Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies

This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 2.94 units per year while that of NADI-MERRA–2 was 1.21 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.

A Paleo Perspective of Alabama and Florida (USA) Interstate Streamflow

Seasonal reconstructions of streamflow are valuable because they provide water planners, policy makers, and stakeholders with information on the range and variability of water resources before the observational period. In this study, we used streamflow data from five gages near the Alabama-Florida border and centuries-long tree-ring chronologies to create and analyze seasonal flow reconstructions. Prescreening methods included correlation and temporal stability analysis of predictors to ensure practical and reliable reconstructions. Seasonal correlation analysis revealed that several regional tree-ring chronologies were significantly correlated (p ≤ 0.05) with March–October streamflow, and stepwise linear regression was used to create the reconstructions. Reconstructions spanned 1203–1985, 1652–1983, 1725–1993, 1867–2011, and 1238–1985 for the Choctawhatchee, Conecuh, Escambia, Perdido, and Pascagoula Rivers, respectively, all of which were statistically skillful (R2 ≥ 0.50). The reconstructions were statistically validated using the following parameters: R2 predicted validation, the sign test, the variance inflation factor (VIF), and the Durbin–Watson (D–W) statistic. The long-term streamflow variability was analyzed for the Choctawhatchee, Conecuh, Escambia, and Perdido Rivers, and the recent (2000s) drought was identified as being the most severe in the instrumental record. The 2000s drought was also identified as being one of the most severe droughts throughout the entire reconstructed paleo-record developed for all five rivers. This information is vital for the consideration of present and future conditions within the system.

The seasonal correlation between atmospheric water vapor and aerosol extinction and the relationship between aerosol, NO2, SO2 and water vapor during haze pollution in Qingdao, China

<p>MAX-DOAS observations was carried out from March 1, 2019 to December 31, 2019 in Qingdao, China, to measure the O<sub>4</sub>, NO<sub>2</sub>, SO<sub>2</sub> and H<sub>2</sub>O absorption, to retrieve AOD and the troposphere vertical column concentration of NO<sub>2</sub>, SO<sub>2</sub> and H<sub>2</sub>O.We use PriAM algorithm which based on the optimal estimation to calculating volume mixing ratio profile of trace gases, aerosol and water vapor during 0 ~ 4 km. The correlation between AOD and H<sub>2</sub>O VCD was analyzed in every month, the results showed that the AOD and H<sub>2</sub>O VCD has good linear relationship in each month., illustrate the increase of water vapor concentration will lead to the increase of moisture absorption of aerosol. The seasonal variation of the four seasonal correlation slopes in the order of summer < autumn < spring < winter. The influence of concentration change of NO<sub>2</sub> VCD, SO<sub>2</sub> VCD, H<sub>2</sub>O VCD and AOD is discussed in a haze episodes occurred in December 2019. Discovery that the H<sub>2</sub>O VCD and AOD was increased at the same time in the haze pollution incident, but with the increase of water vapor concentration, the concentration of NO<sub>2</sub> and SO<sub>2</sub> decreases, indicated that due to the increase of concentration of water vapor, NO<sub>2</sub> and SO<sub>2</sub> heterogeneous reaction will happen to generate nitrate and sulfate aerosols, so that the concentration of NO<sub>2</sub> and SO<sub>2 </sub>concentration was decreased. The relationship between NO<sub>2</sub>, SO<sub>2</sub>, AOD and water vapor mixing ratio of 50m, 200m, 400m and 600m during haze pollution period was also studied, and it was indicated that phenomenon aerosol extinction increased with the increase of water vapor mixing ratio, while NO<sub>2</sub> and SO<sub>2</sub>, on the contrary, were more obvious at 50m and 200m near the ground.</p>

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes.

Spatio-Temporal Trend Analysis of Rainfall and Temperature Extremes in the Vea Catchment, Ghana

This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability in reproducing the climatology of the catchment was evaluated. The extreme rainfall and temperature indices were computed using a RClimdex package by considering seventeen (17) climate change indices from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Trend detection and quantification in the rainfall (frequency and intensity) and temperature extreme indices were analyzed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator. The results show a very high seasonal correlation coefficient (r = 0.99), Nash–Sutcliff efficiency (0.98) and percentage bias (4.4% and −8.1%) between the stations and the gridded data. An investigation of dry and wet years using Standardized Anomaly Index shows 45.5% frequency of drier than normal periods compared to 54.5% wetter than normal periods in the catchment with 1999 and 2003 been extremely wet years while the year 1990 and 2013 were extremely dry. The intensity and magnitude of extreme rainfall indices show a decreasing trend for more than 78% of the rainfall locations while positive trends were observed in the frequency of extreme rainfall indices (R10mm, R20mm, and CDD) with the exception of consecutive wet days (CWD) that shows a decreasing trend. A general warming trend over the catchment was observed through the increase in the annual number of warm days (TX90p), warm nights (TN90p) and warm spells (WSDI). The spatial distribution analysis shows a high frequency and intensity of extremes rainfall indices in the south of the catchment compared to the middle and northern of part of the catchment, while temperature extremes were uniformly distributed over the catchment.

A large sample analysis of seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the High Flow Season (HFS) and the Low Flow Season (LFS) as the three-month and the one-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 European rivers spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in flood frequency estimation, we fit a bivariate Meta-Gaussian probability distribution to peak HFS flow and average pre-HFS flow in order to condition the peak flow distribution in the HFS upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the flood frequency distribution one season in advance in real-world cases. Our findings suggest that there is a traceable physical basis for river memory which in turn can be statistically assimilated into flood frequency estimation to reduce uncertainty and improve predictions for technical purposes.

A Comparative Study on Precipitation Climatology and Interannual Variability in the Lower Midlatitude East Asia and Central Asia

This study examines precipitation climatology and interannual variability in two regions in the lower midlatitude Asia to the east and west of the Tibetan Plateau, one located in monsoonal East Asia (the M region) and the other in semiarid central Asia (the W region). The focus is on the 5-month summer half year (May–September) for the M region and the winter half year (December–April) for the W region, corresponding to their respective rainy seasons. The main mechanism of moisture transport for the M region is the summer lower-tropospheric southerly winds, whereas the winter midtropospheric westerly circulation between 25° and 45°N is responsible for conducting moisture fluxes to the W region. It is further discovered that the winter precipitation series are positively correlated between the two regions (r = 0.47). There is also a weak cross-seasonal correlation between the winter W region precipitation and summer M region precipitation (r = 0.27). Winter westerly circulation over the W region is influenced by both the east Atlantic–western Russia and the polar–Eurasia extratropical teleconnection patterns, while El Niño–Southern Oscillation influences regional circulation patterns in both regions through teleconnections via the Indo-Pacific warm pool convection in winter and its lagged impact on the western North Pacific anticyclone over the Philippine Sea. In the meantime, responses of the regional winter circulation in the M region to the upstream westerly circulation intensity cause the correlation in winter precipitation between the two regions. Such linkages form the basis of the concurrent and cross-seasonal correlations in precipitation between the two remote regions.