scholarly journals Reliability Assessment of the Water Supply Systems under Uncertain Future Extreme Climate Conditions

2013 ◽  
Vol 17 (20) ◽  
pp. 1-27 ◽  
Author(s):  
Mohammad Karamouz ◽  
Erfan Goharian ◽  
Sara Nazif
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Water Supply Systems ◽  
Extreme Conditions ◽  
Reservoir System ◽  
Extreme Climate ◽  
The Future ◽  
Change Impact ◽  
Hbv Model ◽  
Precipitation And Temperature

Abstract Increase in global mean temperature and changes in rainfall amount, pattern, and distribution over the world are all indicative of climate change events. These changes alter the hydroclimatic condition of regions as well as the availability of water resources. In this study, the data generated by 14 general circulation models (GCMs) developed under the Special Report on Emissions Scenarios (SRES) A1B, A2, and B2 are downscaled and utilized to evaluate climate change impact on the hydroclimatic system of the Karaj River basin located in central Iran. The precipitation and temperature of the study region are downscaled using the change factor approach (CFA). The study analyzes future climate data, extreme changes of future climatic conditions of precipitation, and temperature. The Hydrologiska Byråns Vattenbalansavdelning (HBV) model developed by the Swedish Meteorological and Hydrological Institute (SMHI) is used to simulate streamflow under extreme climate change conditions. Two different sources of uncertainty are investigated in this study. First, the model parameters uncertainty is analyzed with the Monte Carlo procedure, and then different datasets of GCMs projection are investigated under the climate of the twentieth-century climate simulation (20C3M). Results show the GCMs projections range can almost capture the historical records during the 1980s through 2000 for the Karaj basin. By applying the HBV model, considerable range of streamflow changes in the future can be projected that will affect the operation scheme of Karaj Reservoir. In this study, the system dynamics (SD) modeling approach is used to simulate the system behavior through time in an integrated fashion and evaluate its overall reliability in supplying water. The results of this study show that the runoff will decrease in the future under the climate change impact. This will result in more than 50% decrease in reliability of the Karaj Reservoir system under the extreme conditions. As a result, this research predicts that the Karaj Reservoir system will face more than 50% decrease in its reliability under the extreme conditions. Consequently, meeting the increasing water demands would be difficult and application of demand management strategies will be unavoidable.

Assessment the climate change impact on the future evapotranspiration and flows from a semi-arid environment

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Salah Ouhamdouch ◽  
Mohammed Bahir ◽  
Driss Ouazar ◽  
Abdelmalek Goumih ◽  
Kamel Zouari
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Arid Environment ◽  
The Future ◽  
Change Impact ◽  
Semi Arid

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.

scholarly journals Water resources change in response to climate change in Changjiang River basin

2010 ◽  
Vol 7 (3) ◽  
pp. 3159-3188 ◽  
Author(s):  
Y. Huang ◽  
W. F. Yang ◽  
L. Chen
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
River Basin ◽  
Climate Change Impact ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Changjiang River ◽  
Runoff Change ◽  
The Future ◽  
Change Impact

Abstract. Doubtlessly, global climate change and its impacts have caught increasing attention from all sectors of the society world-widely. Among all those affected aspects, hydrological circle has been found rather sensitive to climate change. Climate change, either as the result or as the driving-force, has intensified the uneven distribution of water resources in the Changjiang (Yangtze) River basin, China. In turn, drought and flooding problems have been aggravated which has brought new challenges to current hydraulic works such as dike or reservoirs which were designed and constructed based on the historical hydrological characteristics, yet has been significantly changed due to climate change impact. Thus, it is necessary to consider the climate change impacts in basin planning and water resources management, currently and in the future. To serve such purpose, research has been carried out on climate change impact on water resources (and hydrological circle) in Changjiang River. The paper presents the main findings of the research, including main findings from analysis of historical hydro-meteorological data in Changjiang River, and runoff change trends in the future using temperature and precipitation predictions calculated based on different emission scenarios of the 24 Global Climate Modes (GCMs) which has been used in the 4th IPCC assessment report. In this research, two types of macro-scope statistical and hydrological models were developed to simulate runoff prediction. Concerning the change trends obtained from the historical data and the projection from GCMs results, the trend of changes in water resources impacted by climate change was analyzed for Changjiang River. Uncertainty of using the models and data were as well analyzed.

scholarly journals Appropriateness of Potential Evapotranspiration Models for Climate Change Impact Analysis in Yarlung Zangbo River Basin, China

