Statistical Significance Assessment of Streamflow Elasticity of Major Rivers

Abstract

Impacts of climate change on streamflow have long been an issue of concern for water experts. The main aim of this study is to assess the response of streamflow to precipitation and air temperature. In this study elasticity model was used to compute the precipitation and air temperature elasticity of 6 major rivers in Khyber-Pakhtunkhwa (KP) Province, Pakistan. In contrast to temperature elasticity estimator, box plots of precipitation elasticity estimator have low range and standard deviation leading to greater central affinity which produces valid, appropriate, and statistically significant elasticity results. Precipitation is positively correlated with streamflow while the air temperature is both positively and negatively linked with streamflow. 10% variation in precipitation and air temperature produces 12 to 20% and 8 to 18% change in streamflow, respectively. The sensitivity of streamflow to air temperature is higher as compared to precipitation. This research work shows that precipitation elasticity results are statistically valid and realistic as compared to temperature elasticity results. Moreover, it is suggested to support elasticity results by statistical correlation to avoid misleading and unrealistic results. Results of the current study can be used in formulating long term policies regarding streamflow sensitivity in the study region. Doi: 10.28991/cej-2021-03091698 Full Text: PDF

Hydrological impacts of climate and land-use change on flow regime variations in upper Indus basin

“…The stored water will be used in the dry season for agriculture, hydropower, industrial, and domestic purposes. Furthermore, plantations are suggested in the study area to decline the increasing trend of air temperature as the sensitivity of streamflow is higher in the study area (Ahmad et al 2020;Shah et al 2021).…”

Climate-Streamflow Relationship and Consequences of Its Instability in Large Rivers of Pakistan: An Elasticity Perspective

“…In Equation ( 2), variables P and Q are quite general and can be used as instantaneous, monthly, or annual values [77][78][79]. In this study, the mean annual values of streamflow Q and precipitation P for the estimation of ε p were utilized.…”

“…Similarly, it was also found that the regression analysis that includes temperature improves the coefficient of determination (R 2 ) [40]. Since all the subsequent research based on multivariate models suggests that multivariate models are more reliable than bivariate elasticity models [39,42,48,[73][74][75][76][77][78][79][80][81][82], it is believed that the multivariate elasticity results of our study are more authentic than the bivariate elasticity results, although there is very small difference between the results as discussed in Section 3.1 above.…”

“…Thus, the results of temperature are not reliable and are misleading. Furthermore, the existing literature also suggests that there is no significant impact of residual temperature on the streamflow compared to the direct and much more significant impact of precipitation on the streamflow, all because of the opposite correlation between precipitation and temperature [76][77][78][79][80][81][82].…”

Climate change impacts on the flow regime and water quality indicators using an artificial neural network (ANN): a case study in Saskatchewan, Canada

“…Climate change may significantly alter hydrological processes affecting water quality and quantity ( Jamali et al 2013;Abera et al 2018;Ekwueme & Agunwamba 2021;Feistel & Hellmuth 2021;Hassanjabbar et al 2021;Shah et al 2021). Therefore, understanding the impact of other significant drivers such as climate change helps water managers make better contributions and decisions to solve water quality degradation and conserve aquatic ecosystems.…”