Groundwater Level Variations Research Articles

Evaluating pile foundation design due to groundwater level lowering

In Viet Nam, ground subsidence has been occurring at an alarming rate, raising concerns for pile foundation design. This issue is primarily caused by excessive groundwater extraction for water supply, irrigation, aquaculture, and other uses. Thus, in this study, the evaluation of pile capacity due to groundwater level (GWL) lowering as a preliminary guideline for structures is proposed. This study analyzed different cases of GWL lowering from 0 m to 2 m, and pile size varied from 18 x 18 cm to 45 x 45 cm, to evaluate the allowable bearing capacities for single piles and pile groups. The results indicated that when lowering the GWL from 0 to 2 m, the allowed bearing capacity varied by 5.75–174.70 tons for single piles and 11.95–808.18 tons for pile groups. Besides, when the pile size increased, the bearing capacity increased in the range of 6.38–41.46 tons (size 18 x 18 cm) to 16.30–174.70 tons (size 45 x 45 cm) for a single pile and 11.95–191.81 tons (size 18 x 18 cm) to 33.49–808.18 tons (size 45 x 45 cm). The result of this study underscores the significance of predicting the bearing capacity of pile foundations due to the variation in groundwater level.

Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development.

Enhancing groundwater management with GRACE-based groundwater estimates from GLDAS-2.2: a case study of the Almonte-Marismas aquifer, Spain

AbstractThe Almonte-Marismas aquifer, southwestern Spain, is a critical ecohydrogeological system that features extensive groundwater monitoring. This study investigates the utility of gravity recovery and climate experiment (GRACE) satellite data, specifically obtained from the global land data assimilation system (GLDAS) version 2.2, for assessing groundwater storage variations in the Almonte-Marismas aquifer. The presented research emphasizes the practical application of readily available GLDAS products that do not require data preprocessing. The study validates the GLDAS-2.2-based ready-to-use groundwater storage (GWS) time series by correlating it with precipitation and piezometric information, highlighting its effectiveness in medium-scale aquifers. The results reveal a strong agreement between GLDAS-2.2-derived GWS anomalies and in-situ measurements, confirming GLDAS-2.2’s potential for assessing aquifer depletion. The study discusses the consistency of seasonal variations in groundwater levels and GLDAS-2.2 data, emphasizing their close alignment with precipitation and pumping activities. Importantly, the study introduces GLDAS-2.2-derived volumetric groundwater storage (VGWS) as a valuable calibration parameter for numerical groundwater flow models, enhancing their accuracy over time. Moreover, the analysis reveals disparities in annual recharge values between GLDAS-2.2-derived data and the soil-water mass balance. These variations suggest the importance of additional inputs to precipitation, possibly related to subsurface or lateral connections. Overall, this study contributes to the ongoing discourse on the practical applications of GLDAS-2.2-derived GWS data in groundwater management, offering insights into its effectiveness in diverse hydrogeological settings, particularly in areas that lack monitoring infrastructure.

A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models

Agricultural water demand, groundwater extraction, surface water delivery and climate exhibit complex nonlinear relationships with groundwater storage in agricultural regions. As alternatives to computationally intensive physical models, data-driven machine learning methods are frequently employed as surrogates to capture these complex relationships, owing to their high computational efficiency. Inevitably, reliance on a single machine learning model may lead to underestimation of prediction uncertainty and potentially result in reduced accuracy. This paper presents a multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models. The key contributions of the research are threefold: 1) the development of a comprehensive input variable selection process considering the lag effects and driving mechanisms of groundwater level variations; 2) the implementation of an optimization-based hyperparameter tuning method to enhance the performance of individual machine learning models; and 3) the establishment of a multi-model integration framework based on a multi-layer nesting technique. This approach combines the outputs of multiple machine learning models to consolidate predictions and expand the hypothesis space. The effectiveness of the proposed framework is demonstrated through case studies in the Huaihe River Basin, a major agricultural region in China. The results show that the multi-layer nesting and integration approach outperforms the use of individual machine learning models, providing more accurate and reliable groundwater level predictions. This framework offers valuable insights for decision-makers and water resource managers, supporting sustainable groundwater management and addressing the challenges faced in agriculturally intensive areas.

