Xiaoduo Pan

Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China

The spatial resolution of general circulation models (GCMs) is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model (WRF) is a next-generation, fully compressible, Euler non-hydrostatic mesoscale forecast model with a run-time hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/1 h air temperatures by daily observed data of China Meteorological Administration (CMA) stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error (MBE) for each station is less than 2°C; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2°C, the R2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

Toward an improved data stewardship and service for environmental and ecological science data in West China

Abstract Sharing of scientific data can help scientific research to flourish and facilitate more widespread use of scientific data for the benefit of society. The Environmental and Ecological Science Data Center for West China (WestDC), sponsored by the National Natural Science Foundation of China (NSFC), aims to collect, manage, integrate, and disseminate environmental and ecological data from western China. It also aims to provide a long-term data service for multidisciplinary research within NSFCs “Environment and Ecology of West China Research Plan” (NSFC West Plan). An integrated platform has been developed by the WestDC, and this has the function of data sharing, acting as a knowledge repository. Major data sets developed by the WestDC include basic geographic data, the regionalization of global data set for China, scientific data for cold and arid regions in China, scientific data for the cryosphere in countries that neighbor China, data relating to the inland river basins in northwestern China, and data submitted by the NSFC West Plan projects. In compliance with the “full and open” data sharing policy, most data in the WestDC can be accessed online. Highlights include detailed data documentation, the integration of data with bibliographic knowledge, data publishing, and data reference.

Comparison of Downscaled Precipitation Data over a Mountainous Watershed: A Case Study in the Heihe River Basin

AbstractDevelopment of an accurate precipitation dataset is of primary importance for regional hydrological process studies and water resources management. Here, four regional precipitation products are evaluated for the Heihe River basin (HRB): 1) a spatially and temporally disaggregated Climate Prediction Center Merged Analysis of Precipitation (CMAP) at 0.25° spatial resolution (DCMAP); 2) a fusion product obtained by merging China Meteorological Administration station data and Tropical Rainfall Measuring Mission precipitation data at 0.1° spatial resolution supported by the Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (ITP-F); 3) a disaggregated CMAP downscaled by a statistical meteorological model tool at 1-km spatial resolution (DCMAP–MicroMet); and 4) a Weather Research and Forecasting (WRF) Model simulation with 5-km resolution (WRF-P). The validation metrics include spatial pattern, temporal pattern, error analysis with respect to observation data, and precipitation ev...

Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins

Endorheic basins around the world are suffering from water and ecosystem crisis. To pursue sustainable development, quantifying the hydrological cycle is fundamentally important. However, knowledge gaps exist in how climate change and human activities influence the hydrological cycle in endorheic basins. We used an integrated ecohydrological model, in combination with systematic observations, to analyze the hydrological cycle in the Heihe River Basin, a typical endorheic basin in arid region of China. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012. The results showed that climate warming, which has led to greater precipitation, snowmelt, glacier melt, and runoff, is a favorable factor in alleviating water scarcity. Human activities, including ecological water diversion, cropland expansion, and groundwater overexploitation, have both positive and negative effects. The natural oasis ecosystem has been restored considerably, but the overuse of water in midstream and the use of environmental flow for agriculture in downstream have exacerbated the water stress, resulting in unfavorable changes in surface-ground water interactions and raising concerns regarding how to fairly allocate water resources. Our results suggest that the water resource management in the region should be adjusted to adapt to a changing hydrological cycle, cropland area must be reduced, and the abstraction of groundwater must be controlled. To foster long-term benefits, water conflicts should be handled from a broad socioeconomic perspective. The findings can provide useful information on endorheic basins to policy makers and stakeholders around the world.

Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin

To obtain long term accurate high resolution precipitation for the Heihe River Basin (HRB), Weather Research and Forecasting (WRF) model simulations were performed using two different initial boundary conditions, with nine microphysical processes for different analysis parameterization schemes. High spatial-temporal precipitation was simulated from 2000 to 2013 and a suitable set of initial, boundary, and micro parameters for the HRB was evaluated from the Heihe Watershed Allied Telemetry Experimental Research project and Chinese Meteorological Administration data at hourly, daily, monthly, and annual time scales using various statistical indicators. It was found that annual precipitation has gradually increased over the HRB since 2000. Precipitation mostly occurs in summer and is higher in monsoon-influenced areas. High elevations experience winter snowfall. Precipitation is higher in the eastern upstream area than in the western upstream, area; however, the converse occurs in winter. Precipitation gradually increases with elevation from 1000 m to 4000 m, and the maximum precipitation occurs at the height of 3500–4000 m, then the precipitation slowly decreases with elevation from 4000 m to the top over the Qilian Mountains. Precipitation is scare and has a high temporal variation in the downstream area. Results are systematically validated using the in situ observations in this region and it was found that precipitation simulated by the WRF model using suitable physical configuration agrees well with the observation over the HRB at hourly, daily, monthly and yearly scales, as well as at spatial pattern. We also conclude that the dynamic downscaling using the WRF model is capable of producing high-resolution and reliable precipitation over complex mountainous areas and extremely arid environments. The downscaled data can meet the requirement of river basin scale hydrological modeling and water balance analysis.

