Spatial distribution of monthly potential evaporation over mountainous regions: case of the Lhasa River basin, China

Abstract

This paper develops an algorithm for computing spatially-distributed monthly potential evaporation (PE) over a mountainous region, the Lhasa River basin in China. To develop the algorithm, first, correlation analysis of different meteorological variables was conducted. It was observed that PE is significantly correlated with vapour pressure and temperature differences between the land surface and the atmosphere. Second, the Dalton model, which was developed based on the mass transfer mechanism, was modified by including the influence of the related meteorological variables. Third, the influence of elevation on monthly temperature, vapour pressure and wind velocity was analysed, and functions for extending these meteorological variables to any given altitude were developed. Fourth, the inverse distance weighting method was applied to integrate the extended meteorological variables from five stations adjacent to and within the Lhasa River basin. Finally, using the modified Dalton model and the integrated meteorological variables, we computed the spatially-distributed monthly PE. This study indicated that spatially-distributed PE can be obtained using data from sparse meteorological stations, even if only one station is available; the results show that in the Lhasa River basin PE decreases when elevation increases. The new algorithm, including the modified model and the method for spatially extending meteorological variables can provide the basic inputs for distributed hydrological models.

Characteristics of climate change and its relationship with land use/cover change in Yunnan Province, China

“…Therefore, Equation could present the high‐to‐low increasing trend in the annual temperature over this region, and this was consistent with the results reported in a number of studies (e.g. Running et al ., ; Thornton et al ., ; Shi et al ., ) that temperature may decrease along with the increase of elevation. By contrast, the R 2 value of Equation was relatively lower, and longitude and latitude were significant for Equation at the significant level of p < 0.05 and p < 0.01, respectively; however, elevation was not significant for Equation ( p > 0.1).…”

“…Considering both simplicity and accuracy, this study selects the IDW method (e.g. Shi et al ., , , ), the general form of which can be expressed as follows: where N is the number of used meteorological stations, X p is the interpolated value at the point of interest, X i is the value at the i th given station, D i is the distance from the i th given station to the point of interest and β is the power of D i . Following common practice (e.g.…”

“…Considering both simplicity and accuracy, this study selects the IDW method (e.g. Shi et al, 2014Shi et al, , 2016aShi et al, , 2017a, the general form of which can be expressed as follows:…”

Attribution Analysis of Dry Season Runoff in the Lhasa River Using an Extended Hydrological Sensitivity Method and a Hydrological Model

“…The observed pan evaporation data were recorded using an E 20 pan (an evaporimeter with a diameter of 20 cm) or E-601B pan (an evaporimeter with a diameter of 61.8 cm) depending on the external environment. In Tibet, average E 20 pan and E-601B pan coefficients are 0.585 and 0.9, respectively [58]. Therefore, the potential evapotranspiration data used in this study were derived using the E 20 pan or E-601B pan observations multiplied by the corresponding coefficient.…”

Delimitation of Agricultural Areas with Natural Constraints in Greece: Assessment of the Dryness Climatic Criterion Using Geostatistics

“…However, as can be seen in Figure 3b, for the case of ET o its covariance with elevation is weak. As it is also reported by Shi et al [52] and Soulis [25], the relationship between ET o and elevation is complicated and it is very difficult to develop an algorithm for estimating spatially distributed ET o over mountainous regions. In contrast, it would be possible to consider the clearer relationships between meteorological parameters and topography (e.g., temperature-elevation, aspect-solar radiation, etc.)…”