Integrating standardized indices and performance indicators for better drought assessment in semi-arid coastal aquifers

Aquifers in arid and semi-arid coastal regions, such as along the Mediterranean rim, are severely affected by droughts. The natural decrease in water levels is often exacerbated by excessive abstraction, resulting in both degradation of water quality and the risk of seawater intrusion. In these regions it is crucial to conduct thorough monitoring of wells, employing a wide range of indicators to forecast and mitigate the consequences of decreased precipitation and intensified pumping. This study proposes an analysis and monitoring methodology involving the calculation of performance indicators based on the Standardized Groundwater level Index (SGI), supplemented with information on the optimal accumulation time of the Standardized Precipitation Evapotranspiration Index (SPEI). Atmospheric reanalysis datasets and in-situ groundwater level observations are used together to derive the groundwater system memory and find consistent optimal SPEI accumulation times at each individual location. The inverse of memory derived from the autocorrelation of the SGI is used to estimate each well's ability to recover from drought conditions. This value provides the most reliable indication of resilience and sustainability. In the Algarve, the average regional variability of groundwater level is well captured by the SPEI-12 index. However, groundwater memories and optimal SPEI accumulation times are spatially very heterogeneous varying between SPEI-5 and SPEI-48. Wells with shorter memories (<6 months) demonstrate greater sustainability, whereas those with longer memories (>16 months), whether situated inland or along the coast, exhibit lower resilience and lower sustainability. Coastal wells with groundwater levels close to sea level, exhibiting minimal resilience, are of particular concern and require intensified monitoring efforts to adapt management strategies.

Dynamic interactions between groundwater level and discharge by phreatophytes

Many traditional models that predict plant–groundwater use based on groundwater level variations, such as the White method, make various simplifying assumptions. For example, these models often neglect the role of plant hydraulic redistribution, a process that can contribute up to 80% of transpiration. Thus, this work aims to avoid such assumptions and subsequently explore the dynamic interactions between groundwater levels and phreatophytic vegetation, including plant nocturnal transpiration, hydraulic redistribution, and response to atmospheric conditions, in shallow-groundwater ecosystems using Loblolly pine (Pinus taeda) as a model species. The model scenarios are formulated using a stomatal-optimization model coupled to the soil–plant–atmosphere continuum. Flow through soil and groundwater are described using the Richards equation and a linear reservoir approximation, respectively, with groundwater in contact with an external water body of fixed elevation. Results show that nocturnal transpiration, mediated by plant residual conductance, and hydraulic redistribution, are able to reduce groundwater levels at night and alter the groundwater recharge rate. Projected atmospheric conditions of increased carbon dioxide and elevated temperature have opposing effects on groundwater levels, which tend to roughly cancel each other under a projected scenario of 500 ppm carbon dioxide and 1.5 oC warming. Such detailed modeling can be used to provide further insights into coupled interactions between vegetation, climate and groundwater levels in phreatophyte-dominated ecosystems.

Variation in soil water content and groundwater levels across three land cover types in a floodplain of the Kromme catchment, South Africa

AbstractInvasions of floodplains and riparian areas by alien woody species replacing predominantly herbaceous indigenous vegetation have altered the hydrological and ecosystem functioning in catchments. Although existing studies have examined changes in river flows following the establishment or clearing of alien woody vegetation, our understanding of impacts on soil water content and groundwater remains poor. Limited process knowledge restricts our capacity to reliably model and predict the impacts of land cover changes. As such, this work compared temporal variations in soil water content (SWC) and groundwater levels at three locations with different vegetation types: black wattle (Acacia mearnsii) trees, palmiet (Prionium serratum), and grass (dominated by Pennisetum clandestinum spp), within a floodplain site in the Kromme Catchment in the Eastern Cape Province of South Africa. Soil water content and shallow groundwater levels (&lt; 4 m below ground) were monitored from August 2017 to December 2019 using soil moisture probes and piezometers. Rainfall, vegetation type and antecedent conditions were identified as the major factors controlling observed responses. On average, soil water content and water retention were significantly higher (p &lt; 0.05) at the palmiet site, whilst the wattle site had the lowest SWC among the three sites. Shallow groundwater levels were also higher at the palmiet and grass sites and lowest at the wattle site. Results showed the negative impacts of black wattle trees on SWC and groundwater levels. These results are crucial for improved quantitative predictive capacity which would allow for better catchment management, for example, informing water supply planning and guiding restoration programs focusing on alien plant clearing.

