[目的]峰丛洼地是重要的喀斯特地貌类型之一,探明不同景观位置对Ks的影响规律可为深入理解该区水文过程提供参考。[方法]通过测定2种景观位置不同土层Ks和土壤理化性质,采用方差分析、回归分析和通径分析等方法研究不同景观位置Ks的分布特征及其影响因素。[结果]景观位置和土层对Ks分布有显著的影响(p0.05)。景观位置对土壤颗粒组成和全磷影响不明显(p>0.05),而有机碳、全氮和体积质量随景观位置不同发生明显变化(p<0.05)。坡地Ks与粉粒、砂粒、有机碳和全磷显著正相关(p<0.05),与黏粒和体积质量呈显著负相关(p<0.05);洼地Ks与体积质量呈显著正相关(p<0.05)。通径分析表明,体积质量和有机碳是影响坡地和洼地Ks的主要因素。利用多元逐步回归建立坡地和洼地Ks的回归方程,其影响因素方差解释率分别为76.2%和32.6%。[结论]地貌特征分异对土壤水文参数分布有重要影响,可为喀斯特峰丛洼地小流域水土过程研究提供科学依据。

Understanding the characteristics of flood resistance within agricultural ecosystems is essential for the planning of agricultural structures within karst depressions against floods. However, the responses of crops to flooding were rarely investigated under natural precipitation extremes in karst depressions. This study investigates the influence of flooding on agricultural ecosystems (Maize and Napier grass) in a typical karst peak-cluster depression in Southwest China after an extreme rainfall event in 2016. Results showed that this rainfall event was once every 10 years, and caused 78.6 % of Maize and 20.4 % of Napier grass in death. Napier grass performed better than Maize when submerged, due to higher fine root biomass and the stable photosynthetic capacity of the leaves. The submergence critical tolerance time of flooding to Napier grass was 10.2 days; only when the Maize plant protruded 1.5 m above the water level can it survive. In response to this extreme event in 2016, the safe planting height for Napier grass and Maize was 3.09 m and 6.21 m, respectively, relative to the lowest point of the depression elevation (set at 0.00 m). This study implicated that it is necessary to rationally arrange the layout of agroecosystems at the karst depression according to the ability of plants to withstand flooding to cope with future climate change.

Soil water storage (SWS) is a crucial parameter for vegetation restoration and a variety of hydrological processes in semi -arid regions. Its dynamics reflect the integrated influence of different factors operating at various intensities and scales. In this study, we investigated the scale -specific correlations between SWS and six selected environmental factors along a 1340-m long sampling transect in a semi -arid catchment using multivariate empirical mode decomposition (MEMD). The six factors were bulk density, elevation, clay, silt, sand, and soil organic carbon content. The multivariate data series of the SWS and factors of interest were separated into seven intrinsic mode functions (IMFs) and residue. IMF1, IMF3, and IMF5 were identified as the primary scales according to the percentage of total variance (about 50%) in SWS under different soil moisture conditions. The scale -specific correlations between SWS and the corresponding factors varied with scale but were irrelevant to soil moisture condition. SWS at each IMF and residue could be estimated using the associated scale -specific environmental factors at the same IMF or residue. SWS at the measurement scale was satisfactorily estimated by summarizing all the predicted IMFs and residue, which outperformed measurement -scale SWS estimation based on multivariate stepwise linear regression between SWS and the associated factors. Soil moisture condition could affect the relative importance of the environmental factors in overall SWS estimation. In general, soil properties were the dominant predictor of SWS under different soil moisture conditions. MEMD provides a useful opportunity to deal with scale -specific correlations between various variables.

