Seismic velocity recovery in the subsurface: transient damage and groundwater drainage following the 2015 Gorkha earthquake, Nepal

Shallow earthquakes frequently disturb the hydrological and mechanical state of the subsurface, with consequences for hazard and water management. Transient post-seismic hydrological behaviour has been widely reported, suggesting that the recovery of material properties (relaxation) following ground shaking may impact groundwater fluctuations. However, the monitoring of seismic velocity variations associated with earthquake damage and hydrological variations are often done assuming that both effects are independent. In a field site prone to highly variable hydrological conditions, we disentangle the different forcing of the relative seismic velocity variations $\delta v$ retrieved from a small dense seismic array in Nepal in the aftermath of the 2015 Mw 7.8 Gorkha earthquake. We successfully model transient damage effects by introducing a universal relaxation function that contains a unique maximum relaxation timescale for the main shock and the aftershocks, independent of the ground shaking levels. Next, we remove the modeled velocity from the raw data and test whether the corresponding residuals agree with a background hydrological behaviour we inferred from a previously calibrated groundwater model. The fitting of the $\delta v$ data with this model is improved when we introduce transient hydrological properties in the phase immediately following the main shock. This transient behaviour, interpreted as an enhanced permeability in the shallow subsurface, lasts for $\sim$ 6 months and is shorter than the damage relaxation ($\sim$ 1 year). Thus, we demonstrate the capability of seismic interferometry to deconvolve transient hydrological properties after earthquakes from non-linear mechanical recovery.

Water Availability Forecasting Using Univariate and Multivariate Prophet Time Series Model for ACEA (European Automobile Manufacturers Association)

Time series is one of method to forecasting the data. The ACEA company has competition with opened the data in the Water Availability and uses the data to forecast. The dataset namely, Aquifers-Petrignano in Italy in water resources field has five parameters e.g. rainfall, temperature, depth to groundwater, drainage volume, and river hydrometry. In our research will be forecast the depth to groundwater data using univariate and multivariate approach of time series using Prophet Method. Prophet method is one of library which develop by Facebook team. We also use the other approach to making the data clean, or the data ready to forecast. We use handle missing data, transforming, differencing, decomposition time series, determine lag, stationary approach, and Augmented Dickey-Fuller (ADF). The all approach will be uses to make sure that the data not appearing the problem while we tried to forecast. In the other describe, we already get the results using univariate and multivariate Prophet method. The multivariate approach has presented the value of MAE 0.82 and RMSE 0.99, it’s better than while we forecast using univariate Prophet.

The Forecasting of Groundwater Fluctuations using Time Series Analysis and Combination of Data-Driven Models

The Forecasting of Groundwater Fluctuations is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and imposing high costs. In this research, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters Exponential Smoothing (HWES) data-driven models were used for short-term modeling of the groundwater fluctuations in a piezometer around the Gohar Zamin Iron Ore Mine. For this purpose, 250 non-seasonal groundwater fluctuations data in the period 22-Nov-2018 to 29-Jul-2019, 200 data for modeling, and 50 data for prediction were used. To take advantage of all the features of the two developed models, the predictions are combined with different methods and specific weights. The results show better accuracy for the ARIMA method between the two short-term forecasts, while the HWES method requires less time for modeling. Also, among all the predictions made, the highest accuracy for the combined least-squares method is for forecasting the groundwater fluctuations in the short-term. All the forecasts show a decrease in the groundwater fluctuations, indicating pumping wells around the Gohar Zamin Iron Ore Mine area.

Groundwater Withdrawal-Induced Land Subsidence

This entry presents a comprehensive review of models used to predict land surface displacements caused by rock mass drainage, as well as (2) recent advances and (3) a summary of InSAR implementation over recent years to support the aquifer compaction modelling process. Therefore, the study presented would be of benefit to readers who are interested in the topic of interaction between the human population and the hydrogeological system in different regions. The research presented allows readers to better understand the factors, developments and effects of groundwater drainage and thus facilitate large - scale risk assessment and preventive planning.

Vacuum formation and negative pressure transmission under a self-starting drainage process

Drainage can effectively control landslides that are caused by the infiltration of rainfall. Laboratory model tests were conducted in this study to investigate an efficient groundwater drainage method. The tests identified a drainage method that self-starts and creates a vacuum in the permeable section through siphoning. In addition, the value of the vacuum pressure in the permeable section is related to the difference in the height from the outlet of the drain to the permeable section. The greater the height, the greater the vacuum pressure generated in the permeable section. However, the vacuum pressure attenuates continuously in the process of transfer in the soil. It was also proved that there is a smaller attenuation of the amplitude in soil that has good permeability. Most importantly, the self-starting drainage method makes the surrounding soil form a vacuum seepage field and increases the seepage gradient, thereby improving the efficiency of the drainage.

Hydrological Response Mechanism of Aquifer Dynamic Storage during the Dry and Wet Seasons

<p>Streamflow recession usually exists between rainfall events, and the recession can be expressed as the power relationship between the streamflow and the recession rate. Under the assumption that groundwater drainage from the aquifer to the river channel, this method can inversely evaluate the basin-scale groundwater storage using observed streamflow. It has often been used to explore regional groundwater storage conditions and storage-discharge relationships, and even to estimate hydrogeological parameters. However, the groundwater storage generating the streamflow is only a component of the dynamic storage that includes streamflow and evapotranspiration dynamic, or other forms of mobile groundwater. In order to understand the mechanism of the aquifer dynamic storage responses under environmental changes (included climate change, human activities, etc.), this study used the analytic streamflow distribution model to optimize the estimation of recession parameters during the dry and wet seasons, and explored the parameters change over time and the relationship with seasonal evapotranspiration and basin wetness conditions. Combining the water balance methods, we also quantified the dynamic storage relevant to groundwater drainage and vegetation available water (i.e. storage insensitive to streamflow), respectively, to explore the hydrological response mechanism of aquifer dynamic storage in two seasons. The results showed that the difference in recession parameters in the dry and wet seasons is related to the basin wetness condition, and the evapotranspiration effect is relatively limited. In addition, the parameters change over time also indicated that the environmental change has gradually changed the streamflow recession mechanism. By comparing the response of dynamic storage components to rainfall events and evapotranspiration, this study also demonstrated that the variability in different forms of dynamic storages during each season, which is helpful for understanding the store and loss process of groundwater storage at the basin scale and improving the possibility for predicting the different environmental impacts on groundwater storage.</p>