EVALUATION OF GENERAL CIRCULATION MODELS IN PREDICTING MONTHLY RAINFALL FOR THE SANFRANCISCO RIVER BASIN

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.

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Impact of Climate Change on Water Balance Components and Droughts in the Guajoyo River Basin (El Salvador)

Water ◽

10.3390/w11112360 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2360 ◽

Cited By ~ 5

Author(s):

Pablo Blanco-Gómez ◽

Patricia Jimeno-Sáez ◽

Javier Senent-Aparicio ◽

Julio Pérez-Sánchez

Keyword(s):

Climate Change ◽

El Salvador ◽

River Basin ◽

General Circulation ◽

Goodness Of Fit ◽

General Circulation Models ◽

Future Climate ◽

Future Climate Change ◽

Climate Change Scenarios ◽

Historical Series

This study assessed how changes in terms of temperature and precipitation might translate into changes in water availability and droughts in an area in a developing country with environmental interest. The hydrological model Soil and Water Assessment Tool (SWAT) was applied to analyze the impacts of climate change on water resources of the Guajoyo River Basin in El Salvador. El Salvador is in one of the most vulnerable regions in Latin America to the effects of climate change. The predicted future climate change by two climate change scenarios (RCP 4.5 and RCP 8.5) and five general circulation models (GCMs) were considered. A statistical analysis was performed to identify which GCM was better in terms of goodness of fit to variation in means and standard deviations of the historical series. A significant decreasing trend in precipitation and a significant increase in annual average temperatures were projected by the middle and the end of the twenty–first century. The results indicated a decreasing trend of the amount of water available and more severe droughts for future climate scenarios with respect to the base period (1975–2004). These findings will provide local water management authorities useful information in the face of climate change to help decision making.

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Estimation of Runoff under changed climatic scenario of a Meso Scale River by Neural Network Based Gridded Model Approach

10.21203/rs.3.rs-223790/v1 ◽

2021 ◽

Author(s):

Debajit Das ◽

Tilottama Chakraborty ◽

Mrinmoy Majumder ◽

Tarun Kanti Bandyopadhyay

Keyword(s):

Climate Change ◽

River Basin ◽

General Circulation ◽

General Circulation Models ◽

Group Method ◽

North East India ◽

North East ◽

Hadley Centre ◽

Gomati River ◽

The Impact

Abstract As climate change is linked with changes in precipitation, evapotranspiration and changes in other climatological parameters, these changes will be affected runoff of a river basin. Gomati River basin is the largest river basin among all the river basin of Tripura. Due to the increase in settlement in the Gomati river basin and climate change may threaten natural flow patterns that endure its diversity. This study assesses the impact of climate change on total flow of a catchment in North East India (Gomati River catchment). For this assessment, the Group Method of Data Handling Modeling System (GMDH) model was used to simulate the rainfall-runoff relationship of the catchment, with respect to the observed data during the period of 2008–2009. The statistically downscaled outputs of HadGEM2-ES (Hadley Centre Global Environment Model version 2), general circulation models (GCMs) scenario was used to assess the impacts of climate change on the Gomati River Basin. Future projections were developed for the 2030s, 2040s and 2050s projections, respectively. The results from the present study can contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in North East, Tripura.

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

Journal of Water and Climate Change ◽

10.2166/wcc.2021.124 ◽

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.

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Reducing the basin vulnerability by land management practices under past and future climate: a case study of the Nam Ou River Basin, Lao PDR

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-9863-2014 ◽

2014 ◽

Vol 11 (8) ◽

pp. 9863-9905

Author(s):

M. Maharjan ◽

M. S. Babel ◽

S. Maskey

Keyword(s):

