Wenbin Liu

Exploring the water storage changes in the largest lake (Selin Co) over the Tibetan Plateau during 2003–2012 from a basin-wide hydrological modeling

Lake water storage change (ΔSw) is an important indicator of the hydrologic cycle and greatly influences lake expansion/shrinkage over the Tibetan Plateau (TP). Accurate estimation of ΔSw will contribute to improved understanding of lake variations in the TP. Based on a water balance, this study explored the variations of ΔSw for the Lake Selin Co (the largest closed lake on the TP) during 2003–2012 using the Water and Energy Budget-based Distributed Hydrological Model (WEB-DHM) together with two different evapotranspiration (ET) algorithms (the Penman-Monteith method and a simple sublimation estimation approach for water area in unfrozen and frozen period). The contributions of basin discharge and climate causes to the ΔSw are also quantitatively analyzed. The results showed that WEB-DHM could well reproduce daily discharge, the spatial pattern, and basin-averaged values of MODIS land surface temperature (LST) during nighttime and daytime. Compared with the ET reference values estimated from the basin-wide water balance, our ET estimates showed better performance than three global ET products in reproducing basin-averaged ET. The modeled ET at point scale matches well with short-term in situ daily measurements (RMSE = 0.82 mm/d). Lake inflows and precipitation over the water area had stronger relationships with ΔSw in the warm season and monthly scale, whereas evaporation from the water area had remarkable effects on ΔSw in the cold season. The total contribution of the three factors to ΔSw was about 90%, and accounting for 49.5%, 22.1%, and 18.3%, respectively.

Spatial–temporal variations of spring drought based on spring-composite index values for the Songnen Plain, Northeast China

A spring-composite index (s-CI) is proposed in this study that involves slightly altering the use of the accumulated precipitation from the composite index (CI) comparing the value with other three commonly used indices (standardized precipitation index, SPI; self-calibrated Palmer drought severity index, sc-PDSI; and CI). In addition, the spatial–temporal variation of the s-CI in the Songnen Plain (SNP) was investigated using the Mann–Kendall test and empirical orthogonal function (EOF) methods. The results indicated that the proposed s-CI could identify most drought events in 1990s and 2000s and performed relatively better than SPI, sc-PDSI, and CI in this region. Compared with the other three indices, the s-CI had a higher correlation with relative soil moisture in April and May. The recent spring droughts (2000s) were the most severe in April or May. The weather was drier in May compared with April in the 1980s, whereas the weather was wetter in May than in April in the 1960s and 1970s. Moreover, the spatial patterns of the first EOFs for both April and May indicated an obviously east–west gradient in the SNP, whereas the second EOFs displayed north–south drought patterns. The proposed index is particularly suitable for detecting, monitoring, and exploring spring droughts in the Songnen Plain under global warming.

A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Three statistical downscaling methods (conditional resampling statistical downscaling model: CR-SDSM, the generalised linear model for daily climate time series: GLIMCLIM, and the non-homogeneous hidden Markov model: NHMM) for multi-site daily rainfall were evaluated and compared in the North China Plain (NCP). The comparison focused on a range of statistics important for hydrological studies including rainfall amount, extreme rainfall, intra-annual variability, and spatial coherency. The results showed that no single model performed well over all statistics/timescales, suggesting that the user should chose appropriate methods after assessing their advantages and limitations when applying downscaling methods for particular purposes. Specifically, the CR-SDSM provided relatively robust results for annual/monthly statistics and extreme characteristics, but exhibited weakness for some daily statistics, such as daily rainfall amount, dry-spell length, and annual wet/dry days. GLIMCLIM performed well for annual dry/wet days, dry/wet spell length, and spatial coherency, but slightly overestimated the daily rainfall. Additionally, NHMM performed better for daily rainfall and annual wet/dry days, but slightly underestimated dry/wet spell length and overestimated the daily extremes. The results of this study could be applied when investigating climate change impact on hydrology and water availability for the NCP, which suffers from intense water shortages due to climate change and human activities in recent years.

How Will Climate Change Affect the Water Availability in the Heihe River Basin, Northwest China?

AbstractThis paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021–50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981–2010), by 1.2°–1.7°C under scenario RCP4.5 and by 1....

Assessing estimates of evaporative demand in climate models using observed pan evaporation over China

Here we assess estimates of atmospheric evaporative demand over China in 12 state-of-the-art global climate models (GCMs) against observed D20 pan evaporation (E-pan) over the period of 1961-2000. To do that, we use an energy-relevant and physical-based approach, namely, PenPan model, to comprehensively evaluate GCM performance with respect to their ability to simulate annual, seasonal, and monthly statistics of E-pan (and its radiative and aerodynamic components, E-p,E-R and E-p,E-A). The results indicated that most GCMs generally captured the spatial pattern and seasonal cycle of E-pan, E-p,E-R, and E-p,E-A. However, regional means of annual and monthly E-pan, E-p,E-R, and E-p,E-A were underestimated by most GCMs mainly due to negatively biased surface air temperature (T-a) and vapor pressure deficit (vpd) outputted/simulated by the GCMs. Overall, the discrepancies among GCMs in estimating the regional statistics (regional means and seasonal cycles) of E-p,E-A were relatively larger than that of E-p,E-R, which indicates considerable uncertainties in the calculation of the aerodynamic component of evaporation based on the GCM outputs. Moreover, a few GCMs captured negative trends of regional mean annual and seasonal E-pan, E-p,E-R, and E-p,E-A well over the period of 1961- 2000, but most showed positive trends. The underestimation of net radiation (R-n) and overestimation of wind speed at 2m (u(2)) in most GCMs may, to some extent, accentuate/compensate the negative biases in GCM-estimated annual and seasonal E-pan, E-p,E-R, and E-p,E-A. The results demonstrate the importance of incorporating observation of pan evaporation and well-validated PenPan model to evaluate GCM performance on atmospheric evaporative demand that is relevant to projections of future drought and regional water-energy budgets.

