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This study proposes a hydrological drought index, the standardized wetness index (SWI), by combining the structure of the Standardized Precipitation-Evapotranspiration Index and actual-evaporation-based residual water-energy ratio, in which actual evaporation is estimated using the Budyko hypothesis. The SWI requires three parameters-precipitation, potential evaporation, and parameter [ABSTRACT FROM AUTHOR]

As one of the largest karst regions in the world, southwest China is experiencing severe soil erosion due to its special geological conditions, inappropriate land use, and lower soil loss tolerance. Knowledge and accurate estimations of changes in sediment discharge rates is important for finding potential measures to effectively control sediment delivery. This study investigated temporal variation in monthly sediment discharge ( SD ), and developed sediment rating curves and state-space model to estimate SD . Monthly water discharge, SD , precipitation, potential evapotranspiration, and normalized differential vegetation index during 2003–2015 collected from a typical karst catchment of Yujiang River were analyzed in present study. A Mann-Kendal test and Morlet wavelet analysis were employed to detect the changes in SD . Results indicated that a decreasing trend was observed in sediment discharge at monthly and annual scale. The water and sediment discharge both had a significant 1-year period, implying that water discharge has substantial influence on SD . The best state-space model using water discharge was a simple but effective model, accounting for 99% of the variation in SD . The sediment rating curves, however, represented only 78% of the variation in SD . This study provides an insight into the possibility of accurate estimation of SD only using water discharge with state-space model approach. State-space model is recommended as an effective approach for quantifying the temporal relationships between SD and its driving factors in karst regions of southwest China. [ABSTRACT FROM AUTHOR]

Southwest China represents one of the largest continuous karst regions in the world. It is estimated that around 1.7 million people are heavily dependent on water derived from karst springs in southwest China. However, there is a limited amount of water supply in this region. Moreover, there is not enough information on temporal patterns of spring discharge in the area. In this context, it is essential to accurately predict spring discharge, as well as understand karst hydrological processes in a thorough manner, so that water shortages in this area could be predicted and managed efficiently. The objectives of this study were to determine the primary factors that govern spring discharge patterns and to develop a state-space model to predict spring discharge. Spring discharge, precipitation (PT), relative humidity (RD), water temperature (WD), and electrical conductivity (EC) were the variables analyzed in the present work, and they were monitored at two different locations (referred to as karst springs A and B, respectively, in this paper) in a karst catchment area in southwest China from May to November 2015. Results showed that a state-space model using any combinations of variables outperformed a classical linear regression, a back-propagation artificial neural network model, and a least square support vector machine in modeling spring discharge time series for karst spring A. The best state-space model was obtained by using PT and RD, which accounted for 99.9% of the total variation in spring discharge. This model was then applied to an independent data set obtained from karst spring B, and it provided accurate spring discharge estimates. Therefore, state-space modeling was a useful tool for predicting spring discharge in karst regions in southwest China, and this modeling procedure may help researchers to obtain accurate results in other karst regions. [ABSTRACT FROM AUTHOR]

Abstract Spatial distribution of soil moisture (SM) is a prerequisite for research and management of agriculture and ecology. However, it is still a challenge to retrieve SM data in highly heterogeneous landscapes. By investigating environmental factors (soil, vegetation and topography) and comparing different remote sensing sources (Landsat-8, Radarsat-2, ASTER Global Digital Elevation Model (DEM) V002 (ASTGTM2), unmanned aerial vehicle (UAV)) for karst mountainous catchments of southwest China, this study identified key controlling factors on the spatial distribution of SM and built a remote sensing model for SM estimation in highly heterogeneous landscapes. Results showed that vegetation type (35.7%), aspect (7.7%), height index (4.2%), soil bulk density (3.3%), soil total nitrogen (3.1%), aspect interact with vegetation type (3.4%) and soil total phosphorous (1.3%) totally explained 58.8% of the SM variability. The correlations between SM and topographic derivatives varied with DEM resolutions (1–50 m), and generally reached their highest values at 7 m for height index, slope gradient, and aspect, 16 m for flow accumulation and topographic wetness index, and 43 m for curvature. Partial least-squares regression analysis showed that optical and infrared bands from Landsat-8 and topographic derivatives from UAV photogrammetry DEM were more strongly correlated with SM than other datasets. An empirical model (SM = 9.27 ∗ 10−2 HI − 1.82 ∗ 10−5 B 5 + 0.519) with only height index and B5 band from Landsat-8 as inputs is proposed, as it shows acceptable performance (R2 = 0.36; RMSE = 0.076). The results of this study provide useful information for SM remote sensing in karst mountainous area and similar heterogeneous landscapes. Highlights • Vegetation is the major controlling factor for SM in karst region. • Correlation between topographic derivatives and SM varied with resolutions of DEMs. • UAV photogrammetry showed great potential for hydrological research. [ABSTRACT FROM AUTHOR]