River Basin ◽

Land Management ◽

Sediment Yield ◽

General Circulation ◽

Management Practices ◽

Greenhouse Gas Emission ◽

General Circulation Models ◽

Future Climate ◽

Projection Data ◽

Research Station

Abstract. This research evaluates different land management practices for the Nam Ou River Basin in Northern Laos for reducing vulnerability of the basin due to erosion and sediment yield under existing and future climate conditions. We use climate projection data (precipitation and temperature) from three general circulation models (GCMs) for three greenhouse gas emission scenarios (GHGES), namely B1, A1B and A2 and three future periods, namely 2011–2030, 2046–2065 and 2080–2099. These large resolution GCM data are downscaled using the Long Ashton Research Station-Weather Generator (LARS-WG). The Soil and Water Assessment Tool (SWAT), which is a process based hydrological model, is used to simulate discharge and sediment yield and a threshold value of annual sediment yield is applied to identify vulnerable sub-basins. Results show that the change in the annual precipitation is expected to be between −7.60 to 2.64% in 2011–2030, −8.98 to 11.85% in 2046–2065, and −11.04 to 25.84% in 2080–2099. In the meantime, the changes in mean monthly temperature vary from 0.3 to 1.3 °C in the 2011–2030, 1.3 to 2.9 °C in the 2046–2065 and 1.9 to 4.9 °C in the 2080–2099. Five sub-basins are identified vulnerable (critical) under the current climate. Our results show that terracing is the most effective land management practice to reduce sediment yield in these sub-basins followed by strip-cropping and filter strip. Appropriate land management practices applied under future climate scenarios show significant reduction in sediment yield (i.e. up to the tolerance limit) except for some sub-basins. In these exceptional sub-basins, designing an optimum combination of management practices is essential to reduce the vulnerability of the basin.

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

Hydrology ◽

10.3390/hydrology7030063 ◽

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.

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Application of a technique for scenario prediction of climate change impact on the water balance components of northern river basins

Journal of Hydrology and Hydromechanics ◽

10.2478/johh-2014-0025 ◽

2014 ◽

Vol 62 (3) ◽

pp. 197-208 ◽

Cited By ~ 3

Author(s):

Yeugeniy M. Gusev ◽

Olga N. Nasonova

Keyword(s):

Climate Change ◽

Time Series ◽

Water Balance ◽

River Basin ◽

General Circulation ◽

General Circulation Models ◽

River Basins ◽

Surface Model ◽

Water Balance Components ◽

Northern River

Abstract The scenario forecasting technique for assessing changes of water balance components of the northern river basins due to possible climate change was developed. Three IPCC global emission scenarios corresponding to different possible scenarios for economic, technological, political and demographic development of the human civilization in the 21st century were chosen for generating climate change projections by an ensemble of 16 General Circulation Models with a high spatial resolution. The projections representing increments of monthly values of meteorological characteristics were used for creating 3-hour meteorological time series up to 2063 for the Northern Dvina River basin, which belongs to the pan-Arctic basin and locates at the north of the European part of Russia. The obtained time series were applied as forcing data to drive the land surface model SWAP to simulate possible changes in the water balance components due to different scenarios of climate change for the Northern Dvina River basin

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Isolating the impacts of land use and climate change on streamflow

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-2201-2015 ◽

2015 ◽

Vol 12 (2) ◽

pp. 2201-2242 ◽

Cited By ~ 4

Author(s):

I. Chawla ◽

P. P. Mujumdar

Keyword(s):

