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The intensification of water resource usage, correlated with changes in land use and cover as well as climate variability, has led to significant alterations in the hydrological cycle, impacting water availability in basins. This study assesses hydrological trends in the section of the Janeiro River basin located in the Brazilian savannah (Cerrado biome), focusing on the influence of anthropogenic activities and climate variability between 1985 and 2017. Using precipitation, actual evapotranspiration, streamflow, land use and cover, and water use authorization data, we applied statistical tests (Mann–Kendall, Sen's slope, Pettitt, and RHO Spearman) to identify trends, abrupt changes, and correlations. The results show a decreasing trend in average and minimum flows, with reductions of 30 to 40%, respectively, compared to the historical series average, not attributable to significant changes in precipitation but rather to an expansion of agricultural areas and an intensification of water consumption for irrigation. There was a reduction from 76.5% to the sum of Natural and Forest Formation areas and an increase of 71.1% in Agricultural areas. The correlation between land use changes and streamflows suggests that the conversion of natural vegetation into agricultural lands is directly associated with the decline in water availability. This study highlights the need for sustainable planning and management of water resources, considering the seasonality of water availability and agricultural demands, to mitigate the negative impacts on the hydrological cycle and ensure water sustainability in the Brazilian savannah region. [ABSTRACT FROM AUTHOR]

Climate change significantly impacts water cycle processes and water resource management in the upper Yellow River (UYR), China. Understanding the influence of meteorological factors on low-flow changes is crucial, but the optimal number of antecedent days and the specific contributions of different factors remain unclear. In this study, we use a structural equation model and path analysis to dissect the direct and indirect effects of selected meteorological factors (daily precipitation, P; average temperature, AT; average wind velocity, AWV; average relative humidity, ARH; and total radiation, TR) on four low-flow indices in the UYR. We employ data from 1958 to 2017, collected from six meteorological stations and eight hydrological stations above Lanzhou hydrological station. Our findings reveal that: (1) meteorological factors have varying direct and indirect impacts on low-flow changes at corresponding and cumulative scales. For instance, at the corresponding scale, P, AT, AWV, ARH, and TR have direct impacts of 42%, 54%, 74%, 79%, and 59%, respectively. At the cumulative scale, these values change to 67%, 59%, 67%, 64%, and 60%, respectively. (2) Cumulative effects of meteorological factors enhance the significance and goodness of fit of the analysis model, decreasing residual path coefficients and elevating the contribution of independent variables to the model. (3) The dominant components of meteorological factors affecting low-flow changes differ between corresponding and cumulative scales, explaining the variations in direct and indirect impacts. These insights are valuable for sustainable water resource management in drought-prone regions with water scarcity. [ABSTRACT FROM AUTHOR]

Drought is caused by imbalances in the hydrological cycle's variables, especially lack of rainfall, which is frequently brought on by climate change and can occur anywhere on the Earth. This study aims to conduct a detailed seasonal drought analysis through the seasonal precipitation statistics, groundwater level, and soil moisture in Bankura District during 1991–2022. The study utilized the standardized precipitation index, Standardized water-level Index, and Standardized soil moisture index to assess the meteorological drought, hydrological drought, and agricultural drought at multiple time scales. The three drought indices have been calculated by the 'Standardized Precipitation-Evapotranspiration Index' package using 'R' programming, which make it possible to compare the drought situations in various climatic zones. The three months standardized precipitation index was used in the seasonal analysis of different types of drought. The modified Mann–Kendall trend analysis was used to acquire information on the course of the drought and rains. Correlation analysis was also done to evaluate the dependency of agricultural drought upon the meteorological drought and hydrological drought. Seasonal droughts in meteorological drought, hydrological drought, and agricultural drought have been compared to the production of the four major seasonal crops. The trend of rainfall showed -0.184, -1.149, -1.263 and -3.598 mm decreases in pre-monsoon, winter, post-monsoon and monsoon season respectively. The occurrence of drought with negative standardized precipitation index, standardized water-level index, and standardized soil moisture index values frequently depicted dry events in the study area. The results show that the drought harms the productivity of food grains, with production losses of 122.77 thousand tonnes and yield rate losses of 292.37 kg per hectare from the average, respectively. This study also considered non-structural and structural efforts from the governmental, stakeholder, and research communities to mitigate the seasonal drought, frame drought-resilient agriculture, and promote sustainability. [ABSTRACT FROM AUTHOR]

Integrated Precipitable Water Vapor (IPWV) wields significant influence over atmospheric processes, the climate system, and the hydrological cycle. Spatial and temporal variability characterizes water vapor distribution in the atmosphere, with equatorial regions registering elevated water vapor percentages. There are various types of instruments and methods to assess the quantity of moisture in the air. Global Navigation Satellite System (GNSS) and radiosonde techniques have been widely used to estimate IPWV in the atmosphere. European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-5) is the latest reanalysis IPWV dataset. This study aims to assess the congruence of ERA-5-derived IPWV with GNSS-derived IPWV and study the spatial and temporal variability of IPWV over Indian region. The IPWV data from 16 monitoring stations of GNSS Atmosphere Water Vapor Watch Network of the India Meteorological Department (IMD) have been compared with ERA-5 data. The IPWV data from GNSS and ERA-5 are in excellent agreement as corroborated by correlation coefficients spanning 0.97 to 1.00 and the Root Mean Square Error (RMSE) values varying between 1.5 mm and 5.6 mm. IPWV values exhibit prominent seasonal variations, with minimum values during the winter months and peak appears between June and September, aligning with warm and moist monsoon season of India. The ERA5 data from 1981 to 2020 were used to study variability and trend over Indian region. Strong positive correlations are observed between rainfall and IPWV. The results indicated IPWV trends are moistening especially over Indian landmass, the Indian Ocean, Arabian Sea and Bay of Bengal during all the seasons except winter. Highlights: GNSS retrieved IPWV from 16 Indian stations used for comparison with ERA5 data. Strong agreement observed between ERA5 and GNSS IPWV data. Study examines correlation of IPWV with precipitation and surface air temperature. Study presents long term trend in IPWV and Rainfall. IPWV and rainfall positively correlated but have noteworthy geographic distribution characteristic. Annual mean rainfall and IPWV shows overall increasing trend over Indian subcontinent. Temperature and IPWV are negatively correlated for monsoon and pre-monsoon season. [ABSTRACT FROM AUTHOR]

