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Threshold level method is well-known for drought identification with the advantage of its simplicity. However, there is no criterion for classification of drought classes in this method. Therefore, a K index based on threshold level method was proposed and verified to perform well in drought assessment in the Luanhe River basin, China. Meteorological data and remote sensing data were used to calculate the net primary productivity (NPP) of the basin by the CASA model. Results showed that the model had a good performance in comparison with the downloaded yearly remote sensing NPP and monthly PsnNet. The NPP in the basin tended to increase slowly during the year of 2000–2010, and the NPP in the downstream was generally larger than upstream. In three selected representative drought years, drought reduced NPP by about 15–25%, and during the growth season, drought may reduce NPP by about 4.6%, 2.7%, and 1.9% in July, August, and September, respectively, due to reduced precipitation. NPP on grasslands and agricultural land were more susceptible to drought than forests. The result of gray incidence analysis showed that the effects of drought on NPP had a certain delay with about 5 months. [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]

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 long series of highly accurate soil moisture data was combined. • SWDI was improved and extended to the monthly scaled drought index. • Temporal features of SWDI were characterized in four geographical regions. • The relationship among drought indexes (SPI, SWDI and VCI) was explored. Drought monitoring and assessment are of great importance due to the costly damage caused by drought. Datasets, drought indexes and drought relationships are three critical areas of drought research. Satellite-retrieved soil moisture (SM) products derived from the European Space Agency Climate Change Initiative (ESA CCI) show application potential in drought monitoring. However, the products are missing certain data in some areas. The model-assimilated SM product derived from the Global Land Data Assimilation System (GLDAS) was used to supplement these missing data. The main goals of this paper are to characterize agricultural drought after the utility and applicability of the combined SM product and the monthly scaled soil water deficit index (SWDI) have been evaluated and to investigate the relationships among meteorological, agricultural and vegetation droughts. First, we provided a long series of highly accurate SM products through simple calculations. The drought index, SWDI, was extended to a monthly scale for long-term drought analysis by using the combined SM product. The probability of detection (POD) between the SWDI and in situ drought records performed fairly well. Half of the 566 stations had PODs higher than 0.9, and one-third of these stations had POD values equal to 1. Through correlation analysis and grey incidence analysis (GIA) between the standardized precipitation index (SPI) and SWDI, we found that the propagation time from meteorological drought to agricultural drought was shorter under drier conditions than wetter conditions, and at the regional scale, the response time ranged from 1 month to 2.5 months. Correlation analysis between the SWDI and vegetation condition index (VCI) indicated that there was no delay effect from agricultural to vegetation drought on a monthly scale in most parts of China except in several provinces distributed in the South; additionally, there was a significant time lag in forests, while grassland and agriculture were more inclined to have no time lag or the response time was less than 1 month. [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]

[Display omitted] • The top-down QPEs perform better over the humid and low-altitude regions. • IMERG-F (SM2RAIN-ASCAT) performs best (worst) in detecting drought occurrence. • The drought dynamic evolutions highly depend on the drought indices used. • All QPEs capture drought centroid trajectory better on smaller spatial scales. Satellite-based quantitative precipitation estimates (QPEs) have shown great potential in the precipitation-related extremes estimation. However, little is known about the capability of different QPEs to monitor the dynamic evolutions of drought on various spatio-temporal scales. Here, the drought utility of two top-down QPEs (i.e., Climate Hazards Group InfraRed Precipitation with Stations, CHIRPS; Integrated Multi-SatellitE Retrievals for Global precipitation measurement-Final run, IMERG-F) and one bottom-up QPE (i.e., Soil Moisture to Rain-Advanced SCATterometer, SM2RAIN-ASCAT), is evaluated comprehensively in mainland China, based on three commonly used drought indices (Standardized Precipitation Index, Standardized Precipitation Evapotranspiration Index, Composite index of meteorological drought). The results indicate that the top-down QPEs generally show better performance in detecting drought occurrence over the humid and low-altitude regions in eastern China than the arid and high-altitude regions in western China. IMERG-F performs best in detecting drought occurrence in the entire mainland China, followed by CHIRPS, and the least satisfactory by SM2RAIN-ASCAT. The trajectory of drought centroids and the temporal variability of drought characteristics are highly dependent on both the drought indices selected and QPEs used. IMERG-F and CHIRPS can better reproduce the trajectories of large-scale drought events (e.g., the locations of drought origin and termination) than SM2RAIN-ASCAT. The ability in tracking the drought centroid trajectory of all three QPEs is higher at the smaller spatial scales, also it improves from the semi-arid to humid regions. Our results highlight the superiority of the top-down QPEs in drought monitoring compared with the bottom-up QPE in mainland China, particularly over the humid regions. The application of SM2RAIN-ASCAT in drought monitoring to the large-scale arid areas should be cautious. [ABSTRACT FROM AUTHOR]

• Multi-source remote sensing data, flux data and reanalysis data were collected and integrated. • Vegetation, drought and carbon flux variations in the eastern piedmont of the Taihang mountains from 2010 to 2018 were analyzed. • SIF was better than NDVI and EVI in reflecting GPP variation during drought. • SIF can accurately assess ecosystem GPP losses caused by drought. Drought has become one of the main reasons for the loss of terrestrial ecosystems gross primary productivity (GPP) because of its more frequent occurrence under the background of global warming. Aiming to explore the response mechanism of vegetation under drought conditions, the sun-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were used to monitor vegetation drought and quantify the drought-induced GPP variabilities. MODIS GPP were used for analysis, which was verified by close-path eddy covariance flux (R2 = 0.69, p < 0.01). According to the standard precipitation evapotranspiration index (SPEI) and soil moisture (SM), the drought periods of 2010 and 2014 in the eastern piedmont of Taihang Mountains were selected to analyze drought impacts on GPP. We found that SIF had the ability to reflect the effects of drought prolonged action on vegetation and respond to GPP persistent changes during drought. SIF successfully described GPP loss during droughts in 2010 (20.91 gCm−2month−1) and 2014 (9.52 gCm−2month−1). SIF was more significantly linearly correlated with GPP (R2 = 0.89) than NDVI (R2 = 0.69) and EVI (R2 = 0.84), suggesting that SIF was more sensitive to physiological changes in vegetation. SIF correlated significantly higher to SM than SPEI, and there was a two-month lag to SM, which may be related to the adaptation mechanism of vegetation physiological recovery under drought conditions. Results showed that SIF is more accurate and sensitive than NDVI and EVI in regional vegetation drought monitoring and drought-induced GPP variations. SIF is an effective physiological index for timely detection and regional drought, and an effective tool for ecosystem management and disaster warning. [ABSTRACT FROM AUTHOR]