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Purpose: Soil macropores affect the proportion of rainfall infiltration and regulate the amount of water available to the plants on a field scale. However, the distribution of soil macropores and the effect on hydrologic and ecological regimes have been underestimated at a continental scale. The aim of this study was to investigate the relationships between soil properties and climatic factors on soil macropores and assess the eco-hydrological effect of soil macropores. Materials and methods: We hypothesized that the spatial distribution of soil macropores was controlled by soil properties and climatic factors at the continental scale. Using a spatial error-egression method, the soil effective porosity (EP) and residual effective porosity (REP) across China were calculated. The effects of soil macropores on soil physicochemical properties and climate factors were analyzed by the random forest model. The effect of macropores on soil water content (SWC) and normalized difference vegetation index (NDVI) was also analyzed. Results and discussion: The EP ranged from 0.06 to 0.35 cm3 cm−3 and was significantly related with soil chemical index, particularly, negatively correlated with cation exchange capacity (CEC) and total nitrogen (TN), but positively correlated with pH. There was a negative relationship between REP and mean annual precipitation in drylands. In humid regions, there was a positive relationship between REP and mean annual precipitation. Random forest model showed in humid region mean annual precipitation was more important than the annual temperature difference in explaining REP, but the results were reversed in drylands. The SWC and NDVI had different correlations with REP in drylands and humid regions. Conclusion: Soils with better quality had lower EP at the continental scale. Soil macropores were related simultaneously to precipitation and temperature conditions. Importantly, soil macropores were not conducive to soil water conservation and vegetation development in drylands. [ABSTRACT FROM AUTHOR]

The timely and effective early warning of sudden drought events is of great importance for reducing disaster losses and drought risk. Satellite microwave remote sensing with the advanced microwave scanning radiometer (AMSR) has enabled the global monitoring of multiple components of water, such as soil moisture (SM), vapour pressure deficit (VPD), and open water fraction (OWF), on a daily scale since 2002. In this study, the applicability of the AMSR surface wetness index (ASWI) and drought features in Central Asia were studied from 2002 to 2017 based on AMSR data. This study concluded that (1) the spatial and temporal differences in the various water components and coefficient of variation of the Central Asia land–atmosphere interface were large, which led to large spatial and temporal differences under both dry and wet conditions in Central Asia; (2) although the ASWI was not significantly correlated with other drought indices (i.e., gravity recovery and climate experiment (GRACE) combined climatological deviation index (CCDI) and GRACE drought severity index (DSI)) over Central Asia, the maximum Pearson correlation coefficient (CC) between the ASWI and Palmer drought severity index (PDSI) in the Irtysh River Basin was 0.824 (p < 0.05), corresponding to a moving average window of 11 months. The CCs of ASWI and Standardized Precipitation Evapotranspiration Index on a 6‐month scale (SPEI‐6) in Central Asia were generally more than 0.8 (p < 0.05), indicating that the ASWI was effective for retrieving drought conditions in Central Asia. In addition, these four drought indices (ASWI, PDSI, GRACE‐DSI, and GRACE‐CCDI) revealed that drought occurred continuously after 2003 in Central Asia; (3) the spatial and temporal distribution of dry and wet conditions in Central Asia is closely related to the teleconnection indices (i.e., MEI, SOI, and PDO). Thus, AMSR data are critical for understanding drought events and their evolution in regions influenced by climate change. [ABSTRACT FROM AUTHOR]

