Search Results

You are looking at 11 - 13 of 13 items for :

  • Author or Editor: H. Zhang x
  • Journal of Hydrometeorology x
  • Refine by Access: All Content x
Clear All Modify Search
Xin-Min Zeng
,
B. Wang
,
Y. Zhang
,
Y. Zheng
,
N. Wang
,
M. Wang
,
X. Yi
,
C. Chen
,
Z. Zhou
, and
H. Liu

Abstract

To quantify and explain effects of different land surface schemes (LSSs) on simulated geopotential height (GPH) fields, we performed simulations over China for the summer of 2003 using 12-member ensembles with the Weather Research and Forecasting (WRF) Model, version 3. The results show that while the model can generally simulate the seasonal and monthly mean GPH patterns, the effects of the LSS choice on simulated GPH fields are substantial, with the LSS-induced differences exceeding 10 gpm over a large area (especially the northwest) of China, which is very large compared with climate anomalies and forecast errors. In terms of the assessment measures for the four LSS ensembles [namely, the five-layer thermal diffusion scheme (SLAB), the Noah LSS (NOAH), the Rapid Update Cycle LSS (RUC), and the Pleim–Xiu LSS (PLEX)] in the WRF, the PLEX ensemble is the best, followed by the NOAH, RUC, and SLAB ensembles. The sensitivity of the simulated 850-hPa GPH is more significant than that of the 500-hPa GPH, with the 500-hPa GPH difference fields generally characterized by two large areas with opposite signs due to the smoothly varying nature of GPHs. LSS-induced GPH sensitivity is found to be higher than the GPH sensitivity induced by atmospheric boundary layer schemes. Moreover, theoretical analyses show that the LSS-induced GPH sensitivity is mainly caused by changes in surface fluxes (in particular, sensible heat flux), which further modify atmospheric temperature and pressure fields. The temperature and pressure fields generally have opposite contributions to changes in the GPH. This study emphasizes the importance of choosing and improving LSSs for simulating seasonal and monthly GPHs using regional climate models.

Full access
Jiali Ju
,
Heng Dai
,
Chuanhao Wu
,
Bill X. Hu
,
Ming Ye
,
Xingyuan Chen
,
Dongwei Gui
,
Haifan Liu
, and
Jin Zhang

Abstract

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

Full access
Xiangyu Ao
,
C. S. B. Grimmond
,
H. C. Ward
,
A. M. Gabey
,
Jianguo Tan
,
Xiu-Qun Yang
,
Dongwei Liu
,
Xing Zhi
,
Hongya Liu
, and
Ning Zhang

Abstract

The Surface Urban Energy and Water Balance Scheme (SUEWS) is used to investigate the impact of anthropogenic heat flux Q F and irrigation on surface energy balance partitioning in a central business district of Shanghai. Diurnal profiles of Q F are carefully derived based on city-specific hourly electricity consumption data, hourly traffic data, and dynamic population density. The Q F is estimated to be largest in summer (mean daily peak 236 W m−2). When Q F is omitted, the SUEWS sensible heat flux Q H reproduces the observed diurnal pattern generally well, but the magnitude is underestimated compared to observations for all seasons. When Q F is included, the Q H estimates are improved in spring, summer, and autumn but are poorer in winter, indicating winter Q F is overestimated. Inclusion of Q F has little influence on the simulated latent heat flux Q E but improves the storage heat flux estimates except in winter. Irrigation, both amount and frequency, has a large impact on Q E . When irrigation is not considered, the simulated Q E is underestimated for all seasons. The mean summer daytime Q E is largely overestimated compared to observations under continuous irrigation conditions. Model results are improved when irrigation occurs with a 3-day frequency, especially in summer. Results are consistent with observed monthly outdoor water use. This study highlights the importance of appropriately including Q F and irrigation in urban land surface models—terms not generally considered in many previous studies.

Full access