scholarly journals Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
L. Campozano ◽  
D. Tenelanda ◽  
E. Sanchez ◽  
E. Samaniego ◽  
J. Feyen
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
General Circulation ◽  
General Circulation Models ◽  
Support Vector ◽  
Monthly Precipitation ◽  
Local Conditions ◽  
Southern Ecuador ◽  
Monthly Total Precipitation ◽  
Precipitation Estimates

Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.

scholarly journals An Evaluation of Statistical Downscaling Techniques for Simulating Daily Rainfall Occurrences in the Upper Ping River Basin

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 63
Author(s):  
Sirikanya Cheevaprasert ◽  
Rajeshwar Mehrotra ◽  
Sansarith Thianpopirug ◽  
Nutchanart Sriwongsitanon
Keyword(s):  
River Basin ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Low Frequency ◽  
Daily Rainfall ◽  
General Circulation Models ◽  
Research Area ◽  
Single Site ◽  
Future Research ◽  
Rainfall Occurrences

This study presents an exhaustive evaluation of the performance of three statistical downscaling techniques for generating daily rainfall occurrences at 22 rainfall stations in the upper Ping river basin (UPRB), Thailand. The three downscaling techniques considered are the modified Markov model (MMM), a stochastic model, and two variants of regression models, statistical models, one with single relationship for all days of the year (RegressionYrly) and the other with individual relationships for each of the 366 days (Regression366). A stepwise regression is applied to identify the significant atmospheric (ATM) variables to be used as predictors in the downscaling models. Aggregated wetness state indicators (WIs), representing the recent past wetness state for the previous 30, 90 or 365 days, are also considered as additional potential predictors since they have been effectively used to represent the low-frequency variability in the downscaled sequences. Grouping of ATM and all possible combinations of WI is used to form eight predictor sets comprising ATM, ATM-WI30, ATM-WI90, ATM-WI365, ATM-WI30&90, ATM-WI30&365, ATM-WI90&365 and ATM-WI30&90&365. These eight predictor sets were used to run the three downscaling techniques to create 24 combination cases. These cases were first applied at each station individually (single site simulation) and thereafter collectively at all sites (multisite simulations) following multisite downscaling models leading to 48 combination cases in total that were run and evaluated. The downscaling models were calibrated using atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis database and validated using representative General Circulation Models (GCM) data. Identification of meaningful predictors to be used in downscaling, calibration and setting up of downscaling models, running all 48 possible predictor combinations and a thorough evaluation of results required considerable efforts and knowledge of the research area. The validation results show that the use of WIs remarkably improves the accuracy of downscaling models in terms of simulation of standard deviations of annual, monthly and seasonal wet days. By comparing the overall performance of the three downscaling techniques keeping common sets of predictors, MMM provides the best results of the simulated wet and dry spells as well as the standard deviation of monthly, seasonal and annual wet days. These findings are consistent across both single site and multisite simulations. Overall, the MMM multisite model with ATM and wetness indicators provides the best results. Upon evaluating the combinations of ATM and sets of wetness indicators, ATM-WI30&90 and ATM-WI30&365 were found to perform well during calibration in reproducing the overall rainfall occurrence statistics while ATM-WI30&365 was found to significantly improve the accuracy of monthly wet spells over the region. However, these models perform poorly during validation at annual time scale. The use of multi-dimension bias correction approaches is recommended for future research.

scholarly journals Comparison of different statistical downscaling models and future projection of areal mean precipitation of a river basin under climate change effect

2021 ◽  
Author(s):  
A. Guven ◽  
A. Pala
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Gene Expression Programming ◽  
Circulation Model ◽  
Support Vector ◽  
Group Method ◽  
Monthly Precipitation ◽  
Weather Factors

Abstract Investigation of the hydrological impacts of climate change at the local scale requires the use of a statistical downscaling technique. In order to use the output of a Global Circulation Model (GCM) model, downscaling technique is used. In this study, statistical downscaling of monthly areal mean precipitation of Göksun River basin in Turkey was carried out using the Group Method of Data Handling (GMDH), Support Vector Machines (SVM) and Gene-expression Programming (GEP) techniques. Large-scale weather factors are used for a basin with monthly areal mean precipitation (PM) record from 1971 to 2000 for training and testing periods. The R2-value for precipitation in SVM, GEP and GMDH models are 0.62, 0.59, and 0.6 respectively, for testing periods. The results showed that SVM has the best model performance than the other proposed downscaling models, however, AIC values showed the GEP model has the lowest AIC value. The simulated results for CGCM3 A1B and A2 scenarios show a similarity in their average precipitation prediction. Generally, both scenarios anticipate a decrease in the average monthly precipitation during the simulated periods. Therefore, the results of future projections show that the mean precipitation might decrease during the period of 2021–2100.

