Qin Ju

Impact of climate change on hydrological extremes in the Yangtze River Basin, China

The recent (1970–1999) and future (2070–2099) climates under the SRES A1B scenario, simulated by the regional climate model RegCM4.0 driven with lateral boundary conditions from the ECHAM5 general circulation model, are utilized to force a large-scale hydrological model for assessing the hydrological response to climate changes in the Yangtze River Basin, China. The variable infiltration capacity model (VIC) is utilized to simulate various hydrological components for examining the changes in streamflow at various locations throughout the Yangtze River Basin. In the end of the twenty-first century, most of the Yangtze River Basin stands out as “hotspots” of climate change in China, with an annual temperature increase of approximately 3.5xa0°C, an increase of annual precipitation in North and a decrease in South. Runoff in the upper reach of Yangtze River is projected to increase throughout the year in the future, especially in spring when the increase will be approximately 30xa0%. Runoff from the catchments in the northern part of Yangtze River will increase by approximately 10xa0%, whereas that in the southern part will decrease, especially in the dry season, following precipitation changes. The frequency of extreme floods at three mainstream stations (Cuntan, Yichang, and Datong) is projected to increase significantly. The original extreme floods with return periods of 50, 20, and 10xa0years will change into floods with return periods of no more than 20, 10, and 5xa0years. The projected increase in extreme floods will have significant impacts on water resources management and flood control systems in the Yangtze River Basin.

Spatial and Temporal Scale Effect in Simulating Hydrologic Processes in a Watershed

AbstractSmall-scale variations of hydrologic processes both in space and time have a significant impact on the simulation of hydrologic processes at different scales in the watershed. The objectives of this study were to investigate: (1)xa0how the spatial and temporal grid scales affect the results of hydrologic process simulations; and (2)xa0how the variability of input driving parameters (e.g.,xa0elevation and precipitation intensity) at different grid scales is related to the simulated discharge response. A hydrologic model system (HMS) was used to simulate hydrologic processes at different spatial and temporal grid scales in a small watershed. The spatial distributions of various hydrologic properties, such as soil and land-use/land-cover data, were included in the simulations along with a digital elevation model (DEM) and precipitation data. 5-min precipitation records collected from four gauge stations within the watershed were used to drive fifty model simulations, which were designed to examine the effe...

Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter

Accurate soil moisture information is useful in agricultural practice, weather forecasting, and various hydrological applications. Although land surface modeling provides a viable approach to simulating soil moisture, many factors such as errors in the precipitation can affect the accuracy of soil moisture simulations. This paper examined how precipitation rate and evapotranspiration rate affect the accuracy of soil moisture simulation using simple biosphere model with and without data assimilation through ensemble Kalman filter (EnKF). For each of the two variables, seven levels of relative errors (−20, −10, −5, 0, 5, 10 and 20xa0%) were introduced independently, thus a total of 49 combined cases were investigated. Observations from Wudaogou Hydrology Experimental site in the Huaihe River basin, China, were used to drive and verify the simulations. Results indicate that when the error of precipitation rate is within 10xa0% of the observations, the resulting error in soil moisture simulations is less significant and manageable, thus the simulated precipitation can be used to drive hydrological models in poorly gauged catchments when observations are not available. When the error of evapotranspiration rate is within 20xa0% of the observations, which is partly caused by model structural and parameterization errors, its impact on soil moisture simulation is less significant and can be acceptable. This study also demonstrated that the EnKF can perform consistently well to improve soil moisture simulation with less sensitivity to precipitation errors.

Evaluating Ensemble Kalman, Particle, and Ensemble Particle Filters through Soil Temperature Prediction

AbstractData assimilation is a useful tool in hydrologic and agricultural application studies because of its ability to produce predicted results with high accuracy. However, different data-assimilation methods have different performances for a given application. Although the popular ensemble Kalman filter (EnKF) performs well with Gaussian distribution, the error is difficult to conform to the Gaussian distribution. To take advantage of the EnKF, this study presents a new data-assimilation method, ensemble particle filter (EnPF), which is an integration of the EnKF and the particle filter (PF). This new method was evaluated in comparison with two existing methods (EnKF and PF) through soil temperature predictions. The simple biosphere model (SiB2) and the filters were assessed with observations from the Wudaogou experimental area in the Huaihe River basin, China. Results show that when the time interval increases adequately, all the simulated or assimilated results improve significantly. All of these fil...