The characteristics of soil macropores and water infiltration are closely connected to the growth of plant roots and their root zone environment. However, it is unclear how the root zone environment of oasis farmlands regulates the development of soil macropores and water flow in hyper‐arid regions. The objective of this study was to investigate soil macropores and their effect on water flow under irrigated oasis farmlands using a combination of X‐ray computed tomography (CT) and dye tracer. It was hypothesized that the integration of CT and dye tracer could clearly reveal preferential flow through biopores and large pores of oasis soils. A helical medical CT scanner was used to quantify more information about soil macropores in the root zone, along with an in situ single‐ring dye infiltration experiment to reveal water flow in three different oasis farmlands (piedmont oasis farmland, marginal oasis farmland, and old oasis farmland). Soil macroporosity was 0.44% under crop rows, while soil macroporosity in the interrows was only 0.30% across the oasis farmlands. Biopores contributed 73% of the volume of the total macropores under crop rows. The stable infiltration rate in the interrows was 0.3 mm min−1, which was significantly (p < 0.05) less than that under crop rows (0.7 mm min−1). Water flow under crop rows were mainly transported in biopores and large pores. The contribution of macropores to preferential flow under crop rows was 4.8 times larger than interrows. The integration of CT and dye tracer was a more holistic technique, which adequately revealed that oases had preferential flow affected by biopores and large pores, resulting in higher solute and contaminant transport. Key Points: Soil macropores and preferential flow affected by crop roots were investigated in hyper‐arid regions.The integration of CT and dye tracer adequately identified preferential flowpaths of oasis soils.Biopores were larger under crop rows than interrows.Preferential flow transported in biopores and large soil pores under crop rows. [ABSTRACT FROM AUTHOR]

Landslide and debris flows are typically triggered by rainfall-related weather conditions, including short-duration storms and long-lasting rainfall. The critical precipitation of landslides and debris flow occurrence is different under various hydrometeorological conditions. In this study, the trigger sensitivities of different daily hydrological variables were assessed using 50 days-worth of recorded landslide and debris flows using the Soil and Water Assessment Tool model. The event days were divided into long-lasting rainfall trigger (LLR-trigger) event days and short-duration storm trigger (SDS-trigger) event days with six determinate criteria based on modeled wetness states. The landslide and debris flow prediction model was built using nine hydrometeorological variables, and the predictive performance was tested with simulated data from 2010 to 2012. The results suggest that, except for rainfall, historical hydrological variables and their development provide important information for triggering landslides and debris flows. The prediction model with an area under curve (AUC) value of 0.85 was able to capture most of the landslides and debris flows. The temporal distribution of the two triggering events predicted by the model was consistent with the annual precipitation distribution. In addition, the spatial variations of the specific trigger types could be attributed to the different land covers. Despite some uncertainty, this study provides an idea of improving the landslide and debris flow prediction model. [ABSTRACT FROM AUTHOR]

