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Summary Karst landform represents about 10% of the continental area and plays key roles in water supplies for almost a quarter of the global population. Knowledge of ecohydrological responses of karst landform to climate change is critical for both water resources management and ecological protection in these regions. This study investigated the effects of karst landform on the elasticity of actual evapotranspiration (derived by the Budyko equation), estimated the contribution of climate change and evaluated the implications, on the basis of 13 typical catchments that have different karst landform coverages in southwest China. Catchment properties, including the vegetation coverage, portion of karst landform (POK), drainage area, surface roughness, mean topographic wetness index, mean slope, and mean aspect, were selected to test the influencing factors for the elasticity of actual evapotranspiration. Results indicate that POK is the most influencing factor for the elasticity of actual evapotranspiration in this region. Moreover, the actual evapotranspiration in karst catchments is more sensitive to precipitation change and less sensitive to the potential evapotranspiration change than that in the non-karst catchments. On the other hand, the contribution of climate change to actual evapotranspiration was generally negative in this region. Furthermore, relatively large negative contributions mainly occurred in the karst-dominated catchments, suggesting that the karst catchments were exposed to higher degradation stress brought by the climate change than that in non-karst catchments. [ABSTRACT FROM AUTHOR]

• Introducing climate (CS) and vegetation seasonality (VS) into the Budyko framework. • CS and VS jointly contribute more than the changes in P and ET 0 in water partitioning. • In average the contributions of VS and CS in water partitioning are equally important. Climate and vegetation changes are main factors influencing water partitioning. However, their impacts on water partitioning are still not well understood. To revisit the interactions among climate, vegetation and water partitioning, this study introduces the climate seasonality (CS) and vegetation seasonality (VS) into the Budyko framework, by using a statistical approach combining the three-parameter Log-logistic probability distribution and multiple linear regression, based on the monthly 〈 P - ET 0 〉 (precipitation (P) minus potential evapotranspiration ( ET 0)) and Normalized Difference Vegetation Index (NDVI). This method is then validated and applied in 86 global catchments to quantify the contribution of climate and vegetation changes to evapotranspiration (ET). Results show that this model can predict well (mean R 2 = 0.81 ± 0.05, P < 0.05) the parameter n , a parameter reflecting the interactions between land surface and atmosphere in the Budyko framework. More importantly, CS and VS jointly control the variation in parameter n , and in average jointly contribute more (mean relative contribution of 57.1 ± 2.3 %) than the changes in P and ET 0 (mean of 42.9 ± 2.3%) to ET. These indicate that CS and VS are at least as important as the changes in P and ET 0 in affecting water balance. Furthermore, the VS contributes slightly lower (mean of 24.9 ± 0.3%) than the CS does (mean of 32.2 ± 2.3%), implying that CS and VS play comparable roles in water partitioning. These findings would be valuable for water resources management and assessing the impacts of climate and land surface changes on water balance in a gradually changing environment. [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]

• GPP and ET were positively and linearly correlated to Rn and VPD in a karst forest. • WUE was negatively correlated to meteorological factors but positively to Bowen ratio. • WUE was not proportional to 1/VPD or 1/VPD0.5 but interactively responded to drivers. Water-use efficiency (WUE) is a key parameter that measures the balance between carbon gain and water loss in an ecosystem. Achieving better understanding of how WUE responds to environmental drivers is critical for revealing ecohydrological processes and mechanisms, and for biophysical modelling to investigate the interactions between vegetation and the changing environment. Karst landforms cover ∼10 % of the Earth's land surface; however, to date few studies have focused on the behaviours of WUE in karstic ecosystems. To fill these knowledge gaps, a two-year experiment was conducted to investigate the WUE and its drivers in a climax karst forest in southwestern China. Results showed that both the GPP (Gross Primary Production) and ET (evapotranspiration) showed exponential relationships to Ta (air temperature, P < 0.05), but linear relationships to Rn (net radiation, R 2 = 0.38 (GPP) and 0.78 (ET), P < 0.05) and VPD (water vapour pressure deficit, R 2 = 0.36 (GPP) and 0.28 (ET), P < 0.05), being quite different from those in other ecosystems. The reasons would be the different habits of the karstic plants, and the facts that the root zone water availability rarely dropped to low levels due to the frequent precipitation. The WUE, with the mean of 8.0 ± 4.8 mg CO 2 g−1 H 2 O, exhibited negative relationships to VPD (R 2 = 0.23, P < 0.05), Rn (R 2 = 0.28, P < 0.05), and Ta (air temperature, R 2 = 0.42, P < 0.05), and a positive linear relationship to the Bowen ratio (R 2 = 0.17, P < 0.05). Besides these, results also showed that the effects of a specific driver (e.g., VPD) on WUE was affected obviously by other factors (e.g., Ta, Rn), indicating that whether a driver is important or not to WUE, depends strongly on the environmental conditions. As the WUE-response formulas are often used to investigate and model the interactions between vegetation and environment, these findings suggested that the modelers should caution the versatility of the WUE-response formulas. [ABSTRACT FROM AUTHOR]