Fubao Sun

The contribution of reduction in evaporative cooling to higher surface air temperatures during drought

This research was supported by the Australian Research Council (CE11E0098), the National Natural Science Foundation of China (91125018), and the China Scholarship Council (201306210089).

Decadal Trends in Evaporation from Global Energy and Water Balances

AbstractSatellite and gridded meteorological data can be used to estimate evaporation (E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in “wet” regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments do not match trends in evaporation estimated using three alternative methods: 1) , a model-tree ensemble approach that uses statistical relationships between E measured across the global network of flux stations, meteorological drivers, and remotely sensed fraction of absorbed photosynthetically active radiation; 2) , a Budyko-style hydrometeorological model; and 3) , the Penman–Monteith energy-balance equation coupled with a simple biophysical model for surface conductance. Key model inputs for the estimation of and are remotely sensed radiation an...

ESTIMATING MONTHLY TOTAL NITROGEN CONCENTRATION IN STREAMS BY USING ARTIFICIAL NEURAL NETWORK

Artificial Neural Network (ANN) is a flexible and popular tool for predicting the non-linear behavior in the environmental system. Here, the feed-forward ANN model was used to investigate the relationship among the land use, fertilizer, and hydrometerological conditions in 59 river basins over Japan and then applied to estimate the monthly river total nitrogen concentration (TNC). It was shown by the sensitivity analysis, that precipitation, temperature, river discharge, forest area and urban area have high relationships with TNC. The ANN structure having eight inputs and one hidden layer with seven nodes gives the best estimate of TNC. The 1:1 scatter plots of predicted versus measured TNC were closely aligned and provided coefficients of errors of 0.98 and 0.93 for ANNs calibration and validation, respectively. From the results obtained, the ANN model gave satisfactory predictions of stream TNC and appears to be a useful tool for prediction of TNC in Japanese streams. It indicates that the ANN model was able to provide accurate estimates of nitrogen concentration in streams. Its application to such environmental data will encourage further studies on prediction of stream TNC in ungauged rivers and provide a useful tool for water resource and environment managers to obtain a quick preliminary assessment of TNC variations.

Dependence of trends in and sensitivity of drought over China (1961–2013) on potential evaporation model

The Palmer Drought Severity Index (PDSI) can lead to controversial results in assessing droughts responding to global warming. Here we assess recent changes in the droughts over China (1961–2013) using the PDSI with two different estimates, i.e., the Thornthwaite (PDSI_th) and Penman-Monteith (PDSI_pm) approaches. We found that droughts have become more severe in the PDSI_th but slightly lessened in the PDSI_pm estimate. To quantify and interpret the different responses in the PDSI_th and PDSI_pm, we designed numerical experiments and found that drying trend of the PDSI_th responding to the warming alone is 3.4 times higher than that of the PDSI_pm, and the latter was further compensated by decreases in wind speed and solar radiation causing the slightly wetting in the PDSI_pm. Interestingly, we found that interbasin difference in the PDSI_th and PDSI_pm responses to the warming alone tends to be larger in warmer basins, exponentially depending on mean temperature.

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.

Effects of Climate Change and Human Activities on Surface Runoff in the Luan River Basin

Quantifying the effects of climate change and human activities on runoff changes is the focus of climate change and hydrological research. This paper presents an integrated method employing the Budyko-based Fu model, hydrological modeling, and climate elasticity approaches to separate the effects of the two driving factors on surface runoff in the Luan River basin, China. The Budyko-based Fu model and the double mass curve method are used to analyze runoff changes during the period 1958~2009. Then two types of hydrological models (the distributed Soil and Water Assessment Tool model and the lumped SIMHYD model) and seven climate elasticity methods (including a nonparametric method and six Budyko-based methods) are applied to estimate the contributions of climate change and human activities to runoff change. The results show that all quantification methods are effective, and the results obtained by the nine methods are generally consistent. During the study period, the effects of climate change on runoff change accounted for 28.3~46.8% while those of human activities contributed with 53.2~71.7%, indicating that both factors have significant effects on the runoff decline in the basin, and that the effects of human activities are relatively stronger than those of climate change.

Global variation of transpiration and soil evaporation and the role of their major climate drivers

Although global variation in actual evapotranspiration has been widely investigated, it remains unclear how its two major components, transpiration and soil evaporation, are driven by climate drivers across global land surface. This paper uses a well-validated, process-based model that estimates transpiration and soil evaporation, and for the first time investigates and quantifies how the main global drivers, associated to vegetation process and the water and energy cycle, drive the spatiotemporal variation of the two components. The results show that transpiration and soil evaporation dominate the variance of actual evapotranspiration in wet and dry regions, respectively. Dry southern hemisphere from 13°S to 27°S is highlighted since it contributes to 21% global soil evaporation variance, with only 11% global land area. In wet regions, particularly in the humid tropics, there are strong correlations between transpiration, actual evapotranspiration, and potential evapotranspiration, with precipitation playing a relatively minor role, and available radiative energy is the major contributor to the interannual variability in transpiration and actual evapotranspiration in Amazonia. Conversely in dry regions, there are strong correlations between soil evaporation, actual evapotranspiration, and precipitation. Our findings highlight that ecohydrological links are highly related to climate regimes, and the small region such as Australia has important contribution to interannual variation in global soil evaporation and evapotranspiration, and anthropogenic activities strongly influence the variances in irrigation regions.

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.

Wavelet-Based Hydrological Time Series Forecasting

AbstractThese days wavelet analysis is becoming popular for hydrological time series simulation and forecasting. There are, however, a set of key issues influencing the wavelet-aided data preprocessing and modeling practice that need further discussion. This article discusses four key issues related to wavelet analysis: discrepant use of continuous and discrete wavelet methods, choice of mother wavelet, choice of temporal scale, and uncertainty evaluation in wavelet-aided forecasting. The article concludes with a personal reflection on solving the four issues for improving and supplementing relevant wavelet studies, especially wavelet-based artificial intelligence modeling.

Rainfall statistics, stationarity, and climate change

Significance Precipitation shows large year-to-year variations, and there is interest in whether there have been long-lasting changes. We use a global land-based database (1940–2009) of annual precipitation and find evidence for changes at around 14% of the global land surface. In contrast, around 76% of the global land shows little or no change. Our results emphasize the importance of fully accounting for natural variability when assessing long-term precipitation change. There is a growing research interest in the detection of changes in hydrologic and climatic time series. Stationarity can be assessed using the autocorrelation function, but this is not yet common practice in hydrology and climate. Here, we use a global land-based gridded annual precipitation (hereafter P) database (1940–2009) and find that the lag 1 autocorrelation coefficient is statistically significant at around 14% of the global land surface, implying nonstationary behavior (90% confidence). In contrast, around 76% of the global land surface shows little or no change, implying stationary behavior. We use these results to assess change in the observed P over the most recent decade of the database. We find that the changes for most (84%) grid boxes are within the plausible bounds of no significant change at the 90% CI. The results emphasize the importance of adequately accounting for natural variability when assessing change.