Global warming has affected both natural and artificial systems across continents, bringing a lot of ecohydrological crises to many countries (Gitay et al., 2002; Tirado et al., 2010; Thornton et al., 2014). The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) concluded that global average surface air temperature increased by 0.61 ∘C in 1986–2005 compared to preindustrial periods (IPCC, 2014a). In order to mitigate global warming, the Conference of the Parties of the United Nations Framework Convention on Climate Change (UNFCCC) emphasized in the Paris Agreement that the increase in global average temperature should be controlled within 2 ∘C above preindustrial levels, and further efforts should be made to limit it below 1.5 ∘C. However, whether the temperature controlling goal can be reached is still unknown, with much difficulty under current emission conditions (Peters et al., 2012). In addition, a specific warming level such as 2 ∘C increase would be too high for many regions and countries (James et al., 2017; Rogelj et al., 2015). Therefore, it is necessary to assess changes in the regional hydrological cycle and extremes under 1.5, 2 and even 3 ∘C global warming.

Global warming is mainly caused by greenhouse gases emissions and has a profound influence on hydrosphere and ecosphere (Barnett et al., 2005; Vorosmarty et al., 2000). It alters the hydrological cycle both directly (e.g., influences precipitation and evapotranspiration) and indirectly (e.g., influences plant growth and related hydrological processes) at global (Zhu et al., 2016; McVicar et al., 2012) and local scales (Tang et al., 2013; Zheng et al., 2009; Zhang et al., 2008). Besides affecting the mean states of the hydrological conditions, global warming also intensifies hydrological extremes significantly, such as droughts which were regarded as naturally occurring events when water (precipitation, or streamflow, etc.) is significantly below normal over a period of time (Van Loon et al., 2016; Dai, 2011). Among different types of droughts, hydrological droughts focus on the decrease in the availability of water resources, e.g., surface and/or ground water (Lorenzo-Lacruz et al., 2013). Many researchers paid attention to the historical changes, future evolutions and uncertainties, and causing factors for hydrological droughts (Chang et al., 2016; Kormos et al., 2016; Orlowsky and Seneviratne, 2013; Parajka et al., 2016; Perez et al., 2011; Prudhomme et al., 2014; Van Loon and Laaha, 2015; Wanders and Wada, 2015; Yuan et al., 2017). Most drought projection studies focused on the future changes over a fixed time period (e.g., late 21st century), but recent studies pointed out the importance on hydrological drought evolution at certain warming levels (Roudier et al., 2016; Marx et al., 2018) given the aim of the Paris Agreement. Moreover, the changes in characteristics (e.g., frequency, duration, severity) of hydrological drought events at specific warming levels received less attention. The projection of these drought characteristics could provide more relevant guidelines for policymakers on implementing adaptation strategies.

In the past 5 decades, a significant decrease in channel discharge was observed in the middle reaches of the Yellow River basin over northern China (Yuan et al., 2018; Zhao et al., 2014), leading to an intensified water scarcity in this populated area. In this study, we take a semiarid watershed, the Wudinghe in the middle reaches of the Yellow River basin, as a test bed, aiming to solve the following questions: (1) how do hydrological drought characteristics change at different warming levels over the Wudinghe watershed? (2) What are the causes for the hydrological drought change? (3) What are the contributions of uncertainties from different sources (e.g., climate and land surface hydrological models, representative concentration pathway (RCP) scenarios, and internal variability)?

In this study, the Wudinghe watershed was chosen for hydrological drought analysis. As one of the largest subbasins of the Yellow River basin, the Wudinghe watershed is located in the Loess Plateau and has a drainage area of 30 261 km2 with Baijiachuan hydrological station as the watershed outlet (Fig. 1). It has a semiarid climate with long-term (1956–2010) annual mean precipitation of 356 mm and runoff of 39 mm, resulting in a runoff coefficient of 0.11 (Jiao et al., 2017). Most of the rainfall events are concentrated in summer (June to September) with a large possibility of heavy rains (Mo et al., 2009). Located in the transition zone between cropland–grassland and desert–shrub, the northwest part of the Wudinghe watershed is dominated by sandy soil, while the major soil type for the southeast part is loess soil. During recent decades, the Wudinghe watershed has experienced a significant streamflow decrease (Yuan et al., 2018; Zhao et al., 2014) and suffered from serious water resource scarcity because of climate change, vegetation degradation and human water consumption (Xiao, 2014; Xu, 2011).

The Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation model (GCM) simulations for historical experiments and future projections formed the science basis for the IPCC AR5 reports (IPCC, 2014b; Taylor et al., 2012). In this study, we chose eight CMIP5 GCMs for historical (1961–2005) and future (2006–2099) drought analysis, as they provided daily simulations under all four RCP scenarios (i.e. RCP2.6, 4.5, 6.0 and 8.5). Table 1 listed the details of GCMs used in this paper, where historical simulations included all anthropogenic and natural forcings (ALL). Because of the deficiency in GCM precipitation and runoff simulations, we used the corrected meteorological forcing data from CMIP5 climate models to drive a high-resolution land surface hydrological model to simulate runoff and streamflow.

All CMIP5 simulations were bias corrected before being used as land surface model input. After interpolating CMIP5 simulations and China Meteorological Administration (CMA) station observations to the same resolution (0.01∘ in this study), a modified correction method (Li et al., 2010) based on widely used quantile mapping (Wood et al., 2002; Yuan et al., 2015) was applied to adjust CMIP5/ALL historical simulations and CMIP5/RCP future simulations for each model at each grid cell separately. The bias-corrected daily precipitation and temperature were then further temporally disaggregated to a 6 h interval based on the diurnal cycle information from CRUNCEP 6-hourly dataset (https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/atm/datm7/, last access: 4 September 2016). Other 6-hourly meteorological forcings, i.e., incident solar radiation, air pressure, specific humidity and wind speed, were directly taken from CRUNCEP dataset. Please see Appendix A for details.

