The redistribution of hillslope soil water during and after rainstorms is affected by soil properties and topography. Therefore, understanding how soil‐terrain attributes affect the soil volumetric water content (VWC) distribution under various catchment storages is a prerequisite for accurate hydrological modeling. Herein, the relationships between soil‐terrain attributes and soil VWC were examined in a steep (average slope = 60%), forested, zero‐order catchment. Detailed topography, soil properties, and runoff, and high frequency (6‐min) soil moisture data observed from July 2016 to November 2017 were employed along with resampled VWC data in three time‐steps (daily, hourly, and 6‐min intervals) for correlation analysis between soil‐terrain attributes and measured VWC. The results showed that daily aggregated data overlooked the highly heterogeneous flow conditions under dry‐wet transitions, leading to a constantly high correlation between VWC and the topographic wetness index (TWI). In comparison, the 6‐min data captured short‐lived flow processes, highlighting that, in reality, VWC‐TWI correlations fluctuated frequently as a function of catchment storages and precipitation characteristics, with four wetting patterns identified. Under wet‐dry transitions, the VWC‐TWI correlations increased significantly (from 0.3 to 0.6) as the hillslope gradually drained of lateral subsurface water. Soil water behavior consists of diurnal oscillations superimposed on a declining trend, which demonstrates the existence of unsaturated flow and which contributed to the increase in the strength of the correlations. Finally, through correlation analysis of all drying (n = 4) and wetting (n = 45) periods, we found that soil‐terrain attributes and VWC correlations can be used to identify the dominant soil water processes in various catchment storages.

Soil dielectric sensors have been widely used to obtain real-time soil moisture data, which are important for water resource management. However, soluble salts in the soil significantly affect the accuracy of these sensor measurements. Therefore, it is crucial to select suitable soil dielectric sensors for soil moisture measurements at different salinity levels. Eight mainstream sensors (EC-5, 5TE, Teros12, Hydra-probe II, TDR315L, TDR315H, TDR305H, and CS655) were selected and tested at four different soil salinity levels (EC_1:5 = 3.0, 1.5, 1.0, and 0.75 dS·m⁻¹). The measured values using the factory calibration formulas were compared at six soil moisture levels. The results showed that the measured soil moisture values from various sensors exhibited varying degrees of overestimation, which increased with increasing salinity. Only EC-5 did not exhibit distortion at high-salinity levels, with the measured values showing a good linear trend compared to the standard values. Mutational distortion of the measured apparent dielectric permittivity occurred in TDR315L, TDR315H, Hydra-probe II, and 5TE at EC_1:5 = 3.0 dS·m⁻¹. Insensitive distortion of the measured apparent dielectric permittivity occurred in Teros12 and TDR305H at EC_1:5 = 3.0 dS·m⁻¹ as well as in Teros12, TDR305H, 5TE and Hydra-probe II at EC_1:5 = 1.5 dS·m⁻¹. All tested sensors performed reasonably well at EC_1:5 ≤ 1.0 dS·m⁻¹. Seven sensors (excluding CS655) were calibrated within the distortion threshold. The soil moisture accuracy using the calibrated formulas could reach ±0.02 cm³·cm⁻³. At EC_1:5 ≤ 1.0 dS·m⁻¹, most sensors in this study could be applied with the factory calibration formulas. TDR series, EC-5, 5TE and Teros12 were recommended after calibration for EC_1:5 > 1.0 dS·m⁻¹. For extremely high soil salinity levels, the TDR series and EC-5 may be the best choices.

In humid hilly regions, macropore preferential flow in soils dominates the distribution of event water, thereby influencing the generation and development of runoff. However, the mechanism of how soil functions on macropore drainage and matrix absorption remains poorly understood due to complex soil water dynamics in a multi‐porosity subsurface network. In this study, based on the source‐responsive method that divides the soil into source‐responsive and diffusive domains, the allocation ratio of infiltrated water in macropores recharging the matrix were derived and it was coupled with PIHM (Penn State Integrated Hydrologic Model) as PIHM‐SRM (PS). By simulating the soil moisture process at profile scale and the runoff process at catchment scale, it was found that the PS overcame the difficulty of most hydrologic models in describing the process of replenishing moisture in dry soil. This leads to more satisfactory performance for flood peaks at the outlet (CCC > 0.84) and soil moisture peaks at three profiles (CCC = 0.97) compared to original PIHM models. Moreover, the separate channel of film flow in the PS further improves the simulation accuracy of peak response speed in subsurface floods under rainstorms (TP > 40 mm). Additionally, sensitivity analysis shows that the storage‐discharge capacity of soil profiles dominates torrential flood forecasting in humid headwaters when considering the influence of macropores. Finally, considering the parameter‐predictive property in the PS, field‐based parameterized strategies are vital for distributed catchment modeling. This will enable the PS to improve flash torrent predictions in headwaters and be applied at catchment scales.

Our knowledge of the similarities and differences in ecological systems is vital to understanding the co-evolution of ecological factors. This study proposes a multi-dimensional hydro-climatic similarity and classification framework based on Budyko theory. The framework employs the dryness index (DI), evaporative index (EI), and an empirical parameter (ω) to further sub-divide four climatic zones (humid, semi-humid, semi-arid, and arid zones) in terms of DI. A criterion that define the similarities between stations is proposed to verify the classification to obtain optimal results. This method is applied to Mainland China, and 637 stations are adopted for continental-scale classification experiments. The point cloud of the Budyko curve for all the stations in Mainland China is plotted. We find that the hydrothermal conditions of the vertically distributed stations on the Budyko curve can be quite different in the same climatic zone when DI < 4.0. The higher the vertical locations of the stations on the Budyko curve are, the drier and colder the climates and corresponding natural landscapes. Under the proposed hydro-climatic classification framework, the four climatic zones are further divided into 17 sub-regions, and the hydrothermal conditions for each sub-region are discussed. The results suggest that regional differences of long-term water balance are resulted by not only mean annual hydrothermal factors and catchment forms but also annual distribution of hydrothermal factors. Our framework can provide hydrologically-based classification across continental scale and, thus, provide a profound understanding of hydrothermal conditions of continental-scale hydrological cycles.