Development and evaluation of the soil water balance model in an inland arid delta oasis: Implications for sustainable groundwater resource management

Maihemuti, Balati; Simayi, Zibibula; Alifujiang, Yilinuer; Aishan, Tayierjiang; Abliz, Abdulla; Aierken, Gulaimubaier

doi:10.1016/j.gecco.2020.e01408

Cited by 21 publications

(17 citation statements)

References 51 publications

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“…In desert riparian zones, groundwater is considered as a permanent and steady water source for native plant growth and reproduction due to the limited surface water flows and hyperarid climate conditions induced by severe drought events (Eamus et al., 2006; Evaristo & Mcdonnell, 2017; Li et al., 2020). Besides, in groundwater‐dependent arid ecosystems, plants could suffer from water stress when the groundwater depth is too deep, whereby soil water salinity could restrain plant growth when the water depth is too shallow (Maihemuti et al., 2021). Abiotic stress, such as water stress and salt stress, is one of the most deleterious environmental factors affecting plant growth, reproduction, and survivability during whole developmental stages (Galicia et al., 2020; Ma et al., 2020; Sun et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Responses of two dominant desert plant species to the changes in groundwater depth in hinterland natural oasis, Tarim Basin

Imin

Dai

Shi

et al. 2021

Ecology and Evolution

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Section: Introductionmentioning

confidence: 99%

Responses of two dominant desert plant species to the changes in groundwater depth in hinterland natural oasis, Tarim Basin

Imin

Dai

Shi

et al. 2021

Ecology and Evolution

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“…The Baicheng oases ecological units have high mountainous conditions with abundant precipitation and low temperatures and have developed a large number of modern glaciers above the headstreams of the Muzati River basin, with a glacier coverage of approximately 48.2% and snow and ice meltwater proportion of approximately 93% [31]. The ecological carrying capacity of water resources in the Wei-Ku and Baicheng oases is quite limited, especially during dry periods with sparse precipitation and extreme evapotranspiration (ET), and glacially derived runoff has an essential effect on the water resource management, which influences local ecological sustainability [32]. Therefore, the analysis and prediction of glacially derived runoff in this study area are representative for studying the response of glacially derived runoff in the context of climate influence and regulating the sustainable development of an oasis under water resource constraints.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

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The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

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“…Xiang et al (2020) evaluated the balance between groundwater protection with crop production based on the results of MODFLOW combined with DSSAT (Decision Support System for Agrotechnology Transfer). Maihemuti et al (2021) employed HYDRUS to evaluate the effects of groundwater on plant distribution. However, these physical models usually require boundary conditions and a large number of hydraulic parameters for calibration.…”

Section: Introductionmentioning

confidence: 99%

Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

Liang

Liu

et al. 2021

Front. Environ. Sci.

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Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stationarity of water table depth data, the performance of NNs in areas of over-exploitation is challenging. Therefore, reducing data noise is an essential step before simulating the water table depth. This research proposed a novel method to model the non-stationary time series data of water table depth through combing the advantages of wavelet analysis and Long Short-Term Memory (LSTM) neural network (NN). A typical groundwater over-exploitation area, Baoding, North China Plain (NCP), was selected as a study area. To reflect the impact of anthropogenic activities, the variables harnessed to develop the model includes temperature, precipitation, evaporation, and some socio-economic data. The results show that decomposing the time series of the water table depth into three sub-temporal components by Meyer wavelets can significantly improve the simulation effect of LSTM on the water table depth. The average NSE (Nash-Sutcliffe efficiency coefficient) value of all the sites increased from 0.432 to 0.819. Additionally, a feedforward neural network (FNN) is used to compare forecasts over 12-months. As expected, wavelet-LSTM outperforms wavelet-FNN. As the prediction time increases, the advantages of wavelet-LSTM become more evident. The wavelet-LSTM is satisfactory for forecasting the water table depth at most in 6 months. Furthermore, the importance of input variables of wavelet-LSTM is analysed by the weights of the model. The results indicate that anthropogenic activities influence the water table depth significantly, especially in the sites close to the Baiyangdian Lake, the largest lake in the North China Plain. This study demonstrates that the wavelet-LSTM model provides an option for water table depth simulation and predicting areas of over-exploitation of groundwater.

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Development and evaluation of the soil water balance model in an inland arid delta oasis: Implications for sustainable groundwater resource management

Cited by 21 publications

References 51 publications

Responses of two dominant desert plant species to the changes in groundwater depth in hinterland natural oasis, Tarim Basin

Responses of two dominant desert plant species to the changes in groundwater depth in hinterland natural oasis, Tarim Basin

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

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