Climate change accelerates the changes in the hydrological cycle, which increases or decreases the intensity and duration of the drought events. This study assesses the drought risk under future climatic projections by computing drought hazard and drought vulnerability at a sub-basin scale. A multivariate drought hazard index (MDHI) considers deficits in precipitation and streamflow to quantify droughts. MDHI was analyzed using the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Streamflow Drought Index (SDI). Soil and Water Assessment Tool (SWAT+) simulated hydrological variables under baseline and future SSP scenarios were used to estimate MDHI. Drought vulnerability assessment is estimated by combining exposure, sensitivity and adaptive capacity indicators. The results show that the Drought Hazard Index (DHI) are likely to decrease in future climate change scenarios in comparison with the baseline. The Drought Vulnerability Index (DVI) reveals more than 50% of the basin falls under the high and very high vulnerable category. Based on the drought risk assessment, during the baseline period 48.82%, and 15.25% of the total basin area fall under high and very high drought risk. In the case of the future SSP245 and SSP585 scenarios, the high drought risk area gets reduced extensively with 15.09% and 1.02% respectively. The drought risk will be lower for future scenarios compared to baseline. This study aids the policymakers and water managers in prioritizing the projects on a sub-basin level to solve drought risk in future climatic conditions. [ABSTRACT FROM AUTHOR]

The trend detection in hydro-climatological series is a prime priority in the context of changing climate. Evapotranspiration is a crucial component of the hydrological cycle and surface energy balance that plays a key role in irrigation water management. The present study examined the spatio-temporal variations of annual and seasonal reference evapotranspiration using Mann–Kendall and Innovative trend analysis, in South West and central part of Punjab over the period of 1980–2021. The results revealed that reference evapotranspiration (RET) for the annual, pre-monsoon, and post-monsoon series at Sangrur (Central Zone) and for winter at Bathinda (South-West) were serially correlated using the autocorrelation test of lag-1 at a 5% significance level. The highest aridity index (0.34 ± 0.10) and the lowest moisture index (-0.66 ± 0.10) were detected at Moga station, followed by Sangrur station in Central Punjab. The overall results revealed that annual and seasonal trend of reference evapotranspiration was significantly decreasing over the period 1980–2021. The spatial distribution of Sen's slope percentage change was highest at Mansa station, followed by Sangrur station. Mann–Kendall trend analysis of annual and post-monsoon series revealed that out of a total of six stations, Barnala, Muktsar Sahib, and Sangrur showed a significant decreasing trend. The Sen's slope and ITA trend slope of monsoon series are statistically decreasing at 1% and 5% significance level all over the districts. In the pre-monsoon season, an increasing trend of RET was detected throughout all the stations, with the highest percentage change of 4.3% detected at Mansa station. The ITA trend slope of ET0 exhibited decreasing magnitude in the annual, monsoon, post monsoon except at pre-monsoon season all round the districts. While the contrasting trend is found in MK slope exhibited increasing and decreasing trend in the pre-monsoon season. The innovative trend slope revealed the presence of a hidden trend for RET that was missed by the traditional MK/MMK test. The study may guide planners and policymakers in the effective implementation of crop water management strategies to improve the status of water resources in Punjab. [ABSTRACT FROM AUTHOR]

Drylands, where the water supplies are scarce relative to the atmospheric moisture demands, occupy approximately half of the area of China. With global warming, the response of atmospheric water cycle is a crucial concern for sustainable development in China's drylands. This study investigates the characteristics of the water cycle in China's drylands during 1980–2019, as well as future projections estimated by the Community Earth System Model (CESM) during 2020–2100. The results show that the air temperature in China's drylands has increased significantly since 1980, especially after 2000, with greater warming in the warm season. Meanwhile, China generally tend to be drier from 1980 to 2019 as a whole, especially in the cold season. Both precipitation (P) and evapotranspiration (E) have increased, but the net effect is that drylands become drier (negative P-E) and wetlands become wetter (positive P-E), respectively. In the future, drylands are projected to become more humid due to increased precipitation, showing a pattern of "western wetter and eastern drier" during 2020–2100. These results could improve understanding of the water cycle and provide some guidance for adapting to climate change in drylands. [ABSTRACT FROM AUTHOR]

Global warming has had significant effects on the hydrological cycle. In North Africa, these effects have resulted in a continued decrease in the amount of annual precipitation, greatly influencing the availability of surface and groundwater resources. This paper aims to evaluate drought episodes in a long dataset from Djelfa (Algeria) using a statistical approach based on a decision support system enabled by the Hyfran-plus software, to fit the best statistical distribution model and predict non-exceedance probability of precipitation. The standardized precipitation index is used to highlight sequences of meteorological drought and changes in weather patterns. Shift in precipitation has been identified by Wilcoxon Test for homogeneity; Wold-Wolfowitz test of independence, Kendall's test of trend and new cumulative mean difference curve (CMD) developed to visualize the trend of the precipitation and runoff dataset, Autocorrelation function (ACF) and Fourier spectrum and Deuterium excess isotopes has been used to visualize trends. Log–log plot; the mean excess function (MEF), AIC and BIC criteria are used to select the best distribution fitting model. The statistical results obtained indicate that the dataset follows a heavy-tailed distribution (Pearson type III), Values of excess Deuterium indicate that groundwater recharge has occurred in cold weather different from the actual period. SPI analysis shows that dry years cover more than 51% compared to 49% of wet years. The CMD curve shows a change and trend in the precipitation and runoff time series since 1974. Forecasts show drought recurrences to be between 3 and 200 years. [ABSTRACT FROM AUTHOR]

