Hydrological response to land use and land cover changes in a sub-watershed of West Liaohe River Basin,China

In recent years, the streamflow of the Laohahe Basin in China showed a dramatic decrease during the rainy season as a result of climate change and/or human activities. The objective of this work was to document significant streamflow changes caused by land use and land cover(LULC) changes and to quantify the impacts of the observed changes in Laohahe Basin. In the study area, the observed streamflow has been influenced by LULC changes, dams, and irrigation from rivers, industry, livestock and human consumption. Most importantly, the growth of population and gross domestic product(GDP) accompanied by the growth in industrial and agricultural activities, which led to LULC changes with increased residential land and cropland and decreased grassland since 2000s. Statistical methods and Variable Infiltration Capacity(VIC) hydrological model were used to estimate the effects of climate change and LULC changes on streamflow and evaportranspiration(ET). First, the streamflow data of the study area were divided into three sub-periods according to the Pettitt test. The hydrological process was then simulated by VIC model from 1964 to 2009. Furthermore, we compared the simulated results based on land use scenarios in 1989, 1999 and 2007, respectively for exploring the effect of LULC changes on the spatio-temporal distribution of streamflow and ET in the Laohahe Basin. The results suggest that, accompanied with climate change, the LULC changes and human water consumption appeared to be the most likely factors contributing to the significant reduction in streamflow in the Laohahe Basin by 64% from1999 to 2009.

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.