Role of measurement uncertainty in the comparison of average areal rainfall methods

Abstract

The main motivation for this research is the growing awareness of the impact of climate change and the increasing relevance of the United Nations Sustainable Development Goals, aiming to contribute to the measurement of quantities like precipitation and rate of rainfall. This knowledge is widely used in hydrology, climatology and meteorology, providing data and information applied in modelling, pattern definition and recognition, and forecasting. This work is concerned with estimating the average areal rainfall in a stipulated region from rainfall intensity observations made at measurement stations within that region. It focuses on three straightforward estimation approaches: the arithmetic mean method, the Thiessen polygon method and the isohyetal method. The evaluation of the associated measurement uncertainty, for which the law of propagation of uncertainty and a Monte Carlo method as described in guidance documents from the Joint Committee for Guides in Metrology are applied, is the main consideration. The approaches described may be readily applied by practitioners. A comparison of results from applying these methods to a simple example is made. Such results are required for conformity assessment and support in urban management and water resources management worldwide.

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

“…Since long-term averages of area precipitation distribution [ [66] , [67] , [72] , [73] ] play a larger role in modeling agricultural watershed characterization, precipitation proportionality for model input should be the region of uncertainty based on selected measured observations [ 74 ]. Therefore, the Thiessen polygon method is a good approach for gap minimization and its impact on predicting the water balance and water content of the plant root zone due to the uncertainty of rainfall measurement [ 75 ].…”

“…This time frame is selected for model calibration, which aims to predict plant canopy water content. Given the importance of rainfall windows for crop water uptake from the soil root zone [ 72 ], there is a strong correlation between comparing N nutrient uptake and estimating (NO − 3 -N) leaching/runoff from cropland [ 73 ]. Therefore, accurate prediction of precipitation patterns in May and June is crucial for calibrating the model and accurately estimating plant canopy water content.…”

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination index from cropland N-fertilizer in Bilate downstream

“…Since long-term average values of areal rainfall distribution has a greater role in modeling agricultural watershed characterization, rainfall proportionality for model input is supposed the range of uncertainty based on selected gauged observation (Remesan & Holman, 2015). Hence Thiessen polygon method is a good approach for gap minimization and its influence in predicting crop canopy water content and crop root zone water content due to rain gauge uncertain observation (Ribeiro et al, 2021), Thiessen polygon technique sets to fixe weighting factor for the areal coverage of a station and arithmetic average method assumes that the rainfall field is homogeneous (T. Zhang et al, 2016). since pattern of rainfall homogeneity of study area was tested by others scholars (Orke & Li, 2021).…”

Pengaruh Sebaran Spasial Hujan terhadap Pemilihan Metode Hujan Wilayah Berbasis Analisis Geospasial

“…Metode yang sering dimanfaatkan dalam analisis hujan wilayah adalah Metode Rerata Aritmatik, Isohyet dan Thiessen (Yoo , et al, 2008). Metode rerata aritmatik, Thiessen dan Isohyet merupakan metode analisis hujan wilayah yang menitepretasikan faktor kuantitas fisik alat ukur hujan dalam skala geometris (Ribeiro, et al, 2021).…”