High-resolution Groundwater Research Articles

Machine learning downscaling of GRACE/GRACE-FO data to capture spatial-temporal drought effects on groundwater storage at a local scale under data-scarcity

The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrates: data normalization; interaction of hydrometeorological variables; and the application of a time series split for cross-validation that produces a high spatial resolution groundwater storage anomaly (GWSA) dataset from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission, GRACE Follow-On (GRACE-FO). In the study, the relationship between the terrestrial water storage anomaly (TWSA) from GRACE and other land surface and hydrometeorological variables (e.g., vegetation coverage, land surface temperature, precipitation, and in situ groundwater level data) is leveraged to downscale the GWSA. The predicted downscaled GWSA datasets were tested using monthly in situ groundwater level observations, and the results showed that the model satisfactorily reproduced the spatial and temporal variations in the GWSA in the study area, with Nash-Sutcliffe efficiency (NSE) correlation coefficient values of 0.8674 (random forest) and 0.7909 (XGBoost), respectively. Evapotranspiration was the most influential predictor variable in the random forest model, whereas it was rainfall in the XGBoost model. In particular, the random forest model excelled in aligning closely with the observed groundwater storage patterns, as evidenced by its high positive correlations and lower error metrics (Mean Absolute Error (MAE) of 54.78 mm; R-squared (R²) of 0.8674). The downscaled 5 km GWSA data (based on random forest) showed a decreasing trend in storage associated with variability in the rainfall pattern. An increase in drought severity during El Niño lengthened the full recovery time of groundwater based on historical storage trends. Furthermore, the time lag between the occurrence of precipitation and recharge was likely controlled by the drought intensity and the spatial recharge characteristics of the aquifer. Projected increases in drought severity could further increase groundwater recovery times in response to droughts in a changing climate, resetting storage to a new tipping condition. Therefore, climate change adaptation strategies must recognise that less groundwater will be available to supplement the surface water supply during droughts.

Rewetting impact on the hydrological function of a drained peatland in the boreal landscape

There is a growing interest in peatland restoration as a nature-based solution to mitigate hydrological extremes. To counter the impacts of past peatland degradation and ongoing climate change trajectories, governmental authorities propose rewetting of drained peatlands as a key tool to enhance landscape resilience against floods and droughts by improving water storage. Despite a growing body of literature on this topic, the effectiveness of rewetting to enhance peatland hydrological functions remains insufficiently documented, especially in the boreal region. Therefore, this study utilized high temporal resolution groundwater table level and streamflow data to investigate the impact of peatland rewetting using a before-after-control-impact (BACI) approach. This investigation was conducted on a historically drained peatland located at the Trollberget Experimental Area (TEA) in northern Sweden. The primary aim of the experimental study was to examine the impact of rewetting on (1) the groundwater table level response, (2) runoff dynamics, and (3) water storage and hydrological buffer capacity. Our results showed that peatland rewetting led to a significant increase in the groundwater table level by 60 mm compared to the control. Flowdurationcurve(FDC) analysis demonstrated that the low-flow threshold increased by up to 150% at the rewetted sites. Furthermore, our findings suggested that rewetting resulted in an increase in the groundwater table level threshold at which stream runoff is generated. Additionally, our findings showed a noteworthy shift in the monthly runoff coefficient, with an increase during dry months and a decrease during wet periods. Combined, these observations point towards an enhancement in the peatland’s water storage and hydrological buffer capacity as a positive outcome of the rewetting efforts, but also highlight that within the first three years, full hydrological restoration did not occur.

