scholarly journals Estimation of Climate Change Impact on Mean Annual Runoff across Continental Australia Using Budyko and Fu Equations and Hydrological Models

2012 ◽  
Vol 13 (3) ◽  
pp. 1094-1106 ◽  
Author(s):  
J. Teng ◽  
F. H. S. Chiew ◽  
J. Vaze ◽  
S. Marvanek ◽  
D. G. C. Kirono
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Climate Models ◽  
Hydrological Modeling ◽  
Global Climate ◽  
Annual Runoff ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Change Impact ◽  
Change In Mean

Abstract This paper presents the climate change impact on mean annual runoff across continental Australia estimated using the Budyko and Fu equations informed by projections from 15 global climate models and compares the estimates with those from extensive hydrological modeling. The results show runoff decline in southeast and far southwest Australia, but elsewhere across the continent there is no clear agreement between the global climate models in the direction of future precipitation and runoff change. Averaged across large regions, the estimates from the Budyko and Fu equations are reasonably similar to those from the hydrological models. The simplicity of the Budyko equation, the similarity in the results, and the large uncertainty in global climate model projections of future precipitation suggest that the Budyko equation is suitable for estimating climate change impact on mean annual runoff across large regions. The Budyko equation is particularly useful for data-limited regions, for studies where only estimates of climate change impact on long-term water availability are needed, and for investigative assessments prior to a detailed hydrological modeling study. The Budyko and Fu equations are, however, limited to estimating the change in mean annual runoff for a given change in mean annual precipitation and potential evaporation. The hydrological models, on the other hand, can also take into account potential changes in the subannual and other climate characteristics as well as provide a continuous simulation of daily and monthly runoff, which is important for many water availability studies.

scholarly journals GIS Modeling of Climate Change Impact on Saffron Water Requirement (the case of Ardabil Province)

2017 ◽  
Vol 7 (4) ◽  
pp. 255-262
Author(s):  
Vahid Ashrafi
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Evaluation Criteria ◽  
Water Requirement ◽  
Global Climate Models ◽  
Gis Modeling ◽  
Change Impact

In recent decades, climate change in Iran has led to the introduction of specific plants that have less water requirement than policymakers. Saffron cultivation has a long history in Iran and saffron was ranked as one of these figures on the agenda of policymakers. Here we presented the evaluation of Climate Change Impact on Saffron Water Requirement by GIS Modeling in Ardabil Provincel. We spot periods 1992-2017 and 2017-2040 as base and future period, respectively. For CCCSN, the data from five global climate models (GCMs) from the CGCM3T47 archive were selected that cover three ‘Representative Concentration Pathways’ (RCPs) scenarios. Potential evapotranspiration is estimated by Torrent White method. The accuracy of models at base period was determined by evaluation criteria, such as the RMSE, R2. Results showed that accuracy of CGCM3T47 model on A1B scenario was higher than other AOGCM models which used on base term. Also, it illustrated that water requirement will rise in all capable regions of state on 2040. In universal, average of addition of water requirement is 67, where in Germi, capital of state, we will have maximum variations by 95 mm ascension for the year 2040. Also, pole of production saffron in state, Bilasvar will have 40 mm ascension in saffron water requirement. Mean water requirement of saffron will be constantly increasing. In the meanwhile, the index of 425.52 mm for the year 2017 to 487.61 mm for the year 2040 were performed.

scholarly journals Uncertainty in runoff based on Global Climate Model precipitation and temperature data – Part 2: Estimation and uncertainty of annual runoff and reservoir yield

2014 ◽  
Vol 11 (5) ◽  
pp. 4579-4638 ◽  
Author(s):  
M. C. Peel ◽  
R. Srikanthan ◽  
T. A. McMahon ◽  
D. J. Karoly
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Climate Change Impact ◽  
Global Climate ◽  
Annual Runoff ◽  
Monthly Precipitation ◽  
Change Impact ◽  
The Impact ◽  
Reservoir Yield ◽  
Precipitation And Temperature