Spatiotemporal variations of groundwater and gully impact in two peatland watersheds in the Upper Yellow River, Qinghai‐Tibet Plateau

AbstractThe spatiotemporal variability of groundwater level is an important property of peatland hydrology that directly alternates water storage. Nonetheless, the current understanding of the variations of groundwater level over long periods of time remains limited. In this study, we investigated two peatland watersheds (0.151 km2 for Watershed 1 and 0.844 km2 for Watershed 2) in the Zoige Basin in the upper watershed of the Yellow River to monitor temporal variability of groundwater level using self‐recorded water loggers over 4 years (2017–2021). The main results demonstrate that (1) groundwater level variations were controlled by gully drainage in sites adjacent to the gully but were more affected by rainfall in sites distant from the gully. The groundwater level near the gully downcut was lower than that near the gully without complete downcutting through the pear layer, with a maximum difference of 58.3 cm, indicating the longitudinal effect of groundwater level in the watershed. (2) Because the rainfall had a lag effect on the groundwater level, the length of lag gradually decreased with increased rainfall intensity (i.e., the lag time for sites distant from the gully was about 18 min shorter than that of sites close to the gully in Watershed 1). (3) The peak values of the groundwater level occurred simultaneously with the maximum and minimum rainfall in Watershed 2, and the peak occurrence time was related to the ratio of precipitation to evaporation. In the downstream sites, the groundwater level fluctuated more than the upstream ones in Watershed 2. Moreover, the average groundwater level in the upstream sites was 14.3 cm higher than that of the middle ones, indicating a decreasing trend of water storage along the gully. (4) The differences in groundwater level between wet and dry seasons were clear, but the difference was smaller in the upstream sites due to limited gully incision and higher water storage within the peat layer. Additionally, groundwater level changes were more extreme on rainy days during both the wet and dry seasons, but the different intensities of rainfall resulted in stable groundwater in the dry season and an oscillating groundwater level in the wet season in Watershed 2. This study uncovers the groundwater dynamics in the two peatland watersheds, which is of great significance for understanding runoff variation, ecohydrological processes, and wetland shrinkage.

Satellite gravimetry observations on the state of groundwater level variability in the Arabian Peninsula Region and the associated socio-economic sustainability challenges

Groundwater is an important resource for the Arabian Peninsula Region. The population increase, rise in agricultural activities, and Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) inclination towards economic diversification and tourism promotion have heightened the freshwater demand. As a result of climate change and varying weather patterns, the situation has become more complicated. Due to arid conditions, recharge is mostly less than withdrawal which consequently results in underground water level decline over time. In the research, we have used Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) MASCON solutions, Global Land Data Assimilation System (GLDAS) soil moisture, and the Integrated Multi-satellite Retrievals for Global Precipitation Mission (IMERG) rainfall data to observe the Equivalent Water Thickness (EWT), and rainfall patterns in this region for the past two decades (2002–2023). The results indicate that in Saudi Arabia the water level is declining nearly at a linear rate and the linear regression model fits well with the data (R2 value, the coefficient of determination, for different cities of Saudi Arabia is ≥ 0.94). In the Al Jouf Area, the water decline is the highest at −1.69 cm/year which is 43% greater than the previous calculations. The lowest decline rate is in Sanaa (Yemen) which is −0.13 cm/year. Furthermore, all the other studied locations show a groundwater declining trend. In Saudi Arabia's Makkah, Madina, Riyadh, and Damam the reduction rate is −0.36, −0.48, −0.72, and −0.48 (cm/year) respectively. Kuwait, UAE's Dubai, and Al Ain show a similar groundwater reduction rate of −0.19 cm/year. In Oman's Masqat, the groundwater decline rate is −0.22 cm/year. Also, in the recent data, one can see the higher seasonal amplitudes that are indicative of greater fluctuations in EWT data in recent times. If water mining continues at the same pace, this important resource can become a rare commodity. Limited water supply can likely become a limiting factor for further social, agricultural, and industrial development. That's why major reviews and shifts are necessary in the current policies related to water resource management and conservation.