Climate Change ◽

Land Use ◽

River Basin ◽

General Circulation ◽

General Circulation Models ◽

Hydrologic Model ◽

Combined Effect ◽

Infiltration Capacity ◽

Ganga Basin

Abstract. Streamflow regime is sensitive to changes in land use and climate in a river basin. Quantifying the isolated and integrated impacts of land use and climate change on streamflow is challenging as well as crucial to optimally manage water resources in the river basin. This paper presents a simple hydrologic modelling based approach to segregate the impacts of land use and climate change on streamflow of a river basin. The upper Ganga basin in India is selected as the case study to carry out the analysis. Streamflow in the river basin is modelled using a calibrated variable infiltration capacity hydrologic model. The approach involves development of three scenarios to understand the influence of land use and climate on streamflow. The first scenario assesses the sensitivity of streamflow to land use changes under invariant climate. The second scenario determines the change in streamflow due to change in climate assuming constant land use. The third scenario estimates the combined effect of changing land use and climate over streamflow of the basin. Based on the results obtained from the three scenarios, quantification of isolated impacts of land use and climate change on streamflow is addressed. Future projections of climate are obtained from dynamically downscaled simulations of six general circulation models (GCMs) available from the Coordinated Regional Downscaling Experiment (CORDEX) project. Uncertainties associated with the GCMs and emission scenarios are quantified in the analysis. Results for the case study indicate that streamflow is highly sensitive to change in urban area and moderately sensitive to change in crop land area. However, variations in streamflow generally reproduce the variations in precipitation. Combined effect of land use and climate on streamflow is observed to be more pronounced compared to their individual impacts in the basin. It is observed from the isolated effects of land use and climate change that climate has a more dominant impact on streamflow in the region. The approach proposed in this paper is applicable to any river basin to isolate the impacts of land use change and climate change on the streamflow.

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Linking climate change to soil loss estimation in the Kosi river basin, India

Journal of Water and Climate Change ◽

10.2166/wcc.2021.259 ◽

2021 ◽

Author(s):

Aadil Towheed ◽

Thendiyath Roshni

Keyword(s):

Climate Change ◽

River Basin ◽

Soil Loss ◽

General Circulation ◽

General Circulation Models ◽

Rainfall Data ◽

Rainfall Runoff ◽

Rainfall Analysis ◽

Time Period ◽

Spatio Temporal

Abstract This study assessed the spatio-temporal variability of soil loss based on rainfall–runoff erosivity in the context of climate change in the Kosi river basin. The observed rainfall data (1985–2017) were used for past and present analyses, and the projected rainfall data (2020–2100) interpolated for various general circulation models (GCMs) were used for future analysis. The results of rainfall analysis for the projected period show a maximum percentage variation of 26.2% for a particular GCM and an average of 9.4% increase in the rainfall data from all selected GCMs considering three representative concentration pathways (RCPs). We also evaluated the implications of change in the soil loss due to changes in the rainfall pattern and crop management factor for three time slices. The results for the projected time period showed a concomitant increase in the average soil loss of −13.03–10.39% with respect to the baseline. The average soil loss results for the time period of 2020–2100 are also compared with the average soil loss for each RCP scenario and found very meager changes in the area of soil erosion. The results due to climate change aid in prioritizing the areas with suitable conservation support practices.

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Water resources of the Desna river basin under future climate

Journal of Water and Climate Change ◽

10.2166/wcc.2021.034 ◽

2021 ◽

Author(s):

Osypov Valeriy ◽

Speka Oleh ◽

Chyhareva Anastasiia ◽

Osadcha Nataliia ◽

Krakovska Svitlana ◽

...

Keyword(s):

Water Resources ◽

River Basin ◽

General Circulation ◽

Large Scale ◽

Climate Models ◽

Assessment Tool ◽

General Circulation Models ◽

Future Climate ◽

Moderate Increase ◽

Water Balance Components

Abstract Climate change impact on water resources has been observing in Ukraine since the end of the 20th century. For now, only large-scale climate impact studies cover Ukraine territory, having low credibility for a specific catchment. This study aims to calculate future changes in river discharge, water flow components, and soil water within the Desna basin and evaluate vulnerability trends on this basis. The framework assembles the process-based SWAT (Soil and Water Assessment Tool) model and eight high-resolution regional climate models (RCMs) driven by RCP4.5 and RCP8.5 emission scenarios. The climate models are provided by the Euro-CORDEX initiative and based on three RCMs (RCA4, HIRHAM5, and RACMO22E) forced by five general circulation models (CNRM-CM5, EC-EARTH, IPSL-CM5A-MR, HadGEM2-ES, and MPI-ESM-LR). The results preferably show a moderate increase in the annual discharge till the end of the 21st century. The intra-annual changes of water balance components negatively affect the vegetation period because of higher dryness and temperature stress but reduce flood risk, diffuse pollution, and water erosion in the far future. In the river basin management plan, the highest attention should be paid to adaptive strategies in agriculture because of possible water deficit in the vegetation season under future climate scenarios.

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