Spatial Distribution and Sustainability Implications of the Canadian Groundwater Resources under Changing Climate

Groundwater availability, utilization, sustainability, and climate change implications were assessed at regional and provincial scales of Canada. It remains an unexplored resource, estimated to be renewing between 380 and 625 km3/year. However, the provinces have initiated developing their quantitative and qualitative databases for their accurate inventory. Sustainable groundwater availability at the national scale was estimated as 19,832 m3/person/year (750 km3/year), with high regional variations ranging from 3949 in the densely populated Prince Edward Island (PEI) province to 87,899 in the thinly populated Newfoundland and Labrador (NFL). It fulfills 82%, 43%, and 14% of water requirements of the rural population, irrigation, and industry, respectively. It is the potable water source for more than 9 million people countrywide (24% of the population), and provinces of Quebec, and Ontario (1.3 million people), and PEI (0.15 million people) particularly depend on it. It is mostly a free or nominally charged commodity, but its utilization was found to be well under sustainable limits (40% of recharge) at the provincial scales, i.e., under 4% for all the provinces except New Brunswick (NB), which also had just 8% extraction of sustainable availability. Nevertheless, localized issues of quantitative depletion and qualitative degradation were found at scattered places, particularly in Ontario and Quebec. Climate change impacts of warming and changing precipitations on groundwater underscored its stability with some temporal shifts in recharge patterns. In general, increased recharge in late winters and springs was observed due to reduced frost and more infiltration, and was somewhat decreasing in summers due to more intense rainfall events

Prospective Climates, and Water Availabilities under Different Projections of Environmental Changes in Prince Edward Island, Canada

Climate change impacts on temperatures, precipitations, streamflows, and recharges were studied across eastern, central, and western Prince Edward Island (PEI) between climate normals in 1991–2020, 2021–2050, and 2051–2080 using observed and projected data, and SWAT modeling. Average annual temperature can significantly rise from the existing 5.90–6.86 °C to 8.26–11.09 °C in different parts during the next 30–60 years under different RCP scenarios. Average annual precipitations would not significantly change except in western PEI where a 17% likely increase would offset further warming impact; therefore, current streamflows (~650 mm/year) and recharges (~320 mm/year) would not be much affected there. However, warming and increased pumping together in its Wilmot River watershed could reduce streamflows up to 9%, and 13% during 2021–2050, and 2051–2080, respectively. In the eastern forest-dominated Bear River watershed, no significant reductions in current streamflows (~692 mm/year) or recharges (~597 mm/year) are expected. Nevertheless, near constant precipitation and warming could cumulatively reduce streamflows/recharges up to 8% there, as pumping will be negligible. In the central zone, precipitation could insignificantly increase up to 5%, but current streamflows (~737 mm/year) and recharges (~446 mm/year) would not be significantly affected, except for RCP8.5 under which streamflows could reduce by ~16% during 2051–2080. Overall, more attenuated streamflows and recharges are likely with higher quantities in late winter and early spring, and somewhat lesser ones in summer, which could reduce water supplies during the growing season. Besides, precipitation uncertainty of ~300 mm/year between dry and wet years continues to be a major water management challenge. Adapting policies and regulations to the changing environment would ensure sustainable water management in PEI

Maximization of Water Productivity and Yield of Two Iceberg Lettuce Cultivars in Hydroponic Farming System Using Magnetically Treated Saline Water

