Integration of Numerical Models and InSAR Techniques to Assess Land Subsidence Due to Excessive Groundwater Abstraction in the Coastal and Lowland Regions of Semarang City

This study was carried out to assess land subsidence due to excessive groundwater abstraction in the northern region of Semarang City by integrating the application of both numerical models and geodetic measurements, particularly those based on the synthetic aperture radar interferometry (InSAR) technique. Since 1695, alluvial deposits caused by sedimentations have accumulated in the northern part of Semarang City, in turn resulting in changes in the coastline and land use up to the present. Commencing in 1900, excessive groundwater withdrawal from deep wells in the northern section of Semarang City has exacerbated natural compaction and aggravated the problem of land subsidence. In the current study, a groundwater model equivalent to the hydrogeological system in this area was developed using MODFLOW to simulate the hydromechanical coupling of groundwater flow and land subsidence. The numerical computation was performed starting with the steady-state flow model from the period of 1970 to 1990, followed by the model of transient flow and land subsidence from the period of 1990 to 2010. Our models were calibrated with deformation data from field measurements collected from various sources (e.g., leveling, GPS, and InSAR) for simulation of land subsidence, as well as with the hydraulic heads from observation wells for simulation of groundwater flow. Comparison of the results of our numerical calculations with recorded observations led to low RMSEs, yet high R2 values, mathematically indicating that the simulation outcomes are in good agreement with monitoring data. The findings in the present study also revealed that land subsidence arising from groundwater pumping poses a serious threat to the northern part of Semarang City. Two groundwater management measures are proposed and the future development of land subsidence is accordingly projected until 2050. Our study shows quantitatively that the greatest land subsidence occurs in Genuk District, with a magnitude of 36.8 mm/year. However, if the suggested groundwater management can be implemented, the rate and affected area of land subsidence can be reduced by up to 59% and 76%, respectively.

A Review of Groundwater Management Models with a Focus on IoT-Based Systems

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system that is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

Groundwater management zones and their groundwater water level thresholds in the Tongliao plain

The West Liao River (WLR) basin located in Inner Mongolia, is an important food production area in China. In recent years, the problem of groundwater over exploitation has become increasingly prominent in the basin due to the expansion of agriculture. This paper developed adaptive management initiatives of the local groundwater resources using Tongliao, located in the east part of the WLR basin, as a case study. Groundwater management zones were divided based on hydrogeology, precipitation, land use, the groundwater over exploitation areas, groundwater depth, and the administrative units (Banners/ Counties/ Districts). The Tongliao basin was divided into 21 management zones. Subsequently, assessment rules for determining groundwater level thresholds in each groundwater management zone were developed based on groundwater observation conditions and the current groundwater depth. Based on the assessment rules, in 2020 the management threshold of groundwater level for each zone were determined. The results provided a scientific basis for the ‘Water Availability Based Local Development Initiative’ in the Tongliao plain.

A Review of Groundwater Management Models with a Focus on IoT-Based Systems

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system which is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

Groundwater dependent ecosystems (GDEs) are increasingly threatened worldwide, but the shallow groundwater resources that they are reliant upon are seldom monitored. In this study, we used satellite-based remote sensing to predict groundwater levels under groundwater dependent ecosystems across California, USA. Depth to groundwater was modelled for a 35-years period (1985–2019) within all groundwater dependent ecosystems across the state (n = 95,135). Our model was developed within Google Earth Engine using Landsat satellite imagery, climate data, and field-based groundwater data [n = 627 shallow (< 30 m) monitoring wells] as predictors in a Random Forest model. Our findings show that 1) 44% of groundwater dependent ecosystems have experienced a significant long-term (1985–2019) decline in groundwater levels compared to 28% with a significant increase; 2) groundwater level declines have intensified during the most recent two decades, with 39% of groundwater dependent ecosystems experiencing declines in the 2003–2019 period compared to 27% in the 1985–2002 period; and 3) groundwater declines are most prevalent within GDEs existing in areas of the state where sustainable groundwater management is absent. Our results indicate that declining shallow groundwater levels may be adversely impacting California’s groundwater dependent ecosystems. Particularly where groundwater levels have fallen beneath plant roots or streams thereby affecting key life processes, such as forest recruitment/succession, or hydrological processes, such as streamflow that affects aquatic habitat. In the absence of groundwater monitoring well data, our model and findings can be used to help state and local water agencies fill in data gaps of shallow groundwater conditions, evaluate potential effects on GDEs, and improve sustainable groundwater management policy in California.

Establishing 3D hydrogeological solid model and database for sustainable groundwater management in the Vietnam Mekong delta

The Vietnam Mekong delta (VMD) is a tide-dominated delta formed by the Mekong river system. The sediments are dominantly fine grained and were deposited in the receiving basin with slight inclination of pre-existing deposits in the East sea and gulf of Thailand. The VMD is homeland to about 18 million people that exploit about 4-6 million m3/day of groundwater mainly for domestic use. In recent years, significant groundwater depletion has been occurring in many parts of the VMD due to excessive pumping. Consequently, the VMD has become increasingly faced with serious land subsidence, salt groundwater intrusion, and contamination. Establishing a 3D hydrogeological solid model and database are sorely needed to achieve sustainable groundwater management, and to serve as a basis for further in-depth analyses to quantify contributions from the above-mentioned hazards to current hydrogeological conditions. Therefore, a 3D hydrogeological solid model and database were built based on more than 1000 well logs available from the VMD. An areal distribution of the Holocene, Pleistocene, Pliocene, and Late Miocene subsurfaces from this 3D hydrogeological solid model and database showed zones of tectonic depression and uplift from Early Miocene - Quaternary. Also, the resulting areal distribution aquitards and aquifers thicknesses gave hints of ground saltwater intrusion and contamination.