Mohammad Reza Goodarzi; Alireza Faraji; Mahdi Komasi
Abstract
Introduction: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature, However, uncertainty in downscaling methods have been given less attention. Today many studies have been done about the future impact of climate change on human life and water resources. ...
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Introduction: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature, However, uncertainty in downscaling methods have been given less attention. Today many studies have been done about the future impact of climate change on human life and water resources. Urban development, water conflicts, and Green House Gases increasing will intensify this event in future and will alter rivers flow. Basin catchment has faced to flow recession and also runoff decreasing in few last decades. At this field the climate change effects will intensify this conditions in future decades too. The first step of climate change impacts studies is the projection of future climate variables (e.g precipitation and temperature). GCMS models and their outputs are useful tools for this projection. The main problem is the mismatch of spatial scale between the scale of global climate models and the resolution needed for impacts assessments.
Materials and Methods: The Gharesou River Basin is located in the west of Iran. Its area is approximately equal to 5793km2, and the maximum and minimum of its heights are 1237 and 3350 m, respectively. The average of annual rainfall varies from 300 to 800mm. This study focuses on various climate models from IPCC fourth and fifth reports and has been used two downscaling methods including the statistical and proportional downscaling methods and also scenarios and different climate models for considering different uncertainty. The new scenarios as Representative Concentration Pathways (RCPs) of greenhouse gasses have been used in fifth assessment reports (AR5) of IPCC. The Representative Concentration Pathways describe four different 21st-century pathways of greenhouse gas (GHG) emissions and atmospheric concentrations, air pollutant emissions and land use. The RCPs represent the range of GHG emissions. Different kinds of downscaling method include 1) Proportional downscaling that is adding coarse-scale climate changes to higher resolution observations (the delta approach); 2) Statistical method (eg SDSM model; CLIGEN; GEM; LARS-WG and etc); 3) Dynamical method that is application of regional climate model using global climate model boundary conditions (e.g, RegCM3; MM5 and PRECIS). statistical downscaling method processes establish relating large scale climate features (e.g., 500 MB heights), predictors, to local climate (e.g, daily, monthly temperature at a point), predictands. The SDSM software reduces the task of statistically downscaling daily weather series into seven discrete processes that are consist of quality control and data transformation; screening of predictor variables; model calibration; weather generation (observed predictors); statistical analyses; graphing model output and scenario generation (climate model predictors). HEC-HMS (Hydrologic Modeling System) has been designed by HEC (Hydrologic Engineering Center) for simulation of precipitation-runoff processes in a drainage basin. The HEC-HMS simulation methods represent - Watershed precipitation and evaporation: These describe the spatial and temporal distribution of rainfall on and evaporation from a watershed. - Runoff volume: These address questions about the volume of precipitation that falls on the watershed: How much infiltrates on pervious surfaces? How much runoff of the impervious surfaces? When does it run off? - Direct runoff: including overland flow and interflow. These methods describe what happens as water that has not infiltrated or been stored on the watershed moves over or just beneath the watershed surface. Baseflow: simulate the slow subsurface drainage of water from a hydrologic system into the watershed’s channels.- Channel flow: These so-called routing methods simulate one-dimensional open channel flow, thus predicting time series of downstream flow, stage, or velocity, given upstream hydrographs. HEC-HMS includes several models for calculation of cumulative precipitation losses but only the SMA module is continuous (a module that simulates the losses for both wet and dry weather conditions). Other loss models are event based.
Results and Discussion: The results of criteria and models weighting show that CANESM2 and HADCM3 are better than other models for future temperature and precipitation projection for statistical downscaling and HADCM3 for future precipitation and HADGEM for future temperature assessment for Proportional downscaling. According to various scenarios, future temperature and precipitation projection (2040-2069 period for the statistical and 2040-2052 period for Proportional downscaling) have downscaled and have given to HEC-HMS model for future flow projection. Already the rainfall-runoff model has calibrated and validated base on observed flow data in reference period that daily coefficient of determine was 0.7 for calibrated period and 0.6 for validated period. Finally, flow variation has investigated that Most of GCMS represent increases in winter flows and reductions in other season flows.
S. Babaei Hessar; R. Ghazavi
Abstract
Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, ...
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Introduction: Precipitation is one of the most important and sensitive parameters of the tropical climate that influence the catchments hydrological regime. The prediction of rainfall is vital for strategic planning and water resources management. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Various methods, such as time series and artificial neural network models, have been proposed to predict the level of rainfall. But there is not enough attention to global warming and climate change issues. The main aim of this study is to investigate the conformity of artificial neural network and time series models with climate scenarios.
Materials and Methods: For this study, 50 years of daily rainfall data (1961 to 2010) of the synoptic station of Urmia, Tabriz and Khoy was investigated. Data was obtained from Meteorological Organization of Iran. In the present study, the results of two Artificial Neural Network (ANN) and Time Seri (TS) methods were compared with the result of the Emission Scenarios (A2 & B1). HadCM3 model in LARS-WG software was used to generate rainfall for the next 18 years (2011-2029). The results of models were compared with climate scenarios over the next 18 years in the three synoptic stations located in the basin of the Lake Urmia. At the first stage, the best model of time series method was selected. The precipitation was estimated for the next 18 years using these models. For the same period, precipitation was forecast using artificial neural networks. Finally, the results of two models were compared with data generated under two scenarios (B1 and A2) in LARS-WG.
Results and Discussion: Different order of AR, MA and ARMA was examined to select the best model of TS The results show that AR(1) was suitable for Tabriz and Khoy stations .In the Urmia station MA(1) was the best performance. Multiple Layer Perceptron with a 10 neurons in hidden layer and the output layer consists of five neurons had the lowest MSE and the highest correlation coefficient in modeling the values of annual precipitation. So MLP was determined as the best structure of neural network for rainfall prediction. According to results, precipitation predicted by the ANN model was very close to the results of A2 and B1 scenario, whereas TS has a significant difference with these scenarios. Average rainfall predicted by two A2 and B1 scenarios in Urmia station has more difference than other stations. Based on the B1 scenario, precipitation will increase 11 percent over the next two decades. It will decrease 10.7 percent according to A2 emissions scenario. According to ANN models and two A2 and B1 scenarios, the rates of rainfall will increase in Tabriz and Khoy stations. However, according to TS model, rainfall will decline 5.94 and 3.63 percent for these two stations, respectively.
Conclusion: Global warming and climate change should have adverse effects on groundwater and surface water resources. Different models are used for simulating of thes effects. But, conformity of these models with the results of climate scenarios is an issue that has not been addressed. In the present research coincidence of TS model, ANN model and climate change scenarios was investigated. Results show under emissions scenarios, during the next two decades in Tabriz and Khoy stations, precipitation will increase. In Urmia station B1 and A2 scenario percent increase by 11 percent and 10.5 percent decline predicted, respectively. The results of Roshan and et al (4) and Golmohammad and et al, (7) investigations show increasing trend in the rainfall rate and confirming the results of this study According to results, the performance of ANN model is better than TS model for rainfall prediction and its result is similar to climate change scenarios. Similar results have been reported by Wang et al (29) and the Norani et al (20). Due to the significant difference between the TS and climate scenarios used in the study area, is not recommended, though it can be used as a plausible climate scenario to predict the precipitation of stations in the future studied. At the end, it is suggested that the similar studies carried out in a larger number of stations in the country with respect to global warming and climate change, to determine the validity of the methods used to the predicted rainfall.
