The embedding dimension and the number of nearest neighbors are very important parameters in the prediction of a chaotic time series. In order to reduce the uncertainties in the determination of the ...forgoing two parameters, a new adaptive local linear prediction method is proposed in this study. In the new method, the embedding dimension and the number of nearest neighbors are combined as a parameter set and change adaptively in the process of prediction. The generalized degree of freedom is used to help select the optimal parameters. Real hydrological time series are taken to examine the performance of the new method. The prediction results indicate that the new method can choose the optimal parameters of embedding dimension and the nearest neighbor number adaptively in the prediction process. And the nonlinear hydrological time series perhaps could be modeled better by the new method.
•A BPROME method to quantify the impact of number of events on model performance and uncertainty is constructed.•A regular influence of the model uncertainty decreases and the model performance ...increases with the number of events increases.•At an appropriate range of numbers of events can achieve better parameter calibration effect of the model in data-scare areas.
Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.
Land surface phenology (LSP), defined as the plant's growth rhythm retrieved from satellite sensing products, is proven to shift with climate change and affect the carbon cycles of terrestrial ...ecosystems. Global afforested area is largely increasing and consequently affecting local and global climate. However, how and to what extent revegetation affects LSP remains relatively unexplored. Here we investigated the difference in four LSPs (i.e., greenup, maturity, senescence, and dormancy) and the response of LSP to climate between restored and native vegetation on Loess Plateau, China, where a remarkable process of vegetation restoration happened during 1982–2015. Most study regions showed a longer growing season (LOS) over time, specifically, with a slight delay in greenup but a relatively large delay in senescence. We found that air temperature was the dominant factor affecting greenup and maturity, while precipitation mostly controlled the senescence and dormancy in the study area. Under similar climate conditions, the LSP of restored vegetation (i.e., restored forest and grassland) showed a significant difference (p < 0.05) from native ones during 1999–2015. Compared to the native forest, restored forest from cropland and grassland showed a delayed greenup date by 0.3 and 3.6 days (p < 0.05) and an advanced dormancy date of 6.6 and 9.0 days (p < 0.05), respectively. Furthermore, the restored vegetation became less sensitive to air temperature than native vegetation, while the restored forest was more sensitive to precipitation, and its growth was affected by the water limitation to a larger extent in the study area. Our study highlights the necessity of considering land use management and its effect on the LSP change to better understand the effect of afforestation on global climate and carbon cycles.
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•Revegetation affected the land surface phenology (LSP) on Loess Plateau.•Growing season of restored vegetation was shorter than that of native vegetation.•LSP of restored vegetation became less dependent on air temperature.•LSP of restored forest was more sensitive to precipitation than that of native one.
In this research, drought in Yellow River basin has been studied by using dry spells. Three indices, including the maximum length (MxDS), mean length (MDS) and number of dry spells (NDS), and five ...periods (annual, winter, spring, summer and autumn) are considered. The results show that a south to north gradient for mean MxDS and MDS has been dominantly found in all periods except summer, in which a southwest and southeast to north gradient exists. Mean NDS shows an opposite distribution to that of mean MxDS and MDS. It is surely that the northern part of Yellow River basin, with a higher MxDS and MDS and lower NDS, is much drier than southern part in a regional scale. According to temporal analysis by using the Mann–Kendall trend method, MxDS of most stations show negative but insignificant trends during annual and winter, while the majority of stations show positive trends during spring, summer and autumn. Trends of MDS and NDS dominantly depict positive and negative for most periods, respectively. By comparing the frequency of dry spells during the ENSO events, it can be found that the frequency of intermediate and long dry spells is almost tantamount during the occurrence periods of El Niño and La Niña.
•PML-V2 (or MODIS) gives the best (or worst) performance in mainland China.•ET increases in the majority of mainland China during 2001–2018.•ET products give better performance in humid than in arid ...areas across mainland China.
