scholarly journals Climate change impact on groundwater levels: ensemble modelling of extreme values

2012 ◽  
Vol 9 (6) ◽  
pp. 7835-7875 ◽  
Author(s):  
J. Kidmose ◽  
J. C. Refsgaard ◽  
L. Troldborg ◽  
L. P. Seaby ◽  
M. M. Escrivà
Keyword(s):  
Climate Models ◽  
Extreme Values ◽  
Control Period ◽  
Extreme Value ◽  
Global Climate Models ◽  
Extreme Value Statistics ◽  
Return Periods ◽  
Future Period ◽  
Groundwater Levels ◽  
The Future

Abstract. This paper presents a first attempt to estimate future groundwater levels by applying extreme value statistics on predictions from a hydrological model. Climate for the future period, 2081–2100, are represented by projections from nine combinations of three global climate models and six regional climate models, and downscaled with two different methods. An integrated surface water/groundwater model is forced with precipitation, temperature, and evapotranspiration from the 18 model – and downscaling combinations. Extreme value analyses are performed on the hydraulic head changes from a control period (1991–2010) to the future period for the 18 combinations. Hydraulic heads for return periods of 21, 50 and 100 yr (T21–100) are estimated. Three uncertainty sources are evaluated; climate models, downscaling and extreme value statistics. Of these sources, downscaling dominates for the higher return periods of 50 and 100 yr, whereas uncertainty from climate models and downscaling are similar for lower return periods. Uncertainty from the extreme value statistics only contribute up to around 10% of the uncertainty from the three sources.

scholarly journals Climate change impact on groundwater levels: ensemble modelling of extreme values

2013 ◽  
Vol 17 (4) ◽  
pp. 1619-1634 ◽  
Author(s):  
J. Kidmose ◽  
J. C. Refsgaard ◽  
L. Troldborg ◽  
L. P. Seaby ◽  
M. M. Escrivà
Keyword(s):  
Climate Models ◽  
Extreme Values ◽  
Control Period ◽  
Extreme Value ◽  
Global Climate Models ◽  
Extreme Value Statistics ◽  
Return Periods ◽  
Future Period ◽  
Groundwater Levels ◽  
The Future

Abstract. This paper presents a first attempt to estimate future groundwater levels by applying extreme value statistics on predictions from a hydrological model. Climate scenarios for the future period, 2081–2100, are represented by projections from nine combinations of three global climate models and six regional climate models, and downscaled (including bias correction) with two different methods. An integrated surface water/groundwater model is forced with precipitation, temperature, and potential evapotranspiration from the 18 models and downscaling combinations. Extreme value analyses are performed on the hydraulic head changes from a control period (1991–2010) to the future period for the 18 combinations. Hydraulic heads for return periods of 21, 50 and 100 yr (T21–100) are estimated. Three uncertainty sources are evaluated: climate models, downscaling and extreme value statistics. Of these sources, extreme value statistics dominates for return periods higher than 50 yr, whereas uncertainty from climate models and extreme value statistics are similar for lower return periods. Uncertainty from downscaling only contributes to around 10% of the uncertainty from the three sources.

scholarly journals The Impacts of Climate Change on Maximum Daily Discharge in the Payab Jamash Watershed, Iran

2019 ◽  
Vol 11 (1) ◽  
pp. 1035-1045
Author(s):  
Farzad Parandin ◽  
Asadollah Khoorani ◽  
Ommolbanin Bazrafshan
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Climate Models ◽  
River Flow ◽  
Flow Simulation ◽  
Global Climate Models ◽  
Historical Period ◽  
Future Period ◽  
The Future ◽  
Daily Discharge

Abstract One of the most crucial consequences of climate change involves the alteration of the hydrologic cycle and river flow regime of watersheds. This study was an endeavor to investigate the contributions of climate change to maximum daily discharge (MDD). To this end, the MDD simulation was carried out through implementing the IHACRES precipitation-runoff model in the Payyab Jamash watershed for the 21st century (2016-2100). Subsequently, the observed precipitation and temperature data of the weather stations (1980-2011) as well as 4 multi-model outputs of Global Climate Models (GCMs) under the maximum and minimum Representative Concentration Pathways (RCPs) (2016-2100) were utilized. In order to downscale the output of GCMs, Bias Correction (BC) statistical method was applied. The projections for the 21st century indicated a reduction in Maximum Daily Precipitation (MDP) in comparison with the historic period in the study area. The average projected MDP for the future period was 9 mm/day and 5 mm/ day under 2.6 and 8.5 RCPs (4.6% and 2.6% decrease compared with the historical period), respectively. Moreover, the temperature increased in Jamash Watershed based on 2.6 and 8.5 RCPs by 1∘C and 2∘C(3.7% and 7.4% increase compared with the historical period), respectively. The findings of flow simulation for the future period indicated a decrease in MDD due to the diminished MDP in the study area. The amount of this decrease under RCP8.5 was not remarkable (0.75 m3/s), whereas its value for RCP2.6 was calculated as 40m3/s (respectively, 0.11% and 5.88% decrease compared with the historical period).

scholarly journals Assessment of Changes in Annual Maximum Precipitations in the Iberian Peninsula under Climate Change

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2375 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
The Future

Climate model projections can be used to assess the future expected behavior of extreme precipitation due to climate change. In Europe, the EURO-CORDEX project provides precipitation projections in the future under various representative concentration pathways (RCP), through regionalized outputs of Global Climate Models (GCM) by a set of Regional Climate Models (RCM). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX project are analyzed in the Iberian Peninsula and the Balearic Islands. Precipitation quantiles for a set of exceedance probabilities are estimated by using the Generalized Extreme Value (GEV) distribution function fitted by the L-moment method. Precipitation quantiles expected in the future period are compared with the precipitation quantiles in the control period, for each climate model. An approach based on Monte Carlo simulations is developed to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period to identify statistically significant changes. The higher the significance threshold, the fewer cells with changes are identified. Consequently, a set of maps are obtained for various thresholds to assist the decision making process in subsequent climate change studies.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

scholarly journals Regional climate models downscaling in the Alpine area with multimodel superensemble

