Climate change impacts on the streamflow of Zarrineh River,Iran

Zarrineh River is located in the northwest of Iran,providing more than 40%of the total inflow into the Lake Urmia that is one of the largest saltwater lakes on the earth.Lake Urmia is a highly endangered ecosystem on the brink of desiccation.This paper studied the impacts of climate change on the streamflow of Zarrineh River.The streamflow was simulated and projected for the period 1992-2050 through seven CMIP5(coupled model intercomparison project phase 5)data series(namely,BCC-CSM1-1,BNU-ESM,CSIRO-Mk3-6-0,GFDL-ESM2G,IPSL-CM5A-LR,MIROC-ESM and MIROC-ESM-CHEM)under RCP2.6(RCP,representative concentration pathways)and RCP8.5.The model data series were statistically downscaled and bias corrected using an artificial neural network(ANN)technique and a Gamma based quantile mapping bias correction method.The best model(CSIRO-Mk3-6-0)was chosen by the TOPSIS(technique for order of preference by similarity to ideal solution)method from seven CMIP5 models based on statistical indices.For simulation of streamflow,a rainfall-runoff model,the hydrologiska byrans vattenavdelning(HBV-Light)model,was utilized.Results on hydro-climatological changes in Zarrineh River basin showed that the mean daily precipitation is expected to decrease from 0.94 and 0.96 mm in 2015 to 0.65 and 0.68 mm in 2050 under RCP2.6 and RCP8.5,respectively.In the case of temperature,the numbers change from 12.33℃ and 12.37℃ in 2015 to 14.28℃ and 14.32℃ in 2050.Corresponding to these climate scenarios,this study projected a decrease of the annual streamflow of Zarrineh River by half from 2015 to 2050 as the results of climatic changes will lead to a decrease in the annual streamflow of Zarrineh River from 59.49 m^(3)/s in 2015 to 22.61 and 23.19 m^(3)/s in 2050.The finding is of important meaning for water resources planning purposes,management programs and strategies of the Lake's endangered ecosystem.

Assessment of Climate Change Impacts on the Water Resources of Megech River Catchment, Abbay Basin, Ethiopia

This study was aimed to assess the impact of climate change on the water resource of Megech river catchment. In this study, large scale regional climate model (REMO) output was downscaled statistically to metrological variables at a daily resolution using SDSM model version 5.11. We noticed that statistical downscaling smooth out the bias between REMO output and observed data. According to the projected climate data, the maximum temperature is likely to have an increasing trend +0.57°C while the minimum temperature shows a decreasing trends ﹣0.61°C. There is no clear trend for precipitation, both increasing and decreasing trend observed in the catchment. The HBV-Light hydrological model was successfully calibrated (1991-1995) and validated (1998-2000) using current climatic inputs and observed river flows. The overall performances of the model was good at monthly time scale both on calibration (NSE = 0.91) and validation (NSE = 0.86). Future discharge (2015-2050) was simulated using statistically downscaled 20 ensembles climate scenario data for both A1B and B1 scenarios. HBV-Light model simulation results showed a reduction of the peak discharge in August and September.

Runoff components and the contributions of precipitation and temperature in a highly glacierized river basin in Central As

Understanding the main drivers of runoff components and contributions of precipitation and temperature have important implications for water-limited inland basins,where snow and glacier melt provide essential inputs to surface runoff.To quantify the impact of temperature and precipitation changes on river runoff in the Tarim River basin(TRB),the Hydrologiska Byrans Vattenbalansavdelning(HBV)-light model,which contains a glacier routine process,was applied to analyze the change in runoff composition.Runoff in the headstream parts of the TRB was more sensitive to temperature than to precipitation.In the TRB,overall,rainfall generated 41.22%of the total runoff,while snow and glacier meltwater generated 20.72%and 38.06%,respectively.These values indicate that temperature exerted more major effects on runoff than did precipitation.Runoff compositions were different in the various subbasins and may have been caused by different glacier coverages.The runoff volumes generated by rainfall,snowmelt,glacier melt was almost equal in the Aksu River subbasin.In the Yarkand and Hotan River subbasins,glacier meltwater was the main supplier of runoff,accounting for 46.72%and 58.73%,respectively.In the Kaidu-Kongque River subbasin,80.86%was fed by rainfall and 19.14%was fed by snowmelt.In the TRB,runoff generated by rainfall was the dominant component in spring,autumn,winter,while glacier melt runoff was the dominant component in summer.Runoff in the TRB significantly increased during 1961–2016;additionally,56.49%of the increase in runoff was contributed by temperature changes,and 43.51%was contributed by precipitation changes.In spring,the runoff increase in the TRB was mainly caused by the precipitation increase,opposite result in summer and autumn.Contribution of temperature was negative in winter.Our findings have important implications for water resource management in high mountainous regions and for similar river basins in which melting glaciers strongly impact the hydrological cycle.

Parameter uncertainty and identifiability of a conceptual semi-distributed model to simulate hydrological processes in a small headwater catchment in Northwest China

Introduction:Conceptual hydrological models are useful tools to support catchment water management.However,the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task.Here,we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model,HBV-light,with emphasis on parameter identifiability,uncertainty,and model structural validity.Results:The results of a regional sensitivity analysis(RSA)show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used.Results based on the generalized likelihood uncertainty estimation(GLUE)method further show that most of the model parameters are well constrained,showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used.Finally,the dynamic identifiability analysis(DYNIA)shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff.Conclusions:The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy.Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis.More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and,specifically,improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff.Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.