Evaluation of groundwater sustainability in the arid Hexi Corridor of Northwestern China, using GRACE, GLDAS and measured groundwater data products

Wang, Sijia; Hu, Liu; Yang, Yu; Zhao, Wenzhi; Yang, Qingxi; Liu, Jintao

doi:10.1016/j.scitotenv.2019.135829

Cited by 65 publications

(48 citation statements)

References 63 publications

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“…In Hexi Corridor of northwestern China, the most important commodity grain bases area, wheat is largely adopted 3 . This region has annual mean pan evaporation greater than 2000 mm, leading to serious water shortage 4 . Due to crop water requirement is much greater than precipitation, wheat production relies heavily on irrigation 5 .…”

Section: Introductionmentioning

confidence: 99%

Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas

Tan

Chai

et al. 2021

Sci Rep

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Crop yield is limited by water and nitrogen (N) availability. However, in Hexi Corridor of northwestern China, water scarcity and excessive fertilizer N in wheat (Triticum aestivum L.) production causes serious conflicts between water and N supply and crop demand. A field experiment was conducted from 2016 to 2018 to evaluate whether reducing of irrigation and fertilizer N will reduce grain yield of wheat. There were two irrigation quotas (192 and 240 mm) and three fertilizer N rates (135, 180, and 225 kg N ha−1). The results showed that reducing irrigation to 192 mm and N rate to 180 kg N ha−1 reduced water uptake, water uptake efficiency, and N uptake of spring wheat as compared to local practice (i.e., 240 mm irrigation and 225 kg N ha−1 fertilizer). Whereas, it improved water and N utilization efficiency, and water and N productivity. Consequently, the irrigation and N rate reduced treatment achieved the same quantity of grain yield as local practice. The path analysis showed that interaction effect between irrigation and N fertilization may attributable to the improvement of grain yield with lower irrigation and N rate. The enhanced water and N utilization allows us to conclude that irrigation quota at 192 mm coupled with fertilizer N rate at 180 kg N ha−1 can be used as an efficient practice for wheat production in arid irrigation areas.

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Section: Introductionmentioning

confidence: 99%

Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas

Tan

Chai

et al. 2021

Sci Rep

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“…Therefore, this study attempts to use the thermal inertia model to calculate soil water content and retrieve the shallow groundwater level through the relationship between soil water content and groundwater level. For the method of groundwater evaluation using measured data (Obuobie et al 2012;Wang et al 2020), the distribution of observation sites may lead to a rough spatial grid and uneven distribution of evaluation results. Consequently, the application of remote sensing image to evaluate groundwater can improve the above problems.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Assessing shallow groundwater level using RTI model and long-term MODIS data in Ejina Basin, Northwest China

Luo

Wen

et al. 2021

Earth Sci Inform

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The present study aims to exploit the thermal inertia model to assess the shallow groundwater level distribution, as an attempt to address agricultural water shortage and ecological degradation in arid and semi-arid regions. Apparent thermal inertia (ATI) model is one of the most commonly used and successfully implemented models, whereas in some cases, the scales of the results achieved on different dates are inconsistent, which poses a serious difficulty when monitoring soil moisture over time. To address this restriction, we introduce a soil moisture indicator, called RTI, which derives an approximation of soil thermal inertia, from flexible multi-temporal MODIS observations covering a 2-year period. Taking the Ejina Basin in arid area as an example, to more accurately assess the shallow groundwater level distribution, ATI and RTI models were adopted based on the long time series MODIS data to assess the multi-temporal soil moisture content in the study area. The data of groundwater level measured by observation well and thermal inertia were employed to generate scatter diagrams. Subsequently, a nonlinear equation was developed. Based on the thermal inertia, the temporal and spatial distribution map of the shallow groundwater level in the whole study area was generated with the inverse calculation of the nonlinear equation. It was found that the shallow groundwater level distribution in Ejina Basin tended to be shallower from south to north, as well as from summer to winter. As revealed from the results, both ATI and RTI models can effectively assess the shallow groundwater level in arid areas. The RTI model could better reflect the seasonal changes of soil moisture content and groundwater level.

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“…Groundwater is the major source of water supply for crop irrigation, production and domestic use. However, the rapid population growth, oasis expansion and urbanization have recently increased the amount of groundwater use resulting in an over-extraction problem in this area [70]. The 2016 government report shows that the total area of groundwater overexploitation was 2418.30 km 2 and the amount of over-exploitation reached 192.64 million m 3 in this area.…”

Section: Case Study 41 Study Area and Hydrological Datamentioning

confidence: 99%

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

Feng

Wen

et al. 2021

Water

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Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models considering the uncontrollable error, indefinite inputs and unneglectable uncertainty during the modelling process. The AI model ensembled with the data pretreatment technique, the input selection method, or uncertainty analysis has been successfully used to tackle this issue, whereas studies about the comprehensive deterministic and uncertainty analysis of hybrid models in groundwater level forecast are rarely reported. In this study, a novel hybrid predictive model combining the variational mode decomposition (VMD) data pretreatment technique, Boruta input selection method, bootstrap based uncertainty analysis, and the extreme learning machine (ELM) model named VBELM was developed for 1-, 2- and 3-month ahead groundwater level prediction in a typical arid oasis area of northwestern China. The historical observed monthly groundwater level, precipitation and temperature data were used as inputs to train and test the model. Specifically, the VMD was used to decompose all the input-outputs into a set of intrinsic mode functions (IMFs), the Boruta method was applied to determine input variables, and the ELM was employed to forecast the value of each IMF. In order to ascertain the efficiency of the proposed VBELM model, the performance of the coupled model (VELM) hybridizing VMD with ELM algorithm and the single ELM model were estimated in comparison. The results indicate that the VBELM performed best, while the single ELM model performed the worst among the three models. Furthermore, the VBELM model presented lower uncertainty than the VELM model with more observed groundwater level values falling inside the confidence interval. In summary, the VBELM model demonstrated an excellent performance for both certainty and uncertainty analyses, and can serve as an effective tool for multi-scale groundwater level forecasting.

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Evaluation of groundwater sustainability in the arid Hexi Corridor of Northwestern China, using GRACE, GLDAS and measured groundwater data products

Cited by 65 publications

References 63 publications

Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas

Improving wheat grain yield via promotion of water and nitrogen utilization in arid areas

Assessing shallow groundwater level using RTI model and long-term MODIS data in Ejina Basin, Northwest China

Deterministic Analysis and Uncertainty Analysis of Ensemble Forecasting Model Based on Variational Mode Decomposition for Estimation of Monthly Groundwater Level

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