Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009

If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, statistical methods for environmental pollution monitoring always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.

Statistical Methods For Environmental Pollution Monitoring

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2016RG000549

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

Classical multivariate statistical methods concern models, distributions and inference based on the Gaussian distribution. These are the topics in the first textbook for mathematical statisticians by T.W. Anderson that was published in 1958 and that appeared as a slightly expanded 3rd edition in 2003. Matrix theory and notation is used there extensively to efficiently derive properties of the multivariate Gaussian or the Wishart distribution, of principal components, of canonical correlation and discriminant analysis and of the general multivariate linear model in which a Gaussian response vector variable Ya has linear least-squares regression on all components of an explanatory vector variable Yb. In contrast, many methods for analysing sets of observed variables have been developed first within special substantive fields and some or all of the models in a given class were justified in terms of probabilistic and statistical theory much later. Among them are factor analysis, path analysis, structural equation models, and models for which partial-least squares estimation have been proposed. Other multivariate techniques such as cluster analysis and multidimensional scaling have been often used, but the result of such an analysis cannot be formulated as a hypothesis to be tested in a new study and satisfactory theoretical justifications are still lacking. Factor analysis was proposed by psychologist C. Spearman (1904), (1926) and, at the time, thought of as a tool for measuring human intelligence. Such a model has one or several latent variables. These are hidden or unobserved and are to explain the observed correlations among a set of observed variables, called items in that context. The difficult task is to decide how many and which of a possibly large set of items to include into a model. But, given a set of latent variables, a classical factor analysis model specifies for a joint Gaussian distribution mutual independence of the observed variables given the latent variables. This can be recognized to be one special type of a graphical Markov model; see Cox and Wermuth (1996), Edwards (2000), Lauritzen (1996), Whittaker (1990).

Multivariate Statistical Analysis.

Aims Net primary productivity (NPP) is a key component of the terrestrial carbon cycle. Model simulation is commonly used to estimate regional and global NPP given difficulties to directly measure NPP at such spatial scales. A number of NPP models have been developed in recent years as research issues related to food security and biotic response to climatic warming have become more compelling. However, large uncertainties still exist because of the complexity of ecosystems and difficulties in determining some key model parameters. Methods We developed an estimation model of NPP based on geographic information system (GIS) and remote sensing (RS) technology. The vegetation types and their classification accuracy are simultaneously introduced to the computation of some key vegetation parameters, such as the maximum value of normalized difference vegetation index (NDVI) for different vegetation types. This can remove some noise from the remote sensing data and the statistical errors of vegetation classification. It also provides a basis for the sensitivity analysis of NPP on the classification accuracy. The maximum light use efficiency (LUE) for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The simulated values of LUE are greater than the value used in the CASA model and less than the values simulated with the BIOME-BGC model. The computation of the water restriction factor is driven with ground meteorological data and remote sensing data, and complex soil parameters are avoided. Results are compared with other studies and models. Important findings The simulated mean NPP in Chinese terrestrial vegetation from 1989-1993 is 3.12 Pg C (1 Pg=1015 g). The simulated NPP is close to the observed NPP, and the total mean relative error is 4.5% for 690 NPP observation stations distributed in the whole country. This illustrates the utility of the model for the estimation of terrestrial primary production over regional scales.

Estimation of net primary productivity of chinese terrestrial vegetation based on remote sensing

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.