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 453 ◽  
Author(s):  
Pan ◽  
Xu ◽  
Xuan ◽  
Gu ◽  
Bai
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impact ◽  
Impact Analysis ◽  
Potential Evapotranspiration ◽  
Global Climate Models ◽  
Historical Period ◽  
Yarlung Zangbo River ◽  
The Future ◽  
Change Impact

Evapotranspiration (ET) is an important element in the water and energy cycle. Potential evapotranspiration (PET) is an important measurement of ET. Its accuracy has significant influence on agricultural water management, irrigation planning, and hydrological modelling. However, whether current PET models are applicable under climate change or not, is still a question. In this study, five frequently used PET models were chosen, including one combination model (the FAO Penman-Monteith model, FAO-PM), two temperature-based models (the Blaney-Criddle and the Hargreaves models) and two radiation-based models (the Makkink and the Priestley-Taylor models), to estimate their appropriateness in the historical and future periods under climate change impact on the Yarlung Zangbo river basin, China. Bias correction methods were not only applied to the temperature output of Global Climate Models (GCMs), but also for radiation, humidity, and wind speed. It was demonstrated that the results from the Blaney-Criddle and Makkink models provided better agreement with the PET obtained by the FAO-PM model in the historical period. In the future period, monthly PET estimated by all five models show positive trends. The changes of PET under RCP8.5 are much higher than under RCP2.6. The radiation-based models show better appropriateness than the temperature-based models in the future, as the root mean square error (RMSE) value of the former models is almost half of the latter models. The radiation-based models are recommended for use to estimate PET under climate change in the Yarlung Zangbo river basin.

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.

scholarly journals Climate change and hydropower production in the Swiss Alps: quantification of potential impacts and related modelling uncertainties

2007 ◽  
Vol 11 (3) ◽  
pp. 1191-1205 ◽  
Author(s):  
B. Schaefli ◽  
B. Hingray ◽  
A. Musy
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
System Performance ◽  
Swiss Alps ◽  
Climate Change Scenarios ◽  
Future Period ◽  
The Future ◽  
Modelling Uncertainties ◽  
Change Impact ◽  
Potential Climate Change

Abstract. This paper addresses two major challenges in climate change impact analysis on water resources systems: (i) incorporation of a large range of potential climate change scenarios and (ii) quantification of related modelling uncertainties. The methodology of climate change impact modelling is developed and illustrated through application to a hydropower plant in the Swiss Alps that uses the discharge of a highly glacierised catchment. The potential climate change impacts are analysed in terms of system performance for the control period (1961–1990) and for the future period (2070–2099) under a range of climate change scenarios. The system performance is simulated through a set of four model types, including the production of regional climate change scenarios based on global-mean warming scenarios, the corresponding discharge model, the model of glacier surface evolution and the hydropower management model. The modelling uncertainties inherent in each model type are characterised and quantified separately. The overall modelling uncertainty is simulated through Monte Carlo simulations of the system behaviour for the control and the future period. The results obtained for both periods lead to the conclusion that potential climate change has a statistically significant negative impact on the system performance.

scholarly journals Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies

2020 ◽  
Author(s):  
Mostafa Tarek ◽  
François Brissette ◽  
Richard Arsenault
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Climate Model ◽  
The United States ◽  
Reference Period ◽  
Reference Dataset ◽  
Impact Studies ◽  
Change Impact ◽  
Weather Stations ◽  
Precipitation And Temperature

Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world’s regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged products dataset have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauged-only products (GPCC, CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using ground-based observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I, and ERA5) and one gauged, satellite, and reanalysis merged product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a top-down hydroclimatic modeling chain using 10 CMIP5 GCMs under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071–2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but 4 precipitation datasets outperformed the others for most catchments: they are, in order: MSWEP, CHIRPS, PERSIANN, and ERA5. For the present study, the 2-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the best 4 performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics, but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.

scholarly journals DAMPAK PERUBAHAN IKLIM DAN STRATEGI ADAPTASI TANAMAN BUAH DAN SAYURAN DI DAERAH TROPIS / Climate Change Impact and Adaptation Srategy for Vegetable and Fruit Crops in the Tropic Region

2019 ◽  
Vol 38 (2) ◽  
pp. 65
Author(s):  
Yeli Sarvina
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Negative Impact ◽  
Future Research ◽  
Tropical Region ◽  
Climate Projections ◽  
Fruit Crops ◽  
Extreme Climate ◽  
Circular Model ◽  
Change Impact

