Residual Mass Severity Index (RMSI) – a duration free method to characterise droughts

2020 ◽  
Author(s):  
Rounak Afroz ◽  
Ashish Sharma ◽  
Fiona Johnson
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Severity Index ◽  
General Circulation Models ◽  
Drought Indices ◽  
Severe Drought ◽  
Monthly Precipitation ◽  
Water Shortages ◽  
Simple Method ◽  
Residual Mass

<p>The complexity of representing droughts has led to many drought indices being developed. A common aspect for many of these indices, however, is the need to adopt a predefined time period, over which a drought is characterized. Therefore, to declare a catchment as drought-impacted, 6, 12 or 24-month SPI are required. Actual water allocations, however, are required at all times and are thus duration free; a concept well described by the well-known residual mass curve. Here we propose a new framework to characterize drought, termed as the Residual Mass Severity Index (RMSI). As the name suggests, the RMSI defines drought based on the magnitude of the residual mass in any location which is calculated by performing a water balance using a prescribed demand. Demand here is adopted as the median monthly precipitation for the region. Water shortages only become significant when there is a sustained deficit compared to this demand. The above described residual mass is standardized to formulate the RMSI across Australia. The new RMSI has been validated against established drought indices (such as the SPI) to highlight the advantages of a duration-free drought index.</p><p>RMSI provides a simple method of assessing sustained and severe drought anomalies which is important with expected increases in water scarcity due to anthropogenic climate change. We demonstrate that RMSI can be used as a tool to evaluate the performance of General Circulation Models (GMCs) in representing the sustainability of water resource systems as a product of resilience, reliability, and vulnerability (RRV) of the system. Future projections of drought from GCMs which perform well in representing RMSI in the RRV context in the historical climate are then compared to drought projections from the full CMIP5 ensemble.</p><p>Keywords: Drought, Residual Mass Curve, SPI, RRV, Climate Change, CMIP5 GCMs</p>

scholarly journals Climate change impact on meteorological droughts in watershed scale (case study: southwestern Iran)

2014 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Alireza Nikbakht Shahbazi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Meteorological Data ◽  
Climate Change Impacts ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Monthly Precipitation ◽  
Spring Precipitation ◽  
Data Simulation ◽  
Intergovernmental Panel

Drought is one of the major natural disasters in the world which has a lot of social and economic impacts. There are various factors that affect climate changes; the investigation of this incident is also sensitive. Climate scenarios of future climate change studies and investigation of efficient methods for investigating these events on drought should be assumed. This study intends to investigate climate change impacts on drought in Karoon3 watershed in the future. For this purpose, the atmospheric general circulation models (GCM) data under Intergovernmental Panel on Climate Change (IPCC) scenarios should be investigated. In this study, watershed drought under climate change impacts will be simulated in future periods (2011 to 2099). In this research standard precipitation index (SPI) was calculated using mean monthly precipitation data in Karoon3 watershed. SPI was calculated in 6, 12 and 24 months periods. Statistical analysis on daily precipitation and minimum and maximum daily temperature was performed. To determine the feasibility of future periods meteorological data production of LRAS-WG5 model, calibration and verification was performed for the base year (1980-2007). Meteorological data simulation for future periods under General Circulation Models and climate change IPCC scenarios was performed and then the drought status using SPI under climate change effects analyzed. Results showed that differences between monthly maximum and minimum temperature will decrease under climate change and spring precipitation shall increase while summer and autumn rainfall shall decrease. The most increase of precipitation will take place in winter and in December. Normal and wet SPI category is more frequent in B1 and A2 emissions scenarios than A1B. Wet years increases in the study area during 2011-2030 period and the more continuous drought years gradually increases during 2046-2065 period, the more severe and frequent drought will occur during the 2080-2099 period.

scholarly journals Climate Change Implications for Tropical Islands: Interpolating and Interpreting Statistically Downscaled GCM Projections for Management and Planning

2016 ◽  
Vol 55 (2) ◽  
pp. 265-282 ◽  
Author(s):  
Azad Henareh Khalyani ◽  
William A. Gould ◽  
Eric Harmsen ◽  
Adam Terando ◽  
Maya Quinones ◽  
...  
Keyword(s):  
Climate Change ◽  
Energy Demand ◽  
General Circulation ◽  
Large Scale ◽  
Climate Model ◽  
Model Averaging ◽  
General Circulation Models ◽  
Economic Effects ◽  
Drought Indices ◽  
Tropical Islands