Egypt has limited agricultural land, associated with the scarcity of irrigation water and rapid population growth. Hydroponic farming, seawater desalination and magnetic treatment are among the practical solutions for sustaining rapid population growth. In this regard, the main objective of the present research study was to design and construct a hierarchical engineering unit as a hydroponic farming system (soilless) to produce an iceberg lettuce crop using magnetically treated saline water. The treatments included four types of irrigation water: common irrigation water (IW1) with an electrical conductivity (EC) of 0.96 dS/m as a control treatment, magnetically treated common irrigation water (IW2) with an EC of 0.96 dS/m, saline water (IW3) with an EC of 4.56 dS/m and magnetically treated saline water (IW4) with an EC of 4.56 dS/m; three depletion ratios (DR) of field capacity (DR0 = 50%, DR1 = 60% and DR2 = 70%) and three slopes of hydroponic pipes (S1 = 0.0%, S2 = 0.025% and S3 = 0.075%). The results revealed that seawater contributed 7.15% to produce iceberg lettuce in the hydroponic system. The geometric parameter, the slope of the pipes, influenced the obtained luminous intensity by an average increase of 21% and 71% for S2 and S3, respectively, compared with the zero slope (horizontal pipes). Magnetization of irrigation water increased the total soluble solids (TSS) and enhanced the fresh weight and water productivity of both iceberg lettuce varieties used. The maximum percentages of TSS were 5.20% and 5.10% for lemur and iceberg 077, respectively, for the combination IW4DR2S2. The highest values of fresh weight and water productivity of 3.10 kg/m and 39.15 kg/m3 were recorded with the combinations IW3DR2S3 and IW4DR1S3, respectively, for lemur and iceberg lettuce. The percentages of these increases were 109.46% and 97.78%, respectively, when compared with the combination IW1DR0S1. The highest values of iceberg lettuce 077 fresh weight and water productivity were 2.93 kg/m and 36.15 kg/m3, respectively, which were recorded with the combination IW4DR1S3. The percentages of these increases were 112.32% and 120.56%, respectively, when compared with IW1DR0S1 (the control treatment)

Maximization of Water Productivity and Yield of Two Iceberg Lettuce Cultivars in Hydroponic Farming System Using Magnetically Treated Saline Water

Egypt has limited agricultural land, associated with the scarcity of irrigation water and rapid population growth. Hydroponic farming, seawater desalination and magnetic treatment are among the practical solutions for sustaining rapid population growth. In this regard, the main objective of the present research study was to design and construct a hierarchical engineering unit as a hydroponic farming system (soilless) to produce an iceberg lettuce crop using magnetically treated saline water. The treatments included four types of irrigation water: common irrigation water (IW1) with an electrical conductivity (EC) of 0.96 dS/m as a control treatment, magnetically treated common irrigation water (IW2) with an EC of 0.96 dS/m, saline water (IW3) with an EC of 4.56 dS/m and magnetically treated saline water (IW4) with an EC of 4.56 dS/m; three depletion ratios (DR) of field capacity (DR0 = 50%, DR1 = 60% and DR2 = 70%) and three slopes of hydroponic pipes (S1 = 0.0%, S2 = 0.025% and S3 = 0.075%). The results revealed that seawater contributed 7.15% to produce iceberg lettuce in the hydroponic system. The geometric parameter, the slope of the pipes, influenced the obtained luminous intensity by an average increase of 21% and 71% for S2 and S3, respectively, compared with the zero slope (horizontal pipes). Magnetization of irrigation water increased the total soluble solids (TSS) and enhanced the fresh weight and water productivity of both iceberg lettuce varieties used. The maximum percentages of TSS were 5.20% and 5.10% for lemur and iceberg 077, respectively, for the combination IW4DR2S2. The highest values of fresh weight and water productivity of 3.10 kg/m and 39.15 kg/m3 were recorded with the combinations IW3DR2S3 and IW4DR1S3, respectively, for lemur and iceberg lettuce. The percentages of these increases were 109.46% and 97.78%, respectively, when compared with the combination IW1DR0S1. The highest values of iceberg lettuce 077 fresh weight and water productivity were 2.93 kg/m and 36.15 kg/m3, respectively, which were recorded with the combination IW4DR1S3. The percentages of these increases were 112.32% and 120.56%, respectively, when compared with IW1DR0S1 (the control treatment)

Long-term trend analysis of extreme climate in Sarawak tropical peatland under the influence of climate change

