Calibration of parameters of mathematical models is still a tough task in several engineering problems. Many of the models adopted for the numerical simulations of real phenomena, in fact, are of empirical derivation. Therefore, they include parameters which have to be calibrated in order to correctly reproduce the physical evidence. Thus, the success of a numerical model application depends on the quality of the performed calibration, which can be of great complexity, especially if the number of parameters is higher than one. Calibration is traditionally performed by engineers and researchers through manual trial-and-error procedures. However, since models themselves are increasingly sophisticated, it seems more proper to look at more advanced calibration procedures. In this work, in particular, an optimization technique for a multi-parameter calibration is applied to a two-phase depth-averaged model, already adopted in previous works to simulate morphodynamic processes, such as, for example, the dike erosion by overtopping.
The use of electromagnetic (EM) soil moisture probes is proliferating rapidly, in two broad domains: in field and laboratory research; and in strongly practical applications such as irrigation scheduling in farms or horticultural enterprises, and hydrological monitoring. Numerous commercial EM probes are available for measurement of volumetric water content (θv), spanning a range of measurement principles, and of probe dimensions and sensing volumes. However probe calibration (i.e. the relationship of actual θv to probe electrical output) can shift, often substantially, with variations in parameters such as soil texture, organic matter content, wetness range, electrical conductivity and temperature. Hence a single-valued, manufacturer-supplied calibration function is often inadequate, forcing the user to seek an application-specific calibration. The purpose of this paper is to describe systematic procedures which probe users can use to check or re-determine the calibration of their selected probe(s). Given the wide diversity of operating principles and designs of commercially-available EM probes, we illustrate these procedures with results from our own calibrations of five different short probes (length of 5 to 20 cm). Users are strongly recommended to undertake such calibration checks, which provide both a) pre-use experience, and b) more reliable in-use data. and Používanie elektromagnetických (EM) snímačov vlhkosti pôdy sa rýchlo rozširuje tak v terénnom výskume, ako aj v laboratóriu. Sú používané v praktických aplikáciách ako je riadenie závlah na farmách a záhradách, ako aj v hydrologickom monitoringu. Pre meranie vlhkosti pôdy (θv) sú dostupné početné typy komerčných EM snímačov, založených na viacerých princípoch merania a snímače majú rozdielnu veľkosť snímaných objemov pôdy. Kalibračné krivky takýchto snímačov (t.j. závislosti medzi reálnou vlhkosťou pôdy θv a elektrickým výstupom snímača) sa môžu posúvať - niekedy podstatne - a to v závislosti od rozdielnych parametrov pôdy, ako je jej textúra, obsah organických látok, rozsah vlhkostí, elektrická vodivosť a teplota. Z toho vyplýva, že jednoznačná kalibračná krivka, dodávaná výrobcom je často neadekvátna, čo núti užívateľa snímač kalibrovať v špecifických podmienkach. Cieľom tohto príspevku je opísať procedúry, ktoré môžu byť použité užívateľmi pri rekalibrácii vybraných typov snímačov. Berúc do úvahy širokú paletu princípov EM snímačov, ilustrujeme tieto procedúry výsledkami vlastných kalibračných testov na piatich typoch krátkych snímačov (dĺžka od 5 do 20 cm). Užívateľom odporúčame rekalibráciu komerčných snímačov, ktorými získajú predbežné skúsenosti a spoľahlivejšie výsledky pri meraní vlhkosti pôdy.
Longwave radiation, as part of the radiation balance, is one of the factors needed to estimate potential evapotranspiration (PET). Since the longwave radiation balance is rarely measured, many computational methods have been designed. In this study, we report on the difference between the observed longwave radiation balance and modelling results obtained using the two main procedures outlined in FAO24 (relying on the measured sunshine duration) and FAO56 (based on the measured solar radiation) manuals. The performance of these equations was evaluated in the April–October period over eight years at the Liz experimental catchment and grass surface in the Bohemian Forest (Czech Republic). The coefficients of both methods, which describe the influence of cloudiness factor and atmospheric emissivity of the air, were calibrated. The Penman-Monteith method was used to calculate the PET. The use of default coefficient values gave errors of 40–100 mm (FAO56) and 0–20 mm (FAO24) for the seasonal PET estimates (the PET was usually overestimated). Parameter calibration decreased the FAO56 error to less than 20 mm per season (FAO24 remained unaffected by the calibration). The FAO56 approach with calibrated coefficients proved to be more suitable for estimation of the longwave radiation balance.