The cosmic-ray neutron sensing (CRNS) is an emerging method for continuously monitoring soil water content (SWC) at an intermediate scale. However, when multiple hydrologic units are present within its footprint, the potential application of CRNS in water resources management is restricted. Here we propose a new strategy to predict point-scale SWC in root zone established on CRNS-based soil moisture and improved relative difference method. A total of 768 days of soil moisture data were collected by CRNS at the intermediate scale and EC-TM sensors at the point scale in a karst catchment. The original and improved mean relative difference methods predicted point-scale SWCs within and without the effective measuring depth, respectively. The mean effective measuring depth was 13.16 cm, ranging from 10.13 to 19.23 cm. Both land use type and soil structure played essential roles in regulating point scale SWC in the soil profile. Point-scale SWC in root zone can be predicted accurately (P © 2023 The Authors

Spatiotemporal variability in soil water storage (SWS) is controlled by different factors operating at various intensities and scales. The traditional Pearson's correlation analysis can be used to identify the linear correlations at the measurement scale only. In this study, wavelet coherency analysis was used to investigate scale-specific relationships between SWS and selected controlling factors. SWS of 135 sampling locations were calculated along a 1,340-m long sampling transect established in a typical catchment on the Loess Plateau, China. The selected controlling factors were soil saturated hydraulic conductivity (Ks); clay, silt, sand, and soil organic carbon (SOC) contents; elevation; and aboveground biomass (AGB). The spatial pattern of SWS measured in growing and nongrowing seasons and at different soil depths was similar. At all the sampling points, except for some locations in the depression area, SWS in the growing season was relatively greater than that in the nongrowing season. The influence of factors on SWS varied with scale, with soil Ks, clay and sand contents significantly correlated with SWS at large scales. Season and soil depth had no significant effect on scale-specific relationships between SWS and the controlling factors. The wavelet coherency analysis identified the type of correlation at different scales and locations. The outcomes of this study offer meaningful insights into the hydrological processes that can be observed in the Loess Plateau region and could have significant implications for future hydrological systems and water resources management.

粉垄耕作方式在土壤剖面上形成了独特的“粉垄层”,致使粉垄层内部的水文过程及其水分的植被吸收利用过程发生变化，进一步促使作物达到增产的效果。然而，粉垄耕作方式对土壤结构及其水文功能的影响尚不明晰。本项目以广西旱坡地广泛种植的甘蔗为研究对象，通过野外定位观测试验和室内分析实验，结合无人机技术、CT扫描技术和模拟人工降雨试验，分析粉垄层水分和土壤变化特征，阐明粉垄耕作对土壤性质和甘蔗生长影响的季节变化，评估了粉垄耕作下甘蔗对风灾的抗逆性。主要成果:(1)土壤粉垄耕作有利于土壤容重和土壤孔隙度(尤其在底层)改善，增加土壤储水量，从而优化了根系生长的土壤环境，增加了甘蔗生物量;(2)无人机可以迅速识别出倒伏的甘蔗面积和比例，与传统耕作相比，粉垄耕作条件下甘蔗倒伏的比例较低，表明粉垄耕作条件下植被粗壮的茎秆和发达的根系有助于抵御大风的不利影响;(3)粉垄耕作通过改变土壤理化性质和增加甘蔗生物量来改变侵蚀过程，可有效减少强降雨和作物成熟期的土壤侵蚀；粉垄耕作后小雨强下甘蔗生长初期具有较大的侵蚀量，有必要在此时期采取覆盖等措施减少土壤流失;(4)与传统耕作相比，粉垄耕作对土壤结构的影响更大，主要是因为其高速旋转的钻头可以将土壤粉碎成粉末状；粉垄耕作显著增加了土壤大孔隙度，这主要是通过增加土壤孔隙大小来实现的;(5)粉垄耕作显著改善了土壤孔隙结构，随着粉垄耕作年限的增加，其土壤大孔隙度逐渐减小，这种减小主要发生在底层土壤。研究结果表明粉垄耕作是一种高效的耕作措施，为雨养条件下广西旱坡地甘蔗地管理和调控提供科学依据。