Liquefaction Potential Analysis of Irrigation Canals at Sidera Area-Sigi Regency

Abstract An earthquake measuring 7.5 on the Richter scale that occurred in Palu on 28 September 2018 resulted in liquefaction where the soil lost its bearing capacity due to increased pore water pressure. The liquefaction disaster caused great damage to the Gumbasa Irrigation channel, a large part of which is in the alluvial fan area. This study aims to analyze the potential of liquefaction in irrigation canals in the Sidera village area, Sigi Regency. Using SPT (Standard Penetration Test) data from 2 boreholes with a depth of ± 20 m, MASW data, and Earthquake Risk Map. Researchers analyzed with the Seed Simplified Procedure approach, The researchers analyzed the Simplified Procedure method proposed by Seed, which uses a stress-based approach that uses the ratio of soil shear strength (CRR) and earthquake-induced soil shear stress (CSR). The results of the analysis using Peak Ground Acceleration (PGA) of 0.43 and groundwater level variations of -2.85 m (borehole BM 53) and -12.5m (Borehole BM 49) show that liquefaction occurs at depths of 4-8 m (BM 53) and 14-17 m (BM 49). The value of the Liquefaction Potential Index (LPI) increases and indicates a high liquefaction potential below the water table with the highest value of 17.88. The analysis shows that liquefaction is closely related to the shallow water table, soil type, and low N-SPT values. The high liquefaction potential requires prevention methods as a form of treatment.

Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change

Monitoring groundwater (GW) level variations, or anomalies in multiple wells, over long periods of time is essential to understanding changes in regional groundwater resource availability. However, it is challenging to predict these GW anomalies over the long term in agricultural areas due to complicated boundary conditions, heterogeneous hydrogeological characteristics, and groundwater extraction, as well as nonlinear interactions among these factors. To overcome this challenge, we developed an advanced modeling framework based on a recurrent neural network of long short-term memory (LSTM) as an alternative to complex and computationally expensive physical models. GW anomalies were forecast two months in advance (t + 2) based on the evaluation of drivers that influence GW dynamics in densely irrigated agricultural regions. An application and evaluation of this new approach was conducted in the Wisconsin Central Sands (WCS) region in the U.S., one of the most productive agricultural regions. The modeling framework was developed for the period 1958–2020 by utilizing easily accessible dynamic and static variables to represent hydrometeorological and geological characteristics. GW anomaly observations were acquired from 26 piezometers (wells) installed in the sandy aquifer in WCS over 10–60 years. The subset of GW observations from ∼ 10–60 years, not used in model training, can forecast GW anomalies two months out with a coefficient of determination R2 ∼ 0.8. Additionally, MAE was less than 0.34 m/month across the study region for both temporal and spatial modeling frameworks. Groundwater anomalies showed high spatiotemporal variability, and their responses are influenced differently by boundary conditions, catchment geology, climate, and topography across locations. Sites with higher autocorrelation with previous two-months GW anomalies reduced bias by increasing R2. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. The novelty of this study is identifying the regional drivers of GW fluxes. This case-specific information and location-related simplification, modification, and assumption of LSTM is a unique contribution to the existing literature. Our framework can be used as an alternative method of simulating water availability and groundwater level changes in areas where subsurface properties are unknown or difficult to determine.

Time-Series Analysis of Mining-Induced Subsidence in the Arid Region of Mongolia Based on SBAS-InSAR

Mongolia’s substantial mineral resources have played a pivotal role in its economic progress, with mining activities significantly contributing to this development. However, these continuous mining operations, particularly at the Oyu Tolgoi copper and gold mine, have induced land subsidence that threatens both production activities and poses risks of geological and other natural disasters. This study employs the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor and analyze time-series surface subsidence using 120 Sentinel-1A datasets from 2018 to 2022. The findings reveal that the SBAS-InSAR method successfully captures the subsidence and its spatial distribution at Oyu Tolgoi, with the maximum cumulative subsidence reaching −742.01 mm and the highest annual average subsidence rate at −158.11 mm/year. Key drivers identified for the subsidence include variations in groundwater levels, active mining operations, and changes in surface stress. This research underscores the ongoing subsidence issue at the Oyu Tolgoi mining area, providing crucial insights that could aid in enhancing mining safety and environmental conservation in the region.

