Normalized difference vegetation index as the dominant predicting factor of groundwater recharge in phreatic aquifers: case studies across Iran

Modeling canopy water content in the assessment for rainfall induced surface and groundwater nitrate contamination: The Bilate cropland sub watershed

Nitrate contamination in surface and groundwater remains a widespread problem in agricultural watersheds is primarily associated to high levels of percolation or leakage from fertilized soil, which allows easy infiltration from soil into groundwater. This study was aimed to predict canopy water content to determine the nitrate contamination index resulting from nitrogen fertilizer loss in surface and groundwater. The study used Geographically Weighted Regression (GWR) model using MODIS 006 MOD13Q1-EVI Earth observation data, crop information and rainfall data. Satellite data collection was synchronized with regional crop calendars and calibrated to plant biomass. The average plant biomass during observed plant growth stages was between 0.19 kg/m2 at the minimum and 0.57 kg/m2 at the maximum. These values are based on the growth stages of crops and provide a solid basis for monitoring and validating crop water productivity data. The simulation results were validated with a high correlation coefficient (R2 = 0.996, P < 0.0005) for the observed rainfall in the growing zone compared to the predicted canopy water content. The nitrate contamination index assessment was conducted in 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2018 and 2020. Canopy water content and root zone seasonal water content were measured in (%) per portion as indicators of the NO-3-N-nitrate contamination index in these years (0.391, 0.316, 0.298, 0.389, 0.380, 0.339, 0.242, 0.342 and 0.356).

Rapid groundwater decline and some cases of recovery in aquifers globally

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers. Using the Analytical Hierarchy Process (AHP), groundwater recharge-affecting parameters and classes of each parameter were scored. All thematic layers were integrated using weighted linear combination on a GIS platform to create a groundwater potential zone map. The outcomes of the model indicate that the research region exhibits three distinct groundwater potential zones, namely low (11.928%; 354.884 km2), moderate (76.44%; 2274.4 km2), and high (11.267%; 345.943 km2), in sequential sequence. These categories define the model's output in descending order of how closely it matches the actual conditions. After that, a map removal sensitivity analysis was also executed and found that geology, geomorphology, lineament density and drainage density strongly influence the prediction model for groundwater potential zone identification. The reliability of the results is established by employing a Receiver Operating Characteristic (ROC) curve for evaluation, which demonstrates a prediction accuracy of 81.3%. Authorities responsible for groundwater resource management can use this study's findings to better inform future regulatory initiatives.

Quantifying heterogeneity in ecohydrological partitioning in urban green spaces through the integration of empirical and modelling approaches

Urban green spaces (UGS) can help mitigate hydrological impacts of urbanisation and climate change through precipitation infiltration, evapotranspiration and groundwater recharge. However, there is a need to understand how precipitation is partitioned by contrasting vegetation types in order to target UGS management for specific ecosystem services. We monitored, over one growing season, hydrometeorology, soil moisture, sapflux and isotopic variability of soil water under contrasting vegetation (evergreen shrub, evergreen conifer, grassland, larger and smaller deciduous trees), focussed around a 150-m transect of UGS in northern Scotland. We further used the data to develop a one-dimensional model, calibrated to soil moisture observations (KGE's generally > 0.65), to estimate evapotranspiration and groundwater recharge. Our results evidenced clear inter-site differences, with grassland soils experiencing rapid drying at the start of summer, resulting in more fractionated soil water isotopes. Contrastingly, the larger deciduous site saw gradual drying, whilst deeper sandy upslope soils beneath the evergreen shrub drained rapidly. Soils beneath the denser canopied evergreen conifer were overall least responsive to precipitation. Modelled ecohydrological fluxes showed similar diversity, with median evapotranspiration estimates increasing in the order grassland (193 mm) < evergreen shrub (214 mm) < larger deciduous tree (224 mm) < evergreen conifer tree (265 mm). The evergreen shrub had similar estimated median transpiration totals as the larger deciduous tree (155 mm and 128 mm, respectively), though timing of water uptake was different. Median groundwater recharge was greatest beneath grassland (232 mm) and lowest beneath the evergreen conifer (128 mm). The study showed how integrating observational data and simple modelling can quantify heterogeneities in ecohydrological partitioning and help guide UGS management.