AbstractThe potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to decline in both model ensembles, but the decrease was more severe in the “regionally consistent” models. The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960–90 period to the 2071–99 period was 4.6°–9°C depending on the global emission scenarios and location. This warming may cause increases in CDD, and consequently increasing energy demands. Life zones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

An Improved Daily Weather Generator for the Assessment of Regional Climate Change Impacts

2021 ◽  
Author(s):  
Mohammad Reza Khazaei ◽  
Mehraveh Hasirchian ◽  
Bagher Zahabiyoun
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Regional Climate ◽  
Low Frequency ◽  
Climate Change Impacts ◽  
Regional Climate Change ◽  
General Circulation Models ◽  
Weather Generator ◽  
Weather Data ◽  
Monthly Precipitation

Abstract Weather Generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the Improved Weather Generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulations run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of General Circulation Models (GCMs) scenarios to assess regional climate change impacts, especially hydrological effects.

scholarly journals Climate change and vine protection : the case of mildews management in Burgundy

2018 ◽  
Vol 50 ◽  
pp. 01006 ◽  
Author(s):  
S. Zito ◽  
A. Caffarra ◽  
Y. Richard ◽  
T. Castel ◽  
B. Bois
Keyword(s):  
Climate Change ◽  
Downy Mildew ◽  
Regression Models ◽  
General Circulation ◽  
Plant Protection ◽  
General Circulation Models ◽  
Regression Coefficients ◽  
Monthly Precipitation ◽  
Pesticide Use ◽  
Yearly Average

Viticulture worldwide is currently facing two major challenges: adapting to climate change and reducing its environmental footprint. Plant protection is a central aspect of these challenges, firstly because pests and diseases development is strongly controlled by climate conditions, and secondly, because viticulture requires in many regions large quantities of pesticides. Phytosanitary protection is even more crucial for terroir-based viticulture areas, because the negative image given by excessive pesticide use impacts the whole region which reputation is partly built on environmental friendly practices. Moreover, most of terroir wines sensory properties and fame rely on specific cultivars, which makes it difficult to replace them using diseases resistant varieties. This study addresses the potential impact of climate change on pesticide use to control powdery and downy mildew in Burgundy. To assess the past evolution of diseases risk, a database composed by yearly number of applications of phytosanitary treatments for powdery and downy mildew diseases was built. This information was collected from 400 grapevine growers originating from 5 sub-regions of Burgundy. The data refer to yearly average number of treatments during the 1995-2014 period. Pesticide applications was related to climate by means of multiple linear regression models between the average number of treatments for powdery and downy mildews control and monthly temperature and monthly rainfall indices from April to July. Models providing the lowest error (estimated trough leave-one-out cross-validation) were selected for each of the 5 Burgundy wine sub-regions. According to each region, mildews yearly treatments number were significantly related to monthly climate data. In most models, May and June average temperature were selected with negative regression coefficients while April and May monthly precipitation were selected with positive regression coefficients. These models were fed with 1980-2100 temperature and rainfall projected data using CMIP5 RCP8.5 scenario from 18 GCM (General Circulation Models) statistically downscaled and debiased to match a daily 12 km target resolution. Increasing temperature and a slight ensemble rainfall increase (depending on the GCM) simulated during spring and early summer had inverse effects on models trend. However, regression models project a decreasing trend of the number of treatments for mildews control along the 21st century, based upon GCM data. Spraying numbers are expected to slightly vary in the future (-4,8 to -34.1% reduction), which strengthens the need for alternative plant protection strategies to match both consumer and policies promoting lower pesticides use for viticulture.

scholarly journals Twenty-First Century Drought Analysis Across China using CMIP6 Under Climate Change

2021 ◽  
Author(s):  
Gengxi Zhang ◽  
Thian Yew Gan ◽  
Xiaoling Su
Keyword(s):  
Soil Moisture ◽  
General Circulation ◽  
Coupled Model ◽  
Severity Index ◽  
General Circulation Models ◽  
Mitigation Strategies ◽  
Drought Indices ◽  
Drought Severity ◽  
Moisture Index ◽  
Arid Northwestern China

Abstract Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices (the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)). Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the 21st century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in humid regions. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices are crucial in project representative future droughts and meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact.