Selin Co basin, a representative lake basin situated in the central region of the Tibetan Plateau (TP), is characterized by extensive areas of frozen ground and has undergone a remarkable expansion in its lake area. Evapotranspiration, as an important variable within the lake water cycle, as well as its influencing factors are still under debate at this basin. Actual evapotranspiration (ETa) plays a vital role in influencing land-atmosphere interaction processes and the hydrological cycle. By quantifying the amount of water that is actually transpired by vegetation and evaporated from soil surfaces, ETa provides important information about the mechanisms controlling evapotranspiration. In this study, the advection-aridity (AA) model was employed to estimate ETa in Selin Co basin. The estimated ETa and its influencing factors were investigated between 2001-2018, with an emphasis on examining both spatial and temporal trends. The results demonstrated that the multiyear mean ETa in the basin was 430.3 mm (excluding lakes) with an overall significantly decreasing trend (-5.8 mm/yr). In terms of its seasonal variation, ETa exhibited highest value during summer, while experiencing comparatively lower rates in autumn and spring. Furthermore, the spatial distribution of both the annual and seasonal ETa exhibited a similar decreasing trend from the southeast to the northwest of the basin. The sensitivity of several meteorological variables to modifications in ETa was also examined. Results indicated that specific humidity was the predominant variable, followed by net radiation, air temperature, precipitation, atmospheric pressure, and wind speed. In addition, the area covered by permafrost and seasonally frozen ground was approximately about 1.58 × 104 km2 and 4.22 × 104 km2 in the basin, and permafrost degradation in the northern region of the Selin Co basin resulted in increased average soil moisture content and underground ice melting, leading to a subsequent increase in ETa. [ABSTRACT FROM AUTHOR]

Changes in the hydrological cycle alter the meteorological variables trend over time, leading to higher extreme events. In this study, the spatial and temporal variability of horizontal visibility (VIS), maximum temperature (Tmax), minimum temperature (Tmin), precipitation, and the Standardized Precipitation Evapotranspiration Index (SPEI) are assessed over Saudi Arabia for the study period (34 years, i.e., 1985–2018) and two subperiods (1985–2001 and 2002–2018). The temporal variability of the variables is investigated for five climatic regions, using the Sen's slope, Mann Kendall test, modified MK, and cumulative sum (CUSUM). Results confirm that precipitation over regions has declined by more than 21.1% during the second subperiod (2002–2016) compared to the first subperiod (1985–2001). Combined with the reduction in precipitation, a high increase in minimum temperatures in the second period contributes to the evolution of drought and visibility degradation. The onset of severe drought events (after the late 2000s) corresponds to an acceleration of VIS degradation. The change points for VIS occurred earlier in the arid regions (Northern and Interior regions, 2007 and 2008, respectively) and 2011–2012 for the rest. The change points for precipitation, Tmax, and SPEI series occurred in late 1990, while for Tmin, it occurred in 2010. In the first period, VIS show weak to moderate correlations with drought indices (12-month SPEI) for all regions, except for the Coastal region. In the second period, Northern, Interior, and Coastal regions show strong correlations between VIS and SPEI 12, while mountain regions (Highland and Southern) show moderate to week correlations between VIS and SPEI 12. The abrupt changes in climate teleconnection indices, which has a higher influence on Tmin in the second subperiod, may be the reason for drought evolution and visibility degradations. [ABSTRACT FROM AUTHOR]

Based on satellite observation and reanalysis data, basic features of cloud water and precipitation and the dependence of precipitation efficiency (PE) on environmental factors over the Sichuan Basin and adjacent regions are investigated. Results found that the spatiotemporal distribution characteristics of precipitation and cloud water over the Sichuan Basin and adjacent regions are consistent. The liquid water path (LWP) and ice water path (IWP) in the Sichuan Basin (SCB) are richer than the West Sichuan Plateau (WSP) and Yunnan-Guizhou Plateau (YGP), and the contribution of IWP to precipitation in Sichuan Basin and adjacent regions is greater than that of LWP. Furthermore, the results indicate that PE has the most significant dependence on the low-tropospheric relative humidity (RH) and the convective available potential energy (CAPE) over the Sichuan Basin and adjacent regions. Higher RH and CAPE contribute to a larger PE in the Sichuan Basin. The CAPE has a positive effect on the PE, which indicates that PE is directly affected by precipitation convection, mainly due to the special topography of the Sichuan Basin and adjacent regions, leading to frequent convective activities. The ratio of LWP to IWP (RLI) affects PE. The RLI decreases with the increase of IWP, leading to an increase in PE. RLI is negatively correlated with PE, which further indicates that ice water clouds have a more significant impact on PE over the Sichuan Basin and adjacent regions. Through this study, we can enhance our understanding of the formation processes, spatio-temporal structures, and evolutionary mechanisms of cloud precipitation in the Sichuan Basin and its adjacent areas. This is crucial for unraveling the dynamics of atmospheric water cycle, climate change processes, and optimizing the utilization efficiency of cloud water resources. [ABSTRACT FROM AUTHOR]

This paper analyzes the correlation between precipitation and deforestation and trends in the Brazilian Amazon Biome from 1985 to 2021 using rain gauge measurements. A total of 187 stations were selected after filtering time series with less than 10% of missing data. Deforestation data was acquired using the MapBiomas dataset. Results show that deforestation rates vary across different locations and may be influenced by various factors. The analysis also found significant negative correlations between precipitation and deforestation, with a range of − 0.49 to − 0.33, as well as significant positive correlations, with a range of 0.34 to 0.57. These findings underscore the importance of continued monitoring of precipitation patterns in the region and the urgent need for measures to mitigate deforestation, which is critical for the hydrological cycle of the region. The highest deforestation rates and values were found between latitude 0 and − 12.5, where the arc of deforestation lies. Additionally, this study found a significant negative correlation between precipitation patterns and deforestation, indicating that deforestation led to a reduction in rainfall in the region. [ABSTRACT FROM AUTHOR]

