Downscaling technique uncertainty in assessing hydrological impact of climate change in the Upper Beles River Basin, Ethiopia

2012 ◽  
Vol 44 (2) ◽  
pp. 377-398 ◽  
Author(s):  
Girma Yimer Ebrahim ◽  
Andreja Jonoski ◽  
Ann van Griensven ◽  
Giuliano Di Baldassarre
Keyword(s):  
River Basin ◽  
Daily Precipitation ◽  
Epistemic Uncertainty ◽  
Climate Impact ◽  
Percentage Change ◽  
Research Station ◽  
Model Ensembles ◽  
Ashton Research Station ◽  
Change In Mean ◽  
Precipitation And Temperature

We investigate the uncertainty associated with downscaling techniques in climate impact studies, using the Upper Beles River Basin (Upper Blue Nile) in Ethiopia as an example. The main aim of the study is to estimate the two sources of uncertainty in downscaling models: (1) epistemic uncertainty and (2) stochastic uncertainty due to inherent variability. The first aim was achieved by driving a Hydrologic Engineering Centre-Hydrological Modelling System (HEC-HMS) model with downscaled daily precipitation and temperature using three downscaling models: Statistical Downscaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) and an artificial neural network (ANN). The second objective was achieved by driving the hydrological model with individual downscaled daily precipitation and temperature ensemble members, generated by using the stochastic component of the SDSM. Results of the study showed that the downscaled precipitation and temperature time series are sensitive to the downscaling techniques. More specifically, the percentage change in mean annual flow ranges from 5% reduction to 18% increase. By analyzing the uncertainty of the SDSM model ensembles, it was found that the percentage change in mean annual flow ranges from 6% increase to 8% decrease. This study demonstrates the need for extreme caution in interpreting and using the output of a single downscaling model.

scholarly journals Evaluation of the CMIP5 general circulation models for simulating the precipitation and temperature of the Koshi River Basin in Nepal

2021 ◽  
Author(s):  
Pragya Pradhan ◽  
Sangam Shrestha ◽  
S. Mohana Sundaram ◽  
Salvatore G. P. Virdis
Keyword(s):  
River Basin ◽  
General Circulation ◽  
Performance Indicator ◽  
Extreme Temperature ◽  
General Circulation Models ◽  
Climate Impact ◽  
Climate Indices ◽  
Extreme Precipitation Events ◽  
Koshi River Basin ◽  
Precipitation And Temperature

Abstract This study evaluates the performance of 12 different general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to simulate precipitation and temperature in the Koshi River Basin, Nepal. Four statistical performance indicators: correlation coefficient, normalised root-mean-square deviation (NMRSD), absolute NMRSD, and average absolute relative deviation are considered to evaluate the GCMs using historical observations. Seven different climate indices: consecutive dry days, consecutive wet days, cold spell duration index, warm spell duration index, frost days, very wet days, and simple daily intensity index are considered to identify the most suitable models for the basin and future climate impact assessment studies. Weights for each performance indicator are determined using the entropy method, with compromise programming applied to rank the GCMs based on the Euclidian distant technique. The results suggest that CanESM2 and CSIRO-MK3.6.0 are the most suitable for predicting extreme precipitation events, and BCC-CSM 1.1, CanESM2, NorESM1-M, and CNRM-CM5 for extreme temperature events in Himalayan river basins. Overall, IPSL-CM5A-MR, CanESM2, CNRM-CM5, BCC-CSM 1.1, NorESM1-M, and CSIRO-Mk3.6.0 are deemed suitable models for predicting precipitation and temperature in the Koshi River Basin, Nepal.

scholarly journals Statistical Reconstruction of Daily Precipitation and Temperature Fields in Switzerland back to 1864

2019 ◽  
Author(s):  
Lucas Pfister ◽  
Stefan Brönnimann ◽  
Mikhaël Schwander ◽  
Francesco Alessandro Isotta ◽  
Pascal Horton ◽  
...  
Keyword(s):  
Spatial Information ◽  
Daily Precipitation ◽  
Temperature Reconstruction ◽  
Climate Impact ◽  
Temperature Fields ◽  
Weather Data ◽  
Weather Extremes ◽  
Weather Patterns ◽  
Local Station ◽  
Precipitation And Temperature

