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The characteristics of urban pluvial flooding are altering all over the world due to environmental change. In this paper, generalized additive models for location, scale and shape (GAMLSS) was employed to analyze nonstationary frequency of extreme precipitation at subdaily scale. Nonstationary precipitation return periods were estimated using the expected waiting time (EWT) interpretation. A model coupling SWMM and MIKE21 was established, and calibrated and verified by three historical urban floods. Then, it was utilized to simulate urban floods under stationary and nonstationary rainfall conditions with different return periods. The simulated results illustrated that rainfall depth under nonstationarity was greater than that of stationarity when return period was less than 10 years, but the results reversed when the return period was over 20 years. The main variation of rainfall depth occurred within 6 h. The deviation of the maximum water depth was less than 10% for five return levels, and the difference in the longest inundation lengths was 1.2 h for 50-year return period under two assumptions. It may indicate that slight differences of urban flooding were detected between stationary and nonstationary conditions in the study region, which suggested a further study about urban flood under nonstationarity. [ABSTRACT FROM AUTHOR]

The spatial and temporal characteristics of drought are investigated for Luanhe River basin, using monthly precipitation data from 26 stations covering the common period of 1958–2011. The spatial pattern of drought was assessed by applying principal component analysis (PCA) to the Standardized Precipitation Index (SPI) computed on 3- and 12-month time scales. In addition, annual SPI and seasonal SPIs (including spring SPI, summer SPI, autumn SPI, and winter SPI) were also defined and considered in this study to characterize seasonal and annual drought conditions, respectively. For all seven SPI cases, three distinctive sub-regions with different temporal evolutions of droughts are well identified, respectively, representing the southeast, middle, and northwest of the Luanhe River basin. The Mann-Kendall (MK) trend test with a trend-free pre-whitening (TFPW) procedure and Sen’s method were used to determine the temporal trends in the annual and seasonal SPI time series. The continuous wavelet transform (CWT) was employed for further detecting the periodical features of drought condition in each sub-region. Results of MK and Sen’s tests show a general tendency of intensification in summer drought over the entire basin, while a significant mitigating trend in spring drought. On the whole, an aggravating trend of inter-annual drought is discovered across the basin. Based on the CWT, the drought variability in the basin is generally dominated by 16- to 64-month cycles, and the 2- to 6-year cycles appear to be obvious when concerned with annual and seasonal droughts. Furthermore, a cross wavelet analysis was performed to examine the possible links between the drought conditions and large-scale climate patterns. The teleconnections of ENSO, NAO, PDO, and AMO show significant influences on the regional droughts principally concentrated in the 16- to 64-month period, maybe responsible for the physical causes of the cyclical behavior of drought occurrences. PDO and AMO also highlight a noteworthy correlation with drought variability on a decadal scale (around 128-month period). The findings of this study will provide valuable references for regional drought mitigation and water resource management. [ABSTRACT FROM AUTHOR]

Drought is regarded as one of the most severe natural disasters globally. This is especially the case in Tianjin City, Northern China, where drought can affect economic development and people's livelihoods. Drought forecasting, the basis of drought management, is an important mitigation strategy. In this paper, we evolve a probabilistic forecasting model, which forecasts transition probabilities from a current Standardized Precipitation Index (SPI) value to a future SPI class, based on conditional distribution of multivariate normal distribution to involve two large-scale climatic indices at the same time, and apply the forecasting model to 26 rain gauges in the Luanhe River basin in North China. The establishment of the model and the derivation of the SPI are based on the hypothesis of aggregated monthly precipitation that is normally distributed. Pearson correlation and Shapiro-Wilk normality tests are used to select appropriate SPI time scale and large-scale climatic indices. Findings indicated that longer-term aggregated monthly precipitation, in general, was more likely to be considered normally distributed and forecasting models should be applied to each gauge, respectively, rather than to the whole basin. Taking Liying Gauge as an example, we illustrate the impact of the SPI time scale and lead time on transition probabilities. Then, the controlled climatic indices of every gauge are selected by Pearson correlation test and the multivariate normality of SPI, corresponding climatic indices for current month and SPI 1, 2, and 3 months later are demonstrated using Shapiro-Wilk normality test. Subsequently, we illustrate the impact of large-scale oceanic-atmospheric circulation patterns on transition probabilities. Finally, we use a score method to evaluate and compare the performance of the three forecasting models and compare them with two traditional models which forecast transition probabilities from a current to a future SPI class. The results show that the three proposed models outperform the two traditional models and involving large-scale climatic indices can improve the forecasting accuracy. [ABSTRACT FROM AUTHOR]