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

Analysis of Groundwater Time Series With Limited Pumping Information in Unconfined Aquifer: Response Function Based on Lagging Theory

AbstractGroundwater extraction from aquifers is a common practice for human use, and variations in groundwater levels can provide valuable information on the hydrogeological properties of the aquifer. However, reliable data on pumping rates and distribution are often lacking due to unsupervised groundwater pumping activities. This study presents a new mathematical model for transfer function modeling that depicts the drawdown response caused by pumping in an unconfined aquifer system. To account for the dense and unsupervised pumping events, the uniform pumping approach was used to estimate these effects. To more accurately represent unconfined flow, the model first integrates lagging theory into a response function derived from the Boussinesq equation. The lagging theory accounts for the effects of both inertial force and capillary suction. Furthermore, the model has been used to derive both specific yield and transmissivity along with two lagging parameters simultaneously using only groundwater level information from the Choshui River region in Taiwan. The estimated results suggest that this approach provides reliable estimates of hydrogeological parameters, demonstrating its usefulness for water resource management and water budget evaluation.

Inundation dynamics in seasonally dry floodplain forests in southeastern Brazil

AbstractFloodplains are one of the most threatened ecosystems. Even though the vegetation composition in floodplain forests is expected to reflect the variation in groundwater levels and flood duration and frequency, there is little field data on the inundation dynamics (e.g., the variability in flood duration and flood frequency), especially for the understudied seasonally dry tropics. This limits our understanding of these ecosystems and the mechanisms that cause the flooding. We, therefore, investigated six floodplain forests in the state of Minas Gerais in Brazil for 1.5 years (two wet seasons): Capivari, Jacaré, and Aiuruoca in the Rio Grande basin, and Jequitaí, Verde Grande, and Carinhanha in the São Francisco basin. These locations span a range of climates (humid subtropical to seasonal tropical) and biomes (Atlantic forest to Caatinga). At each location, we continuously measured water levels in five geomorphologically distinct eco‐units: marginal levee, lower terrace, higher terrace, lower plain, and higher plain, providing a unique hydrological dataset for these understudied regions. The levees and terraces were flooded for longer periods than the plains. Inundation of the terraces lasted around 40 days per year. The levees in the Rio Grande basin were flooded for shorter durations. In the São Francisco basin, the flooding of the levees lasted longer and the water level regime of the levees was more similar to that of the terraces. In the Rio Grande basin, flooding was most likely caused by rising groundwater levels (i.e., “flow pulse”) and flood pulses that caused overbank flooding. In the São Francisco basin, inundation was most likely caused by overbank flooding (i.e., “flood pulse”). These findings highlight the large variation in inundation dynamics across floodplain forests and are relevant to predict the impacts of changes in the flood regime due to climate change and other anthropogenic changes on floodplain forest functioning.

Analyzing time series variations in groundwater levels within the Baitarani River Basin, India

Analyzing time series variations in groundwater levels within the Baitarani River Basin, India

Spring erosional processes and small sapping valleys in southwestern lower Michigan, USA

AbstractTributary valleys along the Pigeon River in southwestern Michigan USA possess many previously defined criteria for sapping valleys. Direct observations of seasonal variations in groundwater levels, hydraulic gradients and water temperatures confirm that perennial springs and their streams are present in these small valleys. Estimated spring‐fed stream discharges are low and variable, but continuous. Direct observations, grain‐size distributions and bedload transport estimates indicate that significant transport of fine to medium sand occurs in all seasons.We present a process‐form model for small‐scale sapping processes. Stream terraces and paleo‐meander scarps are being modified by sapping, and elongated valleys are likely being lengthened headward by groundwater sapping. Sapping processes have been occurring for at least the past ~4,500 years after Pigeon River adjusted to a fall in base level from the Nipissing high stand to the modern level of Lake Michigan. Sapping processes are currently producing a variety of small‐scale landforms dependent on four main factors: 1) hydraulic gradients and spring discharges, 2) available grain size of material being eroded, 3) transport rate of sand bedload by spring‐fed streams and finally 4) time available for groundwater sapping process to occur.

