Remote sensing of wetland evolution in predicting shallow groundwater arsenic distribution in two typical inland basins

In semi-arid ecoregions of temperate zones, focused snowmelt water infiltration in topographic depressions is a key, but imperfectly understood, groundwater recharge mechanism. Routine monitoring is precluded by the abundance of depressions. We have used remote-sensing data to construct mass balances and estimate volumes of temporary ponds in the Tambov area of Russia. First, small water bodies were automatically recognized in each of a time series of high-resolution Planet Labs images taken in April and May 2021 by object-oriented supervised classification. A training set of water pixels defined in one of the latest images using a small unmanned aerial vehicle enabled high-confidence predictions of water pixels in the earlier images (Cohen’s Κ = 0.99). A digital elevation model was used to estimate the ponds’ water volumes, which decreased with time following a negative exponential equation. The power of the exponent did not systematically depend on the pond size. With adjustment for estimates of daily Penman evaporation, function-based interpolation of the water bodies’ areas and volumes allowed calculation of daily infiltration into the depression beds. The infiltration was maximal (5–40 mm/day) at onset of spring and decreased with time during the study period. Use of the spatially variable infiltration rates improved steady-state shallow groundwater simulations.

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Improved groundwater geogenic arsenic hazard map for Cambodia

Environmental Chemistry ◽

10.1071/en14006 ◽

2014 ◽

Vol 11 (5) ◽

pp. 595 ◽

Cited By ~ 15

Author(s):

C. Sovann ◽

D. A. Polya

Keyword(s):

Shallow Groundwater ◽

Hazard Map ◽

Geostatistical Model ◽

Arsenic Level ◽

Environmental Chemical ◽

Tonle Sap ◽

Chemical Hazard ◽

Groundwater Arsenic ◽

Geogenic Arsenic ◽

High Arsenic

Environmental context Groundwater arsenic is a major environmental risk to human health in many regions of the world, including Cambodia where groundwater is often used for drinking water. We present data for hitherto poorly sampled regions in Cambodia, notably around Tonle Sap and in the coastal provinces, and provide a geo-statistical model of arsenic in shallow groundwater for the whole country. Abstract Arsenic is a known environmental chemical hazard in shallow groundwaters of Cambodia and is increasingly recognised as a major problem for public health. Notwithstanding this, accurate arsenic data are not available for many wells in potentially arsenic-prone areas, particularly around the Tonle Sap Great Lake (TSL) and in the coastal provinces (CP). We present here new data for shallow groundwater (16–120-m depth) arsenic in the TSL and CP regions as well as an improved regression-kriging (RK) based groundwater arsenic hazard map for the whole country. High arsenic levels (up to 100μgL–1) were found in shallow groundwaters from the TSL and CP regions of Cambodia, but despite strong compositional similarities (near neutral, reducing, Na-Mg-Ca-HCO3 dominated) with high arsenic level groundwaters near the Mekong and Bassac rivers, groundwater arsenic levels in both the TSL and CP regions were most commonly low (interquartile range 0.09–1.2μgL–1). The RK geostatistical model was highly successful, accounting for over 50% of the observed variation in arsenic concentrations countrywide and represents a potentially useful tool for policymakers and those responsible and with the interest and authority to prepare arsenic mitigation and safe water supply plans.

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Distribution of groundwater arsenic and hydraulic gradient along the shallow groundwater flow-path in Hetao Plain, Northern China

Journal of Geochemical Exploration ◽

10.1016/j.gexplo.2012.12.004 ◽

2013 ◽

Vol 135 ◽

pp. 31-39 ◽

Cited By ~ 22

Author(s):

Yilong Zhang ◽

Wengeng Cao ◽

Wenzhong Wang ◽

Qiuyao Dong

Keyword(s):

Groundwater Flow ◽

Shallow Groundwater ◽

Northern China ◽

Flow Path ◽

Hydraulic Gradient ◽

Hetao Plain ◽

Groundwater Arsenic

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An Integrative Approach for Environmental Assessment and Water Resources Management Using Direct Current Resistivity (DC), Geographic Information System (GIS), Remote Sensing, and Gain and Loss Method

10.18122/td.1872.boisestate ◽

2021 ◽

Author(s):

Dina Ragab Desouki Abdelmoneim

Keyword(s):

Remote Sensing ◽

Surface Water ◽

Water Resource ◽

Water Resource Management ◽

Shallow Groundwater ◽

Groundwater Table ◽

Dc Resistivity ◽

Seepage Rate ◽

Loss Method ◽

Shallow Groundwater Table

Sustainable water resource management is a crucial national and global issue (Currell et al., 2012). In arid areas, groundwater is often the major source of water or at least a crucial supplement to other freshwater resources for agriculture, industry and domestic consumption (Vrba and Renaud, 2016). The complexity associated with groundwater-surface water interactions creates uncertainty about water resource sustainability in semi-arid environments, especially with urbanization and population growth. Flood irrigation in the early 1900s increased the shallow groundwater table in the Treasure Valley (TV), but with increasing irrigation efficiencies, they have been declining since the 1960s with a mean decline rate of about 2.9-3.9x10^-9 (m/s) (Contor et al., 2011). Quantifying how much surface water is being exchanged with the shallow groundwater table through canals in the TV is necessary for gaining a better understanding of groundwater-surface water interactions in this heavily managed system. This knowledge would help evaluate alternative management options for achieving sustainable management of existing water resources. The key objectives of this project are to determine the seepage rate through some canal reaches in the TV, evaluate the integration of the gain and loss method, remote sensing, GIS, hydrogeophysical simulation, and direct current (DC) resistivity geophysical methods for water resource management. We hypothesize that the underlying lithology and size of canals affect the magnitude of the seepage rate. Flow measurements were collected weekly between July and August 2020 in canal reaches representing different sizes and lithological units to determine the seepage rate using the reach gain/loss method. Canal variability and measurement uncertainty were included in seepage estimation for the entire TV using 3 alternative scaling approaches. DC resistivity was used as a complementary method to monitor the seepage effect on the shallow GW aquifer over 2 months. This research evaluates to what extent canal size and its underlying lithology affects the seepage rate, and how the integration of methods may provide additional insight into groundwater exchange-surface water.

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Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia

Remote Sensing ◽

10.3390/rs12091361 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1361 ◽

Cited By ~ 3

Author(s):

Fahad Alshehri ◽

Mohamed Sultan ◽

Sita Karki ◽

Essam Alwagdani ◽

Saleh Alsefry ◽

...

Keyword(s):

Remote Sensing ◽

Saudi Arabia ◽

Brightness Temperature ◽

Field Data ◽

Shallow Groundwater ◽

Remote Sensing Data ◽

Data Sets ◽

Backscatter Coefficient ◽

Groundwater Levels ◽

Sensing Data

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km2), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km2). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.

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