A Time-Dependent Drought Index for Non-Stationary Precipitation Series

Many drought indices were proposed to describe drought characteristics, but only few had considered environmental changes. In an attempt to incorporate climate change into meteorological drought index, a nonstationary Gamma distribution with climate indices as covariates was developed for fitting precipitation data and then used for calculating a Nonstationary Standardized Precipitation Index (NSPI) in this study. The performances of the NSPI were compared with those of the traditional Standardized Precipitation Index (SPI), showing that the NSPI capable of taking climate variations into account is more robust than the traditional SPI. Focusing on the Luanhe River basin, historical drought events were described and assessed based on the NSPI and traditional SPI. Moreover, drought characteristics, including drought frequency, peak, duration, and magnitude, were calculated by using the two indices. The results in this study indicated that NSPI using climate indices as covariates could capture drought characteristics in the Luanhe River basin, and this new drought index provides a new concept for constructing the drought index that can effectively adapt to a changing environment.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2015JD023920

A Nonstationary Standardized Precipitation Index incorporating climate indices as covariates

Traditionally, flood frequency analysis under the assumption of stationarity has been a cornerstone and there is mature technology applied in practice. However, recent evidences of the impact of climate variability and anthropogenic factors have thrown into question the applicability of stationary hypothesis. In this study, Kendall’s tau and Spearman’s rho correlation test were adopted to detect the relationship between climate indices (PDO, NAO, AO, NPO and ENSO) and annual flood peak data. The test results showed that NPO and Nino3 had significant correlations with the flood peak which could prove the climate cause of non-stationarity. Nino3 is used herein to describe ENSO. We also proposed a check dam index (CDIp) to represent the effect of human activities that caused nonstationarity on flood. The CDIp was based on the estimated storage capacity and drainage area of large number of check dams and small hydraulic structures. A framework for nonstationary flood frequency analysis was developed through Generalized Additive Models in Location, Scale and Shape (GAMLSS), and two models based on GAMLSS were applied to the annual flood peak. The model results that incorporated climate indices (NPO and Nino3) and CDIp as covariates in the parameters of the selected distribution exhibited an undulate behavior, which could better describe nonstationarity than the model with only time dependence. For a reservoir index (RI) proposed by Lopez and Frances (2013) which is similar to CDIp, we established two contrast models and the result revealed that CDIp is superior to RI. These results highlight the necessity of flood frequency analysis under nonstationary conditions, and alternative definitions of return period should be adapted.

Nonstationary Flood Frequency Analysis for Annual Flood Peak Series, Adopting Climate Indices and Check Dam Index as Covariates

The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China

Loglinear models for three-dimensional contingency tables was used with data from 21 rainfall stations and 7 hydrometric stations in the Luanhe river basin, northeast China, for short term prediction of drought severity class. Loglinear models were fitted to drought class transitions derived from standardized precipitation index (SPI) and standardized runoff index (SRI) time series to find which series was more suitable for hydrological drought class prediction 1 and 2 months ahead, respectively. Expected frequencies for two consecutive transitions between drought classes were first calculated, and based on this the predicted drought classes 1 and 2 months ahead were obtained. The results showed that despite the contingency tables of drought class transitions presented the maintenance of the precedent drought class, results of three-dimensional loglinear modeling presented good results when comparing predicted and observed drought classes. Only for a few cases predictions did not fully match the observed drought class, mainly for 2-month lead and when the SRI values are near the limit of the severity class predicted by SRI time series. Based on the correlation analysis of SPI and SRI, we presented the well-known method of hydrological drought class prediction by SPI time series. It was found that, using loglinear regression method, the accuracy of predictions for 2-month lead predicted by SPI time series was higher than those predicted by SRI time series. When we divided the SPI and SRI time series into 2 sub-periods (pre- and post-1980 where land cover changed), we got the same drought class prediction as that predicted by the entire SPI and SRI time series, which illustrated that changes in land use did not affect predictions of hydrological drought classes in the Luanhe river basin. It could be concluded that loglinear prediction of drought class transitions is a useful tool for short term hydrological drought warning, and the results could provide significant information for water resources managers and policy makers to mitigate drought effects.

Jianzhu Li

Papers

A Time-Dependent Drought Index for Non-Stationary Precipitation Series

A Nonstationary Standardized Precipitation Index incorporating climate indices as covariates

Nonstationary Flood Frequency Analysis for Annual Flood Peak Series, Adopting Climate Indices and Check Dam Index as Covariates

The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China

Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression