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Global climate change is an unequivocal reality that is immensely impacting the available water resources in many regions around the globe. For sustainable water resource management, the present research aims to evaluate the impact of climatic change on streamflow using both the global climate change and hydraulic models. This research presents a novel approach of applying functional data analysis (FDA) to highlight the commonalities and differences between the outcomes of various models for streamflow analysis. Observed temperature, precipitation and streamflow data from 1985 to 2014 of Astore catchment in the Upper Indus River Basin, in Pakistan, were used for this purpose. The precipitation and temperature results of three global climate models (GCMs) were obtained under two scenarios of greenhouse gas concentration, namely RCP 2.6 and RCP 8.5. Results of precipitation and temperature obtained under climate change scenarios were subsequently used to simulate the streamflow using the Hydrologic Engineering Centre-Hydraulic Modeling System (HEC-HMS). The FDA evaluated the Euclidean distances between the streamflow data predicted by various models. The diverging trend found in these distances identified some degree of dissimilarities in the streamflow results. The simulations manifest that the streamflow will increase in the study area (Astore) till 2070, while it is expected to decline in the distant future. The concerned agencies can adopt rational water resource management strategies based on the predicted streamflow in the region.
Abdul Razzaq Ghumman; Ateeq- Ur- Rauf; Abdullah Alodah; Husnain Haider; Shafiquzzaman. Evaluating the impact of climate change on stream flow: integrating GCM, hydraulic modelling and functional data analysis. Arabian Journal of Geosciences 2020, 13, 1 -15.
AMA StyleAbdul Razzaq Ghumman, Ateeq- Ur- Rauf, Abdullah Alodah, Husnain Haider, Shafiquzzaman. Evaluating the impact of climate change on stream flow: integrating GCM, hydraulic modelling and functional data analysis. Arabian Journal of Geosciences. 2020; 13 (17):1-15.
Chicago/Turabian StyleAbdul Razzaq Ghumman; Ateeq- Ur- Rauf; Abdullah Alodah; Husnain Haider; Shafiquzzaman. 2020. "Evaluating the impact of climate change on stream flow: integrating GCM, hydraulic modelling and functional data analysis." Arabian Journal of Geosciences 13, no. 17: 1-15.
While Stochastic Weather Generators (SWGs) are used intensively in climate and hydrological applications to simulate hydroclimatic time series and estimate risks and performance measures linked to climate variability, there have been few investigations into how many realizations are required for a robust estimation of these measures. Given the computational cost and time necessary to force climate-sensitive systems with multiple realizations, the estimation of the optimal number of synthetic time series to generate with a particular SWG for a predefined accuracy when estimating a particular risk or performance measure is particularly important. In this paper, the required number of realizations of five SWGs coupled with a SWAT model (the Soil and Water Assessment Tool) needed in order to achieve a predefined Relative Root Mean Square Error is investigated. The statistical indices used are the mean, standard deviation, skewness, and kurtosis of four hydroclimatic variables: precipitation, maximum and minimum temperature, and annual streamflow obtained for each observed and model-generated time series. While the results vary somewhat across SWGs, variables and indicators, they overall show that the marginal improvement decreases dramatically after 25 realizations. The results also indicate that the benefit of generating more than 100 realizations of climate and streamflow data is very minimal. The methodology presented herein can be applied in further investigations of other set of risk indicators, SWGs, hydrological models, and watersheds to minimize the required workload.
Abdullah Alodah; Ousmane Seidou. Influence of output size of stochastic weather generators on common climate and hydrological statistical indices. Stochastic Environmental Research and Risk Assessment 2020, 34, 993 -1021.
AMA StyleAbdullah Alodah, Ousmane Seidou. Influence of output size of stochastic weather generators on common climate and hydrological statistical indices. Stochastic Environmental Research and Risk Assessment. 2020; 34 (7):993-1021.
Chicago/Turabian StyleAbdullah Alodah; Ousmane Seidou. 2020. "Influence of output size of stochastic weather generators on common climate and hydrological statistical indices." Stochastic Environmental Research and Risk Assessment 34, no. 7: 993-1021.
Countries in arid regions are presently facing challenges in managing their limited water resources. Assessing the evaporation losses from various sources of water is a daunting task that is inevitable for the sustainability of water resource management schemes in these regions. Although several techniques are available for simulating evaporation rates, identifying the parameters of various evaporation equations still needs to be further investigated. The main goal of this research was to develop a framework for determining the parameters influencing the evaporation rate of evaporation pans. Four different equations, including those of Hamon, Penman, Jensen–Haise, and Makkink, were chosen to estimate evaporation from the evaporation pans installed in the Qassim Region of Saudi Arabia. The parameters of these four equations were identified by a state-of-the-art optimization technique, known as the general reduced gradient (GRG). Three types of objective functions used for optimization were tested. Forty-year monitoring records for pan evaporation, temperature, relative humidity, and sunshine hours were collected from the Municipality of Buraydah Al Qassim, for the period of 1976 to 2016. These data were mainly manually recorded at a weather station situated in the Buraydah city. Preliminary data analysis was performed using the Mann–Kendall and Sen’s slope tests to study the trends. The first 20-year (1976–1995) data were used for calibrating the equations by employing an optimization technique and the remaining data were used for validation purposes. Four new equations were finally developed and their performance, along with the performance of the four original equations, was evaluated using the Nash and Sutcliffe Efficiency (NSE) and the Mean Biased Error (MBE). The study revealed that among the original equations, the Penman equation performed better than the other three equations. Additionally, among the new equations, the Hamon method performed better than the remaining three equations.