To explore the reason for less frequent but more severe hydrological droughts, we compared the differences in monthly precipitation; evapotranspiration; total, surface and subsurface runoff; and streamflow between the baseline period and periods reaching 1.5, 2 and 3 ∘C warming levels. Standardized indices for these hydrological variables were used to remove seasonality from monthly time series, and mean values and variabilities of these indices were chosen as indicators.

Figure 7 shows that mean values increase as temperature increases for all standardized hydrological indices, showing a wetter hydroclimate in the future, with more precipitation, evapotranspiration, runoff and streamflow (Fig. 7a). However, variabilities for the standardized indices in the future are much higher than those during baseline period, indicating larger fluctuations and higher chance of extreme droughts and floods at all warming levels (Fig. 7b). For extreme drought events (with an SSI < -1.3, representing a dry condition with a probability of 10%), the ensemble mean amounts of drought events are 4.3, 3.1 and 3.7 at 1.5, 2 and 3 ∘C warming levels, which are much larger than the baseline period with 0.9 (not shown). Focusing on the gaps between baseline and future periods, it is clear that the differences in both evapotranspiration and runoff are larger than those of precipitation for mean values and standard deviations, suggesting the water redistribution through complicated hydrological processes. The increase in the mean value of runoff and consequently streamflow mainly comes from the increase in subsurface runoff. As hydrological drought defined in this paper is based on monthly SSI series, increases in both mean value and variability in precipitation and evapotranspiration indicate a period with less frequent but more severe hydrological drought events.

Another issue is the reliability of results considering large differences among CMIP5 models. Figure 8 shows the uncertainty fractions contributed from internal variability, climate models and RCP scenarios based on multi-model and multi-scenario ensemble projections of temperature, precipitation, streamflow and drought frequency. Uncertainty in temperature projection is mainly contributed by climate models before 2052, and it is then taken over by RCP scenarios. Internal variability contributes to less than 1.5 % of the uncertainty for the temperature projection (Fig. 8a). For precipitation projection, climate models account for a large proportion of uncertainty throughout the century. The internal variability contributes to larger uncertainty than RCP scenarios until the second half of the 21st century (Fig. 8b). Similar to precipitation, a major source of uncertainty for the projections of streamflow and hydrological drought frequency is the climate and land surface hydrological models, while the impacts of both internal variability and RCP scenarios are further weakened (Fig. 8c and d).

Generally for all variables except temperature, GCMs and land surface hydrological model account for over 80 % of total uncertainties, while internal variability contributes to a comparable or larger proportion than RCP scenarios. RCP scenario only contributes to around 5 % of the uncertainties in the projections of streamflow and hydrological drought frequency. These results indicate that the improvement in GCM-simulated precipitation would largely narrow the uncertainties for future projections of hydrological droughts. Besides, previous studies (Marx et al., 2018; Samaniego et al., 2018) have shown that uncertainties contributed from land surface hydrological models can be comparable to that from GCMs, indicating the importance of introducing multiple land surface hydrological models into the analysis of uncertainty, and the significance of exploring more suitable methods in further studies.

There are also some issues for further investigations. As shown in Fig. 3, GCM historical simulations underestimate the increasing trend in temperature and decreasing trend in precipitation and results in underestimations of hydrological drying trends. Although the quantile mapping method used in this study is able to remove the biases in GCM simulations (e.g., mean value, variance), the underestimation of trends could not be corrected. An alternative method is to use regional climate models for dynamical downscaling, which would be useful if regional forcings (e.g., topography, land use change, aerosol emission) are strong. Another issue is the spatially varied warming rates. IPCC AR5 reported (IPCC, 2014c) that global warming for the last 20 years compared to the preindustrial period are 0.3–1.7 ∘C (RCP2.6), 1.1–2.6 ∘C (RCP4.5), 1.4–3.1 ∘C (RCP6.0) and 2.6–4.8 ∘C (RCP8.5). However, temperature increases vary a lot for different regions. For instance, temperature rises faster in high-altitude (Kraaijenbrink et al., 2017) and polar regions (Bromwich et al., 2013), where the rate of regional warming could be 3 times that of global warming. Actually, reaching periods for regional warming thresholds in the Wudinghe watershed are earlier than the global ones (not shown here), which suggest that the regional warming would be more severe at specific global warming levels.

In this paper, we bias-corrected future projections of meteorological forcings from eight CMIP5 GCM simulations under four RCP scenarios to drive a newly developed land surface hydrological model, CLM-GBHM, to project changes in streamflow and hydrological drought characteristics over the Wudinghe watershed. After determining the time periods reaching 1.5, 2 and 3 ∘C global warming levels for each GCM–RCP combination, we focused on the changes in regional hydrological drought characteristics at all warming levels. Moreover, projection uncertainties from different sources were separated and analyzed. The main conclusions are listed as follows:

With CMIP5 GCM simulations as forcing data, the model ensemble mean hindcast can reproduce the significant decreasing trend of streamflow and increasing trend of hydrological drought frequency in the historical period (1961–2005), but the drying trend is underestimated because of GCM uncertainties. Streamflow increases and hydrological drought frequency decreases in the future under all RCP scenarios.