Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones.

Core Ideas: Deep drainage rates under young cropland in the desert‐oasis region were investigated. Deep drainage rates were estimated by a simulation approach based on deep soil moisture data. Annual deep drainage averaged 468 mm; the annual deep drainage coefficient averaged 43%. Deep drainage reduces agricultural water productivity under cropland recently converted from native desert soils (i.e., young cropland) and increases the risks of nutrient and pesticide leaching into groundwater in the desert‐oasis ecotone. However, the deep drainage rates under young cropland in these oasis environments remain unclear, especially for winter irrigation, a common practice in Northwest China. The objective of this study was to estimate the deep drainage rate using the HYDRUS‐1D model based on soil moisture data in the deep vadose zone. Soil moisture at depths ranging from 0 to 200 cm was measured using HydraProbe II soil sensors in maize ( Zea mays L.) and wheat ( Triticum aestivum L.) fields in 2015 and 2017, respectively. Using a novel simulation approach based on soil moisture data in the deep vadose zone, the HYDRUS‐1D model provided reliable estimates of deep drainage as confirmed by comparison with estimates from the soil water balance method and prior studies in the region. The annual deep drainage averaged 468 mm, and the annual deep drainage coefficient averaged 43% in the young croplands. The winter irrigation amount averaged 265 mm, and the deep drainage coefficient during winter averaged 21% in the young croplands. The sandy soil of the young cropland and inefficient irrigation scheduling are detrimental to water conservation, causing relatively large deep drainage losses and enhancing the risks of groundwater pollution.

Core Ideas: Useful daily drainage estimates obtained from soil moisture measurements at 60 cm. Annual drainage estimates indicate potential recharge rates to groundwater aquifers. Uncertainty in the drainage estimates was relatively high at sites with high precipitation. Groundwater supplies ~20% of global freshwater withdrawals, and accurate information regarding groundwater recharge rates is needed for sustainable groundwater management. Recharge rates are often limited by the rates of drainage from the soil profile, which are influenced by soil moisture conditions. Soil moisture monitoring has expanded dramatically in recent decades with the advent of large‐scale networks like the Oklahoma Mesonet, which has monitored soil moisture statewide since 1996. Using those data with site‐specific soil hydraulic properties and a unit‐gradient assumption, we estimated daily drainage rates at 60 cm for 78 sites for up to 17 yr. Our working hypothesis was that these drainage rates are indicative of potential groundwater recharge rates. Mean annual drainage rates ranged from 6 to 266 mm yr−1, with a statewide median of 67 mm yr−1. These rates agreed well with prior recharge estimates for major Oklahoma aquifers. To provide a further independent check on our results, drainage was modeled using HYDRUS‐1D for four focus sites across 17 yr. Soil‐moisture‐based drainage rates and HYDRUS‐1D drainage rates agreed to within 10 mm yr−1 at the drier two sites but had discrepancies of > 150 mm yr−1 at two sites with > 1000 mm yr−1 precipitation. Simulations also showed that for a semiarid site the unit‐gradient assumption was likely violated at the 60‐cm depth, highlighting the need for deeper soil moisture monitoring. Despite these limitations, this simple method for estimating drainage through long‐term soil moisture monitoring shows unique potential to provide valuable information for hydrology and groundwater management.