scholarly journals Impact of Climate Change on Runoff Prediction in Ogbese River Watershed

2021 ◽  
Vol 6 (4) ◽  
Author(s):  
Obinna A. Obiora-Okeke ◽  
James R. Adewumi ◽  
Ochuko M. Ojo
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Sandy Loam ◽  
Circulation Model ◽  
Climate Scenario ◽  
Curve Number ◽  
Runoff Simulation ◽  
Maximum Rainfall ◽  
River Watershed ◽  
Peak Runoff

Increased rainfall amounts are projected in the humid southern parts of Nigeria due to climate change. The consequence of higher rainfall in future years would result to higher peak runoffs and flood stages in streams in these parts. The focus of this study is to simulate peak runoff at the outlet of Ogbese river watershed for future years of 2030, 2040, 2050 and 2060. Local twenty years (2000-2019) historical rainfall depths were used to statically downscale General Circulation Model outputs in the future for RCP 4.5 climate scenario. Downscaled rainfall depths were inputted in HEC-HMS model version 4.2 for rainfall-runoff simulation. The watershed was delineated with DEM in ArcGIS while four land use and land cover classifications were extracted with QGIS. Maximum rainfall depths projected in years 2030, 2040, 2050 and 2060 were 38.5mm/hr, 39mm/hr, 42mm/hr and 46mm/hr respectively. Peak runoff discharge simulated for RCP 4.5 climate scenario in years 2030, 2040, 2050 and 2060 are 1771m3/s, 1826 m3/s, 1897 m3/s and 2200 m3/s respectively. This represents 24.2% increase peak discharge between 2030 and 2060. Land area delineated for the catchment is 1946.2 km2. The LULC classification areas for urban area, forest, rock outcrop and bare land are 81.59 km2, 1721.84 km2, 146.27 km2 and 4.11 km2 respectively. The soil types are sandy clay loam (92.51 %), sandy loam (6.84 %), and clay (0.65 %). Curve Number and Initial abstraction parameter values are 70.27 and 2.89 respectively. Keywords- Climate change, GCM, HEC-HMS , Ogbese river, Peak runoff 

scholarly journals STOCHASTIC TYPHOON MODEL AND ITS APPLICATION TO FUTURE TYPHOON PROJECTION

2011 ◽  
Vol 1 (32) ◽  
pp. 16 ◽  
Author(s):  
Tomohiro Yasuda ◽  
Hajime Mase ◽  
Shoji Kunitomi ◽  
Nobuhito Mori ◽  
Yuta Hayashi
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Circulation Model ◽  
Stochastic Modelling ◽  
Climate Scenario ◽  
Future Climate ◽  
Statistical Characteristics ◽  
Climate Change Signal ◽  
Change Scenario ◽  
Climate Conditions

This study presents a stochastic typhoon model (STM) for estimating the characteristics of typhoons in the present and future climate conditions. Differences of statistical characteristics between present and future typhoons were estimated from projections by an Atmospheric General Circulation Model (AGCM) under a climate change scenario and are taken into account in the stochastic modelling of future typhoons as a climate change signal. From the STM results which utilize the Monte Carlo simulation, it was found that the frequency of typhoon landfall in Osaka bay area, Japan, will decrease, although the mean value of atmospheric central pressure of typhoon will not change significantly. The arrival probability of stronger typhoons will increase in the future climate scenario.

“Modes of Variability” and Climate Change

2009 ◽  
Vol 22 (10) ◽  
pp. 2639-2658 ◽  
Author(s):  
Grant Branstator ◽  
Frank Selten
Keyword(s):  
Climate Change ◽  
Stochastic Model ◽  
General Circulation ◽  
Climate Changes ◽  
Circulation Model ◽  
Meridional Wind ◽  
Model Atmosphere ◽  
Linear Pattern ◽  
Modes Of Variability ◽  
Mean State