Terrestrial evapotranspiration (ET) is a critical variable connecting water, energy and carbon cycles. Many high spatial resolution (0.1°×0.1°) remote sensing based ET (ETRS) products have been released and provide useful information for ET-related analysis. However, the accuracy of these ET products differs largely in space and time due to the estimation method and forcing data in producing the ET data, which raises the demand for the intercomparison of the capacity and applicability of these datasets to give an integrated suggestion of how to choose the optimal dataset for a specific region. In this study, we intercompare the performance of six ETRS products (AVHRR, GLASS, GLEAM, IDAHO, MODIS, and PML-V2) in mainland China during 2001–2018. The comparison of eddy covariance (EC) between ET from flux sites (ETOBS) and the estimated ET given by the terrestrial water balance method (ETTWB) at an annual time scale demonstrated good performance of these ET products in capturing the ET changes in mainland China, although there still exist some discrepancies among different ET products. PML-V2 outperforms other products with the lowest root-mean-squared-deviation (RMSE) and the highest Nash-Sutcliffe efficiency coefficient (NSE) compared to the ground-based observations from the flux network. GLEAM has the best correlation with ETTWB. A southeast-to-northwest ET gradient can be found across the study area with the range of 350–450 mm/yr and the majority of mainland China exhibited an increasing ET trend from 0.213 mm/yr2 to 3.178 mm/yr2 during 2001–2018. We also find that PML-V2 and GLASS are the most reliable products in the humid and semi-humid areas of mainland China, while GLEAM are the most effective products in arid and semi-arid areas of China. The results of this study can provide useful information on how to choose the relative ET products for water resources management and planning.
•A new urban hydrological model considering different land covers is proposed.•The TVGM_Urban model outperforms other two models in flood simulations.•The TVGM_Urban model has less prediction ...uncertainty than other two models.
Rapid urbanization has great potential to adversely impact the local hydrological cycle, the environment, and ecosystems, as well as trigger severe problems, such as urban flooding, waterlogging, and water contamination. Understanding runoff generation mechanisms in urban areas helps identify the impacts of urbanization on runoff response and better simulate urban floods. However, in most urban hydrological models, urban surfaces are coarsely classified into impervious and pervious ones, which likely ignores differential rainfall-runoff responses of different urban surfaces and leads to large discrepancies between simulated and observed runoff. Here we developed the TVGM_Urban model, a new urban hydrological model based on the time variant gain model (TVGM), that represents nonlinear rainfall-runoff relationships for different urban surfaces. We applied the model to the Fenghuangcheng region of Shenzhen City, China and compared the results to those of two commonly used urban hydrological models: the Horton infiltration and Soil Conservation Service Curve Number (SCSCN) model. For each of these models, we conducted model uncertainty analysis using the GLUE method. The results showed that the TVGM_Urban model outperformed the other two models in terms of total runoff and peak flow. This could be due to the consideration of different land covers and both saturation excess and infiltration excess runoff generation in the TVGM_Urban model. In addition, the uncertainty analysis indicated better performance of the TVGM_Urban model in reducing structural uncertainty and prediction uncertainty. This study highlights the need to account for detailed land covers and different runoff generation mechanisms in urban hydrological modeling.
In recent decades, extreme precipitation events have been a research hotspot worldwide. Based on 12 extreme precipitation indices, the spatiotemporal variation and statistical characteristic of ...precipitation extremes in the middle reaches of the Yellow River Basin (MRYRB) during 1960–2013 were investigated. The results showed that the values of most extreme precipitation indices (except consecutive dry days (CDD)) increased from the northwest to the southeast of the MRYRB, reflecting that the southeast was the wettest region in the study area. Temporally, the precipitation extremes presented a drying trend with less frequent precipitation events. Generalized extreme value (GEV) distribution was selected to fit the time series of all indices, and the quantiles values under the 50-year return period showed a similar spatial extent with the corresponding precipitation extreme indices during 1960–2013, indicating a higher risk of extreme precipitation in the southeast of the MRYRB. Furthermore, the changes in probability distribution functions of indices for the period of 1960–1986 and 1987–2013 revealed a drying tendency in our study area. Both El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) were proved to have a strong influence on precipitation extremes in the MRYRB. The results of this study are useful to master the change rule of local precipitation extremes, which will help to prevent natural hazards caused.
