Evaluating the Timing and Interdependence of Hydrologic Processes at the Watershed Scale Based on Continuously Monitored Data

Abstract

A quantitative understanding of the interplay between the different components of the hydrologic cycle at the watershed scale can be gained from analyzing high-frequency hydrologic time series. High-frequency measurements of precipitation, soil water content, shallow groundwater, and streamflow were collected and analyzed in Otter Creek, a 122 km 2 watershed located in Northeast Iowa, USA. For selected rainfall events occurring in 2014, it was found that there is at least 4 h of delay between soil water content and water table time series response and streamflow peak. This is true even when the water table was approximately 6.5 m below the ground surface before rainfall started. Data reveal a strong linear dependence between the soil water content and the water table, which suggests the existence of a capillary fringe that extends approximately 2.5 m above the water table. The highest streamflow values in Otter Creek occurred when both the water table was close to the ground surface and the near surface soil (top 65 cm) was close to full saturation. Analyses show that, in the study area, data on depth to water table or deep soil water content have the potential to play a key role in the development of a flood warning system. The transformation of rainfall into streamflow is a complex process that we simplified in this study. Additional analyses using physically based coupled surface-subsurface models or non-linear or stochastic models are recommended for more rigorous analysis.

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

“…They reported quicker streamflow response to rainfall in the case of watersheds coupled with forests and steep topography compared to pasture and flat topography [8]. In another study, soil moisture was identified as a critical component in the development of the flood warning system [9].…”

A Dynamic Study of a Karst Spring Based on Wavelet Analysis and the Mann-Kendall Trend Test

“…A multiple regression model was established to examine the average water level during two different periods (1960-1967 and 1968-1989) and precipitation in the current year, urban exploitation, peripheral exploitation and precipitation in the previous year. The following equations were used: Y = 30.417 + 0.002X 1 − 0.278X 2 + 0.002X 3 + 0.0004X 4 (13) Y = 27.362 + 0.002X 1 − 0.002X 2 − 0.05X 3 + 0.001X 4 (14) where X 1 is current precipitation; X 2 is urban exploitation; X 3 is external extraction; and X 4 is precipitation of the previous year. Regression analysis results (Table 2) show that from the 1960s to the 1990s, the main factor affecting groundwater level changed from precipitation to artificial exploitation, and the influence of peripheral mining has exceeded that of urban mining.…”

“…However, the loose rock type water and karst fissure water dynamics are quite different. A karst aqueous medium is highly heterogeneous and anisotropic, and its water circulation process is complex, resulting in karst fissures having a strong water flow [13,14]. This has led to an increase in laboratory-based physical model experiments investigating karst water flow movement [15,16].…”