Understanding subsurface structure and groundwater flow in deltaic aquifers is essential for evaluating the vulnerability of groundwater resources in delta systems. Deltaic aquifers contain coarse‐grained paleochannels that preserve a record of former surface river channels as well as fine‐grained floodplain deposits. The distribution of these deposits and how they are interconnected control groundwater flow and contaminant transport. In this work, we link depositional environments of deltaic aquifers to stratigraphic (static) and flow and transport (dynamic) connectivity metrics. Numerical models of deltaic stratigraphy were generated using a reduced‐complexity numerical model (DeltaRCM) with different input sand fractions (ISF) and rates of sea‐level rise (SLR). The groundwater flow and advective transport behavior of these deltas were simulated using MODFLOW and MODPATH. By comparing the static and dynamic metrics calculated from these numerical models, we show that groundwater behavior can be predicted by particular aspects of the subsurface architecture, and that horizontal and vertical connectivity display different characteristics. We also evaluate relationships between connectivity metrics and two environmental controls on delta evolution: ISF and SLR rate. The results show that geologic setting strongly influences both static and dynamic connectivity in different directions. These results provide insights into quantitatively differentiated subsurface hydraulic behavior between deltas formed under different external forcing (ISF and SLR rate) and they are a potential link in using information from delta surface networks and depositional history to predict vulnerability to aquifer contamination.

Deriving rainfall thresholds is one of the most convenient and effective empirical methods for formulating landslide warnings. The previous rainfall threshold models only considered the threshold values for areas with landslide data. This study focuses on obtaining a threshold for each single landslide via the geostatistical interpolation of historical landslide–rainfall data. We collect the occurrence times and locations of landslides, along with the hourly rainfall data, for Dazhou. We integrate the short-term and long-term rainfall data preceding the landslide occurrences, categorizing them into four groups for analysis: 1 h–7 days (H1–7), 12 h–7 days (H12–D7), 24 h–7 days (H24–D7), and 72 h–7 days (H72–D7). Then, we construct a rainfall threshold distribution map based on the 2014–2020 data by means of Kriging interpolation. This process involves applying different splitting coefficients to distinguish the landslides triggered by short-term versus long-term rainfall. Subsequently, we validate these thresholds and splitting coefficients using the dataset for 2021. The results show that the best splitting coefficients for H1–D7, H12–D7, H24–D7, and H72–D7 are around 0.19, 0.52, 0.55, and 0.80, respectively. The accuracy of the predictions increases with the duration of the short-term rainfall, from 48% for H1–D7 to 67% for H72–D7. The performance of these threshold models indicates their potential for practical application in the sustainable development of geo-hazard prevention. Finally, we discuss the reliability and applicability of this method by considering various factors, including the influence of the interpolation techniques, data quality, weather forecast, and human activities.

River deltas are densely populated regions of the world with vulnerable groundwater reserves. Contamination of these groundwater aquifers via saline water intrusion and pollutant transport is a growing threat due to both anthropogenic and climate changes. The arrangement and composition of subsurface sediment is known to have a significant impact on aquifer contamination; however, developing accurate depictions of the subsurface is challenging. In this work, we explore the relationship between surface and subsurface properties and identify the metrics most sensitive to different forcing conditions. To do so, we simulate river delta evolution with the rule‐based numerical model, DeltaRCM, and test the influence of input sand fraction and steady sea level rise (SLR) on delta evolution. From the model outputs, we measure a variety of surface and subsurface metrics chosen based on their applicability to imagery and modeling results. The Kullback‐Leibler (KL) divergence is then used to quantitatively gauge which metrics are most indicative of the imposed forcings. Both qualitative observations and the KL divergence analysis suggest that estimates of subsurface connectivity can be constrained using surface information. In particular, more variable shoreline roughness values and higher surface wetted fraction values correspond to increased subsurface connectivity. These findings complement traditional methods of estimating subsurface structure in river‐dominated delta systems and represent a step toward the identification of a direct link between surface observations and subsurface form.