated with historical changes in forcings. Here the Hadley cell widening is assessed in current GCMs from historical simulations of the twentieth century as well as future climate projections and preindustrial control runs. The authors find that observed widening cannot be explained by natural variability. This observed widening is also significantly larger than in simulations of the twentieth and twenty-first centuries. These results illustrate the need for further investigation into the discrepancy between the observed and simulated widening of the Hadley cell.

scholarly journals Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Vimal Mishra ◽  
Udit Bhatia ◽  
Amar Deep Tiwari
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
South Asia ◽  
General Circulation ◽  
Coupled Model ◽  
General Circulation Models ◽  
River Basins ◽  
Climate Projections ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951–2014) and projected (2015–2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3–5°C) and wetter (13–30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate change impact assessment in South Asia and hydrologic impact assessment in the sub-continental river basins.

scholarly journals Nearest Neighbor–Genetic Algorithm for Downscaling of Climate Change Data from GCMs

2016 ◽  
Vol 55 (3) ◽  
pp. 773-789
Author(s):  
Soojun Kim ◽  
Jaewon Kwak ◽  
Hung Soo Kim ◽  
Younghun Jung ◽  
Gilho Kim
Keyword(s):  
Climate Change ◽  
Genetic Algorithm ◽  
General Circulation ◽  
Nearest Neighbor ◽  
Coupled Model ◽  
Temporal Correlation ◽  
General Circulation Models ◽  
Temperature Fields ◽  
Change Scenario ◽  
Limited Applicability

AbstractThe spatial and temporal resolution of readily available climate change projections from general circulation models (GCM) has limited applicability. Consequently, several downscaling methods have been developed. These methods predominantly focus on a single meteorological series at specific sites. Spatial and temporal correlation of the precipitation and temperature fields is important for hydrologic applications. This research uses a nearest neighbor–genetic algorithm (NN–GA) method to analyze the Namhan River basin in the Korean Peninsula. Using the simulation results of the CNRM-CM for the RCP 8.5 climate change scenario, archived in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the GCM projections are downscaled through the NN–GA. The NN–GA simulations reproduce the features of the observed series in terms of site statistics as well as across variables and sites.

scholarly journals Future discharge drought across climate regions around the world modelled with a synthetic hydrological modelling approach forced by three General Circulation Models

2013 ◽  
Vol 1 (6) ◽  
pp. 7701-7738 ◽  
Author(s):  
N. Wanders ◽  
H. A. J. van Lanen
Keyword(s):  
General Circulation ◽  
Reference Model ◽  
Control Period ◽  
General Circulation Models ◽  
Hydrological Drought ◽  
Severe Drought ◽  
Drought Duration ◽  
Increased Risk ◽  
The World ◽  
Drought Occurrence

Abstract. Hydrological droughts characteristics (drought in groundwater and streamflow) likely will change in the 21st century as a results of climate change. Magnitude and directionality of these changes and their dependency on climatology and catchment characteristics, however, is largely unknown. In this study a conceptual hydrological model was forced by downscaled and bias-corrected outcome from three General Circulation Models for the A2 emission scenario (GCM forced models), and the WATCH Forcing Data re-analysis dataset(reference model). The threshold level method was applied to investigate drought occurrence, duration and deficit volume. Results for the control period (1971–2000) show that the drought characteristics of each GCM forced model reasonably agree with the reference model for most of the climate types, suggesting that the climate model's results after post-processing produce realistic outcome for global drought analyses. For the near future (2021–2050) and far future (2071–2100) the GCM forced models show a decrease in drought occurrence for all major climates around the world and increase of both average drought duration and deficit volume of the remaining drought events. The largest decrease in hydrological drought occurrence is expected in cold (D-)climates where global warming results in a decreased length of the snow season and an increased precipitation. In the dry B-climates the smallest decrease in drought occurrence is expected to occur, which probably will lead to even more severe water scarcity. However, in the extreme climate regions (desert and polar), the analysis for the control period showed that projections are in these regions most uncertain. On a global scale the increase in hydrological drought duration and severity will lead to a higher impact of drought events, which urges water resources managers to timely anticipate on the increased risk on more severe drought in groundwater and streamflow and to design pro-active measures.

scholarly journals Downscaling approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia

2015 ◽  
Vol 369 ◽  
pp. 147-155 ◽  
Author(s):  
H. M. S. M. Herath ◽  
P. R. Sarukkalige ◽  
V. T. V. Nguyen
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Extreme Rainfall ◽  
General Circulation Models ◽  
Correction Function ◽  
Climate Projections ◽  
Time Regime ◽  
Intensity Duration Frequency ◽  
Environmental Prediction ◽  
Spatial Downscaling

Abstract. Downscaling of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, downscaling techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial downscaling of GCMs' output and still there is a high demand to develop temporal downscaling approaches. As the main objective of this study, combined approach of spatial and temporal downscaling is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed downscaling approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical Downscaling Model (SDSM) is used to spatial downscaling and numerical model based on scaling invariant concept is used to temporal downscaling of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of downscaled annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily downscaled rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.

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