Analysis of groundwater-level fluctuation and linear regression modeling for prediction of initial groundwater level during irrigation of rice paddies in the Nasunogahara alluvial fan, central Japan

Tsuchihara, Takeo; Yoshimoto, Shuhei; Shirahata, Katsushi; Nakazato, Hiroomi; Ishida, Satoshi

doi:10.1007/s12665-023-11174-w

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…Although physically based models necessitate a thorough understanding of complex input/output relationships and extensive meteorological, hydrological, and geological data for the study area [13,14], their application is hindered by the demand for substantial measurements. In addition to physically based models, alternative methods such as water balance models [15][16][17] and statistical regression models [18][19][20] have been developed and used. However, they both need large amount of input parameter values and observations to fulfill the calculation.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Mirzaei,

Shirmohammadi

2024

Water

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Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site.

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Section: Introductionmentioning

confidence: 99%

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Mirzaei,

Shirmohammadi

2024

Water

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An Evaluation on the Effect of Water-Saving Renovation on a Large-Scale Irrigation District: A Case Study in the North China Plain

Liu,

Wu,

et al. 2024

Agronomy

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The construction of water-saving renovation projects can bring substantial benefits to the development of agriculture, but it may also be accompanied by negative impacts, especially in a large-scale irrigation district. Hence, there is always controversy, and it is vital and necessary to investigate the effectiveness of water-saving renovation. In this study, the Yahekou irrigation district, one of the largest districts in China, is selected as the case to explore the beneficial influence and adverse impact. Kriging interpolation, Pearson correlation analysis, and linear regression analysis are employed to study the temporal evolution, spatial distribution, and domain influencing factors. The results show that the water-saving renovation project in the Yahekou irrigation district had significant water-saving benefits during the period of 1998–2021, and the average annual water use of the irrigation district decreased by 61%. Canal lining is an important influencing factor for the decrease in irrigation water use, with a correlation coefficient of −0.538, B of −18.669, and R2 of 0.290. Furthermore, the water-saving renovation of irrigation districts is not the dominant reason for changes in groundwater depth. Meanwhile, the groundwater depth below ground level (the level DBGL) in the irrigation district increased by 82%. The combined effects of atmospheric precipitation, canal lining, river sand mining, and groundwater exploitation have led to a sustained increase in groundwater depth in the study area. The results obtained from this study can provide valuable and scientific reference for agricultural water resource management over the large-scale irrigation district. This article focuses on the impact of water-saving renovation on ecological and technical indicators such as water use and the groundwater DBGL. However, the impact of water-saving renovation in irrigation districts is multifaceted; subsequent research can explore the impact of water-saving renovation projects on society and the economy.

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Analysis of groundwater-level fluctuation and linear regression modeling for prediction of initial groundwater level during irrigation of rice paddies in the Nasunogahara alluvial fan, central Japan

Cited by 2 publications

References 49 publications

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

An Evaluation on the Effect of Water-Saving Renovation on a Large-Scale Irrigation District: A Case Study in the North China Plain

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