Characterizing spatial dependence of boron, arsenic, and other trace elements for Permian groundwater in Northern Anhui plain coal mining area, China, using spatial autocorrelation index and geostatistics

Anthropogenic and geological factors play an essential role in the variability of groundwater quality, resulting in a weak spatial dependence of groundwater trace elements. Thus, it is an essential study to investigate the factors affecting groundwater quality and its spatial abundance of trace elements (including As, B, and other metalloids). In this study, samples are obtained from a Permian sandstone fracture aquifer in a coal mining area. A multivariate statistical analysis, hydrogeochemistry modeling, and spatial autocorrelation analysis were used to analyze the data. The results showed that Moran index was positive for all trace elements, which had good spatial autocorrelation. The Local indicators of spatial association (LISA) indicated that trace elements were clustered. The hydrogeochemical modeling results indicated that the precipitation and stability of iron-phase minerals, such as rhodochrosite and arsenic (As) absorption on the surface of iron-phase minerals in the aquifer, may limit concentrations in the southern region. The spatial autocorrelations of both As and Boron (B) were positive (high-high) in the western areas, indicating that As contamination occurred from both natural geological causes and human coal mining activities. In contrast, B contamination was mainly linked to the influence of human agricultural or industrial activities. Over 96% of the groundwater concentrations of As (10 μg/L) and B (300 μg/L) in the study area exceeded World Health Organization (WHO) limits. Overall, the results of this work could help decision-makers involved in regional water quality management visualize disperse zones where specific anthropogenic and geological processes may threaten groundwater quality.

Local neural-network-weighted models for occurrence and number of down wood in natural forest ecosystem

The natural forest ecosystem has been affected by wind storms for years, which have caused several down wood (DW) and dramatically modified the fabric and size. Therefore, it is very important to explain the forest system by quantifying the spatial relationship between DW and environmental parameters. However, the spatial non-stationary characteristics caused by the terrain and stand environmental changes with distinct gradients may lead to an incomplete description of DW, the local neural-network-weighted models of geographically neural-network-weighted (GNNWR) models are introduced here. To verify the validity of models, our DW and environmental factors were applied to investigate of occurrence of DW and number of DW to establish the generalized linear (logistic and Poisson) models, geographically weighted regression (GWLR and GWPR) models and GNNWR (GNNWLR and GNNWPR) models. The results show that the GNNWR models show great advantages in the model-fitting performance, prediction performance, and the spatial Moran’s I of model residuals. In addition, GNNWR models can combine the geographic information system technology for accurately expressing the spatial distribution of DW relevant information to provide the key technology that can be used as the basis for human decision-making and management planning.

Tracing Real-Time Transnational Hydrologic Sensitivity and Crop Irrigation in the Upper Rhine Area over the Exceptional Drought Episode 2018–2020 Using Open Source Sentinel-2 Data

Climate and regional land-use and landcover change (LUCC) impact the ecosystem of the Upper Rhine Area (URA) and transform large parts of the landscape into strongly irrigated agricultural cropland. The increase of long-term drought periods and the trend towards low summer precipitation totals trigger an increase in groundwater scarcity and amplify the negative effects of extensive irrigation purposes and freshwater consumption in a hydrologically sensitive region in Central Europe. This article presents qualitative transnational open source remote sensing temporal series of vegetation indices (NDVI) and groundwater level development to tracing near real-time vegetation change and socio-ecological feedbacks during periods of climate extremes in the Upper Rhine Area (2018–2020). Increased freshwater consumption caused a dramatic drop in groundwater availability, which eventually led to a strong degradation of the vegetation canopy and caused governmental regulations in July 2020. Assessing vegetation growth behavior and linking groundwater reactions in the URA through open source satellite data contributes to a rapidly accessible understanding of the ecosystem’s feedbacks on the local to the transnational scale and further enables risk management and eco-political regulations in current and future decision-making processes.