Field evaluation of farm irrigation systems, described in detail by Merriam and Keller , plays a fundamental role in improving surface irrigation. Evaluations provide information used to advise irrigators on how to improve their system design and/or operation, as well as information on improving design, model validation and updating, optimization programming, and developing real-time irrigation management decisions. Basic field evaluation includes observation of
• inflow and outflow rates and volumes (volume balance);
• timing of the irrigation phases, particularly advance and recession;
• soil water requirements and storage;
• slope, topography, and geometry of the field; and
• management procedures used by the irrigator.
More thorough evaluations require independent measurement of the infiltration using a furrow or basin infiltrometer, as well as estimates of water stored on the soil surface and/or surface roughness. When soils are erodible, erosion observations also should be performed .
The most difficult to measure and important parameter affecting surface irrigation design and performance is infiltration. Field data from evaluations can be used to estimate the infiltration parameters for the Kostiakov equation. The application of the volume balance method to the advance phase of sloping furrows and borders led to the development of a well-proved methodology for estimating infiltration parameters—the two-point method . Several authors have reported on successful use of this method, and usefulness to design and evaluation is well established . Smerdon et al.  provide an interesting evaluation of methodologies, and Blair and Smerdon  analyze several forms of advance and infiltration equations. A standard engineering practice for furrow evaluation (ASAE EP419.1) has been developed .
Estimation of the infiltration parameters and the roughness coefficient for surface flow also can be done through the inverse surface irrigation problem by numerical simulation models [37, 38]. Techniques to optimize the infiltration and roughness parameters by using the simulation models interactively are available [39,40]. Of particular interest are the methodologies aimed at real-time control of irrigation. The examples offered by Mailhol  and Eisenhauer et al.  describe simplified, easy-to-implement approaches.
The two main performance parameters—the distribution uniformity DU[Eq. (5.119)] and the application efficiency ea [Eq. (5.118) or (5.117)] can be computed from field data. Distribution uniformity primarily depends on the parameters characterizing the irrigation event; ea also is influenced by the irrigation scheduling decision, i.e., the irrigation timing (SWD) and depth. DU can be functionally described by
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