Mapping specific groundwater nitrate concentrations from spatial data using machine learning: A case study of chongqing, China

Groundwater resources is not only important essential water resources but also imperative connectors within the intricate framework of the ecological environment. High nitrate concentrations in groundwater can exerting adverse impacts on human health. It is imperative to accurately delineate the distribution characteristics of groundwater nitrate concentrations. Four different machine learning models (Gradient Boosting Regression (GB), Random Forest Regression (RF), Extreme Gradient Boosting Regression (XG) and Adaptive Boosting Regression (AD)) which combine spatial environmental data and different radius contributing area was developed to predict the distribution of nitrate concentration in groundwater. The models use 595 groundwater samples and included topography, remote sensing, hydrogeological and hydrological, climate, nitrate input, and socio-economic predictor. Gradient Boosting Regression model outperforms the other models (R2 = 0.627, MAE = 0.529, RMSE = 0.705, PICP = 0.924 for test dataset) under 500 m radius contributing area. A high-resolution (1 km) groundwater nitrate concentration distribution map reveal in the majority of the study area, groundwater nitrate concentrations are below 1 mg/L and high nitrate concentration (>10 mg/L) proportion in southeast, northeast and central main urban area karst valley regions is 1.89%, 0.91%, and 0.38% respectively. In study area, hydrogeological conditions, soil parameters, nitrogen input factors, and percentage of arable land are among the most influential explanatory factors. This work, serving as the inaugural application of utilizing effective spatial methods for predicting groundwater nitrate concentrations in Chongqing city, furnish decision-making support for the prevention and control of groundwater pollution, particularly in areas primarily dependent on groundwater for water supply and holds profound significance as a milestone achievement.

Estimating spatiotemporal pumping amounts using multiple signal decomposition methods

Effective water resource management requires accurate estimation of the volume of water withdrawn through anthropogenic groundwater pumping. However, insufficient pumping information makes accurate estimation difficult. To address this issue, this study proposes a novel method for estimating pumping volumes that does not rely on pumping information. The proposed method uses high-resolution groundwater level (GWL) data from densely monitored wells and integrates Hilbert–Huang transform and empirical orthogonal function analysis to extract pump-associated signals, which can then be used to estimate total pumping. Our method was validated by analyzing GWL data from monitoring stations on the Choshui River alluvial fan in Taiwan. The results indicate the estimated pumping amount of 1.71×109 – 2.05×109 m3/year consistent with previous estimates obtained using survey and physics-based methods ranging from 9×108 m3/year to 2.2×109 m3/year. In the areas with abundant groundwater head observations, the proposed method can efficiently estimate pumping rates with a high space–time resolution, and may serve as a useful reference for groundwater management.

Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin

High-resolution, continuous groundwater data is important for place-based adaptive aquifer management. This information is unavailable in many areas due to spatial sparsity of and temporal gaps in groundwater monitoring. This study advances the ability to generate high-resolution (1 km2), temporally continuous estimates of groundwater level (GWL) changes by incorporating 1 km2 covariates and existing piezometer observations into predictive modeling. We employed a hybrid machine learning (ML) model, primarily using the geographically weighted random forest (RFgw) model. To assess the performance of the RFgw model, we conducted a comprehensive comparison with the SGS geostatistical method and non-spatial ML models (RF and XGBoost). The framework was implemented across the Indus Basin using biannual (July and Oct) GWL data from piezometers and local covariates from 2003 to 2020. The RFgw model demonstrated superior accuracy in predicting GWLs, improving R2 by 10 %, 17 %, and 22 % compared to SGS, RF, and XGBoost, respectively. Notably, SGS, RF, and XGBoost substantially underestimated the GWL in deeper wells (7–11 m), whereas RFgw showed a much smaller underestimation (up to ∼ 3 m). The 90 % prediction interval revealed that RFgw had less uncertainty (1–3 m) followed by RF (2–5 m), and SGS and XGBoost (up to 8 m) for most testing piezometers. Incorporating high-resolution covariates into RFgw predictive modeling provided reliable estimates of GWL changes for unmonitored sites. Using the reconstructed GWL data, we examined the GWL changes in head (i.e., upstream) and tail (i.e., downstream) farms within canal distributaries, illustrating faster groundwater drawdown in tail farms (e.g., 0.82 m/yr) than head farms (0.02 m/yr in the Hakra canal distributary). Densely populated urban areas (e.g., Lahore, Multan, and Faisalabad) had the highest GWL decline (e.g., up to 0.9 m/yr). The framework can be used in other groundwater-stressed regions to support better aquifer management in the face of limited in-situ observations.