2013 ◽  
Vol 17 (5) ◽  
pp. 2017-2028 ◽  
Author(s):  
D. Cane ◽  
S. Barbarino ◽  
L. A. Renier ◽  
C. Ronchi
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Temperature Fields ◽  
Optimal Interpolation ◽  
Climate Change Scenarios ◽  
Alpine Area ◽  
Ensembles Project

Abstract. The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization. Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique. Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.

scholarly journals Selection of Bias Correction Methods to Assess the Impact of Climate Change on Flood Frequency Curves

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2266 ◽  
Author(s):  
Enrique Soriano ◽  
Luis Mediero ◽  
Carlos Garijo
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Control Period ◽  
Flood Frequency ◽  
Climate Projections ◽  
The Future ◽  
Flood Quantiles ◽  
The Impact

Climate projections provided by EURO-CORDEX predict changes in annual maximum series of daily rainfall in the future in some areas of Spain because of climate change. Precipitation and temperature projections supplied by climate models do not usually fit exactly the statistical properties of the observed time series in the control period. Bias correction methods are used to reduce such errors. This paper seeks to find the most adequate bias correction techniques for temperature and precipitation projections that minimizes the errors between observations and climate model simulations in the control period. Errors in flood quantiles are considered to identify the best bias correction techniques, as flood quantiles are used for hydraulic infrastructure design and safety assessment. In addition, this study aims to understand how the expected changes in precipitation extremes and temperature will affect the catchment response in flood events in the future. Hydrological modelling is required to characterize rainfall-runoff processes adequately in a changing climate, in order to estimate flood changes expected in the future. Four catchments located in the central-western part of Spain have been selected as case studies. The HBV hydrological model has been calibrated in the four catchments by using the observed precipitation, temperature and streamflow data available on a daily scale. Rainfall has been identified as the most significant input to the model, in terms of its influence on flood response. The quantile mapping polynomial correction has been found to be the best bias correction method for precipitation. A general reduction in flood quantiles is expected in the future, smoothing the increases identified in precipitation quantiles by the reduction of soil moisture content in catchments, due to the expected increase in temperature and decrease in mean annual precipitations.

scholarly journals Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

scholarly journals Statistics of the largest geomagnetic storms per solar cycle (1844-1993)

1997 ◽  
Vol 15 (6) ◽  
pp. 719-728 ◽  
Author(s):  
D. M. Willis ◽  
P. R. Stevens ◽  
S. R. Crothers
Keyword(s):  
Statistical Analysis ◽  
Solar Cycle ◽  
Geomagnetic Storm ◽  
Extreme Values ◽  
Geomagnetic Storms ◽  
Extreme Value ◽  
Extreme Value Statistics ◽  
Relative Dispersion ◽  
Solar Cycles ◽  
Aa Index

Abstract. A previous application of extreme-value statistics to the first, second and third largest geomagnetic storms per solar cycle for nine solar cycles is extended to fourteen solar cycles (1844–1993). The intensity of a geomagnetic storm is measured by the magnitude of the daily aa index, rather than the half-daily aa index used previously. Values of the conventional aa index (1868–1993), supplemented by the Helsinki Ak index (1844–1880), provide an almost continuous, and largely homogeneous, daily measure of geomagnetic activity over an interval of 150 years. As in the earlier investigation, analytic expressions giving the probabilities of the three greatest storms (extreme values) per solar cycle, as continuous functions of storm magnitude (aa), are obtained by least-squares fitting of the observations to the appropriate theoretical extreme-value probability functions. These expressions are used to obtain the statistical characteristics of the extreme values; namely, the mode, median, mean, standard deviation and relative dispersion. Since the Ak index may not provide an entirely homogeneous extension of the aa index, the statistical analysis is performed separately for twelve solar cycles (1868–1993), as well as nine solar cycles (1868–1967). The results are utilized to determine the expected ranges of the extreme values as a function of the number of solar cycles. For fourteen solar cycles, the expected ranges of the daily aa index for the first, second and third largest geomagnetic storms per solar cycle decrease monotonically in magnitude, contrary to the situation for the half-daily aa index over nine solar cycles. The observed range of the first extreme daily aa index for fourteen solar cycles is 159–352 nT and for twelve solar cycles is 215–352 nT. In a group of 100 solar cycles the expected ranges are expanded to 137–539 and 177–511 nT, which represent increases of 108% and 144% in the respective ranges. Thus there is at least a 99% probability that the daily aa index will satisfy the condition aa < 550 for the largest geomagnetic storm in the next 100 solar cycles. The statistical analysis is used to infer that remarkable conjugate auroral observations on the night of 16 September 1770, which were recorded during the first voyage of Captain Cook to Australia, occurred during an intense geomagnetic storm.

scholarly journals Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

2020 ◽  
Vol 20 (1) ◽  
pp. 19-29
Author(s):  
Minsu Jeong ◽  
Taesam Lee ◽  
JooHeon Lee ◽  
Hyeonseok Choi ◽  
Sunkwon Yoon
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Differential Evolution ◽  
Frequency Analysis ◽  
Climate Models ◽  
Time Series Data ◽  
Global Climate Models ◽  
Series Data ◽  
Hourly Rainfall ◽  
The Future

In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

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