Improving snow process modeling with satellite‐based estimation of near‐surface‐air‐temperature lapse rate

In distributed hydrological modeling, surface air temperature (Tair) is of great importance in simulating cold region processes, while the near-surface-air-temperature lapse rate (NLR) is crucial to prepare Tair (when interpolating Tair from site observations to model grids). In this study, a distributed biosphere hydrological model with improved snow physics (WEB-DHM-S) was rigorously evaluated in a typical cold, large river basin (e.g., the upper Yellow River basin), given a mean monthly NLRs. Based on the validated model, we have examined the influence of the NLR on the simulated snow processes and streamflows. We found that the NLR has a large effect on the simulated streamflows, with a maximum difference of greater than 24% among the various scenarios for NLRs considered. To supplement the insufficient number of monitoring sites for near-surface-air-temperature at developing/undeveloped mountain regions, the nighttime Moderate Resolution Imaging Spectroradiometer land surface temperature is used as an alternative to derive the approximate NLR at a finer spatial scale (e.g., at different elevation bands, different land covers, different aspects, and different snow conditions). Using satellite-based estimation of NLR, the modeling of snow processes has been greatly refined. Results show that both the determination of rainfall/snowfall and the snowpack process were significantly improved, contributing to a reduced summer evapotranspiration and thus an improved streamflow simulation.

Precipitation variability and response to changing climatic condition in the Yarlung Tsangpo River basin, China

Hydroclimatic process in the Yarlung Tsangpo River (YTR) basin, a sensitive area to climate change, is obviously changing during recent years, but there has limited understanding about it. In this study, we investigated the spatiotemporal variation of precipitation over last four decades in the basin and the impact thereon of the changing Indian summer monsoon at interannual and decadal time scales. All the precipitation series have similar scaling behavior, reflecting similar climatic regime throughout the basin. However, the effect of the Indian monsoon strengthens from the downstream to upstream, causing spatial variability in the seasonal distribution of precipitation, and on this basis, the YTR basin is roughly divided into three regions: east, middle, and west. Both the occurrence times and magnitude of precipitation extremes, ranging 25-50mm/d, are exhibiting downward trends over the last four decades, which bodes well for water disaster controls in the basin. The Indian summer monsoon index, as an intensity indicator for the Indian summer monsoon, shows a positive relationship with the summer precipitation in the YTR basin. Periodic variability of the Indian monsoon determines the interannual nonstationary fluctuations of precipitation. Especially, the weakening effect of the Indian summer monsoon has caused an obvious decrease in precipitation over the rainy season after 1998. If the Indian summer monsoon keeps weakening, the precipitation would decrease and potentially water shortage would become more severe in the basin. Effective adaptation strategy should therefore be developed proactively to handle the unfavorable water situation, which is likely to occur in the future.

Development of a land surface model with coupled snow and frozen soil physics

Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

Projecting and Attributing Future Changes of Evaporative Demand over China in CMIP5 Climate Models

AbstractAtmospheric evaporative demand plays a pivotal role in global water and energy budgets, and its change is very important for drought monitoring, irrigation scheduling, and water resource management under a changing environment. Here, future changes of pan evaporation Epan, a measurable indicator for atmospheric evaporative demand, are first projected and attributed over China through a physically based approach, namely, the PenPan model, forced with outputs from 12 state-of-the-art climate models from phase 5 of the Coupled Model Intercomparison Project. An equidistant quantile mapping method was also used to correct the biases in GCMs outputs to reduce uncertainty in Epan projection. The results indicated that Epan would increase during the periods 2021–50 and 2071–2100 relative to the baseline period 1971–2000 under the representative concentration pathway (RCP) 4.5 and 8.5 scenarios, which can mainly be attributed to the projected increase in air temperature and vapor pressure deficit over Chin...

The Predictability of Annual Evapotranspiration and Runoff in Humid and Nonhumid Catchments over China: Comparison and Quantification

AbstractClimate change and its potential threats on water security call for reliable predictions of evapotranspiration (ET) and runoff Q at different time scales, but current knowledge of the differences in their predictability between humid and nonhumid regions is limited. Based on spatially distributed catchments in China, the authors characterized their predictability and provided plausible explanations. Using the Budyko framework, it was confirmed that annual ET is predictable in nonhumid regions but less predictable in humid regions, and annual Q is predictable in humid regions but less reliable in nonhumid regions. The main cause of the varied predictability lies in the variation of water storage change ΔS in the water balance equation. It affects both the estimation and the variability of Q in nonhumid catchments more than that in humid catchments, which increases the challenge of predicting annual Q in nonhumid regions, while the opposite effect occurs in annual ET prediction between humid and non...