Comparative analyses of covariance matrix adaptation and iterative ensemble smoother on high-dimensional inverse problems in high-resolution groundwater modeling

Parameter estimation is an inverse problem which is crucial to reliable groundwater model predictions and management. Numerous techniques have been developed to address this challenging problem. This study aims to compare the performance of a stochastic optimization method, covariance matrix adaptation-evolution strategy (CMA-ES), and a data assimilation method, Levenberg-Marquardt based iterative ensemble smoother (ES-LM), on solving inverse problems in groundwater modeling. This study also presents a parallelization strategy to accelerate their implementation. The comparative analysis involves synthetic inverse problems and a real-world case study of calibrating a high-resolution groundwater model. Results from the synthetic problems suggest that both the CMA-ES and the ES-LM are able to achieve desirable data fitting, but the ES-LM generally exhibits greater efficiency than the CMA-ES. Also, with appropriate stopping criteria, both methods can perform well on propagating uncertainty of model predictions. However, both methods can underestimate the uncertainty of model parameters derived from Bayesian posterior. The underestimation is particularly pronounced by the ES-LM when using a small ensemble size, whereas uncertainty quantification by the CMA-ES is less affected by population size. Moreover, calibration of the highly parameterized groundwater model in Louisiana and Southwest Mississippi indicates that the ES-LM can outperform the CMA-ES for high-dimensional inverse problems regarding both data match and computational costs. With proper ensemble size, the two methods can produce comparable uncertainty of model parameters and predictions. This study contributes to the scientific understanding and efficient application of the CMA-ES and the ES-LM.

Multiple-point statistical modeling of three-dimensional glacial aquifer heterogeneity for improved groundwater management

Quaternary glacial aquifers are important water sources for irrigation in many agricultural regions, including eastern Nebraska, USA. Quaternary glacial aquifers are heterogeneous, with juxtaposed low-permeability and high-permeability hydrofacies. Managing groundwater in such aquifers requires a realistic groundwater-flow model parameterization, and characterization of the aquifer geometry, spatial distribution of aquifer properties, and local aquifer interconnectedness. Despite its importance in considering uncertainty during decision-making, hydrofacies probabilities generated from multiple-point statistics (MPS) are not widely applied for groundwater model parameterization and groundwater management zone delineation. This study used a combination of soft data, a cognitive training image, and hard data to generate 100 three-dimensional (3D) conditional aquifer heterogeneity realizations. The most probable model (probability of hydrofacies) was then computed at node spacing of 200 × 200 × 3 m and validated using groundwater-level hydrographs. The resulting hydrofacies probability grids revealed variations in aquifer geometry, locally disconnected aquifer systems, recharge pathways, and hydrologic barriers. The profiles from hydrofacies probability at various locations show spatial variability of the streambed and aquifer connectivity. Groundwater-level hydrographs show evidence of these aquifer characteristics, verifying the general structure of the model. Using the MPS-generated 3D hydrofacies probability and hydrologic data, a novel workflow was developed in order to better define high-resolution groundwater management zones and strategies. In general, the conditional probability of hydrofacies helps improve the understanding of glacial aquifer heterogeneity, the characterization of aquifer-to-aquifer and streambed-aquifer connections, and the delineation of groundwater management zones. This MPS workflow can be adapted to other areas for modeling 3D aquifer heterogeneity using multisource data.

N-Map: High-resolution groundwater N-retention mapping and modelling by integration of geophysical, geological, geochemical, and hydrological data