Variation of groundwater level due to land use, precipitation, and earthquake in Yogyakarta City from 2005 to 2020

Yogyakarta City not only experiences rapid land use conversion and precipitation decline but also experiences frequent earthquake events. These factors can have a significant impact on groundwater variation dynamics in this city. Therefore, the aim of this research is to analyze the factors that can influence groundwater oscillation based on precipitation, land use, earthquake, and river discharge changes. The method used to analyze spatio-temporal groundwater level and precipitation are by applying space-time Bayesian statistics since Bayesian has lower root mean square error compared to the space-time variogram and inverse distance methods. Land use is classified with false color composite for Landsat ETM and infrared color composite for Landsat 8 since these color composites well classify built-up and non-built-up areas. River discharge is analyzed as a primary indication of earthquake event impact on groundwater level variation.There is no significant variation of groundwater level from 2005 to 2020. Groundwater fluctuation is classified as stable since the magnitude of change is approximately less than 1.5 m from dry to wet period. It is also probably because there is abundances of groundwater resources in Yogyakarta City that shallow aquifer have very high hydraulic conductivity. The conversion speed from non-built-up area into built-up area is 26% from 2005 to 2015, while 8% from 2015 to 2020. At least 78% of Yogyakarta City is built-up areas in 2020. Abrupt changes in groundwater level is due to: a) declining trends of precipitation on May 2009 to July 2016 with average accumulative monthly residual rainfall ranging from 500 mm month−1 to 1300 mm month−1, and b) high magnitude earthquake occurrences on May 2006, November 2010 and 2015 when groundwater patterns and levels including discharge rivers showed distinct behavior. This study shows that precipitation and earthquake are the major contribution to groundwater level variation compared to land use.

EUROPEAN CLIMATE PROTECTION STRATEGY UNDER CONSIDERATION OF BUILT HERITAGE PRESERVATION

Climate change affects all areas of life. The European Union strives for climate-neutrality of their members until 2050 with the project European Green Deal. By this, the emissions of greenhouse gases should be reduced to zero by 2050 with simultaneous economic development decoupled from resource use. 600 Billion Euros the European Union will spend to lower net greenhouse gas emissions until 2030 by at least 55%, compared to 1990 levels. Climate change affects historical structures, thus by variations in groundwater levels, hydrological cycle, or intense precipitation. However, also climate protection activities such as renewable energy installations may result in negative effects. Solar panels or wind turbines do not emit carbon dioxide and other greenhouse gases but, installed nearby historical buildings, affect the historical appearance of structures. That may result in conflicts between ecological and heritage conservation interests. The authors inform about the European Green Deal and analyze effects of the planned measures on built heritage with a focus on solar and wind energy installations. They also discuss requirements from heritage preservation regulations. The manuscript analyzes how renewable energy installations may affect built heritage in its historical appearance. For that, the authors use available information about the European Green Deal, discuss current research literature and relevant German court cases. It is concluded that renewable energy installations may negatively affect the appearance of built heritage. Interests of climate protection and built preservation need to be balanced to ensure that future generations even can experience and enjoy the cultural heritage.

EUROPEAN CLIMATE PROTECTION STRATEGY UNDER CONSIDERATION OF BUILT HERITAGE PRESERVATION

Climate change affects all areas of life. The European Union strives for climate-neutrality of their members until 2050 with the project European Green Deal. By this, the emissions of greenhouse gases should be reduced to zero by 2050 with simultaneous economic development decoupled from resource use. 600 Billion Euros the European Union will spend to lower net greenhouse gas emissions until 2030 by at least 55%, compared to 1990 levels. Climate change affects historical structures, thus by variations in groundwater levels, hydrological cycle, or intense precipitation. However, also climate protection activities such as renewable energy installations may result in negative effects. Solar panels or wind turbines do not emit carbon dioxide and other greenhouse gases but, installed nearby historical buildings, affect the historical appearance of structures. That may result in conflicts between ecological and heritage conservation interests. The authors inform about the European Green Deal and analyze effects of the planned measures on built heritage with a focus on solar and wind energy installations. They also discuss requirements from heritage preservation regulations. The manuscript analyzes how renewable energy installations may affect built heritage in its historical appearance. For that, the authors use available information about the European Green Deal, discuss current research literature and relevant German court cases. It is concluded that renewable energy installations may negatively affect the appearance of built heritage. Interests of climate protection and built preservation need to be balanced to ensure that future generations even can experience and enjoy the cultural heritage.