Surface soil hydraulic properties play critical roles in controlling water infiltration, evaporation, and soil water storage, in‐turn affecting the evolution of soil pore structure in cultivated desert soils of arid areas. The variability in soil pores and hydraulic properties caused by conversion of native semidesert soils to agroforestry use alters soil water storage and water budget in the desert–oasis ecotone. The objective of this study was to investigate that land use conversion alters surface soil hydraulic properties and enhances the contribution of soil macropores to water flow in the Linze desert–oasis ecoregion. Water infiltration and soil pores characteristics with nine replicates were measured using a disc tension infiltrometer across four habitat types (Haloxylon ammodendron shrublands, Populus gansuensis forests, Medicago sativa Linn grasslands, and Zea mays croplands) during the growing season. Soil properties and hydraulic conductivity characteristics clearly varied across the four habitat types. Saturated hydraulic conductivity was 0.177, 0.126, 0.118, and 0.031cm min−1 in the forest, grassland, shrubland, and cropland, respectively, with the corresponding average soil moisture being 7.6, 6.7, 0.9, and 19.2%. Clay content, bulk density, and initial soil moisture were the key soil physicochemical properties affecting saturated hydraulic conductivity (Ks). The macropores (> 0.5 mm in radius) accounted for 15–60% of total water flow, while 0.25–0.5 mm pores contributed to 12–24% across the four habitat types. The contribution of >0.5 mm pores to water flow in the forest was 3.3–4.0‐times greater than in shrubland and cropland, due to greater soil macroporosity (58 cm−3 cm3) and undisturbed soil pore structure. Land use conversion has a positive impact on soil properties, structure features, and soil hydraulic properties, and as a consequence, enhance the complexity of water exchange in the arid areas. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three NDVI vegetation correction schemes with CRNS were provided. • The ratio of parameter N 0 between different landscapes reached 1.42. • N 0 was negatively correlated with NDVI (y = 373.9 (x + 0.015)−0.136, R2 = 0.77) • Three methods reduced RMSE from 0.093 to 0.063, 0.054, and 0.032 g g−1, respectively. Cosmic-ray neutron sensors (CRNS) are a powerful tool for measuring soil moisture on a hectometer scale, and mobile cosmic-ray neutron technology holds significant importance for upscaling soil moisture. However, vegetation strongly affects with CRNS soil moisture measurements. Variations in vegetation over time change how the CRNS is affected, which is more challenging than a stable vegetation cover, particularly during CRNS roving, where one crosses many vegetation covers. To correct vegetation, we hypothesized that Normalized Difference Vegetation Index (NDVI) can represent vegetation hydrogen pools. From 2020 to 2023, at the 74 plots with varying vegetation cover near the Qilian Mountains, the experiments for soil moisture measurements were conducted in shrubs, forests, deserts, farmlands, and grasslands using a Cosmic-ray Neutron Rover. The measured neutron intensity clearly varied across different landscapes, and the calibrated parameter N 0 differed among 74 plots. The variations in vegetation cover resulted in low measurement accuracy for CRNS. All three NDVI vegetation correction methods (NDVI-θ NDVI method, NDVI-NDVI ave method, and NDVI-N 0 method) offered by the study could improve accuracy of soil moisture measurements. In the optimal NDVI-N 0 method, the relationship between NDVI and N 0 was established using a power function y = 373.9 (x + 0.015)−0.136 (R2 = 0.77), which had the capability to decrease the root mean square error between oven-dried and measured soil moisture from 0.093 to 0.032 g g−1 in our study area. Employing NDVI instead of biomass for CRNS vegetation correction can substantially reduce workload and effectively enhance CRNS soil moisture measurements, showing the potential for upscaling soil moisture. [ABSTRACT FROM AUTHOR]

Flash flood disaster is a prominent issue threatening public safety and social development throughout the world, especially in mountainous regions. Rainfall threshold is a widely accepted alternative to hydrological forecasting for flash flood warning due to the short response time and limited observations of flash flood events. However, determination of rainfall threshold is still very complicated due to multiple impact factors, particular for antecedent soil moisture and rainfall patterns. In this study, hydrological simulation approach (i.e., China Flash Flood-Hydrological Modeling System: CNFF-HMS) was adopted to capture the flash flood processes. Multiple scenarios were further designed with consideration of antecedent soil moisture and rainfall temporal patterns to determine the possible assemble of rainfall thresholds by driving the CNFF-HMS. Moreover, their effects on rainfall thresholds were investigated. Three mountainous catchments (Zhong, Balisi and Yu villages) in southern China were selected for case study. Results showed that the model performance of CNFF-HMS was very satisfactory for flash flood simulations in all these catchments, especially for multimodal flood events. Specifically, the relative errors of runoff and peak flow were within ± 20%, the error of time to peak flow was within ± 2 h and the Nash-Sutcliffe efficiency was greater than 0.90 for over 90% of the flash flood events. The rainfall thresholds varied between 93 and 334 mm at Zhong village, between 77 and 246 mm at Balisi village and between 111 and 420 mm at Yu village. Both antecedent soil moistures and rainfall temporal pattern significantly affected the variations of rainfall threshold. Rainfall threshold decreased by 8-38 and 0-42% as soil saturation increased from 0.20 to 0.50 and from 0.20 to 0.80, respectively. The effect of rainfall threshold was the minimum for the decreasing hyetograph (advanced pattern) and the maximum for the increasing hyetograph (delayed pattern), while it was similar for the design hyetograph and triangular hyetograph (intermediate patterns). Moreover, rainfall thresholds with short time spans were more suitable for early flood warning, especially in small rural catchments with humid climatic characteristics. This study was expected to provide insights into flash flood disaster forecasting and early warning in mountainous regions, and scientific references for the implementation of flash flood disaster prevention in China. [ABSTRACT FROM AUTHOR]

