scholarly journals South Asia river-flow projections and their implications for water resources

2015 ◽  
Vol 19 (12) ◽  
pp. 4783-4810 ◽  
Author(s):  
C. Mathison ◽  
A. J. Wiltshire ◽  
P. Falloon ◽  
A. J. Challinor
Keyword(s):  
Water Resources ◽  
South Asia ◽  
River Flow ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Regional Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
The South ◽  
River Flows

Abstract. South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

scholarly journals South Asia river flow projections and their implications for water resources

2015 ◽  
Vol 12 (6) ◽  
pp. 5789-5840 ◽  
Author(s):  
C. Mathison ◽  
A. J. Wiltshire ◽  
P. Falloon ◽  
A. J. Challinor
Keyword(s):  
Water Resources ◽  
South Asia ◽  
Water Resource ◽  
River Flow ◽  
Regional Climate ◽  
Climate Projections ◽  
The South ◽  
River Flows ◽  
Flow Rates ◽  
The Future

Abstract. South Asia is a region with a large and rising population and a high dependance on industries sensitive to water resource such as agriculture. The climate is hugely variable with the region relying on both the Asian Summer Monsoon (ASM) and glaciers for its supply of fresh water. In recent years, changes in the ASM, fears over the rapid retreat of glaciers and the increasing demand for water resources for domestic and industrial use, have caused concern over the reliability of water resources both in the present day and future for this region. The climate of South Asia means it is one of the most irrigated agricultural regions in the world, therefore pressures on water resource affecting the availability of water for irrigation could adversely affect crop yields and therefore food production. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. ERA-Interim, together with two global climate models (GCMs), which represent the present day processes, particularly the monsoon, reasonably well are downscaled using a regional climate model (RCM) for the periods; 1990–2006 for ERA-Interim and 1960–2100 for the two GCMs. The RCM river flow is routed using a river-routing model to allow analysis of present day and future river flows through comparison with river gauge observations, where available. In this analysis we compare the river flow rate for 12 gauges selected to represent the largest river basins for this region; Ganges, Indus and Brahmaputra basins and characterize the changing conditions from east to west across the Himalayan arc. Observations of precipitation and runoff in this region have large or unknown uncertainties, are short in length or are outside the simulation period, hindering model development and validation designed to improve understanding of the water cycle for this region. In the absence of robust observations for South Asia, a downscaled ERA-Interim RCM simulation provides a benchmark for comparison against the downscaled GCMs. On the basis that these simulations are among the highest resolution climate simulations available we examine how useful they are for understanding the changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows, with timing of maximum river flows broadly matching the available observations and the downscaled ERA-Interim simulation. Typically the RCM simulations over-estimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model although comparison with the downscaled ERA-Interim simulation is more mixed with only a couple of the gauges showing a bias compared with the downscaled GCM runs. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century; this trend is generally masked by the large annual variability of river flows for this region. The future seasonality of river flows does not change with the future maximum river flow rates still occuring during the ASM period, with a magnitude in some cases, greater than the present day natural variability. Increases in river flow during peak flow periods means additional water resource for irrigation, the largest usage of water in this region, but also has implications in terms of inundation risk. Low flow rates also increase which is likely to be important at times of the year when water is historically more scarce. However these projected increases in resource from rivers could be more than countered by changes in demand due to reductions in the quantity and quality of water available from groundwater, increases in domestic use due to a rising population or expansion of other industries such as hydro-electric power generation.

scholarly journals Modelling climate change impact on water resources of the Upper Indus Basin

2021 ◽  
Author(s):  
Jamal H. Ougahi ◽  
Mark E. J. Cutler ◽  
Simon J. Cook
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
River Flow ◽  
Assessment Tool ◽  
Global Climate Models ◽  
Indus Basin ◽  
Water Yield ◽  
River Flows ◽  
Upper Indus Basin