A key aspect of protecting aquatic ecosystems from agricultural nitrogen (N) is to locate (i) farmlands where nitrate leaches from the bottom of the root zone and (ii) denitrifying zones in the aquifers where nitrate is removed before entering the surface water (N-retention). N-retention affects the choice of field mitigation measures to reduce delivered N to surface water. Farmland parcels associated with high N-retention gives the lowest impact of the targeted field measures and vice versa. In Denmark, a targeted N-regulation approach is currently implemented on small catchment scale (approx. 15 km2). Although this regulatory scale is much more detailed than what has been used previously, it is still so large that regulation for most individual fields will be either over- or under-regulated due to large spatial variation in the N-retention. The potential cost reduction for farmers is of up to 20–30% from detailed retention mapping at the field scale compared to the current small catchment scale.In this study, we present a mapping framework (N-Map) for differentiating farmland according to their N-retention, which can be used for improving the effectiveness of targeted N-regulation. The framework currently only includes N-retention in the groundwater. The framework benefits from the incorporation of innovative geophysics in hydrogeological and geochemical mapping and modelling. To capture and describe relevant uncertainties a large number of equally probable realizations are created through Multiple Point Statistical (MPS) methods. This allows relevant descriptions of uncertainties of parts of the model structure and includes other relevant uncertainty measures that affects the obtained N-retention. The output is data-driven high-resolution groundwater N-retention maps, to be used by the individual farmers to manage their cropping systems due to the given regulatory boundary conditions.The detailed mapping allows farmers to use this information in the farm planning in order to optimize the use of field measures to reduce delivered agricultural N to the surface water and thereby lower the costs of the field measures. From farmer interviews, however, it is clear that not all farms will have an economic gain from the detailed mapping as the mapping costs will exceed the potential economic gains for the farmers. The costs of N-Map is here estimated to 5–7 €/ha/year plus implementation costs at the farm. At the society level, the N-retention maps allow authorities to point out opportunities for a more targeted implementation of field measures to efficiently reduce the delivered N-load to surface waters.

Persistent groundwater reduction induced by dynamic stresses from the 2019 Ridgecrest earthquake observed within a fractured aquifer near Ash Meadows, Nevada, USA

Groundwater observations in Ash Meadows, Nevada are dedicated to protect its unique ecosystem and to manage groundwater resources. The groundwater levels are sensitive to natural factors such as earth tides and earthquakes, allowing us to study dynamic hydrologic properties. During the 2019 M6.4 and M7.1 Ridgecrest earthquakes, Ash Meadows groundwater levels declined in the fractured carbonate aquifer by 7-9 cm, whereas no coseismic groundwater level response was observed in the overlying (∼280 m) alluvial aquifer. We analyzed the static and dynamic stresses produced by the earthquake, and found static stresses should have produced only coseismic water level increases in the Ash Meadows region. At the same time, dynamic stresses were large enough to induce coseismic water level response and are likely the driving force for groundwater level reductions in the carbonate aquifer. Although tidal analysis does not show an observable long-term change in hydraulic parameters, we observed a temporary diffusivity enhancement in the 6 days following the M6.4 foreshock. In addition, we did not observe a postseismic recovery of water level in the carbonate aquifer. Instead, a subsequent 2-year observation of water levels indicates a persistent decline at a rate of 1.7−4.6×10−5 m/day, which is in direct contrast to a generally increasing water level before the earthquake. As of August 2021, water levels in the carbonate aquifer have declined between 1.4-3.7 cm since the earthquake. Persistent water level declines induced by the passage of seismic waves may modify aquifer properties in the short (days) and long-term (months to years). High resolution groundwater monitoring is needed to better understand the postseismic response in hydrologically sensitive aquifers such as Ash Meadows.

High-resolution monitoring of groundwater quality in unconsolidated aquifers using UV-Vis spectrometry

In many countries, groundwater quality is routinely monitored in yearly or quarterly intervals. Such frequencies, however, may not be sufficient for investigations into the temporal dynamics of water quality, due to seasonal variability or responses to extreme events. One approach to overcome this limitation is the use of in-situ sensors, such as UV-Vis spectrometers. With a focus on nitrate as an abundant groundwater contaminant, we explored the suitability of these sensors for high-resolution groundwater monitoring in shallow unconsolidated oxic aquifers. In-situ nitrate measurements in four selected wells compared well with values from lab analyses determined in pumped groundwater samples. Most importantly, our results revealed distinct seasonal patterns and, at two sites, pronounced short-term fluctuations in nitrate concentrations concomitant to patterns observed in precipitation, hydraulic head and groundwater temperature. Such dynamics in water quality, and thus the causal relationship with hydrological events and land use, will unfortunately be overlooked when following conventional sampling schemes with the risk of non-representative time series if routine sampling is timed unfavorably. Finally, we provide practical advice for deployment of the sensor.

Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale

Abstract. In the context of changing climate and increasing water demand, large-scale hydrological models are helpful for understanding and projecting future water resources across scales. Groundwater is a critical freshwater resource and strongly controls river flow throughout the year. It is also essential for ecosystems and contributes to evapotranspiration, resulting in climate feedback. However, groundwater systems worldwide are quite diverse, including thick multilayer aquifers and thin heterogeneous aquifers. Recently, efforts have been made to improve the representation of groundwater systems in large-scale hydrological models. The evaluation of the accuracy of these model outputs is challenging because (1) they are applied at much coarser resolutions than hillslope scale, (2) they simplify geological structures generally known at local scale, and (3) they do not adequately include local water management practices (mainly groundwater pumping). Here, we apply a large-scale hydrological model (CWatM), coupled with the groundwater flow model MODFLOW, in two different climatic, geological, and socioeconomic regions: the Seewinkel area (Austria) and the Bhima basin (India). The coupled model enables simulation of the impact of the water table on groundwater–soil and groundwater–river exchanges, groundwater recharge through leaking canals, and groundwater pumping. This regional-scale analysis enables assessment of the model's ability to simulate water tables at fine spatial resolutions (1 km for CWatM, 100–250 m for MODFLOW) and when groundwater pumping is well estimated. Evaluating large-scale models remains challenging, but the results show that the reproduction of (1) average water table fluctuations and (2) water table depths without bias can be a benchmark objective of such models. We found that grid resolution is the main factor that affects water table depth bias because it smooths river incision, while pumping affects time fluctuations. Finally, we use the model to assess the impact of groundwater-based irrigation pumping on evapotranspiration, groundwater recharge, and water table observations from boreholes.

Constructing high-resolution groundwater drought at spatio-temporal scale using GRACE satellite data based on machine learning in the Indus Basin

The complicated phenomenon induced by inadequate precipitation is a drought that impacts water resources and human life. Traditional methods to assess groundwater drought events are hindered due to sparse groundwater observations on a spatio-temporal scale. These groundwater drought events are not well studied in the study area of the Indus Basin Irrigation System (IBIS) holistically. This study applied four machine learning models to the training datasets of Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) and Groundwater Storage (GWS) data to improve resolution to 0.25° from 1°. The Extreme Gradient Boosting (XGBoost) model outperformed the four models and results showed Pearson correlation (R) (0.99), Nash Sutcliff Efficiency (NSE) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm). The GRACE Groundwater Drought Index (GGDI) was calculated by normalizing XGBoost-downscaled GWS. The trend characteristics, the temporal evolution, and spatial distribution of GGDI were analyzed across the IBIS from 2003 to 2016. The wavelet coherence approach was used to evaluate the relationship between teleconnection factors and GGDI. The XGBoost downscaling model can accurately reproduce local groundwater behavior, with the acceptable correlation of coefficient values for validation (ranging from 0.02 to 0.84). The accumulated Standardized Precipitation Evapotranspiration Index (SPEI) with the time of 1, 3, and 6 months, and self-calibrated Palmer Drought Severity Index (sc-PDSI) were used to validate GGDI. The findings have demonstrated that GGDI has comparable drought patterns to SPEI-3 and SPEI-6 and sc-PDSI. The teleconnection factors have a significant impact on the GGDI shown by the wavelet coherence technique. The impact of the sea surface temperature index (namely, NINO3.4) on GGDI was observed significantly high among other teleconnection factors in the IBIS. The proposed framework can serve as a useful tool for drought monitoring and a better understanding of extreme hydroclimatic conditions in the IBIS and other similar climatic regions.

R2ogs5: Calibration of Numerical Groundwater Flow Models with Bayesian Optimization in R.