The global climatology (spatial and temporal) of the total cloud fraction (precipitating cloud (rain cloud + snow cloud) + non-precipitating cloud) during 2007–2016 is investigated based on the observations derived from the CloudSat. It is revealed that the total cloud fraction occurs in the tropical belt (~±15° latitude) over all seasons. Among them, the mean fraction of precipitation clouds is ~12% observed in all seasons and dominates over the high-latitude oceans. The monthly mean fraction of precipitating clouds (~12%) is lower than the non-precipitating clouds (~35%) during 2007–2016. The distribution mode of rain cloud fraction is similar to that of a precipitation cloud in the mid-latitude and Intertropical Convergence Zone over the oceanic environment. In contrast, the precipitation cloud fraction has a greater snow cloud fraction in high latitudes (maximum of ~40%) over the hemispheres (north and south) during the winter season. Also, the snow cloud fraction presents a relatively inter-annual variation compared to rain clouds for all months, especially after 2011. It is attributed to the reduction of instrument detection performance, or the deviation of daytime detection data after 2011 and the sum of daytime and nighttime data before 2011. On the other hand, it may also be related to the increase of extreme precipitation under the effects of global warming, resulting in the increase of fluctuation in standard deviation year by year after 2011. The results obtained highlight the importance of cloud processes for the global hydrological cycle and may lead to an enhanced parameterization of precipitation in general circulation models. [ABSTRACT FROM AUTHOR]

The research on precipitation stable isotopes is beneficial for gaining insight into the water cycle in the monsoon region. However, the variability of monsoon and lack of high-resolution collection of precipitation stable isotopes in the monsoon region has been posing a limitation on learning the controlling factors on its variations. This study collects the daily precipitation stable isotopes in Guiyang from April 2022 to March 2023. The research results show that the precipitation stable isotopes display seasonal variations with the slope and intercept of the fitted local meteoric water line (LMWL) being 8.9 and 19.69, respectively. During the year, precipitation δ18O has a weak negative correlation with precipitation amount and temperature, but the correlation between precipitation δ18O and local environmental factors is not as significant as the one between precipitation δ18O and precipitation amount and outgoing longwave radiation (OLR) of the upstream area. During the monsoon season (MS), as the Intertropical Convergence Zone (ITCZ) migrates northward, the oceanic water vapor and upstream convective activity cause the decrease in precipitation stable isotopes. While during the non-monsoon season (NMS), the local circulation water vapor becomes the dominant water vapor source, leading to the increase in precipitation stale isotopes. The above studies demonstrate that water vapor sources and upstream convective activities play a critical role on variations of precipitation stable isotopes in Guiyang, which can provide basic information for future research on the water cycle in the monsoon region. [ABSTRACT FROM AUTHOR]

Reference crop evapotranspiration (ETO) is a basic component of the hydrological cycle and its estimation is critical for agricultural water resource management and scheduling. In this study, three tree-based machine learning algorithms (random forest [RF], gradient boosting decision tree [GBDT], and extreme gradient boosting [XGBoost]) were adopted to determine the essential factors for ETO prediction. The tree-based models were optimized using the Bayesian optimization (BO) algorithm and were compared with three standalone models in terms of daily ETO and monthly mean ETO estimations in North China, with different input combinations of essential variables. The results indicated that solar radiation (Rs) and air temperature (Ts), including the maximum, minimum, and average temperatures, in daily ETO were the key variables affecting model prediction accuracy. Rs was the most influential factor in the monthly average ETO model followed by Ts. Both relative humidity (RH) and wind speed at 2 m (U2) had little impact on ETO prediction at different scales, although their importance differed. Compared with the GBDT and RF models, the XGBoost model exhibited the best performance for daily ETO and monthly mean ETO estimations. The hybrid tree-based models with the BO algorithm outperformed standalone tree-based models. Overall, compared with the other inputs, the model with three inputs (Rs, Ts, and RH/U2) had the highest accuracy. The BO-XGBoost model exhibited superior performance in terms of the global performance index (GPI) for daily ETO and monthly mean ETO predictions and is recommended as a more accurate model for predicting daily ETO and monthly mean ETO in North China or areas with a similar climate. [ABSTRACT FROM AUTHOR]

A classification was achieved to obtain some daily atmospheric circulation types (days that have a similar atmospheric circulation pattern), capable of reproducing sequences specific to the solid precipitations' events that occur in the mountains of the Southern Alps (Mediterranean region). These atmospheric circulation types were statistically studied to get the evolution of the frequency of the synoptic conditions favorable and unfavorable to solid precipitations. The classification produced six atmospheric circulation types, including two synoptic snow classes and two dry classes. The south circulation with a trough on the Western of Europe (STW) is the major snow classes. STW reproduced 62% of the total solid precipitations' events, 68% of those with a medium intensity level, and 100% of those with the high intensity level, which shows the effectiveness of the approach. The total number of days of presence per decades of STW decreased between 1950 and 2019. This decreasing frequency of occurrence also applies to the other synoptic class of snow, the depression over central Europe (DCE). Conversely, west anticyclonic (WA) and southwest anticyclonic (SWA) circulations, did not produce any solid precipitations events and indicated the opposite logic, with an increase in their occurrences. This could lead to a strong change in the climate of the South of France, with major impact on the thickness and duration of snow cover. A sharp decrease in snow cover would have heavy economic consequences, the water cycle would be modified, the management of water resources could then be severely called into question, and more worryingly, it would increase the intensity of periods of drought. The most plausible explanation for this phenomenon would come from the spread of Hadley's cell, which would push the Azores anticyclone further north and leads to a higher frequency of occurrence of WA and SWA. [ABSTRACT FROM AUTHOR]