Abstract A 62-member ensemble of coupled general circulation model (GCM) simulations of the years 1940–2080, including the effects of projected greenhouse gas increases, is examined. The focus is on the interplay between the trend in the Northern Hemisphere December–February (DJF) mean state and the intrinsic modes of variability of the model atmosphere as given by the upper-tropospheric meridional wind. The structure of the leading modes and the trend are similar. Two commonly proposed explanations for this similarity are considered. Several results suggest that this similarity in most respects is consistent with an explanation involving patterns that result from the model dynamics being well approximated by a linear system. Specifically, the leading intrinsic modes are similar to the leading modes of a stochastic model linearized about the mean state of the GCM atmosphere, trends in GCM tropical precipitation appear to excite the leading linear pattern, and the probability density functions (PDFs) of prominent circulation patterns are quasi-Gaussian. There are, on the other hand, some subtle indications that an explanation for the similarity involving preferred states (which necessarily result from nonlinear influences) has some relevance. For example, though unimodal, PDFs of prominent patterns have departures from Gaussianity that are suggestive of a mixture of two Gaussian components. And there is some evidence of a shift in probability between the two components as the climate changes. Interestingly, contrary to the most prominent theory of the influence of nonlinearly produced preferred states on climate change, the centroids of the components also change as the climate changes. This modification of the system’s preferred states corresponds to a change in the structure of its dominant patterns. The change in pattern structure is reproduced by the linear stochastic model when its basic state is modified to correspond to the trend in the general circulation model’s mean atmospheric state. Thus, there is a two-way interaction between the trend and the modes of variability.

scholarly journals Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

2012 ◽  
Vol 12 (6) ◽  
pp. 3131-3145 ◽  
Author(s):  
A. P. K. Tai ◽  
L. J. Mickley ◽  
D. J. Jacob ◽  
E. M. Leibensperger ◽  
L. Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Transport Model ◽  
Circulation Model ◽  
Multiple Linear Regression Model ◽  
The United States ◽  
Chemical Transport Model ◽  
Modes Of Variability ◽  
The Us ◽  
Cyclone Frequency

Abstract. We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004–2008 PM2.5 observations from ~1000 sites (~200 sites for PM2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM2.5 variability, and show that 20–40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996–2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM2.5, and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies.

scholarly journals Impact of climate change on sediment yield in the Mekong River basin: a case study of the Nam Ou basin, Lao PDR

2013 ◽  
Vol 17 (1) ◽  
pp. 1-20 ◽  
Author(s):  
B. Shrestha ◽  
M. S. Babel ◽  
S. Maskey ◽  
A. van Griensven ◽  
S. Uhlenbrook ◽  
...  
Keyword(s):  
Climate Change ◽  
Sediment Yield ◽  
General Circulation ◽  
Climate Models ◽  
Greenhouse Gas Emission ◽  
Circulation Model ◽  
Sediment Flux ◽  
Seasonal Temperature ◽  
Impact Of Climate Change ◽  
The Impact

Abstract. This paper evaluates the impact of climate change on sediment yield in the Nam Ou basin located in northern Laos. Future climate (temperature and precipitation) from four general circulation models (GCMs) that are found to perform well in the Mekong region and a regional circulation model (PRECIS) are downscaled using a delta change approach. The Soil and Water Assessment Tool (SWAT) is used to assess future changes in sediment flux attributable to climate change. Results indicate up to 3.0 °C shift in seasonal temperature and 27% (decrease) to 41% (increase) in seasonal precipitation. The largest increase in temperature is observed in the dry season while the largest change in precipitation is observed in the wet season. In general, temperature shows increasing trends but changes in precipitation are not unidirectional and vary depending on the greenhouse gas emission scenarios (GHGES), climate models, prediction period and season. The simulation results show that the changes in annual stream discharges are likely to range from a 17% decrease to 66% increase in the future, which will lead to predicted changes in annual sediment yield ranging from a 27% decrease to about 160% increase. Changes in intra-annual (monthly) discharge as well as sediment yield are even greater (−62 to 105% in discharge and −88 to 243% in sediment yield). A higher discharge and sediment flux are expected during the wet seasons, although the highest relative changes are observed during the dry months. The results indicate high uncertainties in the direction and magnitude of changes of discharge as well as sediment yields due to climate change. As the projected climate change impact on sediment varies remarkably between the different climate models, the uncertainty should be taken into account in both sediment management and climate change adaptation.