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

Individually, ground-based, in situ observations, remote sensing, and regional climate modeling cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrains. Data assimilation techniques can be used to bridge the gap between observations and models by assimilating ground observations and remote sensing products into models to improve precipitation simulation and forecasting. However, only a small portion of satellite-retrieved precipitation products assimilation research has been implemented over complex terrains in an arid region. Here, we used the weather research and forecasting (WRF) model to assimilate two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM 3B42 and Fengyun-2D: FY-2D) using the 4D-Var data assimilation method for a typical inland river basin in northwest China’s arid region, the Heihe River Basin, where terrains are very complex. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly over regions with complex terrains.

Synthesis method for simulating snow distribution utilizing remotely sensed data for the Tibetan Plateau

Abstract The complex terrain, shallow snowpack, and cloudy conditions of the Tibetan Plateau (TP) can greatly affect the reliability of different remote sensing (RS) data, and available station data are scarce for simulating and validating the snow distribution. Aiming at these problems, we design a synthesis method for simulating the snow distribution in the TP where the snow is patchy and shallow in most regions. Different RS data are assimilated into the SnowModel, using the ensemble Kalman filter method. The station observations are used for the validation of assimilated snow depth. To avoid the scale effect during validation, we design a random sampling comparison method by constructing a subjunctive region near each station. For years 2000 to 2008, the root-mean-square error of the assimilated results are in the range [0.002 m, 0.008 m], and the range of Pearson product-moment correlation coefficients between the in situ observations and the assimilated results are in the range [0.61, 0.87]. The result suggests that the snow depletion curve is the most important parameter for the simulation of the snow distribution in ungauged regions, especially in the TP where the snow is patchy and shallow.

Simulation of Forest Evapotranspiration Using Time-Series Parameterization of the Surface Energy Balance System (SEBS) over the Qilian Mountains

We propose a long-term parameterization scheme for two critical parameters, zero-plane displacement height (d) and aerodynamic roughness length (z0m), that we further use in the Surface Energy Balance System (SEBS). A sensitivity analysis of SEBS indicated that these two parameters largely impact the estimated sensible heat and latent heat fluxes. First, we calibrated regression relationships between measured forest vertical parameters (Lorey’s height and the frontal area index (FAI)) and forest aboveground biomass (AGB). Next, we derived the interannual Lorey’s height and FAI values from our calibrated regression models and corresponding forest AGB dynamics that were converted from interannual carbon fluxes, as simulated from two incorporated ecological models and a 2009 forest basis map These dynamic forest vertical parameters, combined with refined eight-day Global LAnd Surface Satellite (GLASS) LAI products, were applied to estimate the eight-day d, z0m, and, thus, the heat roughness length (z0h). The obtained d, z0m and z0h were then used as forcing for the SEBS model in order to simulate long-term forest evapotranspiration (ET) from 2000 to 2012 within the Qilian Mountains (QMs). As compared with MODIS, MOD16 products at the eddy covariance (EC) site, ET estimates from the SEBS agreed much better with EC measurements (R2 = 0.80 and RMSE = 0.21 mm·day−1).

Impact Analysis of Climate Change on Snow over a Complex Mountainous Region Using Weather Research and Forecast Model (WRF) Simulation and Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra Fractional Snow Cover Products

Climate change has a complex effect on snow at the regional scale. The change in snow patterns under climate change remains unknown for certain regions. Here, we used high spatiotemporal resolution snow-related variables simulated by a weather research and forecast model (WRF) including snowfall, snow water equivalent and snow depth along with fractional snow cover (FSC) data extracted from Moderate Resolution Imaging Spectroradiometer Data (MODIS)-Terra to evaluate the effects of climate change on snow over the Heihe River Basin (HRB), a typical inland river basin in arid northwestern China from 2000 to 2013. We utilized Empirical Orthogonal Function (EOF) analysis and Mann-Kendall/Theil-Sen trend analysis to evaluate the results. The results are as follows: (1) FSC, snow water equivalent, and snow depth across the entire HRB region decreased, especially at elevations over 4500 m; however, snowfall increased at mid-altitude ranges in the upstream area of the HRB. (2) Total snowfall also increased in the upstream area of the HRB; however, the number of snowfall days decreased. Therefore, the number of extreme snow events in the upstream area of the HRB may have increased. (3) Snowfall over the downstream area of the HRB decreased. Thus, ground stations, WRF simulations and remote sensing products can be used to effectively explore the effect of climate change on snow at the watershed scale.

Development and validation of remote sensing products of hydrological cycle to close water balance at river basin scale

Development and validation of hydrological cycle elements derived from remote sensing observations are of utmost importance for the study of hydrology at different scales, especially at watershed scale. This paper presents the progress we have made in developing and validating watershed scale hydrological cycle products, mainly including precipitation, snow cover area (SCA), soil moisture (SM), evapotranspiration (ET) and groundwater variation. Corresponding high quality remote sensing products (RSPs) have been produced. In addition, to validate the RSPs of water cycle variables, we established several ground observation networks which can provide extensive and high quality validation dataset. Our efforts significantly improve our understanding in watershed water cycle variables, and the developed water cycle products and validation data products have been widely used in several research domains, providing supporting for several key research projects. Based on these efforts, the developed and validated RSPs having been merged into hydrological and land surface models with the aid of land data assimilation method, to allow us to close the water cycle at the basin scale, and further improve our knowledge on terrestrial water study.