One‐dimensional soil temperature simulation with Common Land Model by assimilating in situ observations and MODIS LST with the ensemble particle filter

Soil temperature plays an important role in hydrology, agriculture, and meteorology. In order to improve the accuracy of soil temperature simulation, a soil temperature data assimilation system was developed based on the Ensemble Particle Filter (EnPF) and the Common Land Model (CLM), and then applied in the Walnut Gulch Experimental Watershed (WGEW) in Arizona, United States. Surface soil temperature in situ observations and Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) data were assimilated into the system. In this study, four different assimilation experiments were conducted: (1) assimilating in situ observations of instantaneous surface soil temperature each hour, (2) assimilating in situ observations of instantaneous surface soil temperature once per day, (3) assimilating verified MODIS LST once per day, and (4) assimilating original MODIS LST once per day. These four experiments reflect a transition from high-quality and more frequent in situ observations to lower quality and less frequent remote sensing data in the data assimilation system. The results from these four experiments show that the assimilated results are better than the simulated results without assimilation at all layers except the bottom layer, while the superiority gradually diminishes as the quality and frequency of the observations decrease. This demonstrates that remote sensing data can be assimilated using the ensemble particle filter in poorly gauged catchments to obtain highly accurate soil variables (e.g., soil moisture, soil temperature). Meanwhile, the results also demonstrate that the ensemble particle filter is effective in assimilating soil temperature observations to improve simulations, but the performance of the data assimilation method is affected by the frequency of assimilation and the quality of the input data.

Response of Hydrologic Processes to Future Climate Changes in the Yangtze River Basin

AbstractRecent climate changes have observable impacts on hydrologic processes and will further affect hydrologic systems in the future. The temperature and precipitation modeled with eight global circulation models (GCMs) (selected from 22 GCMs published in the Fourth Assessment of the Intergovernmental Panel on Climate Change) under three typical emission scenarios entitled A1B, A2, and B1 were evaluated in this study for future projections in the Yangtze River Basin, China. The artificial neural network model was used to assess the evolutional trend of hydrologic processes (e.g.,xa0streamflow) and the possibility of extreme floods in the Yangtze River Basin by using data generated by selected GCMs under future climate changes. The results indicate that the future annual streamflow tends to decrease in the Yangtze River Basin. The future average annual flow is reduced by 500u2009u2009m3/s compared with that of the historic record (1951–2005) observed at Yichang Hydrologic Station of the Middle Yangtze River, and ...

Climate Change Hotspots Identification in China through the CMIP5 Global Climate Model Ensemble

China is one of the countries vulnerable to adverse climate changes. The potential climate change hotspots in China throughout the 21st century are identified in this study by using a multimodel, multiscenario climate model ensemble that includes Phase Five of the Coupled Model Intercomparison Project (CMIP5) atmosphere-ocean general circulation models. Both high (RCP8.5) and low (RCP4.5) greenhouse gas emission trajectories are tested, and both the mean and extreme seasonal temperature and precipitation are considered in identifying regional climate change hotspots. Tarim basin and Tibetan Plateau in West China are identified as persistent regional climate change hotspots in both the RCP4.5 and RCP8.5 scenarios. The aggregate impacts of climate change increase throughout the 21st century and are more significant in RCP8.5 than in RCP4.5. Extreme hot event and mean temperature are two climate variables that greatly contribute to the hotspots calculation in all regions. The contribution of other climate variables exhibits a notable subregional variability. South China is identified as another hotspot based on the change of extreme dry event, especially in SON and DJF, which indicates that such event will frequently occur in the future. Our results can contribute to the designing of national and cross-national adaptation and mitigation policies.

Adaptivity of Budyko Hypothesis in Evaluating Interannual Variability of Watershed Water Balance in Northern China

AbstractThis study evaluates the performance of three Budyko-type equations (Fu’s equation, Turc-Pike’s equation, and Milly’s equation) in modeling annual evapotranspiration in 32 watersheds covering both humid and arid regions in Northern China. Daily meteorological data and monthly runoff data are used to calculate potential and actual evapotranspirations in the 32 watersheds. The results show that the Budyko-type equations are adaptive in predicting annual evapotranspiration over most of the watersheds, and Fu’s and Turc-Pike’s equations perform better than Milly’s. In addition, the validity of the framework by Koster and Suarez in predicting the evapotranspiration deviation ratio (EDR) (i.e.,xa0the ratio of the standard deviation of evapotranspiration to the standard deviation of rainfall) based on Fu’s and Ture-Pike’s equations is also examined. Given the unexpected Nash–Sutcliffe efficiency values (−0.915 and −1.026 in Fu’s and Ture-Pike’s, respectively), a linear one-variable model is employed to imp...