Extreme climate is one of the important variables which determine the capability of tropical peatland to act as either carbon sink and/or carbon source. The purpose of this study is to reveal the spatio-temporal trend in the long-term time series of extreme rainfall and temperature in Sarawak peatland cause by climate change. Gridded-based Princeton datasets were used for trend analysis spanning 68-year (1948–2016) based on Modified Mann-Kendall (m-MK) test which has the capability of distinguishing unidirectional trend with multi-scale variability. The m-MK test was also used to confirm the increasing or decreasing trend produce by Mann-Kendall (MK), and to discriminate the exaggeration in trend caused by serial auto-correlation due to the high effect of large scale climate events regulating the climate in the region. By using R-based program, RClimDex for extreme climate indices output, extreme climate under Northeast (NE) and Southwest (SW) monsoon showed lower grid point with significant changes under m-MK test compared to MK test at 95% significance level. Here, the exaggeration of trend by MK test has been reduced by using m-MK test which can accommodate the scaling effect in the time series due to inherent natural climate variability. Diurnal temperature range (DTR) was expected to decrease for both monsoons in the central-coastal region as minimum temperature (TN) increased more than maximum temperature (TX). Significant increase in extreme rainfall (R10, R20, Rnn) was spatially observed more during SW monsoon compared to NE monsoon, although with high spatial variability. Significant increase of TN indices of TNn and TN90p might cause increased rainfall intensity in the south and central-coastal region, while high TX indices of TXn might cause increased rainfall intensity in the north. Due to the imminent threat of climate change, this study gives scientists an essential view on the behavior of different extreme climate variables and its potential impact on the peatland area which is susceptible to flood and risk of fire during the NE and SW monsoon, respectively

Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region

Quantification of the soil physicochemical properties is one of the essential process in the field of soil geo-science. In the current research, three types of machine learning (ML) models including support vector machine (SVM), random forest (RF), and gradient boosted decision tree (GBDT) were developed for Total Dissolved Salt (TDS) prediction over several locations in Iraq region. Various physicochemical soil properties were used as predictors for the TDS prediction. Four modeling scenarios are constructed based on the types of the associated soil input variables properties. The applied ML models were analyzed and discussed based on several statistical measures and graphical presentations. Based on the correlation analysis; Gypsum concentration, Sulfur trioxide (SO3), Chloride (Cl), and organic matter (OR) were the essential soil properties for the TDS concentration influence. The prediction results indicated that incorporating all the types of input variables including chemical, soil consistency limits, and soil sieve analysis attained the best prediction process. In quantitative terms, the SVM model attained the maximum coefficient of determination (R-2 = 0.849) and minimum root mean square error (RMSE = 3.882). Overall, the development of the ML models for the TDS of soil prediction provided a robust and reliable methodology that contributes to the soil geoscience field

Projection of agricultural water stress for climate change scenarios: A regional case study of Iraq

Assessment of possible changes in crops water stress due to climate alteration is essential for agricultural planning, particularly in arid regions where water supply is the major challenge for agricultural development. This study aims to project climatic water availability (CWA) and crop water demand (CWD) to outline the possible future agricultural water stress of Iraq for different radiative concentration pathways (RCPs). The ensemble means of downscaled precipitation and temperature projections of the selected global climate models (GCMs) were used in a simple water balance model for this purpose. The modified Mann–Kendall (mMK) trend test was employed to estimate the tendency in CWA and the Wilcoxon rank test to evaluate CWD alteration in three future time horizons compared to the base period (1971–2000). The results revealed a decrease in CWA at a rate of up to -34/year during 2010–2099 for RCP8.5. The largest declination would be in summer (-29/year) and an insignificant decrease in winter (-1.3/year). The study also showed an increase in CWD of all major crops for all scenarios. The highest increase in CWD would be for summer crops, approximately 320 mm, and the lowest for winter crops, nearly 32 mm for RCP8.5 in the far future (2070–2099). The decrease in CWA and increase in CWD would cause a sharp rise in crop water stress in Iraq. This study indicates that the increase in temperature is the main reason for a large increase in CWD and increased agricultural water stress in Iraq

Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity, non-stationarity, and feature redundancy. Various artificial intelligence (AI) modeling frameworks have been introduced to solve river sediment problems. The present survey is designed to provide an updated account of the latest and most relevant AI-based applications for modeling the sediment transport in river basin systems. The review is established to capture the subsequent developments in the advanced AI models applied for river sediment transport prediction. Also, several hydrological and environmental aspects are identified and analyzed according to the results produced in those studies. The merits and constraints of the well-established AI models are further discussed in much detail, particularly considering state-of-the art, modeling frameworks and their application-specific appraisal, and some of the key proposed future research directions. Together with the synthesis of such information to drive a new understanding of models and methodologies related to suspended river sediment prediction, this review provides a future research vision for hydrologists, water scientists, water resource engineers, oceanography and environmental planners