Land-Use Optimization for Sustainable Agricultural Water Management in Pajaro Valley, California

Garza-Díaz, Laura E.; DeVincentis, Alyssa J.; Solís, Samuel Sandoval; Azizipour, Mohammad Javad; Ortiz‐Partida, J. Pablo; Mahlknecht, Jürgen; Cahn, Michael; Medellín–Azuara, Josué; Zaccaria, Daniele; Kisekka, Isaya

doi:10.1061/(asce)wr.1943-5452.0001117

Cited by 14 publications

(5 citation statements)

References 25 publications

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“…[ [27][28][29] On the basis of meeting ecological water use, water resources can maintain the maximum socio-economic scale of limited development goals. [1,12] Maximum capacity to support socio-economic development within the context of rational use and optimal allocation of water resources Under the socio-economic and technological conditions of different stages in the region, on the premise of rational development and utilization of water resources, the regional natural water resources can maintain and support the population, economy and environmental scale.…”

Section: Maximum Development Capacity Of Water Resourcesmentioning

confidence: 99%

Research Progress on the Evaluation of Water Resources Carrying Capacity

Li¹,

Liu²,

Chang³

et al. 2023

Pol. J. Environ. Stud.

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The imbalanced, maladjusted, and uncontrolled phenomenon in the regional water resources system is caused by the combined disturbances of climate change and human activities. The socio-economic and ecological systems are restricted, and the combined carrying capacity also changes accordingly. In order to explore the above changes, based on the influence mechanism of climate change, socioeconomic system and ecosystem on water resources system, this paper systematically reviews the relevant research progress in water resources carrying capacity (WRCC), from the basic theory of water resources carrying capacity, the construction of evaluation index system, the classification criteria and evaluation methods, etc. This review provides basic theoretical and technical support for the study of generalized water resources carrying capacity considering the interlinked climate-ecology-society system, which is conducive to the promotion of water resources carrying capacity and the synergistic development of socio-economic and ecological environment under a changing climate.

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Section: Maximum Development Capacity Of Water Resourcesmentioning

confidence: 99%

Research Progress on the Evaluation of Water Resources Carrying Capacity

Li¹,

Liu²,

Chang³

et al. 2023

Pol. J. Environ. Stud.

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“…Protection projects such as aquifer replenishment can be constructed to alleviate seawater intrusion by artificially increasing groundwater recharge in the aquifer more than what occurs naturally (Abdalla and Al-Rawahi, 2012;Lu et al, 2019). The replenishment programs have been operated in the developed areas such as Perth, Western Australia, and California, USA (Garza-Díaz et al, 2019). The infrastructures tend to be costly and out of reach for many developing countries.…”

Section: Introductionmentioning

confidence: 99%

Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks

Zhang

Dong

Chen

et al. 2023

Hydrol. Earth Syst. Sci.

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Abstract. Prediction of groundwater level is of immense importance and challenges coastal aquifer management with rapidly increasing climatic change. With the development of artificial intelligence, data-driven models have been widely adopted in hydrological process management. However, due to the limitation of network framework and construction, they are mostly adopted to produce only 1 time step in advance. Here, the temporal convolutional network (TCN) and models based on long short-term memory (LSTM) were developed to predict groundwater levels with different leading periods in a coastal aquifer. The initial data of 10 months, monitored hourly in two monitoring wells, were used for model training and testing, and the data of the following 3 months were used as prediction with 24, 72, 180, and 360 time steps (1, 3, 7, and 15 d) in advance. The historical precipitation and tidal-level data were incorporated as input data. For the one-step prediction of the two wells, the calculated R2 of the TCN-based models' values were higher and the root mean square error (RMSE) values were lower than that of the LSTM-based model in the prediction stage with shorter running times. For the advanced prediction, the model accuracy decreased with the increase in the advancing period from 1 to 3, 7, and 15 d. By comparing the simulation accuracy and efficiency, the TCN-based model slightly outperformed the LSTM-based model but was less efficient in training time. Both models showed great ability to learn complex patterns in advance using historical data with different leading periods and had been proven to be valid localized groundwater-level prediction tools in the subsurface environment.

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“…The replenishment programs have been operated in developed area such as Perth, Western Australia, and California, USA (Garza-Díaz et al, 2019). The infrastructure tends to be costly and out of reach for many developing countries.…”

Section: Introductionmentioning

confidence: 99%

Advance prediction of coastal groundwater levels with temporal convolutional network

Zhang

Fan

Chen

et al. 2022

Preprint

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Abstract. Prediction of groundwater level is of immense importance and challenges for the coastal aquifer management with rapidly increasing climatic change. With the development of artificial intelligence, the data driven models have been widely adopted in predicting hydrological processes. However, due to the limitation of network framework and construction, they are mostly adopted to produce only one-time step in advance. Here, a TCN-based model is developed to predict groundwater level variations with different leading periods in a coastal aquifer. The historical precipitation and tidal level data are incorporated as input data. The first hourly-monitored ten-month data were used for model training and testing, and the data of the following three months were predicted with 24, 72, 18 and 360 time steps in advance. For one-step prediction of the two wells, the calculated R2 are higher than 0.999 in the prediction stage. The performance is meanwhile compared with a powerful network in the field of time-series prediction, long short-term memory (LSTM) recurrent network. The corresponding R2 of the LSTM-based model are 0.996 and 0.998. While the RMSE values of TCN-based model are less than that of LSTM-based model with shorter running times. For the advanced prediction, the model accuracy greatly decreases with the increase of advancing period from 1-day to 3-, 7- and 15-days. Overall, the TCN- and LSTM-based models show great ability to learn complex patterns in advance using historical data within the time series. Considering the simulation accuracy and efficiency, the TCN-based model outperforms the LSTM-based model and has been proved to be a valid localized groundwater prediction tool in the subsurface environment.

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Land-Use Optimization for Sustainable Agricultural Water Management in Pajaro Valley, California

Cited by 14 publications

References 25 publications

Research Progress on the Evaluation of Water Resources Carrying Capacity

Research Progress on the Evaluation of Water Resources Carrying Capacity

Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks

Advance prediction of coastal groundwater levels with temporal convolutional network

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