Afghanistan, as a landlocked country located within central and southwestern Asia, has an arid to semi-arid climate. Most of the people are involved in agricultural activities, and a major part of the country's gross domestic product depends on agriculture, but the country has the lowest water storage capacity. Consecutive periods of drought and rapid snowmelt due to climate change have made it more challenging for suitable water resource management practices. This study investigates the historical meteorological drought characteristics across the whole country by employing the Reconnaissance Drought Index for the period 1979–2019 using data from 55 meteorological stations. Trends in precipitation and temperature are also investigated using the Mann-Kendall's and the Sen's slope statistical tests. A four-decadal countrywide drought map is generated. Extreme and severe droughts were observed in 1999 and 2000 across the whole country. Moderate drought events have started to occur with a frequency of 3 to 5 years since 1999. The decadal annual rainfall values in each river basin indicate that rainfall has decreased in the last two decades with a significant decline in 1999-2008. The trends of increase in temperature and decrease in precipitation are indications of rapid climate change in the country, especially in the south, west, and southwest regions. Due to the intensity and frequency of the droughts, river flow rates have decreased; and therefore, there is a need for the upstream and downstream neighboring countries to come to terms with the phenomenon of a new normal in the hydrological cycle and accordingly revise new water sharing treaties. [ABSTRACT FROM AUTHOR]

In this study, modeling of temporal trends for commonly climatic variables for the base line period and future time interval was undertaken for Gelgel Belese watershed in Upper Blue Nile Basin, Ethiopia. The climatic variables data from the period 1998 to 2019 was considered the baseline period and the future climate variables data were downscaled using dynamic downscaling method from the Coordinated Regional Climate Downscaling Experiment over the African Domain for 2020 to 2100. Mann–Kendall's trend test and time series linear model were used to test the temporal trends of climatic variables in the study area. The test result of observed precipitation indicated non-significant trend, while the average annual temperature indicated an increasing trend with the rate changes of 0.0067 ∘ C /year and 0.005 ℃/year in Dangila and Chagni stations, respectively. Whereas, in the same time interval non-significant increasing trend in average annual temperature in Pawi station was common. In line with average annual temperature, evapotranspiration indicated significant increasing trend with the rate change of 0.68 mm/5 year in Dangila station and non-significant increasing trend in Chagni and Pawi stations. Future average annual temperature and annual evapotranspiration imply significant increasing trend, whereas mean annual future precipitation exhibited non-significant decreasing trend in the majority of the RCPs in all stations in the future time interval. This change may be responsible for alterations of the hydrological cycle in the study area. Higher increment of temperature and evapotranspiration and reduction of rainfall in the future over the study watershed may result in decreasing pattern of total flow of the river in the area. Due to this, water demand dissatisfaction for different water users in the study area may become common event. Thus, the designs of different water harvesting infrastructures for different water users should be taken into account in the study area in the future. [ABSTRACT FROM AUTHOR]

Reanalysis products are the most often used data to characterize the Congo Basin climate system due to the lack of observational data. However, although some studies assessed their performance in reproducing the central equatorial Africa rainfall system, there is still a knowledge gap about the uncertainties that feature these datasets. Yet, they are widely used in the process-based evaluation of climate models over the basin. The present study is a first step towards assessing these uncertainties, i.e., the analysis of discrepancies between the ERA5, MERA2, and NCEP2 reanalysis datasets in the simulation of the thermodynamic processes governing the moisture balance. We focus on the Congo Basin's atmospheric hydrological cycle while decaying the moisture budget into its dynamic and thermodynamic components during rainfall peaks in March–April–May (MAM) and September–October–November (SON). The results show that reanalysis data consistently simulate a prevalence of thermodynamic processes in the moisture provision into the basin during the little rainy season MAM, but instead a prominence of dynamic mechanisms during the great rainy season SON. Apart from MERRA2, there is also an agreement that the evaporation rate weakens in SON, in association with a decrease in the energy required to evaporate soil moisture. However, large uncertainties feature mechanisms contributing to the thermodynamic component during both seasons. Indeed, in MAM, surface solar radiation is the factor maintaining high evaporation in ERA5 and NCEP2 while soil moisture does so in MERRA2. In SON, higher values of soil moisture together with the surface latent heat flux are factors inducing higher evaporation in MERRA2 than in ERA5 and NCEP2. Also, the high rate of evaporation in NCEP2 (3.61 mm/day) than in ERA5 (3.52 mm/day) is associated with the difference in their surface irradiation (23 W. m−2). [ABSTRACT FROM AUTHOR]

As ideal tracers of the global water cycle, water stable isotopes show a temperature effect at mid-high latitudes and an "amount effect" at mid-low latitudes. However, the interpretation of stable isotopes in the monsoon region remains uncertain. To understand the factors controlling daily precipitation δ18O values in the midlatitude monsoon region, continuous observations of precipitation isotopes in Rizhao from 2021 to 2022 were made. We investigate the drivers of precipitation δ18O by analyzing their relationship with temperature, precipitation amount, relative humidity, and outgoing longwave radiation (OLR) data. Back trajectory analysis with the HYSPLIT model based on precipitation events was also used to trace moisture sources. The results show that the controlling factors of stable isotopes in precipitation vary during different seasons. In the spring–summer season, precipitation δ18O is associated with heavy precipitation over the region of the Asian continent, the tropical Indian Ocean, the South China Sea, and the local sampling site, consistent with the spatial patterns of OLR, indicating that convection activities are the main driver of precipitation isotopic values in those seasons. In the autumn–winter season, the positive correlation between precipitation δ18O and local and regional-scale temperature confirms that Rayleigh distillation controls moisture transport. These findings improve regional-scale understanding of hydrological cycles in the East Asian mid-latitude monsoon region and have the potential to improve our understanding of isotopic variations in the proxy archives of the East Asian monsoon region. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is a key factor in the hydrologic cycle and quantifying ETo for future periods is essential for the efficient management of water resources. The objective of this research was to project the possible changes in ETo in future periods in Iran. For this purpose, observed climate data in 40 stations across Iran were collected from 1984 to 2014 and ETo was calculated using these data and the FAO56–PM method. A multi-model ensemble of 27 CMIP6 models under two scenarios, SSP1-2.6 and SSP5-8.5, for the periods 2031–2060 and 2061–2090, was used. A linear regression (LR) model was applied for downscaling and bias correction of CMIP6 temperature using the observed temperature data. The output of the LR model was entered into an artificial neural network (ANN) model with an optimized structure, trained by the observed temperature and the calculated ETo, to project the future ETo based on the downscaled and bias-corrected CMIP6 future temperature. Results showed that ETo will increase in both 2031–2060 and 2061–2090 periods under SSP1–2.6 and SSP5–8.5 scenarios, although the rate of increment was higher under SSP5-8.5 in all stations. Considering all stations, the average of ETo changes was ~ 0.9 mm/day for 2061–2090 under SSP5-8.5. It was found that the ETo rising rate was higher in arid environments, and therefore, shifting to drier conditions with higher water demands will be expectable in such climates in the future. This could cause severe water crises in these environments. [ABSTRACT FROM AUTHOR]

Standardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and bagging tree models, were adopted to predict the standardized precipitation index (SPI) at the Upper Godavari Basin for various periods (3 months, 6 months, and 12 months). The data-driven models' input data was pre-processed with machine learning models to increase quality and the model's performance a priori. These four models predicted the SPI-3, SPI-6, and SPI-12 months based on three metrological station data. Based on the statistical performance metrics such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE), our findings showed that the bagging was the best model for predicting SPI-3 and SPI-6 while the M5P the best for SPI-12 estimation in station 1, while in stations 2 and 3, M5P was superlative in predicting the SPI-3 and SPI-12 months, and the bagging was best in SPI-6. All the best models had acceptable mid-term drought forecasting based on the SPI-3, SPI-6, and SPI-12 months for three stations in the Upper Godavari Basin in India. The machine learning models created in this study produced satisfactory results in short-term and mid-term drought forecasting, and it will be a new strategy for water developers and planners to use for future management and scheduling. [ABSTRACT FROM AUTHOR]

Previous studies depicted that the global gridded hydroclimatic products mostly lack precision and are inconsistent throughout the real-world water cycle. The current study evaluates the efficiency of the eight streamflow datasets (FLDAS, GLDAS 2.1, GLDAS 2.0, ERA5, TERRA, ERA5_Land, MERRA2, and GRUN) in two large-scale watersheds with different climate conditions (Karkhe River basin in Iran and Rio Itapecuru River basin in Brazil). To correct the products via different statistical metrics, two tuning procedures (including scale factors and the network-based Muskingum method) have been used. Based on the results showing the paramount impacts on the correction procedures of the products, GRUN, ERA_land, and GLDAS 2 perform the best accuracy in the Karkhe River basin; however, the worst product in the watershed is GLDAS 2.1. In the Rio Itapecuru River basin, MERRA2 and TERRA have the best and worst performance, respectively. In the first watershed, the KGE and TaylorS in GLDAS 2.1 improved by 0.85 and 0.23 in the case of correction using scale factor, and these statistics also significantly increased using coupled scale factor and Muskingum routing methods by 0.84 and 0.21, respectively. In the second watershed, these statistics increased by 9.16 and 0.56 in the worst case using scale factors; these metrics also levelled up by 9.32 and 0.62 via coupled scale factor and Muskingum routing methods. In addition, it appeared that the corrected products could better simulate the streamflow in terms of oscillation time series and the extreme temporal values of the watersheds. [ABSTRACT FROM AUTHOR]

In the present research, the hydrologic cycle in the big watershed of Khafr (1108.05 km2), Fars province, Iran, affected by land use in a 9-year period (2009–2017) was investigated. The flow of water and the trends of temperature (6.1–27.2 °C) and rainfall (300–600 mm) (1967–2018) were investigated using Mann–Kendall and Sen's tests. The hydrologic cycle was investigated using 22 observing wells and was subsequently simulated using the WetSpass (Water and Energy Transfer between Soil, Plants and Atmosphere under quasi Steady State) model in which the input variables including climate, potential evapotranspiration, soil layer, ground water level, topography, slope and land use were integrated in ArcView GIS maps. The land use in the region was illustrated by Landsat, and was processed by ENVI for the production of the final maps for use in the model. The output variables of the model were plant cover, land use, flooding pathways, rain-harvesting, rainfall-runoff, and charging and discharging of groundwater level. The maximum total yearly runoff of 724 mm, cold season of 632 mm and warm season of 250 mm were estimated by the model. The maximum of yearly groundwater charging of 91 mm, discharge (warm season) of 25 mm and yearly rain-harvesting of 145 mm were also estimated by the model. The WetSpass model simulated the hydrologic cycle in the region with a high accuracy. If land use is managed properly in the region, it would be possible to increase the efficiency of water sources. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau Vortex (TPV) is an important low-pressure system that affects the weather and climate in the Qinghai-Tibet Plateau. It is of great importance to study the water cycle of the TPV to further understand the TPV and its resulting disastrous weather. This study is different from the previous studies of TPV cases or of the atmospheric water cycle of the Qinghai Tibet Plateau. Focusing on the moving TPV weather system, from the perspective of climatology, the characteristics of the atmospheric water cycle of the TPV and the atmospheric circulation that affects the activity of the TPV water cycle are studied using statistical methods based on the reanalysis data. The results show that the TPV water cycle is mainly affected by external circulation. The overall water vapour utilization efficiency of the TPV water cycle first decreases and then increases, and its activity degree first decreases and then increases. The high-value area of TPV precipitation is located in the middle of the plateau and annual TPV precipitation has a good correlation with TPV water cycle activity. The large-scale system is closely related to the vortex water cycle of the plateau. When the intensity of the South Asian high (SAH) is relatively strong, the West Pacific subtropical high (WPSH) advances westward, and there is a stronger warm rising area over the main body of the plateau. When the southerly airflow on the south side of the low-level plateau is strong, the water cycle of the TPV is more active, easily moves eastward and produces more precipitation, and vice versa. [ABSTRACT FROM AUTHOR]