Abstract. Spatial information on past weather contributes to better understand the processes behind day-to-day weather variability and to assess the risks arising from weather extremes. For Switzerland, daily-resolved spatial information on meteorological parameters is restricted to the period starting from 1961, whereas prior to that local station observations are the only source of daily, long-term weather data. While attempts have been made to reconstruct spatial weather patterns for certain extreme events, the task of creating a continuous spatial weather reconstruction dataset for Switzerland has so far not been addressed. Here, we aim to reconstruct daily, high-resolution precipitation and temperature fields for Switzerland back to 1864 with an analogue resampling method (ARM) using station data and a weather type classification. Analogue reconstructions are post-processed with an ensemble Kalman fitting (EnKF) approach and quantile mapping. Results suggest that the presented methods are suitable for daily precipitation and temperature reconstruction. Evaluation experiments reveal an excellent skill for temperature and a good skill for precipitation. As illustrated on the example of the avalanche winter 1887/88, these weather reconstructions have a great potential for various analyses of past weather and for climate impact modelling.

scholarly journals Statistical reconstruction of daily precipitation and temperature fields in Switzerland back to 1864

2020 ◽  
Vol 16 (2) ◽  
pp. 663-678 ◽  
Author(s):  
Lucas Pfister ◽  
Stefan Brönnimann ◽  
Mikhaël Schwander ◽  
Francesco Alessandro Isotta ◽  
Pascal Horton ◽  
...  
Keyword(s):  
Spatial Information ◽  
Daily Precipitation ◽  
Temperature Reconstruction ◽  
Climate Impact ◽  
Temperature Fields ◽  
Weather Data ◽  
Weather Extremes ◽  
Weather Patterns ◽  
Local Station ◽  
Precipitation And Temperature

Abstract. Spatial information on past weather contributes to better understanding the processes behind day-to-day weather variability and to assessing the risks arising from weather extremes. For Switzerland, daily resolved spatial information on meteorological parameters is restricted to the period starting from 1961, whereas prior to that local station observations are the only source of daily long-term weather data. While attempts have been made to reconstruct spatial weather patterns for certain extreme events, the task of creating a continuous spatial weather reconstruction dataset for Switzerland has so far not been addressed. Here, we aim to reconstruct daily high-resolution precipitation and temperature fields for Switzerland back to 1864 with an analogue resampling method (ARM) using station data and a weather type classification. Analogue reconstructions are post-processed with an ensemble Kalman fitting (EnKF) approach and quantile mapping. Results suggest that the presented methods are suitable for daily precipitation and temperature reconstruction. Evaluation experiments reveal excellent skill for temperature and good skill for precipitation. As illustrated with the example of the avalanche winter of 1887/88, these weather reconstructions have great potential for various analyses of past weather and for climate impact modelling.

scholarly journals Daily Precipitation Generation using a Hidden Markov Model with Correlated Emissions for the Potomac River Basin

PAMM ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Gerson C. Kroiz ◽  
Reetam Majumder ◽  
Matthias K. Gobbert ◽  
Nagaraj K. Neerchal ◽  
Kel Markert ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
River Basin ◽  
Daily Precipitation ◽  
Hidden Markov ◽  
Potomac River ◽  
Potomac River Basin

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

scholarly journals Impact of climate change on hydro-meteorological drought over the Be River Basin, Viet Nam

2021 ◽  
Author(s):  
Dao Nguyen Khoi ◽  
Truong Thao Sam ◽  
Pham Thi Loi ◽  
Bui Viet Hung ◽  
Van Thinh Nguyen
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Southern Oscillation ◽  
Meteorological Drought ◽  
Climate Projections ◽  
Research Station ◽  
Climate Change Scenarios ◽  
Study Region ◽  
Meteorological Droughts

