Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011-2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011)(2012)(2013)(2014)(2015) and validated (2016)(2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R 2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management.Water 2020, 12, 5 2 of 18 procedure that requires thorough knowledge of physical hydrological parameters, big data, hydrological models, model inputs, and the geometry of watersheds [3]. Aspects of hydrogeology-i.e., geological factors affecting the distribution and movement of groundwater underneath the soil surface-need to be properly understood when modeling GWLs and manipulating the modeling results. Watershed scale fluctuations in GWLs occur over a period of several decades, and the resulting cumulative effects on streamflow depletion may not be fully realized for years [4]. Resultantly, depending upon the distance of the pumping station from the stream and the geologic characteristics of the aquifer, the groundwater system may take decades to recover from streamflow depletion caused by intermittent pumping. Components of the surface-and the sub-surface physical hydrology of a watershed-i.e., streamflow and groundwater flow, respectively-are interconnected, making the stream-aquifer interaction one of the key processes governing the groundwater flow pattern in a watershed [1]. Groundwater fluctuations affect streamflow and vice versa, as the pumping wells capture groundwater that would otherwise discharge to connected streams, rivers, and other surface-water bodies [4]. Francis [5] reported that, in typical watersheds of Prince Edward Island, the base flow represents almost 80% of the streamflow in the late summer and fall months. Stream length in these island watersheds ranges from less than 1 km to 2...

Climate change induced uneven patterns of rainfall emphasize the use of supplemental irrigation in rainfed agriculture. The Penman-Monteith method was used to calculate supplemental irrigation for water budgeting of a potato crop in Prince Edward Island, Canada. Cumulative gaps between rainfall and crop evapotranspiration (ETc) during August and September of the study years were due to high crop coefficient factor, justifying the need for supplemental irrigation. Pressurized irrigation systems, including sprinklers, fertigation, and drip irrigation were installed, to evaluate the impact of scheduled supplemental irrigation in offsetting deficits in irrigation water requirements in comparison with conventional practice of rainfed cultivation (control). A two-way ANOVA examined the effect of irrigation methods and year on potato tuber yield, water productivity, tuber quality, and payout. Sprinkler and fertigation systems performed better than drip and control treatments. In terms of payout returns and potato tuber quality (percentage of marketable potatoes), the sprinkler treatment performed significantly better than the other treatments. However, for water productivity, fertigation treatment performed significantly better than control and sprinkler treatments during both years. The use of supplemental irrigation is recommended for profitable cultivation of potatoes in soil, agricultural, and environmental conditions resembling to those of Prince Edward Island.

No abstract

Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011–2015) and testing (2016–2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016–2017) root mean square errors were recorded in range of 0.38–0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.

No abstract

No abstract

No abstract

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.