The Relationship between Surface Displacement and Groundwater Level Change and Its Hydrogeological Implications in an Alluvial Fan: Case Study of the Choshui River, Taiwan

Modeling the optimal management of land subsidence due to aquifers overexploitation

The study of land subsidence has recently been expanded due to its increased occurrence and magnitude worldwide. This paper develops and applies an optimal control model of groundwater extractions under conditions of land subsidence. We include, in a traditional groundwater management model, two types of negative externalities associated with land subsidence: damage to infrastructure and to economic activities, and the loss of aquifer storage capacity. Using a two-stage optimal control method, characterized by two sub-problems corresponding to the phase before and after the occurrence of subsidence, we find the economically sustainable paths of groundwater extractions and water table levels under the existence of land subsidence impacts. The theoretical results indicate that the presence of land subsidence dictates the optimal paths of groundwater withdrawals and water table levels. The model has been applied to the Alto Guadalentín over-exploited aquifer system in the Segura River Basin of Spain. The empirical outcomes indicate that by following the optimal paths, groundwater extractions should be curtailing to avoid reaching the critical water level at which subsidence takes place. Results suggest that regional net present value of welfare over the planning period, under the two land subsidence scenarios, is reduced by nearly 1-5%, compared to the no land subsidence scenario. Furthermore, under subsidence, even with relatively small impacts of both types of externalities, groundwater optimal extractions are kept at levels that avoid these externalities. These outcomes clearly call for government intervention in order to reduce groundwater withdrawals in aquifers with propensity to face undesirable subsidence effects.

Modeling of land subsidence using GIS-based artificial neural network in Yunlin County, Taiwan

In this study, the land subsidence in Yunlin County, Taiwan, was modeled using an artificial neural network (ANN). Maps of the fine-grained soil percentage, average maximum drainage path length, agricultural land use percentage, electricity consumption of wells, and accumulated land subsidence depth were produced through geographic information system spatial analysis for 5607 cells in the study area. An ANN model based on a backpropagation neural network was developed to predict the accumulated land subsidence depth. A comparison of the model predictions with ground-truth leveling survey data indicated that the developed model had high accuracy. Moreover, the developed model was used to investigate the relationship of electricity consumption reduction with reductions in the total area of land with severe subsidence (> 4 cm per year); the relationship was approximately linear. In particular, the optimal results were obtained when decreasing the electricity consumption from 80 to 70% of the current value, with the area of severe land subsidence decreasing by 13.66%.

The Role of Bedload Transport in the Development of a Proglacial River Alluvial Fan (Case Study: Scott River, Southwest Svalbard)

This study, which was conducted between 2010 and 2013, presents the results of direct, continuous measurements of the bedload transport rate at the mouth section of the Scott River catchment (NW part of Wedel-Jarlsberg Land, Svalbard). In four consecutive melt seasons, the bedload flux was analyzed at two cross-sections located in the lower reaches of the gravel-bed proglacial river. The transported bedload was measured using two sets of River Bedload Traps (RBTs). Over the course of 130 simultaneous measurement days, a total of 930 bedload samples were collected. During this period, the river discharged about 1.32 t of bedload through cross-section I (XS I), located at the foot of the alluvial fan, and 0.99 t through cross-section II (XS II), located at the river mouth running into the fjord. A comparison of the bedload flux showed a distinctive disproportion between cross-sections. Specifically, the average daily bedload flux QB was 130 kg day−1 (XS I) and 81 kg day−1 (XS II) at the individual cross-profiles. The lower bedload fluxes that were recorded at specified periods in XS II, which closed the catchment at the river mouth from the alluvial cone, indicated an active role of aggradation processes. Approximately 40% of all transported bedload was stored at the alluvial fan, mostly in the active channel zone. However, comparative Geomorphic Change Detection (GCD) analyses of the alluvial fan, which were performed over the period between August 2010 and August 2013, indicated a general lowering of the surface (erosion). It can be assumed that the melt season’s average flows in the active channel zone led to a greater deposition of bedload particles than what was discharged with high intensity during floods (especially the bankfull stage, effectively reshaping the whole surface of the alluvial fan). This study documents that the intensity of bedload flux was determined by the frequency of floods. Notably, the highest daily rates recorded in successive seasons accounted for 12–30% of the total bedload flux. Lastly, the multi-seasonal analysis showed a high spatio-temporal variability of the bedload transport rates, which resulted in changes not only in the channel but also on the entire surface of the alluvial fan morphology during floods.

Seasonal Surface Fluctuation of a Slow-Moving Landslide Detected by Multitemporal Interferometry (MTI) on the Huafan University Campus, Northern Taiwan

A slow-moving landslide on the Huafan University campus, which is located on a dip slope in northern Taiwan, has been observed since 1990. However, reliable monitoring data are difficult to acquire after 2018 due to the lack of continuous maintenance of the field measurement equipment. In this study, the multitemporal interferometry (MTI) technique is applied with Sentinel-1 SAR images to monitor the slow-moving landslide from 2014–2019. The slow-moving areas detected by persistent scatterer (PS) pixels are consistent with the range of previous studies, which are based on in situ monitoring data and field surveys. According to the time series of the PS pixels, a long period gravity-induced deformation of the slow-moving landslide can be clearly observed. Moreover, a short period seasonal surface fluctuation of the slow-moving landslide, which has seldom been discussed before, can also be detected in this study. The seasonal surface fluctuation is in-phase with precipitation, which is inferred to be related to the geological and hydrological conditions of the study area. The MTI technique can compensate for the lack of surface displacement data, in this case, the Huafan University campus, and provide information for evaluating and monitoring slow-moving landslides for possible landslide early warning in the future.