<p>Climate change has significant negative impact on agriculture in tropical region. Inrecent years, research on climate change has focused mainly on food crops while horticultural crops have received little attention. This paper is an overview of Indonesian future climate projection for precipitation, temperature and extreme climate, climate change impact and adaptation strategies on vegetable and fruit crops and future challenge for horticultural development under climate change. The climate change will decrease crop productivity and quality, increase the incidence of new pest and disease, and the outbreaks on vegetable and fruit crops. Further climate change will disrupt water availability, alter climate-crop suitability and cause crop failure due to extreme climate. Several adaptation measures have been developed in farming system, among other adjustment of planting time, using resistant varieties to environmental strees, adopting irrigation technology for efficient water use, using green house and increasing farmers and extention service capacity through climate field school. For future research it is necessary to assess climate projections with several scenarios and Global Circular Models (GCMs) and their impact on future vegetable and fruits crops by developing crop modeling which should be given a priority of in agriculture. This information crucially needed for adaptation strategy and a long term agricultural planning in the future.</p><p>Keywords: Vegetable, fruit, climate change, global circular model, adaptation </p><p> </p><p><strong>Abstrak</strong></p><p>Perubahan iklim berdampak negatif terhadap pertanian di daerah tropis. Selama ini penelitian dampak perubahan iklim terhadap pertanian lebih banyak dilakukan pada tanaman pangan, sementara pada tanaman hortikultura, khususnya sayuran dan buah-buahan masih terbatas. Tulisan ini merupakan tinjauan tentang proyeksi dampak perubahan iklim di Indonesia yang meliputi curah hujan, suhu udara, dan iklim ekstrim terhadap produksi tanaman buah dan sayuran, di samping berbagai upaya adaptasi yang telah dilakukan dan tantangan pembangunan hortikultura ke depan. Perubahan iklim pada tanaman sayuran dan buah-buahan terbukti menurunkan kuantitas dan kualitas produksi, munculnya hama penyakit baru, meningkatnya serangan hama dan penyakit, gagal panen, penurunan kapasitas air irigasi, perubahan kesesuian lahan dan tanaman. Beberapa langkah adaptasi yang sudah dilakukan yaitu penyesuaian sistem usaha tani yang meliputi penggunaan varietas toleran cekaman lingkungan, penyesuian waktu tanam, penggunaan teknik irigasi hemat air, pengembangan teknologi pencarian sumber daya air baru, penggunaan rumah kasa/rumah plastik, peningkatan kemampuan petani dan penyuluh dalam memahami perubahan iklim melalui sekolah lapang. Ke depan masih perlu dilakukan kajian proyeksi iklim dengan berbagai skenario dan berbagai Global circular model (GCM) serta kajian dampak perubahan iklim terhadap tanaman sayur dan buah unggulan melalui pengembangan pemodelan sistem usaha tani. Informasi proyeksi dampak perubahan iklim diperlukan sebagai upaya adaptasi dan perencanaan pembangunan pertanian yang dikaitkan dengan perubahan iklim.</p><p>Kata kunci: Buah-buahan, sayuran, perubahan iklim, global circular model, adaptasi </p>

scholarly journals Large-Scale Analysis of Global Gridded Precipitation and Temperature Datasets for Climate Change Impact Studies

2020 ◽  
Vol 21 (11) ◽  
pp. 2623-2640
Author(s):  
Mostafa Tarek ◽  
François P. Brissette ◽  
Richard Arsenault
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Large Scale ◽  
Simple Task ◽  
Minimum Requirement ◽  
Impact Studies ◽  
Large Scale Analysis ◽  
Change Impact ◽  
Intercomparison Study ◽  
Precipitation And Temperature

AbstractCurrently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/near-global precipitation datasets and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30 years of available data, so they could all potentially be used as reference datasets for climate change impact studies. The precipitation datasets include two gauged-only products (GPCC and CPC-Unified), two satellite products corrected using ground-based observations (CHIRPS V2.0 and PERSIANN-CDR V1R1), four reanalysis products (NCEP CFSR, JRA55, ERA-Interim, and ERA5), and one merged product (MSWEP V1.2). The temperature datasets include one gauge-based (CPC-Unified) and two reanalysis (ERA-Interim and ERA5) products. High-resolution gauge-based gridded precipitation and temperature datasets were combined as the reference dataset for this intercomparison study. To assess dataset performance, all combinations were used as inputs to a lumped hydrological model. The results showed that all temperature datasets performed similarly, albeit with the CPC performance being systematically inferior to that of the other three. Significant differences in performance were, however, observed between the precipitation datasets. The MSWEP dataset performed best, followed by the gauge-based, reanalysis, and satellite datasets categories. Results also showed that gauge-based datasets should be preferred in regions with good weather network density, but CHIRPS and ERA5 would be good alternatives in data-sparse regions.

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