scholarly journals Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran

2021 ◽  
pp. 49-60
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Monthly Precipitation ◽  
Efficiency Coefficient ◽  
Synoptic Station ◽  
Average Maximum ◽  
Ocean General Circulation Models ◽  
The Future ◽  
Ocean General Circulation

INTRODUCTION: Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables. METHODS: In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software. FINDINGS: It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation. CONCLUSION: According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.

scholarly journals Projecting the Impact of Climate Change on Temperature, Precipitation, and Discharge in the Bago River Basin

2020 ◽  
Vol 15 (3) ◽  
pp. 324-334 ◽  
Author(s):  
Hnin Thiri Myo ◽  
Win Win Zin ◽  
Kyi Pyar Shwe ◽  
Zin Mar Lar Tin San ◽  
Akiyuki Kawasaki ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Meteorological Data ◽  
Coupled Model ◽  
General Circulation Models ◽  
Monthly Precipitation ◽  
Rcp Scenarios ◽  
Impact Of Climate Change ◽  
The Impact

Climate change affects both the temperature and precipitation, leading to changes in river runoff. The Bago River basin is one of the most important agricultural regions in the Ayeyarwady Delta of Myanmar, and this paper aims to evaluate the impact of climate change on it. Linear scaling was used as the bias-correction method for ten general circulation models (GCMs) participating in the fifth phase of the Coupled Model Intercomparison Project. Future climate scenarios are predicted for three 27-year periods: the near future (2020–2046), middle future (2047–2073), and far future (2074–2100) with a baseline period of (1981–2005) under two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5 of the IPCC Assessment Report 5 (AR5). The Hydrologic Engineering Center-Hydrologic Modeling System model is used to predict future discharge changes for the Bago River considering future average precipitation for all three future periods. Among the GCMs used to simulate meteorological data in the Ayeyarwady Delta zone, the Model for Interdisciplinary Research on Climate-Earth System is the most suitable. It predicts that average monthly precipitation will fluctuate and that average annual precipitation will increase. Both average monthly and annual temperatures are expected to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios. The simulation shows that the Bago River discharge will increase for all three future periods under both scenarios.

scholarly journals Do Downscaled General Circulation Models Reliably Simulate Historical Climatic Conditions?

2018 ◽  
Vol 22 (10) ◽  
pp. 1-22 ◽  
Author(s):  
Andrew R. Bock ◽  
Lauren E. Hay ◽  
Gregory J. McCabe ◽  
Steven L. Markstrom ◽  
R. Dwight Atkinson
Keyword(s):  
General Circulation ◽  
Statistical Tests ◽  
Circulation Model ◽  
General Circulation Models ◽  
Climatic Conditions ◽  
Low Flow ◽  
Balance Model ◽  
Monthly Precipitation ◽  
Significance Level ◽  
Ks Test

Abstract The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.

scholarly journals Mid-Holocene climate change in Europe: a data-model comparison

2007 ◽  
Vol 3 (3) ◽  
pp. 499-512 ◽  
Author(s):  
S. Brewer ◽  
J. Guiot ◽  
F. Torre
Keyword(s):  
Climate Change ◽  
Data Model ◽  
General Circulation ◽  
Model Comparison ◽  
General Circulation Models ◽  
Growing Degree Days ◽  
Degree Days ◽  
European Continent ◽  
Holocene Climate Change ◽  
Correct Location

Abstract. We present here a comparison between the outputs of 25 General Circulation Models run for the mid-Holocene period (6 ka BP) with a set of palaeoclimate reconstructions based on over 400 fossil pollen sequences distributed across the European continent. Three climate parameters were available (moisture availability, temperature of the coldest month and growing degree days), which were grouped together using cluster analysis to provide regions of homogenous climate change. Each model was then investigated to see if it reproduced 1) similar patterns of change and 2) the correct location of these regions. A fuzzy logic distance was used to compare the output of the model with the data, which allowed uncertainties from both the model and data to be taken into account. The models were compared by the magnitude and direction of climate change within the region as well as the spatial pattern of these changes. The majority of the models are grouped together, suggesting that they are becoming more consistent. A test against a set of zero anomalies (no climate change) shows that, although the models are unable to reproduce the exact patterns of change, they all produce the correct signs of change observed for the mid-Holocene.

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