TY - BOOK ID - 134287613 TI - Application of Climatic Data in Hydrologic Models AU - Valipour, Mohammad AU - Bateni, Sayed M. PY - 2022 PB - Basel MDPI Books DB - UniCat KW - Technology: general issues KW - History of engineering & technology KW - statistical weather generator KW - stochastic process KW - Diyala River basin KW - Wilks’ technique KW - hydrological models KW - rainfall KW - surface runoff KW - linear regression models KW - curve number KW - SCS.CN model KW - mulching KW - wildfire KW - prescribed fire KW - n/a KW - CHIRPS KW - GPM-IMERG KW - rainfall data scarcity KW - agro-hydrology KW - Rift Valley Lake Basin KW - hydrological research basin KW - precipitation KW - temperature KW - long-term trends KW - climate change KW - evapotranspiration KW - groundwater recharge KW - thresholds KW - seasonality KW - spatiotemporal variations KW - regional-scale KW - long-term KW - HydroBudget model KW - cold and humid climates KW - Quebec (Canada) KW - tank cascade system KW - dry zone KW - water governance KW - flood control KW - traditional knowledge KW - community participation KW - Sri Lanka KW - Wilks' technique UR - https://www.unicat.be/uniCat?func=search&query=sysid:134287613 AB - Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models have been developed to simulate hydrologic processes. Obtaining accurate climatic data on local/meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts. Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of newly available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water, and groundwater storage/fluxes, land–atmosphere interactions, have been optimized for previously data-limited conditions. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models. Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. This book aims to present recent advances concerning climatic data and their applications in hydrologic models. ER -