As one of the inland rivers in arid areas of Northwest China, precipitation is an important supply for the Shiyang River. Daily-scale stable isotopes of precipitation can provide record evidence for the moisture sources, so we collected daily δ2H and δ18O data of precipitation obtained from 11 sampling points and reanalysis meteorological data in the Shiyang River Basin from 2016 to 2020, to explore the moisture sources and the impact on moisture fluxes and precipitation isotope. The results showed the δ2H and δ18O values increase from upstream area to midstream and downstream areas, but the spatial variation of d-excess values is reversed. Dominated by westerly moisture, the moisture flux is higher when the westerly wind strengthens in winter. And the strength of the monsoon determines the water vapor content in summer. The δ18O is depleted in precipitation related to maritime sources than continental sources. Significant depletion of δ18O in precipitation is associated with the Atlantic Ocean (− 13.9‰) and Indian Ocean (− 13.5‰) sources, attributed to enhanced Rayleigh distillation caused by longer transport distances. The enrichment of δ18O in precipitation is associated with continental sources affected by evapotranspiration and secondary evaporation, such as inland China (− 8.0‰), Europe (− 8.6‰), and Mongolia-Siberia (− 9.0‰). This study indicates the moisture sources and transport processes in inland river basins of arid area, contributing to the understanding of the water cycle. [ABSTRACT FROM AUTHOR]

Climate system anomalies and intensified human disturbances have different impacts on the hydrological cycle at different scales, chiefly leading to prominent spatio-temporal heterogeneities in precipitation distributions. It is of great significance to accurately detect the non-stationary changes of precipitation. Focusing on the Inner Mongolia section of the Yellow River basin which is considered as the key ecological barrier in northern China, the best probabilistic model with time-varying moments was established to fit the wet-season precipitation series from 1988 to 2017 for 38 meteorological stations. Considering four three-parameter distributions, time was used as covariate to describe the linear or nonlinear change of each parameter, and model optimization was performed by Akaike Information Criterion. Combined with conventional methods including the Trend-Free Pre-Whitening Mann–Kendall trend test and Sen's slope estimator, the non-stationary behaviors of wet-season precipitation variability were quantitatively captured. Results showed that the generalized gamma distribution performed best in fitting the wet-season precipitation series in the study area, characterized with high skewness and heavy tails. The non-stationary characteristics of the wet-season precipitation were obvious in most areas during the past 30 years, especially in the central region. The non-stationarities of wet-season precipitation manifested in a downward trend in the mean value, an increase in dispersion degree and significant changes in distribution shape with time, consequently adding the uncertainty to wet-season precipitation process and raising the risk of extreme conditions. The findings of this study provide scientific references to water resources management, drought resistance, and disaster reduction. [ABSTRACT FROM AUTHOR]

The global hydrological cycle is predicted to be intensified under the warming climate, with more extreme precipitation events and longer dry spell in between. Here, we evaluated how extreme precipitation events (EPEs) with antecedent dry (dry-EPEs) and wet (wet-EPEs) water conditions influence carbon exchange along gradient of arid, semi-arid, and sub-humid ecosystems based on eddy covariance datasets. After EPEs, ecosystem respiration and gross primary productivity (GPP) were stimulated by pulses of soil moisture in arid and semi-arid regions, but suppressed by decreased soil temperature in sub-humid region. Antecedent water condition determined asynchronous response of ecosystem respiration and GPP to EPEs, and therefore fluctuations in net carbon balance. Net carbon uptake capacity was enhanced immediately following wet-EPEs because of more rapid and greater response of GPP than respiration. However, after dry-EPEs, net carbon uptake capacity decreased immediately and increased thereafter because the response of GPP to dry-EPEs lagged behind ecosystem respiration. More antecedent precipitation further stimulated accumulative net carbon uptake. In general, the accumulated net carbon uptake within 3 weeks after wet-EPEs was two times and seven times of that after dry-EPEs in arid and semi-arid region, respectively. In sub-humid region, ecosystems acted as a carbon sink after wet-EPEs, but as a carbon source after dry-EPEs. We conclude that antecedent water conditions and local climate regimes need to be considered when interpreting the response of carbon exchange to EPEs in dryland ecosystems. [ABSTRACT FROM AUTHOR]

Alpine snow is an important part of the water cycle in arid/semiarid regions and has a great influence on runoff and ecosystems. In this study, both optical and passive microwave remote sensing data were used to characterize the vertical distribution of snow cover area (SCA) and snow water equivalent (SWE) on the north slope of Central Tianshan Mountain, northwest China, focusing on exploring the different spatial–temporal characteristics between SCA and SWE, as well as their different responses to climatic factors at different altitudes. The results show that (1) the SCA and SWE have similar vertical distribution characteristics but show different changing rates and changing directions over time at different altitudes. (2) In the snow accumulation period and snowmelt period, the correlation relationships between precipitation/temperature and SCA are the same (positive or negative) as those between precipitation/temperature and SWE at low-middle altitudes but opposite at high altitudes. (3) The influence of precipitation/temperature on SWE has a linear relationship with altitude but not with SCA. (4) The relative importance of precipitation/temperature for SCA is the same as that for SWE at different altitudes. The results illustrated the different vertical responses to climatic factors between SCA and SWE, which could be beneficial to predicting the changes in mountain snow under the climate change scenario. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the main fluxes in the global water cycle. As the Brazilian Pampa biome carries a rich biodiversity, accurate information on the ET dynamics is essential to support its proper monitoring and establish conservation strategies. In this context, we assessed an operational methodology based on the Simplified Surface Energy Balance Index (S-SEBI) model to estimate energy fluxes over the natural grasslands of the Pampa between 2014 and 2019. The S-SEBI is an ET model that requires a minimum of meteorological inputs and has demonstrated reasonable accuracy worldwide. Therefore, we investigated the model performance considering radiation data from both ERA5 reanalysis and Eddy Covariance measurements from a flux tower. Furthermore, comparisons from satellite-based estimates with in situ measurements were performed with and without energy balance closure (EBC). Results indicated that the meteorological inputs have low sensitivity on daily ET estimates from the S-SEBI model. In contrast, the instantaneous energy balance components are more affected. The strong seasonality impacts the evaporative fraction, which is more evident in late summer and autumn and may compromise the performance of the model in the biome. The effects in the daily ET are lower when in situ data without EBC are considered as ground truth. However, they are less correlated with the remote sensing-based estimates. These insights are useful to monitor water and energy fluxes from local to regional scale and provide the opportunity to capture ET trends over the natural grasslands of the Pampa. [ABSTRACT FROM AUTHOR]