Abstract In this paper, the responses of hydro-meteorological drought to changing climate in the Be River Basin located in Southern Vietnam are investigated. Climate change scenarios for the study area were statistically downscaled using the Long Ashton Research Station Weather Generator tool, which incorporates climate projections from Coupled Model Intercomparison Project 5 (CMIP5) based on an ensemble of five general circulation models (Can-ESM2, CNRM-CM5, HadGEM2-AO, IPSL-CM5A-LR, and MPI-ESM-MR) under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). The Soil and Water Assessment Tool model was employed to simulate streamflow for the baseline time period and three consecutive future 20 year periods of 2030s (2021–2040), 2050s (2041–2060), and 2070s (2061–2080). Based on the simulation results, the Standardized Precipitation Index and Standardized Discharge Index were estimated to evaluate the features of hydro-meteorological droughts. The hydrological drought has 1-month lag time from the meteorological drought and the hydro-meteorological droughts have negative correlations with the El Niño Southern Oscillation and Pacific Decadal Oscillation. Under the climate changing impacts, the trends of drought severity will decrease in the future; while the trends of drought frequency will increase in the near future period (2030s), but decrease in the following future periods (2050 and 2070s). The findings of this study can provide useful information to the policy and decisionmakers for a better future planning and management of water resources in the study region.

scholarly journals Evaluation of Wheat Genotypes in Far Western Hills of Nepal

2015 ◽  
Vol 3 (3) ◽  
pp. 417-422
Author(s):  
Hari Kumar Prasai ◽  
Jiban Shrestha
Keyword(s):  
Grain Yield ◽  
River Basin ◽  
Treatment Effect ◽  
Agricultural Research ◽  
Research Station ◽  
Combined Analysis ◽  
Wheat Genotypes ◽  
Yield Trial ◽  
The Difference ◽  
Year 2013

Coordinated Varietal Trial (CVT) and Advanced Varietal Trial (AVT) of wheat were conducted at Regional Agricultural Research Station,Doti during the year 2012 and 2013. Microplot Yield Trial (MPYT) were conducted during the year 2013. Total 20 genotypes were includedin CVT experiment of both years. Although the difference in grain yield due to genotypes was not found significant during the year 2012, NL1144 recorded the highest grain yield (4309 kg/ha) followed by NL 1140 (4295 kg/ha) and NL 1147 (4165 kg/ha) respectively. But in the year2013, NL 1097 produced the highest grain yield (4641 kg/ha) followed by NL 1135 (4383 kg/ha) and NL 1164 (4283 kg/ha) respectively.Statistically, the difference in grain yield due to genotypes was not found significant in the year 2013. Combined analysis over years was alsocarried out. Out of 20, only 10 genotypes were included in the CVT experiment, which were found similar in both years. Genotypes NL 1097(4079 kg/ha), NL 1140 (3814 kg/ha) and NL 1093 (3773 kg/ha) were found high yielding genotypes for river basin agro-environment of farwestern hills. Statistically, effect of year in tested characters was found significant whereas treatment effect was observed non-significant.Similarly, 20 genotypes of wheat were included in AVT of wheat during the year 2012 and 2013. Out of the genotypes included in AVT duringthe year 2012, KISKADEE No.1recorded the highest grain yield (3824 kg/ha) followed by CHEWINK No. 1 (3643 kg/ha) and WK 2120 (3583kg/ha). Statistically all the tested characters except grain yield were found significantly different due to genotypes. But in the same experimentof the year 2013, WK 2412 genotype recorded the highest grain yield (4407 kg/ha) followed by WK 2411 (4329 kg/ha) and Munal-1 (4054kg/ha). Statistically the difference in grain yield and other tested characters were found significantly different. Due to dissimilarity in the testedgenotypes we could not carry-out the combined analysis over years. Total 30 genotypes were included in the MPYT experiment of the year2013. Genotype WK 2272 recorded the highest grain yield (6080 kg/ha) followed by the genotypes WK 2274 (5152 kg/ha) and WK 2278(4480 kg/ha) respectively. Statistically, the difference in grain yield and other tested characters were found significantly different due togenotypes.Int J Appl Sci Biotechnol, Vol 3(3): 417-422

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