The Yellow River is the largest sandy river in the world with an annual transport capacity of approximately 1.6 billion tons. There is growing concern of water availability and soil erosion in this basin. Understanding the runoff regime and sediment transport in the upper Yellow River basin (UYRB), especially in the context of climate change, is crucial for sustainable water resource management and soil–water conservation. Herein, the authors attempt to quantify the response of water and sediment discharge to climate change in the UYRB. The authors employed a distributed hydrological model, i.e., the Soil and Water Assessment Tool (SWAT), to simulate the runoff and sediment load under different scenarios, including climate change and detrended climate conditions. To predict the future trend, the authors designed scenarios based on downscaled forcing data from three global climate models (GCMs) which are with respect to the representative concentration pathway 8.5 (RCP8.5) of the Coupled Model Intercomparison Project Phase 5 (CMIP5). In response to the decrease in precipitation and increase in temperature from 1966 to 2009, annual runoff and sediment load have significantly decreased at a rate of $-11.6\mathrm{mm}/\mathrm{decade}$ and $-1.3\mathrm{Mt}/\mathrm{decade}$ , respectively. Precipitation plays a dominant role in reshaping these trends, with a contribution more than four times greater than that of temperature. Moreover, sediment yields may decline in the near future (2049–2064), especially during late summer and early fall. Runoff change holds substantial uncertainty owing to the different projection based on the GCMs.

The hydrological regime in arid and semi-arid regions is quite sensitive to climate and land cover changes (LCC). The Three-North region (TNR) in China experiences diverse climate conditions, from arid to humid zones. In this region, substantial LCC has occurred over the past decades due to ecological restoration programs and urban expansion. At a regional scale, the hydrological effects of LCC have been demonstrated to be less observable than the effects of climate change, but it is unclear whether or not the effects of LCC may be intensified by future climate conditions. In this study, we employed remote sensing datasets and a macro-scale hydrological modeling to identify the dependence of the future hydrological regime of the TNR on past LCC. The hydrological effects over the period from 2020–2099 were evaluated based on a Representative Concentration Pathway climate scenario. The results indicated that the forest area increased in the northwest (11,691 km²) and the north (69 km²) of China but declined in the northeast (30,042 km²) over the past three decades. Moreover, the urban area has expanded by 1.3% in the TNR. Under the future climate condition, the hydrological regime will be influenced significantly by LCC. Those changes from 1986 to 2015 may alter the future hydrological cycle mainly by promoting runoff (3.24 mm/year) and decreasing evapotranspiration (3.23 mm/year) over the whole region. The spatial distribution of the effects may be extremely uneven: the effects in humid areas would be stronger than those in other areas. Besides, with rising temperatures and precipitation from 2020 to 2099, the LCC may heighten the risk of dryland expansion and flooding more than climate change alone. Despite uncertainties in the datasets and methods, the regional-scale hydrological model provides new insights into the extended impacts of ecological restoration and urbanization on the hydrological regime of the TNR.

Watershed characteristics such as patterns of land use and land cover (LULC), soil structure and river systems, have substantially changed due to natural and anthropogenic factors. To adapt hydrological models to the changing characteristics of watersheds, one of the feasible strategies is to explicitly estimate the changed parameters. However, few approaches have been dedicated to these non-stationary conditions. In this study, we employ an ensemble Kalman filter (EnKF) technique with a constrained parameter evolution scheme to trace the parameter changes. This technique is coupled to a rainfall-runoff model, i.e., the Xinanjiang (XAJ) model. In addition to a stationary condition, we designed three typical non-stationary conditions, including sudden, gradual and rotational changes with respect to two behavioral parameters of the XAJ. Synthetic experiments demonstrated that the EnKF-based method can trace the three types of parameter changes in real time. This method shows robust performance even for the scenarios of high-level uncertainties within rainfall input, modeling and observations, and it holds an implication for detecting changes in watershed characteristics. Coupling this method with a rainfall-runoff model is useful to adapt the model to non-stationary conditions, thereby improving flood simulations and predictions.