Large-scale and high-resolution groundwater models are currently becoming increasingly important in order to clarify the extent to which climate trends and extreme weather affect the groundwater balance regionally. As a result, the parameterization of groundwater models is becoming more detailed and more complex, making conventional calibration methods too time-consuming. Moderating the computational demand to find optimal solutions for the resulting potentially multi-modal objective function requires intelligent and efficient global optimization methods. Moreover, the increasing use of modern scripting languages R and Python to craft environmental analysis workflows calls to integrate groundwater flow simulators in such. Here we introduce and exemplify r2ogs5, a tool that integrates version 5 of the open-source simulation software OpenGeoSys into the programming language R. r2ogs5 allows for calibration of numerical groundwater flow models with a sequential model-based optimization approach that combines Bayesian optimization (BO) with surrogate modeling. Here, we describe the structure and function of r2ogs5 as well as the implemented calibration method. We then demonstrate the calibration method by calibrating 4 and 12 parameters of two simple groundwater flow models. The results indicate that this method needs fewer runs of the groundwater flow model than conventional gradient search and Latin hypercube sampling in case of the 12 parameter model. We believe that our method offers the potential to calibrate computationally expensive groundwater flow models. r2ogs5 supports groundwater flow modelers to access the statistical analysis and visualization capabilities of the R language and is a valuable tool for geoscientists already using R.

Combining high resolution input and stacking ensemble machine learning algorithms for developing robust groundwater potentiality models in Bisha watershed, Saudi Arabia

The present research aims to build a unique ensemble model based on a high-resolution groundwater potentiality model (GPM) by merging the random forest (RF) meta classifier-based stacking ensemble machine learning method with high-resolution groundwater conditioning factors in the Bisha watershed, Saudi Arabia. Using high-resolution satellite images and other secondary sources, twenty-one parameters were derived in this study. SVM, ANN, and LR meta-classifiers were used to create the new stacking ensemble machine learning method. RF meta classifiers were used to create the new stacking ensemble machine learning algorithm. Each of these three models was compared to the ensemble model separately. The GPMs were then confirmed using ROC curves, such as the empirical ROC and the binormal ROC, both parametric and non-parametric. Sensitivity analyses of GPM parameters were carried out using an RF-based approach. Predictions were made using six hybrid algorithms and a new hybrid model for the very high (1835–2149 km2) and high groundwater potential (3335–4585 km2) regions. The stacking model (ROCe-AUC: 0.856; ROCb-AUC: 0.921) beat other models based on ROC's area under the curve (AUC). GPM sensitivity study indicated that NDMI, NDVI, slope, distance to water bodies, and flow accumulation were the most sensitive parameters. This work will aid in improving the effectiveness of GPMs in developing sustainable groundwater management plans by utilizing DEM-derived parameters.

Developing a pan-European high-resolution groundwater recharge map – Combining satellite data and national survey data using machine learning

Groundwater recharge quantification is essential for sustainable groundwater resources management, but typically limited to local and regional scale estimates. A high-resolution (1 km × 1 km) dataset consisting of long-term average actual evapotranspiration, effective precipitation, a groundwater recharge coefficient, and the resulting groundwater recharge map has been created for all of Europe using a variety of pan-European and seven national gridded datasets. As an initial step, the approach developed for continental scale mapping consists of a merged estimate of actual evapotranspiration originating from satellite data and the vegetation controlled Budyko approach to subsequently estimate effective precipitation. Secondly, a machine learning model based on the Random Forest regressor was developed for mapping groundwater recharge coefficients, using a range of covariates related to geology, soil, topography and climate. A common feature of the approach is the validation and training against effective precipitation, recharge coefficients and groundwater recharge from seven national gridded datasets covering the UK, Ireland, Finland, Denmark, the Netherlands, France and Spain, representing a wide range of climatic and hydrogeological conditions across Europe. The groundwater recharge map provides harmonised high-resolution estimates across Europe and locally relevant estimates for areas where this information is otherwise not available, while being consistent with the existing national gridded datasets. The Pan-European groundwater recharge pattern compares well with results from the global hydrological model PCR-GLOBWB 2. At country scale, the results were compared to a German recharge map showing great similarity. The full dataset of long-term average actual evapotranspiration, effective precipitation, recharge coefficients and groundwater recharge is available through the EuroGeoSurveys' open access European Geological Data Infrastructure (EGDI).

