Estimation of daily actual evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in North China

2008 ◽  
Vol 29 (11) ◽  
pp. 3295-3315 ◽  
Author(s):  
Yanchun Gao ◽  
Di Long ◽  
Zhao‐Liang Li
Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 118
Author(s):  
Giovanni Ruggieri ◽  
Vincenzo Allocca ◽  
Flavio Borfecchia ◽  
Delia Cusano ◽  
Palmira Marsiglia ◽  
...  

In many Italian regions, and particularly in southern Italy, karst aquifers are the main sources of drinking water and play a crucial role in the socio-economic development of the territory. Hence, estimating the groundwater recharge of these aquifers is a fundamental task for the proper management of water resources, while also considering the impacts of climate changes. In the southern Apennines, the assessment of hydrological parameters that is needed for the estimation of groundwater recharge is a challenging issue, especially for the spatial and temporal inhomogeneity of networks of rain and air temperature stations, as well as the variable geomorphological features and land use across mountainous karst areas. In such a framework, the integration of terrestrial and remotely sensed data is a promising approach to limit these uncertainties. In this research, estimations of actual evapotranspiration and groundwater recharge using remotely sensed data gathered by the Moderate Resolution Imaging Spectrometer (MODIS) satellite in the period 2000–2014 are shown for karst aquifers of the southern Apennines. To assess the uncertainties affecting conventional methods based on empirical formulas, the values estimated by the MODIS dataset were compared with those calculated by Coutagne, Turc, and Thornthwaite classical empirical formulas, which were based on the recordings of meteorological stations. The annual rainfall time series of 266 rain gauges and 150 air temperature stations, recorded using meteorological networks managed by public agencies in the period 2000–2014, were considered for reconstructing the regional distributed models of actual evapotranspiration (AET) and groundwater recharge. Considering the MODIS AET, the mean annual groundwater recharge for karst aquifers was estimated to be about 448 mm·year−1. In contrast, using the Turc, Coutagne, and Thornthwaite methods, it was estimated as being 494, 533, and 437 mm·year−1, respectively. The obtained results open a new methodological perspective for the assessment of the groundwater recharge of karst aquifers at the regional and mean annual scales, allowing for limiting uncertainties and taking into account a spatial resolution greater than that of the existing meteorological networks. Among the most relevant results obtained via the comparison of classical approaches used for estimating evapotranspiration is the good matching of the actual evapotranspiration estimated using MODIS data with the potential evapotranspiration estimated using the Thornthwaite formula. This result was considered linked to the availability of soil moisture for the evapotranspiration demand due to the relevant precipitation in the area, the general occurrence of soils covering karst aquifers, and the dense vegetation.


2017 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely-sensed data based ET algorithms and distributed hydrological models has provided improved spatially-upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the two methods of up to 48 %, 21 % and 16 % were observed at respectively 1 km2, 5 km2 and 10 km2 spatial resolutions. Land cover differences, mismatches between the two methods and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.


2018 ◽  
Vol 22 (5) ◽  
pp. 2775-2794 ◽  
Author(s):  
Olanrewaju O. Abiodun ◽  
Huade Guan ◽  
Vincent E. A. Post ◽  
Okke Batelaan

Abstract. In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance, which are difficult to estimate, particularly over complex terrain. In recent decades, the advent of remotely sensed data based ET algorithms and distributed hydrological models has provided improved spatially upscaled ET estimates. However, information on the performance of these methods at various spatial scales is limited. This study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with the ET estimates from a SWAT hydrological model on graduated spatial scales for the complex terrain of the Sixth Creek Catchment of the Western Mount Lofty Ranges, South Australia. ET from both models was further compared with the coarser-resolution AWRA-L model at catchment scale. The SWAT model analyses are performed on daily timescales with a 6-year calibration period (2000–2005) and 7-year validation period (2007–2013). Differences in ET estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and 9 % were observed at respectively 1, 4, 9, 16 and 25 km2 spatial resolutions. Based on the results of the study, a spatial scale of confidence of 4 km2 for catchment-scale evapotranspiration is suggested in complex terrain. Land cover differences, HRU parameterisation in AWRA-L and catchment-scale averaging of input climate data in the SWAT semi-distributed model were identified as the principal sources of weaker correlations at higher spatial resolution.


2019 ◽  
Vol 34 (12) ◽  
pp. 2717
Author(s):  
WANG Hao ◽  
LUO Ge-ping ◽  
WANG Wei-sheng ◽  
PACHIKIN Konstantin ◽  
LI Yao-ming ◽  
...  

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