scholarly journals Impacts of climate change on runoff in the source area of the Yellow River

2021 ◽  
Author(s):  
Dongying Yi ◽  
Yue Xu ◽  
Nan Wang ◽  
Xiaoyi Ma
Keyword(s):  
General Circulation ◽  
Yellow River ◽  
Swat Model ◽  
Circulation Model ◽  
Source Area ◽  
Annual Runoff ◽  
The Yellow River ◽  
Runoff Simulation ◽  
Climate System Model ◽  
Average Annual Runoff

The primary approach to realizing long-term runoff prediction involves combining a hydrological model with general circulation model. Previous studies on the Source area of the Yellow River were all based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) data sets with defects in physical mechanisms. In this paper, the Beijing Climate Center Climate System Model (BCC-CSM2-MR) of CMIP6, which proved to perform well in arid and semi-arid regions, will be used to drive the Soil & Water Assessment Tool (SWAT) model and evaluate its applicability in runoff simulation at Tang Nahai Hydrological Station from 2011 to 2019. The occurrence of the extreme value of runoff, its change trend, and the year of abrupt change of runoff in the four Shared Socio-economic Pathway (SSP) scenarios (SSP1-2.6, 2-4.5, 3-7.0, and 5-8.5) during 2021-2100 were analyzed. The results show that: (1) the runoff simulation evaluation index of SWAT driven by BCC-CSM2-MR in the research area from 2011 to 2019 is excellent, and the runoff simulation in the future is reliable and effective. (2) only the average annual runoff in scenario 5-8.5 (708.5m /s) from 2021 to 2100 was significantly higher than that in 2011-2019. Other scenarios are close to or less than the annual runoff observed. Most importantly, the maximum and minimum annual runoff values under the four scenarios all occurred during 2060-2080, so the attribution analysis of runoff extremum during 2060-2080 is worth further study. (3) it is necessary to evaluate whether the existing reservoirs and hydropower stations in the Yellow River basin can reasonably regulate and utilize the annual runoff under scenario 5-8.5.

scholarly journals Evaluation of Climate Change Impacts on Cotton Yield using Cropsyst and Regression Models

2018 ◽  
Vol 8 ◽  
pp. 1433-1451 ◽  
Author(s):  
Pantazis Georgiou ◽  
Panagiota Koukouli
Keyword(s):  
Climate Change ◽  
Regression Models ◽  
General Circulation ◽  
Management Practices ◽  
Potential Evapotranspiration ◽  
Circulation Model ◽  
Planting Date ◽  
Climate Variables ◽  
Cotton Yield ◽  
Cotton Production

The regional as well as the international crop production is expected to be influenced by climate change. This study describes an assessment of simulated potential cotton yield using CropSyst, a cropping systems simulation model, in Northern Greece. CropSyst was used under the General Circulation Model CGCM3.1/T63 of the climate change scenario SRES B1 for time periods of climate change 2020-2050 and 2070-2100 for two planting dates. Additionally, an appraisal of the relationship between climate variables, potential evapotranspiration and cotton yield was done based on regression models. Multiple linear regression models based on climate variables and potential evapotranspiration could be used as a simple tool for the prediction of crop yield changes in response to climate change in the future. The CropSyst simulation under SRES B1, resulted in an increase by 6% for the period 2020-2050 and a decrease by about 15% in cotton yield for 2070-2100. For the earlier planting date a higher increase and a slighter reduction was observed in cotton yield for 2020-2050 and 2070-2100, respectively. The results indicate that alteration of crop management practices, such as changing the planting date could be used as potential adaptation measures to address the impacts of climate change on cotton production.

scholarly journals Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

2021 ◽  
Author(s):  
Saeed Farzin ◽  
Mahdi Valikhan Anaraki
Keyword(s):  
Climate Change ◽  
Suspended Sediment ◽  
General Circulation ◽  
Sediment Load ◽  
Fuzzy Inference ◽  
Circulation Model ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
Inference System ◽  
New Strategy

Abstract In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038).

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