Abdul Razzaq Ghumman; Yousry Mehmood Ghazaw; Abdullah Alodah; Ateeq Ur Rauf; Shafiquzzaman; Husnain Haider. Identification of Parameters of Evaporation Equations Using an Optimization Technique Based on Pan Evaporation. Water 2020, 12, 228 .
AMA StyleAbdul Razzaq Ghumman, Yousry Mehmood Ghazaw, Abdullah Alodah, Ateeq Ur Rauf, Shafiquzzaman, Husnain Haider. Identification of Parameters of Evaporation Equations Using an Optimization Technique Based on Pan Evaporation. Water. 2020; 12 (1):228.
Chicago/Turabian StyleAbdul Razzaq Ghumman; Yousry Mehmood Ghazaw; Abdullah Alodah; Ateeq Ur Rauf; Shafiquzzaman; Husnain Haider. 2020. "Identification of Parameters of Evaporation Equations Using an Optimization Technique Based on Pan Evaporation." Water 12, no. 1: 228.
A quantitative assessment of the likelihood of all possible future states is lacking in both the traditional top-down and the alternative bottom-up approaches to the assessment of climate change impacts. The issue is tackled herein by generating a large number of representative climate projections using weather generators calibrated with the outputs of regional climate models. A case study was performed on the South Nation River Watershed located in Eastern Ontario, Canada, using climate projections generated by four climate models and forced with medium- to high-emission scenarios (RCP4.5 and RCP8.5) for the future 30-year period (2071–2100). These raw projections were corrected using two downscaling techniques. Large ensembles of future series were created by perturbing downscaled data with a stochastic weather generator, then used as inputs to a hydrological model that was calibrated using observed data. Risk indices calculated with the simulated streamflow data were converted into probability distributions using Kernel Density Estimations. The results are dimensional joint probability distributions of risk-relevant indices that provide estimates of the likelihood of unwanted events under a given watershed configuration and management policy. The proposed approach offers a more complete vision of the impacts of climate change and opens the door to a more objective assessment of adaptation strategies.
Abdullah Alodah; Ousmane Seidou. Assessment of Climate Change Impacts on Extreme High and Low Flows: An Improved Bottom-Up Approach. Water 2019, 11, 1236 .
AMA StyleAbdullah Alodah, Ousmane Seidou. Assessment of Climate Change Impacts on Extreme High and Low Flows: An Improved Bottom-Up Approach. Water. 2019; 11 (6):1236.
Chicago/Turabian StyleAbdullah Alodah; Ousmane Seidou. 2019. "Assessment of Climate Change Impacts on Extreme High and Low Flows: An Improved Bottom-Up Approach." Water 11, no. 6: 1236.
Stochastic weather generators are designed to produce synthetic sequences that are commonly used for risk discovery, as they would contain rare events that can lead to potentially catastrophic impacts on the environment, or even human lives. These time series are sometimes used as inputs to rainfall-runoff models to simulate the hydrological impacts of these rare events. This paper puts forward a method that evaluates the usefulness of weather generators by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space, and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space. Assessment of weather generator’s performance is based on visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out on the South Nations watershed in Ontario, Canada, using five different weather generators: two versions of a single-site Weather Generator, two versions of a multi-site Weather Generator (MulGETS) and the K-Nearest Neighbour weather generator (k-nn). Results show that the MulGETS model often outperformed the other weather generators for that particular study area because: (a) the observations were well centered within a point cloud of the synthetically-generated time series in both spaces, and (b) the points generated using MulGETS had a smaller Mahalanobis distance to the observations than those generated with the other weather generators. The \(k\)-nn weather generator performed particularly well in simulating temperature variables, but was poor at modelling precipitation and streamflow statistics.
Abdullah Alodah; Ousmane Seidou. The adequacy of stochastically generated climate time series for water resources systems risk and performance assessment. Stochastic Environmental Research and Risk Assessment 2018, 33, 253 -269.
AMA StyleAbdullah Alodah, Ousmane Seidou. The adequacy of stochastically generated climate time series for water resources systems risk and performance assessment. Stochastic Environmental Research and Risk Assessment. 2018; 33 (1):253-269.
Chicago/Turabian StyleAbdullah Alodah; Ousmane Seidou. 2018. "The adequacy of stochastically generated climate time series for water resources systems risk and performance assessment." Stochastic Environmental Research and Risk Assessment 33, no. 1: 253-269.
Abdullah Alodah; Ousmane Seidou; S. Boukalova; C. A. Brebbia. THE REALISM OF STOCHASTIC WEATHER GENERATORS IN RISK DISCOVERY. Water Resources Management IX 2017, 1, 239 -249.
AMA StyleAbdullah Alodah, Ousmane Seidou, S. Boukalova, C. A. Brebbia. THE REALISM OF STOCHASTIC WEATHER GENERATORS IN RISK DISCOVERY. Water Resources Management IX. 2017; 1 ():239-249.
Chicago/Turabian StyleAbdullah Alodah; Ousmane Seidou; S. Boukalova; C. A. Brebbia. 2017. "THE REALISM OF STOCHASTIC WEATHER GENERATORS IN RISK DISCOVERY." Water Resources Management IX 1, no. : 239-249.