scholarly journals Modelling Groundwater Flow with MIKE SHE Using Conventional Climate Data and Satellite Data as Model Forcing in Haihe Plain, China

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1295 ◽  
Author(s):  
Yunqiao Shu ◽  
Hongjun Li ◽  
Yuping Lei
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Groundwater Flow ◽  
Groundwater Level ◽  
Remote Sensing Data ◽  
Resource Assessment ◽  
Groundwater Resource ◽  
Mike She ◽  
Sensing Data ◽  
Groundwater Resource Assessment

In North China Plain, accurate spatial and temporal ET and precipitation pattern is very important in the groundwater resource assessment. This study demonstrated the potential for modelling ET and groundwater processes using remote sensing data for distributed hydrological modelling with MIKE SHE codes in the Haihe Plain, China. The model was successfully validated against independent groundwater level measurements following the calibration period and the model also provided a reasonable match of the lysimeter measurements of ET. The remote sensing data included ET derived from global radiation products of Fengyun-2C geostationary meteorological satellite (FY-2C) and FY-2C precipitation products. The comparisons show that precipitation is a critical factor for the hydrological response and for the spatial distribution of ET and groundwater flow. FY-2C precipitation products has a spatial resolution of about 11 km, which thus adds more spatial variability to the most important driving variable. The ET map based on FY-2C data has a higher spatial variability than that map based on conventional data, which are caused by higher resolution of ground information. The groundwater level changes in the aquifer system are shown in the quite different spatial patterns under two models, which is affected by the significant difference between two types of precipitation. In the Haihe Plain, accurate spatial and temporal ET pattern is very important in the groundwater resource assessment that determines the recharge to the saturated zone.

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China

2011 ◽  
Vol 91 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Aidi Huo ◽  
Xunhong Chen ◽  
Huike Li ◽  
Ming Hou ◽  
Xiaojing Hou
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Groundwater Level ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Desert Area ◽  
Groundwater Levels ◽  
Sensing Data ◽  
Desert Areas ◽  
Regional Groundwater

Huo, A., Chen, X., Li, H., Hou, M. and Hou, X. 2011. Development and testing of a remote sensing-based model for estimating groundwater levels in aeolian desert areas of China. Can. J. Soil Sci. 91: 29–37. Regional groundwater level is an important data set for understanding the relationships between groundwater resources and regional ecological environments. The decline in water table levels leads to vegetation degradation and thus affects the ecological environment. Such a negative effect is especially apparent in the desertification areas. In this study, a remote-sensing based method was proposed to predict the distribution of the regional groundwater level in an aeolian desert area in northern China. The study used the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and field investigations. Based on field investigation of groundwater level, soil moisture, and other supporting information in the aeolian desert area, as well as the soil moisture distribution derived from the MODIS images, empirical equations describing the relationship between the soil moisture and groundwater level were obtained. The groundwater levels derived using the MODIS image data were verified by groundwater levels measured from 58 wells. The results show that the correlation coefficient between the measured groundwater levels and the remote sensing-based estimated water levels was 0.868, indicating that the error is small and the predictions closely reflect the real water levels. This model can be used to predict groundwater levels in aeolian desert areas based on remote sensing data sets.

scholarly journals Using Remote Sensing Data to Improve Groundwater Supply Estimations in Gujarat, India

2013 ◽  
Vol 17 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Pennan Chinnasamy ◽  
Jason A. Hubbart ◽  
Govindasamy Agoramoorthy
Keyword(s):  
Remote Sensing ◽  
Resource Availability ◽  
Remote Sensing Data ◽  
Groundwater Resource ◽  
Irrigated Agriculture ◽  
Groundwater Depletion ◽  
Sensing Data ◽  
Groundwater Supply ◽  
Well Data ◽  
Regional Groundwater

Abstract India is the greatest groundwater consumer in the world, with estimated annual withdrawals exceeding 230 km3. More than 60% of irrigated agriculture, 85% of drinking water supplies, and 50% of urban and industrial water needs are dependent on sustainable groundwater management. Regardless, groundwater overextraction is a growing problem in many regions. Predictions of groundwater resource availability in India are problematic in part because of a limited number of monitoring sites and insufficient data quality and quantity. Regional groundwater assessments are further complicated because of sporadic and low-frequency data. To help overcome these issues and more accurately quantify groundwater resource availability, scientists have begun using satellite-derived remote sensing data. In this study, the authors used seasonal and annual hydrologic signals obtained by NASA Gravity Recovery and Climate Experiment (GRACE) satellites and simulated soil moisture variations from land data assimilation systems to show groundwater depletion trends in the northwest state of Gujarat (surface area of 196 030 km2), India. Results were evaluated using direct measurement data from 935 wells. Remote sensing generated results compared favorably with well data (e.g., r2 = 0.89 for Gandhinagar, a representative highly urbanized district in Gujarat: confidence interval (CI) = 0.05 and P = 0.002). Results show that remote sensing is an effective tool to compliment and interpolate observed regional groundwater well data and improve groundwater storage estimations in Gujarat, India. Properly implemented, the method will supply reliable science-based information to enhance management of groundwater resources in India and other geographic locations.

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