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 Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

2014 ◽  
Vol 18 (18) ◽  
pp. 1-28 ◽  
Author(s):  
Aneesh Goly ◽  
Ramesh S. V. Teegavarapu ◽  
Arpita Mondal
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Global Climate Model ◽  
Monthly Rainfall ◽  
Specific Humidity ◽  
Support Vector ◽  
Climate Modelling ◽  
Monthly Precipitation ◽  
Statistical Measures ◽  
Rainfall Statistics

Abstract Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

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 Weights for general circulation models from CMIP3/CMIP5 in a statistical downscaling framework and the impact on future Mediterranean precipitation

2019 ◽  
Vol 39 (8) ◽  
pp. 3639-3654 ◽  
Author(s):  
Irena Kaspar‐Ott ◽  
Elke Hertig ◽  
Severin Kaspar ◽  
Felix Pollinger ◽  
Christoph Ring ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
General Circulation Models ◽  
The Impact

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.

Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach

2016 ◽  
Vol 7 (4) ◽  
pp. 683-707
Author(s):  
D. A. Sachindra ◽  
F. Huang ◽  
A. Barton ◽  
B. J. C. Perera
Keyword(s):  
General Circulation Model ◽  
Statistical Downscaling ◽  
General Circulation ◽  
Emission Scenario ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Maximum Temperature ◽  
Monthly Precipitation ◽  
Regression Equations ◽  
Highly Correlated

Using a key station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950–2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key station. The stations that were highly correlated with that key station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key stations and the member stations for each calendar month. The downscaling models at the key stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000–2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.

scholarly journals Downscaling of GCM-Simulated Precipitation Using Model Output Statistics

2014 ◽  
Vol 27 (1) ◽  
pp. 312-324 ◽  
Author(s):  
Jonathan M. Eden ◽  
Martin Widmann
Keyword(s):  
Statistical Downscaling ◽  
Temporal Variability ◽  
General Circulation ◽  
Large Scale ◽  
Principal Component ◽  
General Circulation Models ◽  
Model Output ◽  
Local Scaling ◽  
Simulated Precipitation ◽  
Model Output Statistics

Abstract Producing reliable estimates of changes in precipitation at local and regional scales remains an important challenge in climate science. Statistical downscaling methods are often utilized to bridge the gap between the coarse resolution of general circulation models (GCMs) and the higher resolutions at which information is required by end users. As the skill of GCM precipitation, particularly in simulating temporal variability, is not fully understood, statistical downscaling typically adopts a perfect prognosis (PP) approach in which high-resolution precipitation projections are based on real-world statistical relationships between large-scale atmospheric predictors and local-scale precipitation. Using a nudged simulation of the ECHAM5 GCM, in which the large-scale weather states are forced toward observations of large-scale circulation and temperature for the period 1958–2001, previous work has shown ECHAM5 skill in simulating temporal variability of precipitation to be high in many parts of the world. Here, the same nudged simulation is used in an alternative downscaling approach, based on model output statistics (MOS), in which statistical corrections are derived for simulated precipitation. Cross-validated MOS corrections based on maximum covariance analysis (MCA) and principal component regression (PCR), in addition to a simple local scaling, are shown to perform strongly throughout much of the extratropics. Correlation between downscaled and observed monthly-mean precipitation is as high as 0.8–0.9 in many parts of Europe, North America, and Australia. For these regions, MOS clearly outperforms PP methods that use temperature and circulation as predictors. The strong performance of MOS makes such an approach to downscaling attractive and potentially applicable to climate change simulations.

scholarly journals Deterministic and probabilistic evaluation of raw and post-processing monthly precipitation forecasts: a case study of China

2021 ◽  
Author(s):  
Yujie Li ◽  
Bin Xu ◽  
Dong Wang ◽  
QJ Wang ◽  
Xiongwei Zheng ◽  
...  
Keyword(s):  
General Circulation ◽  
Critical Role ◽  
Joint Probability ◽  
General Circulation Models ◽  
Mean Value ◽  
Monthly Precipitation ◽  
Post Processing ◽  
Probabilistic Evaluation ◽  
Forecasting Performance

Abstract Monthly Precipitation Forecasts (MPF) play a critical role in drought monitoring, hydrological forecasting and water resources management. In this study, we applied two advanced Machine Learning Models (MLM) and latest General Circulation Models (GCM) to generate deterministic MPFs with a resolution of 0.5° across China. Then the Bayesian Joint Probability (BJP) modeling approach is employed to calibrate and generate corresponding ensemble MPFs. Raw and post-processing MPFs were put against gridded observations over the period of 1981–2015. The results indicated that: (1) for deterministic evaluation, the forecasting performance of MLMs was more inclined to generate random forecasts around the mean value, while the GCMs could reflect the increasing or decreasing trend of precipitation to some degree; (2) for probabilistic evaluation, the four BJP calibrated ensemble MPFs were unbiased and reliable. Compared to climatology, reliability and sharpness were all significantly improved. However, in terms of overall accuracy metric, the ensemble MPFs generated from MLMs were similar to climatology. In contrast, the ensemble MPFs generated from GCMs achieved better forecasting skill and was not dependent on forecasting regions and months. Moreover, the post-processing method is necessary that achieve not only bias-free but also reliable as well as skillful ensemble MPFs.

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