Runoff variations were influenced by climate variability and land surface change, but the mechanism was not clear, and quantification of their effects was not mature. To find a reliable method, we selected four sub-watersheds in Luanhe watershed in which runoff series had significant downward trends. We first calibrated and validated the Soil and Water Assessment Tool (SWAT) hydrological model by using the hydrometeorological data in reference period in the four sub-watersheds. The simulated runoff series agreed well with the observed ones, which demonstrated the model performed well. Then we reconstructed the runoff series in impaired period under 1970 and 2000 land use conditions without check dams. According to the simulation results, the contributions of precipitation variability, land use change and construction of large number of check dams were quantified by traditional method. It was found that precipitation variability was the main cause of runoff decrease. In this study, we proposed a new perception for distinguishing the contributions of land surface change and precipitation variability, and got different results from traditional method. Precipitation variability was the main factor for runoff decrease in Luanhe and Wuliehe sub-watersheds, and land surface change contributed more in Yixunhe and Liuhe sub-watersheds. The combined effects were not the summation of single effect of precipitation variability and land surface change. It is of great necessity to do further research on this issue, and will provide reliable information for water resources managers. [ABSTRACT FROM AUTHOR]

In a rapidly changing environment, a greater concern about the establishment and improvement of drought indices is expected. The main goal of this study is to develop and apply a time-dependent Standardized Precipitation Index (SPIt) that takes account of the possible non-stationary behaviors in precipitation records. Summer precipitation observations (1959 ~ 2011) from 21 raingauge stations in the Luanhe River basin are fitted with non-stationary Gamma distributions respectively by means of the Generalized Additive Models in Location, Scale and Shape (GAMLSS). The temporal variability of the distribution's parameter (related to the mean) is flexibly described by an optimized polynomial function. Based on the non-stationary distribution, the SPIt is calculated and then employed to assess the spatio-temporal characteristics of summer drought in the basin. Results of the non-stationary modeling indicate an overall decreasing trend in the summer precipitation during 1959 ~ 2011, and especially a significant decrease in the period of 2000 to 2011. The SPIt is found to be more robust and reliable compared with the traditional Standardized Precipitation Index (SPI). Moreover, remarkable difference is observed between the historical drought assessments of SPIt and SPI in the Luanhe River basin, implying that the non-stationarity of hydrological time series cannot be ignored in drought analyses and forecasts. The proposed SPIt method can be a feasible alternative for drought monitoring under non-stationary conditions, intended to provide a valuable reference for further studies. [ABSTRACT FROM AUTHOR]

• A new eddy flux tower was constructed to study the ecosystem carbon cycle and budget. • Using Universal Global Optimization algorithm to calibrate VPM calibration period (R2 = 0.94, RMSE = 0.47 g C m −2 d −1) based on the field measured eddy flux data. • Using remote sensing data and meteorological reanalysis data, the dynamic variation of GPP from 2000 to 2020 are analyzed, with an average value of 545.6 g C m −2 y −1. • The relationship between SPI and GPP is analyzed by Pearson correlation coefficients, with a cumulative effect of 9 months and a lag effect of 6–7 months. As the largest flux in the global terrestrial carbon cycle, Gross Primary Productivity (GPP) is a key factor for the terrestrial carbon budget. Based on the eddy flux tower data, remote sensing data and the meteorological precipitation data, we calibrated three parameters of maximum light use efficiency, optimum temperature, and maximum temperature in the Vegetation Photosynthesis Model (VPM) to reproduce the GPP spatiotemporal characteristic in Liulin watershed of Taihang Mountains. We further analyzed the time lag effect and cumulative effect of the standard precipitation index (SPI) on GPP by the Pearson correlation coefficients. Results show that VPM captures the GPP characteristics of eddy flux tower observation in Liulin watershed. During the calibration period (R2 = 0.94, RMSE = 0.47 g C m -2· d -1) and validation period (R2 = 0.91, RMSE = 0.60 g C m -2 d -1), the adjusted VPM model successfully reproduced the temporal dynamics of site-observed GPP. From 2000 to 2020, VPM model can effectively reproduce the long-term variations in GPP (R2 = 0.69 vs. NIRv_GPP), with the GPP ranging from 446.4 to 828.1 g C m -2 y -1 and an average value of 545.6 g C m -2 y -1 in Liulin watershed. The GPP exhibited an increasing trend from 2000 to 2020. A mutation in the GPP occurred in 2010, with an average annual increase of 13.9 g C m -2 y -1 from 2000 to 2010 (p < 0.05), followed by an average annual increase of 10.22 g C m -2 y -1 from 2011 to 2020 (p > 0.05). The SPI effectively monitored drought events in the Liulin watershed. With a cumulative effect of 9 months and a lag effect of 6–7 months, the time lag and cumulative effects of drought had a long-term effect on GPP. It is imperative to have long-term eddy flux tower data and meteorological data to better evaluate the spatiotemporal dynamics of GPP and the impact of drought. [ABSTRACT FROM AUTHOR]

Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area. [ABSTRACT FROM AUTHOR]