Abstract Climate change has implications for water resources by increasing temperature, shifting precipitation patterns and altering the timing of snowfall and glacier melt, leading to shifts in the seasonality of river flows. Here, the Soil & Water Assessment Tool was run using downscaled precipitation and temperature projections from five global climate models (GCMs) and their multi-model mean to estimate the potential impact of climate change on water balance components in sub-basins of the Upper Indus Basin (UIB) under two emission (RCP4.5 and RCP8.5) and future (2020–2050 and 2070–2100) scenarios. Warming of above 6 °C relative to baseline (1974–2004) is projected for the UIB by the end of the century (2070–2100), but the spread of annual precipitation projections among GCMs is large (+16 to −28%), and even larger for seasonal precipitation (+91 to −48%). Compared to the baseline, an increase in summer precipitation (RCP8.5: +36.7%) and a decrease in winter precipitation were projected (RCP8.5: −16.9%), with an increase in average annual water yield from the nival–glacial regime and river flow peaking 1 month earlier. We conclude that predicted warming during winter and spring could substantially affect the seasonal river flows, with important implications for water supplies.

scholarly journals Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

2016 ◽  
Vol 11 (2) ◽  
pp. 670-678 ◽  
Author(s):  
N. S Vithlani ◽  
H. D Rank
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Extremes ◽  
Global Climate Models ◽  
Climate Indices ◽  
Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

scholarly journals Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

2021 ◽  
Author(s):  
Guillaume Evin ◽  
Samuel Somot ◽  
Benoit Hingray
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Uncertainty Assessment ◽  
Global Climate Models ◽  
Climate Projections ◽  
Total Uncertainty ◽  
Near Surface

Abstract. Large Multiscenarios Multimodel Ensembles (MMEs) of regional climate model (RCM) experiments driven by Global Climate Models (GCM) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse Scenario-GCM-RCM matrices, these large ensembles are however very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest ensemble ever produced for regional projections in Europe. This analysis provides i) the most up-to-date and balanced estimates of mean changes for near-surface temperature and precipitation in Europe, ii) the total uncertainty of projections and its partition as a function of time, and iii) the list of the most important contributors to the model uncertainty. For changes of total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in Central-Eastern Europe for instance, the difference between balanced and direct estimates are up to 0.8 °C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

Investigation of future climate characteristics over the Carpathian Region by “tuning” CORDEX temperature time series – a new perspective

2021 ◽  
Author(s):  
Csaba Zsolt Torma
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Standard Procedure ◽  
Regional Climate ◽  
Global Climate Models ◽  
Climate Projections ◽  
Global Changes ◽  
Regional Warming ◽  
Mean Temperature

<p>The answers to the following questions ‘What are the consequences of climate change (warming)…?’ and ‘By when do we have to be prepared for that level of climate change (warming)?’ must be given only with caution. On the one hand, regional or local changes can be inconsistent with global changes, as local processes might not accurately interpreted by global climate models (GCMs) due to their relative coarse resolution. On the other hand, climate model simulations’ outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. In this context, while the magnitude of expected change described by a climate model may seem to be reasonable, but the projected temperature is not necessarily realistic (considering the model’s relative bias compared to observations). More specifically, the standard procedure of assessing climate change can be illustrated by taking the mean for a future period (e.g. 2070–2099) and compute the change relative to a reference period (e.g. 1976−2005). Keeping in mind the expected changes based on those projections might come with high degree of uncertainty as simulations might show different mean temperature values for the same assessed periods with even a range of few degrees of °C. When regional climate change is assessed based on at a given regional warming level (WL, e.g. 1.5 °C) added to the observed mean, then the aforementioned uncertainty range is reduced as the models (GCM or regional climate models) are assessed with respect to the same 30-year mean temperature value, but not for the same periods (noting that the WL is defined at regional and not at global scale). Thus the uncertainty of expected changes with regard to temperature can be significantly reduced. In this case an additional uncertainty factor might rise: time, as climate models can reach that WL at different times. Accordingly, we can give information on relative changes with a specific uncertainty as a metric based on the timing of reaching the assessed WL. Aim of the present work is to illustrate the feasibility of this concept for the region of the Carpathian Basin based on high-resolution EURO- and Med-CORDEX simulations.</p>

scholarly journals An Analysis of Regional Climate Simulations for Western Australia’s Wine Regions—Model Evaluation and Future Climate Projections

2017 ◽  
Vol 56 (7) ◽  
pp. 2113-2138 ◽  
Author(s):  
Rebecca Firth ◽  
Jatin Kala ◽  
Thomas J. Lyons ◽  
Julia Andrys
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Global Climate Models ◽  
Future Climate ◽  
Climate Indices ◽  
Climate Projections ◽  
Model Errors ◽  
Current Climate ◽  
Temperature Indices

AbstractThe Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia’s wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate–viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981–2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030–59) minus present (1970–99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia’s wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate.

scholarly journals Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

2021 ◽  
Vol 12 (4) ◽  
pp. 1543-1569
Author(s):  
Guillaume Evin ◽  
Samuel Somot ◽  
Benoit Hingray
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Uncertainty Assessment ◽  
Global Climate Models ◽  
Climate Projections ◽  
Total Uncertainty ◽  
Near Surface

Abstract. Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

scholarly journals Dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs

2021 ◽  
Author(s):  
Yuan Qiu ◽  
Jinming Feng ◽  
Zhongwei Yan ◽  
Jun Wang
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Climate Changes ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
High Elevation ◽  
Global Climate Models ◽  
Climate Projections

Abstract Central Asia (CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need to achieve robust projection of regional climate change. In this study, we applied three bias-corrected global climate models (GCMs) to conduct 9km-resolution regional climate simulations in CA for the present (1986–2005) and future (2031–2050) periods. Dynamical downscaling based on multiple bias-corrected GCM outputs obtains numerous added values not only in reproducing the historical climate but also in projecting the climate changes in CA, in comparison to the original GCMs. The regional climate model (RCM) simulations indicate significant warming over CA in the near-term future, with the regional mean increase of annual daily mean temperature (Tmean) in a range of 1.63–2.01℃, relative to the present period. This increase is expected to be higher north of ~ 45°N in each season except summer and the high-elevation areas have a weaker warming signal than the plains through the year. The season with the largest warming rate is not consistent among the RCM simulations, highlighting the necessity of using multiple GCMs as the boundary conditions to give a range of the projected climate changes. A slight increase in annual precipitation is consistently projected in most plain areas, although the changes over few areas are statistically significant. The climate projections presented here serve as a robust scientific basis for assessment of future risk from climate change in CA.

Regional Climate Scenarios for use in Nordic Water Resources Studies

2003 ◽  
Vol 34 (5) ◽  
pp. 399-412 ◽  
Author(s):  
M. Rummukainen ◽  
J. Räisänen ◽  
D. Bjørge ◽  
J.H. Christensen ◽  
O.B. Christensen ◽  
...  
Keyword(s):  
Water Resources ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Scenarios ◽  
Soil Frost ◽  
Nordic Region ◽  
Regional Climate Projections

According to global climate projections, a substantial global climate change will occur during the next decades, under the assumption of continuous anthropogenic climate forcing. Global models, although fundamental in simulating the response of the climate system to anthropogenic forcing are typically geographically too coarse to well represent many regional or local features. In the Nordic region, climate studies are conducted in each of the Nordic countries to prepare regional climate projections with more detail than in global ones. Results so far indicate larger temperature changes in the Nordic region than in the global mean, regional increases and decreases in net precipitation, longer growing season, shorter snow season etc. These in turn affect runoff, snowpack, groundwater, soil frost and moisture, and thus hydropower production potential, flooding risks etc. Regional climate models do not yet fully incorporate hydrology. Water resources studies are carried out off-line using hydrological models. This requires archived meteorological output from climate models. This paper discusses Nordic regional climate scenarios for use in regional water resources studies. Potential end-users of water resources scenarios are the hydropower industry, dam safety instances and planners of other lasting infrastructure exposed to precipitation, river flows and flooding.

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