• Infiltration is directly affected by soil moisture, rather than rainfall duration. • Horton infiltration equation is improved by adopting soil moisture variation. • Improved equation is better in simulation performance and uncertainty intervals. Soil water infiltration simulation is a subject receiving great interest in hydrological cycle modelling. The traditional Horton equation is based on the curve of infiltration capacity-rainfall duration time. However, the infiltration process is directly affected by soil moisture content, rather than rainfall duration. The objective of this study was to determine a relationship between infiltration capacity and soil moisture content in order to improve the Horton infiltration equation. Artificial rainfall-infiltration experiments were used to determine a series of power functions. The improved equation was cross-validated with observations from 32 experiments of multiple rainfall intensities and antecedent soil moisture. The simulation performance and uncertainty of the improved equation were compared with those of the original Horton equation to verify its accuracy. The results showed that infiltration rate decreases nonlinearly as soil moisture increases, and finally approaches a stable infiltration rate when the soil is saturated. Overland flow simulations by the improved Horton equation closely matched the observations from all 32 experiments over a soil moisture range of 0.222–0.349 m3/m3. The simulation performance was rated as good for most of the experiments for both the calibration and validation data sets. Compared with the original Horton equation, the simulation performance of the improved equation clearly improved estimation of infiltration, particularly as quantified by the Nash-Sutcliffe efficiency coefficient (NSE) and the coefficient of determination (R 2). The number of simulations with NSE values greater than 0.65 increased 11.59% and 2.50% for the calibration and validation data sets, respectively. The number of simulations with R2 values greater than 0.90 increased 31.14% and 22.50%, respectively. The uncertainty intervals of the improved Horton equation became a little greater than those of the original Horton equation. For all 32 experimental simulations, the average relative length of the uncertainty interval at the 95% confidence level increased from 40.52% with the original Horton equation to 49.17% with the improved Horton equation. The number of observations falling within the 95% confidence interval increased from 92.13% to 95.94% with the improved Horton equation. Most of the observations were accurately simulated using the improved Horton equation. The greatest improvements in simulating overland flow were seen for the experiments with low flow simulations. The study results provide insights into soil infiltration mechanisms, and also provide references to support improved infiltration simulation by considering soil moisture variation. [ABSTRACT FROM AUTHOR]

• Soil respiration including winter CO 2 efflux was studied in the desert-oasis region. • Annual soil CO 2 emissions varied from 176 to 918 g C m−2 across the study ecosystems. • The ratio of cold season CO 2 emissions to the annual was 6–15% across ecosystems. • Land use conversion influenced soil respiration across the desert-oasis ecoregion. Quantifying annual soil CO 2 emissions and the effects of physical and geochemical factors on soil CO 2 efflux will improve the carbon budget estimates, as well as enhance predictions of how soil CO 2 emission will response to climate change in arid areas. To data, variability in soil respiration rates caused by land use conversion in the desert-oasis ecoregion of Northwest China remains poorly characterized, especially the contribution of cold season (November-February) CO 2 emission. Soil respiration was measured in a Haloxylon ammodendron desert shrubland, a Zea mays cropland, a Populus gansuensis forest, and a Tamarix chinensis Lour wetland using a LI-COR 8100 Soil Respiration Observation System from April 2016 to March 2019. Annual soil CO 2 emissions totaled 176, 778, 918, and 397 g C m−2 in the desert shrubland, cropland, forest, and wetland, respectively. The ratio of cold season CO 2 emissions to the annual varied from 6 to 15% across ecosystems. Therefore, ignoring cold season CO 2 emissions would lead to underestimates of carbon losses in the desert-oasis ecoregion. The soil respiration rate at 10 °C was influenced by soil organic carbon content in all four ecosystems. In the cropland and forest, soil respiration rate was affected by the interaction of soil moisture and soil temperature, while in the wetland and desert shrubland, soil respiration was sensitive to a single factor, soil temperature. Land use conversion induced differences in organic matter accumulation, soil temperature, and soil moisture, and as a consequence, produced variability in soil respiration rates across the desert-oasis ecoregion. [ABSTRACT FROM AUTHOR]