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between Global Climate Models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) datasets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to approximate within-GCM uncertainty of monthly precipitation and temperature projections and assess its impact on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. To-date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2014) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), temperature (MAT) and runoff (MAR), the standard deviation of annual precipitation (SDP) and runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 world-wide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainty from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were: MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma procedure was applied to each annual runoff time-series for hypothetical reservoir capacities of 1× MAR and 3× MAR and the average uncertainty in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were: 25.1% (1× MAR) and 11.9% (3× MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1× MAR or 3× MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change

2021 ◽  
Author(s):  
Thedini Asali Peiris ◽  
Petra Döll
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Net Radiation ◽  
Ensemble Mean ◽  
The Impact

<p>Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO<sub>2</sub> concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO<sub>2</sub> and climate. This active vegetation response consists of three components. With higher CO<sub>2</sub> concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.</p><p>Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.</p><p>The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.</p><p>We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).</p><p>Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.</p>

scholarly journals Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data

2015 ◽  
Vol 19 (4) ◽  
pp. 1615-1639 ◽  
Author(s):  
M. C. Peel ◽  
R. Srikanthan ◽  
T. A. McMahon ◽  
D. J. Karoly
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Climate Change Impact ◽  
Global Climate ◽  
Annual Runoff ◽  
Monthly Precipitation ◽  
Change Impact ◽  
The Impact ◽  
Reservoir Yield ◽  
Precipitation And Temperature

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Risk-based climate-change impact assessment for the water industry

2009 ◽  
Vol 59 (3) ◽  
pp. 443-451 ◽  
Author(s):  
O. M. Thorne ◽  
R. A. Fenner
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Climate Change Impact ◽  
Climate Models ◽  
Assessment Tool ◽  
Global Climate Models ◽  
Future Climate ◽  
Water Industry ◽  
Climate Change Impact Assessment ◽  
Change Impact

In response to a rapidly changing and highly variable climate, engineers are being asked to perform climate-change impact assessments on existing water industry systems. There is currently no single method of best practice for engineers to interpret output from global climate models (GCMs) and calculate probabilistic distributions of future climate changes as required for risk-based impact assessments. The simplified climate change impact assessment tool (SCIAT) has been developed to address the specific needs of the water industry and provides a tool to translate climate change projections into ‘real world’ impacts or for detailed statistical analysis. Through the use of SCIAT, water system operators are provided with knowledge of potential impacts and an associated probability of occurrence, enabling them to make informed, risk-based adaptation and planning decisions. This paper demonstrates the application of SCIAT to the consideration of the impacts of climate change on reservoir water quality under future climate scenarios.

scholarly journals An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

2013 ◽  
Vol 17 (2) ◽  
pp. 565-578 ◽  
Author(s):  
J. A. Velázquez ◽  
J. Schmid ◽  
S. Ricard ◽  
M. J. Muerth ◽  
B. Gauvin St-Denis ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Hydrological Model ◽  
Global Climate ◽  
Regional Climate ◽  
Structural Complexity ◽  
Global Climate Models ◽  
Hydrological Models ◽  
The Impact

Abstract. Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000) and a future (2041–2070) period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model.

scholarly journals Assessment of the Future Impact of Climate Change on the Hydrology of the Mangoky River, Madagascar Using ANN and SWAT

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1239
Author(s):  
Mirindra Finaritra Rabezanahary Tanteliniaina ◽  
Md. Hasibur Rahaman ◽  
Jun Zhai
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Assessment Tool ◽  
Global Climate ◽  
Annual Runoff ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Adaptation To Climate Change ◽  
Artificial Neural Network Ann

