Preface

Reference evapotranspiration is one of the most difficult components of the hydrologic cycle to quantify accurately. Estimation or measurement of evapotranspiration is demanding because there are a number of climatic parameters that can affect the process, notably temperature, relative humidity, wind speed, and solar radiation. However, estimation methods are constantly evolving and accuracy should continually improve further. This is precisely the purpose of this book: to explore improvements in the accuracy of estimates for evapotranspiration.

Direct methods have the limitations of measurement errors, expense, and the impracticality of acquiring point measurements for spatially variable locations, whereas indirect methods have the limitations of the unavailability of all necessary climate data and a lack of generalizability (the need for local calibration). In contrast to conventional methods, soft computing models can estimate reference evapotranspiration accurately with minimum climate data, which may have the advantages of being inexpensive, independent of specific climatic conditions and unaware of physical relations, and precise modeling of the nonlinear complex system. Results of studies in India suggest that artificial neural network models perform better compared to multiple linear regression for all locations.

Taking the same limitations of current evapotranspiration methods into account, researchers from the United States focused on the developing evapotranspiration method using general meteorological data and the Normalized Difference Vegetation Index. Moreover, they evaluated the potential use of the evapotranspiration method for drought monitoring to support agricultural risk management and food security.

Another team has been exploring improvements in the accuracy of estimates for evapotranspiration over complete growing seasons and for monthly periods when more frequent Landsat imagery was available. By assessing decreases in the accuracy of evapotranspiration estimated values as the frequency of available Landsat images reduces, it was found that for the studied area, a four-day revisit time, as represented by the full run of analysis, was required to ensure robustness in the development of time-integrated evapotranspiration estimates over months and growing seasons. South Asian researchers also studied farmers' livelihoods to identify a set of measures for improving both agricultural land and water productivity under changing climates in recent decades.

Green infrastructure is a common solution for stormwater management in an urban environment, with associated environmental benefits such as flood control, urban heat island relief, adaptations to climate change, biodiversity protection, air pollution reduction, and food production. Evapotranspiration controls a green infrastructure's hydrologic performance and affects all related benefits. This book

is an interesting study that summarizes the current research progress and existing challenges regarding the benefits, measurements, and simulation of the evapotranspiration process from green infrastructures.

> **Dr. Daniel Bucur** Professor, University of Agricultural Science and Veterinary Medicine in Iasi, Iasi, Romania

> > Section 1

Estimating

Evapotranspiration

1

Section 1