Anthropogenic depletion of Iran’s aquifers

Global groundwater assessments rank Iran among countries with the highest groundwater depletion rate using coarse spatial scales that hinder detection of regional imbalances between renewable groundwater supply and human withdrawals. Herein, we use in situ data from 12,230 piezometers, 14,856 observation wells, and groundwater extraction points to provide ground-based evidence about Iran's widespread groundwater depletion and salinity problems. While the number of groundwater extraction points increased by 84.9% from 546,000 in 2002 to over a million in 2015, the annual groundwater withdrawal decreased by 18% (from 74.6 to 61.3 km3/y) primarily due to physical limits to fresh groundwater resources (i.e., depletion and/or salinization). On average, withdrawing 5.4 km3/y of nonrenewable water caused groundwater tables to decline 10 to 100 cm/y in different regions, averaging 49 cm/y across the country. This caused elevated annual average electrical conductivity (EC) of groundwater in vast arid/semiarid areas of central and eastern Iran (16 out of 30 subbasins), indicating "very high salinity hazard" for irrigation water. The annual average EC values were generally lower in the wetter northern and western regions, where groundwater EC improvements were detected in rare cases. Our results based on high-resolution groundwater measurements reveal alarming water security threats associated with declining fresh groundwater quantity and quality due to many years of unsustainable use. Our analysis offers insights into the environmental implications and limitations of water-intensive development plans that other water-scarce countries might adopt.

A participatory approach to assessing groundwater recharge under future climate and land-cover scenarios, Tutuila, American Samoa

Study regionOceania, South Pacific, Polynesia. Study FocusChanging climates have the potential to significantly impact global water resources availability. On many volcanic islands, groundwater is the primary drinking water source, thereby making it essential to manage this limited resource carefully. In this study, we developed high temporal and spatial resolution groundwater recharge estimates for the Island of Tutuila, American Samoa using the Soil Water-Balance-2 (SWB2) model. Additionally, we predicted future recharge by running the calibrated model with combinations of dynamically downscaled general circulation climate model (GCM) predictions, and future land-cover scenarios developed collectively with local stakeholder groups. New hydrological insightsPresent-day results indicate 57 % of Tutuila’s rainfall becomes groundwater recharge, 8 % evaporates from the canopy, 15 % evapotranspires, and 20 % discharges as stormflow-runoff. Future climate scenarios suggest recharge may increase by 8 % or 14 % depending on global emissions. Land-cover was a less significant driver of hydrologic change, although increases in impervious surfaces showed a negative impact on recharge. This work is maintained as an active open-source project on GitHub, the world’s leading software development platform, thereby enhancing transparency, reproducibility, and participation from stakeholders and managers in American Samoa. This study is the first of its kind from a location within the South Pacific Convergence Zone, and provides insights into how human activities on global and local levels affect the future sustainability of essential resources.

Improving the Resolution and Accuracy of Groundwater Level Anomalies Using the Machine Learning-Based Fusion Model in the North China Plain.