Simulation of future climate changes, especially temperature and rainfall, is critical for water resource management, disaster mitigation, and agricultural development. Based on the category-wise indicator method, two preferred Global Climate Models (GCMs) for the Ishikari River basin (IRB), the socio-economic center of Hokkaido, Japan, were examined from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6). Climatic variables (maximum/minimum temperature and precipitation) were projected by the Statistical DownScaling Model (SDSM) under all shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4OS, and SSP5-8.5) in two phases: 2040-2069 (2040s) and 2070-2099 (2070s), with the period of 1985-2014 as the baseline. Predictors of SDSM were derived from CMIP6 GCMs and the reanalysis dataset NOAA-CIRES-DOE 20th Century Reanalysis V3 (20CRv3). Results showed that CMIP6 GCMs had a significant correlation with temperature measurements, but could not represent precipitation features in the IRB. The constructed SDSM could capture the characteristics of temperature and precipitation during the calibration (1985-1999) and validation (2000-2014) phases, respectively. The selected GCMs (MIROC6 and MRI-ESM-2.0) generated higher temperature and less rainfall in the forthcoming phases. The SSP-RCP scenarios had an apparent influence on temperature and precipitation. High-emission scenarios (i.e., SSP5-8.5) would project a higher temperature and lower rainfall than the low-emission scenarios (e.g., SSP1-1.9). Spatial-temporal analysis indicated that the northern part of the IRB is more likely to become warmer with heavier precipitation than the southern part in the future. Higher temperature and lower rainfall were projected throughout the late twenty-first century (2070s) than the mid-century (2040s) in the IRB. The findings of this study could be further used to predict the hydrological cycle and assess the ecosystem's sustainability. © 2023. The Author(s).

Soil water storage (SWS) dynamics are affected by many factors in semiarid regions such as the Loess Plateau. Spatiotemporal variation of SWS varies greatly with different landscape positions due to the rolling and fragmented terrain landscape of the Loess Plateau. However, information on the SWS and its temporal stability at different landscape positions is lacking. In this study, hillslope, dam land and gully landscape positions were selected in a typical small watershed in the Loess Plateau. A soil water observation belt with 28 sample locations was set up at each landscape position, and temporal stability analysis was conducted using SWS data of 12 sampling events. The results showed that the variation characteristics of profile SWS are influenced by landscape position. The persistence of spatial SWS patterns in different landscape positions increases with the increase of soil depth. On each landscape position, representative locations can accurately predict their SWS based on temporal stability analysis. Moreover, the stability of SWS at the representative locations was more important than its content during the prediction process. The dominant factors that lead to the difference of temporal stability characteristics in different landscape positions varied with the change in soil depths. The results provide an important basis for vegetation reconstruction and water management in small Loess Plateau watersheds.

Knowledge of spatiotemporal dynamics of soil water storage (SWS) is essential for hydrological modeling and vegetation restoration in semi-arid areas. However, characterizing the temporal stability of SWS at a regional scale requires time-consuming and labor-intensive manual sampling. Moreover, the influence of soil depth on temporal stability of SWS is not systematic. This study aimed to investigate the influences of sampling frequency and soil depth on SWS and SWS temporal stability. We measured soil moisture at 20-cm intervals in the soil profiles to a depth of 3 m using a neutron probe at 135 locations along a 1340-m long transect on 14 dates from 2012 to 2013. Results showed that sampling frequency did not influence the mean SWS (P < 0.05), while sampling frequency significantly affected temporal stability characteristics including Spearman's rank correlation coefficient (r(s)), standard deviation of mean relative difference (SDRD), the number of locations with SDRD < 5%, and the representative locations. Temporal stability of SWS increased with the increasing soil thickness and depth, which increases the possibility of the number of representative locations in deep soil. Although the mean SWSs of all soil depths can be predicted accurately at each sampling frequency, the prediction accuracy improved when sampling frequency was reducing. The values of R-2 ranged from 0.769 to 0.978 at 15-day sampling frequency, and from 0.987 to 0.998 at 45-day sampling frequency. Soil moisture stability may be more important than the soil water regime during prediction of soil moisture. These findings can provide guidelines for optimizing soil moisture sampling strategies and benefit management of water resources in semiarid watershed.