Abstract Vegetation restoration was implemented to control soil erosion in the karst regions of southwest China. It is essential to assess the soil function and quality scientifically during this process and to adopt suitable management practices for this area. However, few studies have been conducted to comprehensively evaluate the effect of vegetation restoration on soil quality in this severely eroded karst area. By taking 302 soil samples from 11 vegetation types, this study investigated the influence of different types of vegetation restoration on soil quality using an integrated soil quality index (SQI) and a generalized linear model (GLM). Vegetation types had significant effects on soil properties and thus on soil quality. SQI was developed by using TN, TP, TK, AP, and clay content; TN had highest weighting values (0.58), which indicated that it contributed the most to final SQI. The highest and lowest SQI values were observed for primary forest and cropland, respectively. Overall, vegetation restoration (e.g., natural restoration, artificial forests and artificial grassland) improved soil quality significantly. A GLM model explained 73.20% of the total variation in SQI, and vegetation type explained the largest proportion (46.39%) of the variation, which implies that the vegetation restoration practices can greatly enhance the soil quality in karst landscapes of southwest China. The results of this study may be used to improve implication of ecological restoration and management in degraded regions. Graphical abstract Unlabelled Image Highlights • TN, TP, TK, AP, and clay content were representative indicators for soil quality index (SQI). • SQI of any vegetation restoration type was significantly greater than that of cropland. • Vegetation type is the dominant factor influencing SQI. [ABSTRACT FROM AUTHOR]

Quantifying the impacts of climate and human activities on water and sediment discharge has become a central topic in climate and hydrologic research. This issue, however, has so far received little attention in karst regions around the world. Seven karst catchments located in southwest China were chosen to explore water and sediment discharge responses to different driving factors during the period from the 1950s to 2011. The non-parametric Mann-Kendall test was used to detect both the trends and abrupt changes in water and sediment discharge. The double mass curve method was used to quantify the effects of climate and human activities on water and sediment discharge. Results indicated that the annual water discharge showed a decreasing trend in all catchments (−0.21 to −3.68 × 10 8 m 3 yr −1 ), and the sediment discharge exhibited a significant decreasing trend (−7 to −101 × 10 4 t yr −1 ) for six out of the seven catchments. A rapid decline (abrupt change) in sediment discharge occurred since 2000 for all except Liujiang catchment where the sediment discharge has a slight increase since 1983 as no large dams were constructed in this catchment. Specifically, the magnitude of reduction in sediment discharge (%) significantly increases with the extent of flow regulation as measured by the ratio of the area upstream the dam to the total catchment area for the seven catchments ( R 2 = 0.98, P < 0.01). This study demonstrated that water discharge was mainly influenced by precipitation, while sediment discharge was mainly influenced by human activities (relative contribution 70–111%, regardless of whether the effect is negative or positive). Ecological restoration played somehow important roles in the decrease in sediment discharge (negative relationships of sediment discharge with the Normalized Differential Vegetation Index (NDVI)), but dam construction was likely to be the principal cause of the significant decrease in sediment discharge. This study is of use for better catchment management in karst regions in southwest of China. [ABSTRACT FROM AUTHOR]