The assessment of the impacts of climate change on hydrology is important for better water resources management. However, few studies have been conducted in semi-arid Africa, even less in Madagascar. Here we report, climate-induced future hydrological prediction in Mangoky river, Madagascar using an artificial neural network (ANN) and the soil and water assessment tool (SWAT). The current study downscaled two global climate models on the mid-term, noted the 2040s (2041–2050) and long-term, noted 2090s (2091–2099) under two shared socioeconomic pathways (SSP) scenarios, SSP 3–7.0 and SSP 5–8.5. Statistical indices of both ANN and SWAT showed good performance (R2 > 0.65) of the models. Our results revealed a rise in maximum temperature (4.26–4.69 °C) and minimum temperature (2.74–3.01 °C) in the 2040s and 2090s. Under SSP 3–7.0 and SSP 5–8.5, a decline in the annual precipitation is projected in the 2040s and increased the 2090s. This study found that future precipitation and temperature could significantly decrease annual runoff by 60.59% and 73.77% in the 2040s; and 25.18% and 23.45% in the 2090s under SSP 3–7.0 and SSP 5–8.5, respectively. Our findings could be useful for the adaptation to climate change, managing water resources, and water engineering.

scholarly journals Using Integrated Hydrological Models to Assess the Impacts of Climate Change on Discharges and Extreme Flood Events in the Upper Yangtze River Basin

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 299
Author(s):  
Yanjuan Wu ◽  
Gang Luo ◽  
Cai Chen ◽  
Zheng Duan ◽  
Chao Gao
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Flood Events ◽  
Temperature And Precipitation ◽  
Extreme Flood ◽  
Impacts Of Climate Change

Amongst the impacts of climate change, those arising from extreme hydrological events are expected to cause the greatest impacts. To assess the changes in temperature and precipitation and their impacts on the discharge in the upper Yangtze Basin from pre-industrial to the end of 21st century, four hydrological models were integrated with four global climate models. Results indicated that mean discharge was simulated to increase slightly for all hydrological models forced by all global climate models during 1771–1800 and 1871–1900 relative to the 1971–2000 reference period, whereas the change directions in mean discharge were not consistent among the four global climate models during 2070–2099, with increases from HadGEM2-ES and MIROC5, and decreases from GFDL-ESM2M and IPSL-CM5A-LR. Additionally, our results indicated that decreases in precipitation may always result in the decrease in mean discharge, but increases in precipitation did not always lead to increases in discharge due to high temperature rise. The changes in extreme flood events with different return intervals were also explored. These extreme events were projected to become more intense and frequent in the future, which could have potential devastating impacts on the society and ecosystem in this region.

scholarly journals Climate change impact on available water resources obtained using multiple global climate and hydrology models

2013 ◽  
Vol 4 (1) ◽  
pp. 129-144 ◽  
Author(s):  
S. Hagemann ◽  
C. Chen ◽  
D. B. Clark ◽  
S. Folwell ◽  
S. N. Gosling ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Change Impact ◽  
Climate Models ◽  
Global Climate ◽  
Future Climate Change ◽  
Available Water ◽  
Hydrological Changes ◽  
Eastern Australia ◽  
Change Impact

Abstract. Climate change is expected to alter the hydrological cycle resulting in large-scale impacts on water availability. However, future climate change impact assessments are highly uncertain. For the first time, multiple global climate (three) and hydrological models (eight) were used to systematically assess the hydrological response to climate change and project the future state of global water resources. This multi-model ensemble allows us to investigate how the hydrology models contribute to the uncertainty in projected hydrological changes compared to the climate models. Due to their systematic biases, GCM outputs cannot be used directly in hydrological impact studies, so a statistical bias correction has been applied. The results show a large spread in projected changes in water resources within the climate–hydrology modelling chain for some regions. They clearly demonstrate that climate models are not the only source of uncertainty for hydrological change, and that the spread resulting from the choice of the hydrology model is larger than the spread originating from the climate models over many areas. But there are also areas showing a robust change signal, such as at high latitudes and in some midlatitude regions, where the models agree on the sign of projected hydrological changes, indicative of higher confidence in this ensemble mean signal. In many catchments an increase of available water resources is expected but there are some severe decreases in Central and Southern Europe, the Middle East, the Mississippi River basin, southern Africa, southern China and south-eastern Australia.

Sign in / Sign up

Export Citation Format

Share Document