Soil moisture signatures provide a promising solution to overcome the difficulty of evaluating soil moisture dynamics in hydrologic models. Soil moisture signatures are metrics that quantify the dynamic aspects of soil moisture timeseries and enable process‐based model evaluations. To date, soil moisture signatures have been tested only under limited land‐use types. In this study, we explore soil moisture signatures' ability to discriminate different dynamics among contrasting land‐uses. We applied a set of nine soil moisture signatures to datasets from six in‐situ soil moisture networks worldwide. The dataset covered a range of land‐use types, including forested and deforested areas, shallow groundwater areas, wetlands, urban areas, grazed areas, and cropland areas. Our set of signatures characterized soil moisture dynamics at three temporal scales: event, season, and a complete timeseries. Statistical assessment of extracted signatures showed that (1) event‐based signatures can distinguish different dynamics for all the land‐uses, (2) season‐based signatures can distinguish different dynamics for some types of land‐uses (deforested vs. forested, urban vs. greenspace, and cropped vs. grazed vs. grassland contrasts), (3) timeseries‐based signatures can distinguish different dynamics for some types of land‐uses (deforested vs. forested, urban vs. greenspace, shallow vs. deep groundwater, wetland vs. non‐wetland, and cropped vs. grazed vs. grassland contrasts). Further, we compared signature‐based process interpretations against literature knowledge; event‐based and timeseries‐based signatures generally matched well with previous process understandings from literature, but season‐based signatures did not. This study will be a useful guideline for understanding how catchment‐scale soil moisture dynamics in various land‐uses can be described using a standardized set of hydrologically relevant metrics.

The effects of land use change on the occurrence and frequency of preferential flow (fast water flow through a small fraction of the pore space) and piston flow (slower water flow through a large fraction of the pore space) are still not fully understood. In this study, we used a five year high resolution soil moisture monitoring dataset in combination with a response time analysis to identify factors that control preferential and piston flow before and after partial deforestation in a small headwater catchment. The sensor response times at 5, 20 and 50 cm depths were classified into one of four classes: (1) non-sequential preferential flow, (2) velocity based preferential flow, (3) sequential (piston) flow, and (4) no response. The results of this analysis showed that partial deforestation increased sequential flow occurrence and decreased the occurrence of no flow in the deforested area. Similar precipitation conditions (total precipitation) after deforestation caused more sequential flow in the deforested area, which was attributed to higher antecedent moisture conditions and the lack of interception. At the same time, an increase in preferential flow occurrence was also observed for events with identical total precipitation. However, as the events in the treatment period (after deforestation) generally had lower total, maximum, and mean precipitation, this effect was not observed in the overall occurrence of preferential flow. The results of this analysis demonstrate that the combination of a sensor response time analysis and a soil moisture dataset that includes pre- and post-deforestation conditions can offer new insights in preferential and sequential flow conditions after land use change.

Core Ideas: WTC analysis was used to elucidate the non‐stationary C/N relationship at different time scales. Water transit times (≈groundwater contribution) were key drivers for the C/N relationship. Phase shifts between the C/N time series increased with rising groundwater influence. WTC analysis can help to identify spatiotemporally dependent relationships in cachtments. Understanding natural controls on N and C biogeochemical cycles is important to estimate human impacts on these cycles. This study examined the spatiotemporal relationships between time series of weekly monitored stream and groundwater N and C (assessed by NO ₃ ⁻ and dissolved organic C [DOC]) in the forested Wüstebach catchment (Germany). In addition to traditional correlation analysis, we applied wavelet transform coherence (WTC) analysis to study variations in the correlation and lag time between the N and C time series for different time scales. Median transit times were used to connect hydrologic and water chemistry data. We defined three stream‐water groups: (i) subsurface runoff dominated locations with strong seasonal fluctuations in concentrations, short transit times, and strong negative C/N correlations with short time lags, (ii) groundwater dominated locations, with weaker seasonal fluctuations, longer transit times, and weaker C/N correlations with lags of several months, and (iii) intermediate locations, with moderate seasonal fluctuations, moderate transit times, and strong C/N correlations with short time lags. Water transit times could be identified as key drivers for the C/N relationship and we conclude that C and N transport in stream water can be explained by mixing of groundwater and subsurface runoff. Complemented by transit times and the hydrochemical time series, WTC analysis allowed us to discriminate between different water sources (groundwater vs. subsurface runoff). In conclusion, we found that in time series studies of hydrochemical data, e.g., DOC and NO ₃ ⁻ , WTC analysis can be a viable tool to identify spatiotemporally dependent relationships in catchments.