Vegetation phenology is undergoing profound changes in response to the recent increases in the intensity and frequency of drought events. However, the mechanisms by which drought affects the start of ...the growing season (SGS) are poorly understood particularly in arid and semi-arid regions. Here, we identified varying degrees of preseason drought events and analyzed the sensitivity of the SGS to preseason drought across the Northeast China Transect (NECT). Our results showed that drought caused a delayed SGS in grassland ecosystems, but an advanced SGS within forest ecosystems. These contrasting responses to preseason drought reflected different adaptive strategies between vegetation types. The SGS was shown to be highly sensitive to short timescales drought (1–3 months) in semi-arid grasslands where annual precipitation is 200–300 mm (i.e. SAGE200–300). Biomes within this region were found to be most vulnerable out of all the ecosystems to drought. Given the frequent nature of droughts in the mid-latitudes, a drought early warning system was recommended accompanied by improved modeling of how the SGS will be affected by intensified drought under future climate change.
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•The sensitivity of the SGS to drought severity across the NECT was investigated.•Preseason drought caused a delayed SGS in grassland ecosystems, but an earlier SGS in forest ecosystems.•Semi-arid grassland ecosystem with mean annual precipitation between 200 and 300 mm is more vulnerable to preseason drought.
Based on the daily runoff data from 20 hydrological stations above the Bengbu Sluice in the Huaihe River Basin during 1956-2010, run test, trend test and Mann-Kendall test are used to analyze the ...variation trend of annual maximum runoff series. The annual maximum series （AM） and peaks over threshold series （POT） are selected to describe the extreme distributions of generalized extreme value distribution （GEV） and generalized Pareto distri- bution （GPD）. Temporal and spatial variations of extreme runoff in the Huaihe River Basin are analyzed. The results show that during the period 1956-2010 in the Huaihe River Basin, annual maximum runoff at 10 stations have a decreasing trend, while the other 10 stations have an unobvious increasing trend. The maximum runoff events almost occurred in the flood period during the 1960s and 1970s. The extreme runoff events in the Huaihe River Basin mainly occurred in the mainstream of the Huaihe River, Huainan mountainous areas, and Funiu mountainous areas. Through Kolmogorov-Smirnov test, GEV and GPD distributions can be well fitted with AM and POT series respectively. Percentile value method, mean ex- cess plot method and certain numbers of peaks over threshold method are used to select threshold, and it is found that percentile value method is the best of all for extreme runoff in the Huaihe River Basin.
Abstract
In partial plastic-mulch-covered croplands, the complicated coexistence of bare soil surface, mulched soil surface, and dynamically changing canopy surface results in challenges in ...accurately estimating field surface albedo (
α
) and its components (bare soil surface albedo
α
b
, mulched soil surface albedo
α
m
, and canopy surface albedo (
α
c
) during the whole growth period. To accurately estimate
α
,
α
b
,
α
m
, and
α
c
, and to quantify the three surfaces’ contributions to field shortwave radiation reflections (
F
b
,
F
m
,
F
c
), 1) a modified two-stream (MTS) approximation solution that considered the effect of plastic mulch has been proposed to accurately estimate
α
and 2) dynamic variations of
α
b
,
α
m
,
α
c
and
F
b
,
F
m
,
F
c
have been characterized. Therein,
α
b
and
α
m
were determined from corresponding parameterization schemes, and
α
c
was determined using mulched irrigated croplands surface albedo (MICA) relationship between
α
and
α
b
,
α
m
, and
α
c
that was established in this study. Results indicated that 1) compared with measurements, considering the effect of plastic mulch will significantly improve estimation of
α
when the ground surface is not fully covered by the crop canopy, while not underestimating
α
by a mean value of 0.061 in the early growth period, and 2) mean values of
α
,
α
b
,
α
m
, and
α
c
during the whole growth period were 0.198, 0.174, 0.308, and 0.160, respectively, while the corresponding
F
b
,
F
m
, and
F
c
were 0.08, 0.42, and 0.50, respectively.
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