The launch of GRACE satellites has provided a new avenue for studying the terrestrial water storage anomalies (TWSA) with unprecedented accuracy. However, the coarse spatial resolution greatly limits its application in hydrology researches on local scales. To overcome this limitation, this study develops a machine learning-based fusion model to obtain high-resolution (0.25°) groundwater level anomalies (GWLA) by integrating GRACE observations in the North China Plain. Specifically, the fusion model consists of three modules, namely the downscaling module, the data fusion module, and the prediction module, respectively. In terms of the downscaling module, the GRACE-Noah model outperforms traditional data-driven models (multiple linear regression and gradient boosting decision tree (GBDT)) with the correlation coefficient (CC) values from 0.24 to 0.78. With respect to the data fusion module, the groundwater level from 12 monitoring wells is incorporated with climate variables (precipitation, runoff, and evapotranspiration) using the GBDT algorithm, achieving satisfactory performance (mean values: CC: 0.97, RMSE: 1.10 m, and MAE: 0.87 m). By merging the downscaled TWSA and fused groundwater level based on the GBDT algorithm, the prediction module can predict the water level in specified pixels. The predicted groundwater level is validated against 6 in-situ groundwater level data sets in the study area. Compare to the downscaling module, there is a significant improvement in terms of CC metrics, on average, from 0.43 to 0.71. This study provides a feasible and accurate fusion model for downscaling GRACE observations and predicting groundwater level with improved accuracy.

Coupling high-resolution field monitoring and MODIS for reconstructing wetland historical hydroperiod at a high temporal frequency

Historical wetland hydrology data are instrumental to support the design of wetland management and restoration strategies but are rarely available. In this study, we tested the capabilities and limitations of a simple methodological framework based on publicly available MODIS Land Reflectance Products to estimate wetland soil surface saturation and inundation spatiotemporal dynamics. Using supervised learning and high-resolution groundwater and surface water elevation data, the framework searches for spectral algorithms, referred to as the wet/dry wetland status classifier (WSC) and the continuous wetland dynamics identifier (WDI), that best predict upper soil layer wetness status in the study wetland. We used Google Earth Engine (GEE) for fast access and processing of the full range of MODIS data. The capabilities of GEE also enabled us to readily conduct a comparative assessment of the MODIS 8-day composite and daily collections and test various pixel-level quality filters to select reliable data at the highest possible temporal resolution. We tested the framework on the internationally-recognized Ramsar site Palo Verde National Park wetland in Costa Rica, and we obtained good results (overall prediction accuracy of 86.6% and kappa coefficient of 0.7 for the WSC; r2 of 0.71 for the WDI). High-resolution water level data allowed us to assess the challenges, promises and limitations of using MODIS products for wetland hydrology applications. We then applied the WSC and WDI to map the 2000-2016 sub-weekly wetland hydroperiod at 500m resolution, achieving a temporal resolution rarely matched in remote sensing for wetland studies. The analysis of the end-products, combined with the field water elevation data, provided new insights into the drivers controlling the spatiotemporal dynamics of hydroperiod within the Palo Verde wetland and did not reveal any significant temporal trends. The WSC and WDI framework developed here can be useful for reconstructing long-term hydroperiod variability and uncovering its drivers for other wetland systems globally.

Assessment of Groundwater Potential Zones by Using Geoinformatics in Angolelana Tera Woreda, North Shewa Zone, Ethiopia

Groundwater is the least known resource in terms of volume, location, quality, and spatial distribution. This problem is further aggravated in Ethiopia due to a lack of a groundwater spatial database system. The study was conducted in Angolelana Tera Woreda which covers a total area of 782.48 km2. The main objective of this study was to identify and delineate groundwater potential areas by using Geographic Information Systems (GIS) and Remote Sensing (RS) techniques. Multi-criteria decision analysis method in GIS environment was employed to identify groundwater potential zone. To do this, the geological, geomorphologic, slope, soil, lineament, land use/land cover and drainage density data were used. By integrating these data in GIS environment, therefore, groundwater potential zones were identified, and the output was classified as very high, high, moderate, low and very low potential zones. Based on the result, 109.2 km2 (12.9%), 129.53 km2 (16.55%), 435.52 km2 (55.66%), 63.28 km2 (8.09%) and 53.23 km2 (6.8%) of the study area has very high, high, moderate, low and very low groundwater potential, respectively. It can be concluded that relatively the study area has high groundwater potential. So in order to utilize this important resource for different uses, high resolution groundwater potential map and spatial based groundwater analysis techniques should be required. Therefore, the woreda administrator bodies and other concerned stakeholders who might utilize and manage the available groundwater resource of the study area should use this research output as an input to develop an overall master plan for groundwater exploitation and management.