One of the significant components of the hydrological cycle is evapotranspiration. Monthly meteorological parameters of 35 years from 19 meteorological stations across the Northern Region of Nigeria (NRN) were obtained and utilized for the calibration of Hargreaves–Samani (HS) model by comparing between potential evapotranspiration (ETo) values estimated from the original HS and the Penman–Monteith (FAO-56 PM) models. The calibrated HS equation was assessed using trend patterns and some statistical indices. The average value of root mean square error (RMSE) and the mean absolute error (MAE) decreased by 37.1 and 40%, respectively, after the calibration of the model. Also, the correlation coefficients (R) of stations that had values > 0.8 increased from 6 to 11 and the minimum R value increased by 12% above that of the original HS equation. The trend and spatial map of the statistical tests conducted also indicate better performance in most climatic regions after calibration. The precision of the HS equation improved significantly after calibration for semi-arid, humid, and sub-humid regions. However, few stations in the semi-arid, humid, and sub-humid regions did not show drastic improvement due to the peculiarity of the location and high variations in the wind speed and relative humidity parameters. [ABSTRACT FROM AUTHOR]

The transport of atmospheric moisture presents a crucial role in the global hydrological cycle. In this paper, we performed an empirical orthogonal function (EOF) investigation of integrated moisture transport (IVT) which was observed to be connected with the renowned regional climate phenomenon named El Nino–Southern Oscillation (ENSO) in China. Results explained that in the Eastern and Southern China Sea, and Southeastern China, IVT variances were high, and could change significantly yearly. However, in Northern China, variances were quite low, meaning that monthly IVT was more predictable. The spatial distribution of IVT anomalies was different with the seasons. Summer, in particular, exhibited significantly higher IVT values across the equatorial western Pacific, and there was also a transfer of moisture from the tropical Indian Ocean to the Northwestern and Southeastern China. The primary six modes of the EOFs together explained about 52.3%, which accounted for greater than half of the variation in the IVT anomalies. The main mode was related to ENSO and it explained approximately 16.4% variance in the Southern China Sea and central equatorial Pacific, while the second mode illustrated about 10.8% variance and was strongest in the East China Sea and entire Southeastern China. The cross-wavelet coherence indicated that the IVT and ENSO time-series were coherent, having a duration of about 2–8 years. This thus implied that increased IVT was linked with enhanced ENSO activities. This study could contribute to a better perception of how atmospheric circulation patterns could modify the general flow of the atmosphere to affect normal weather conditions in China. [ABSTRACT FROM AUTHOR]

High-resolution seasonal and annual precipitation climatologies for the Upper Indus Basin were developed on the basis of 1995–2017 precipitation normals obtained from four-gridded datasets (APHRODITE, CHIRPS, PERSIANN-CDR, and ERA5) and the quality-controlled high- and mid-elevation station observations. Monthly precipitation is estimated through the anomaly method at the catchment scale, and then, it is compared with the observed discharges over the 1975–2017 period for verification and detection of changes in the hydrological cycle. Running trends and spectral analysis on the precipitation gridded dataset were performed. The Mann–Kendall test was employed to detect the significance of trends whereas the Pettitt test was used to identify change points in precipitation and discharge time series. The results indicate that the bias corrected CHIRPS precipitation, followed by the ERA5, performed better in terms of RMSE, MAE, MAPE, and BIAS against the rain gauge observations. The running trend analysis exhibits a slight increase in annual precipitation, but it shows significant increase in winter precipitation. A runoff coefficient greater than one, especially in the glacierized sub-catchments of Shigar, Shyok, Astore, and Gilgit, indicates that precipitation is likely to be underestimated and glacial melt provides excess runoff volumes in a warming climate. Streamflow variability is found to be pronounced at the seasonal rather than at the annual scale. The annual discharges at Shyok, Gilgit, and Indus at Kachura gauges are slightly significantly increasing. Seasonal discharge analysis reveals more complex regimes, varying in different catchments, and its comparison with precipitation variability favors a deeper understanding of precipitation, snow-, and ice-melt runoff dynamics, addressing the hydroclimatic behavior of the Karakoram region and some weaknesses in the monitoring network at high altitude. [ABSTRACT FROM AUTHOR]

In recent decades, the climate in North and Northeast Brazil has undergone significant changes, which indicate potential risks for water and food security due to increasing temperatures, changing precipitation patterns, and greater frequency of some extreme events. For example, there is evidence of changes in the strength of the hydrological cycles on North and Northeast Brazil river basins, but the magnitude and extension of the precipitation changes are highly dependent on the precipitation dataset under consideration and the methodology. Here, we analyze the precipitation trends in North and Northeast Brazil hydrological basins and the possible associated mechanisms and present the rainfall climatological cycles in these basins using multi-datasets. The results showed that, in most river basins, there is a clear distinction between the rainy and dry seasons, with a contrast between the basins of the North (which receive a higher amount of rainfall) and the Northeast Brazil (with low rainfall volume, especially in the Parnaíba and Eastern Northeast Atlantic basins). Moreover, the annual rainfall has undergone an increasing trend in western Brazil river basins and a decreasing trend in the eastern. This east–west dipole is associated with the strengthening (weakening) of the Walker and Hadley circulations in western (eastern) Brazil in recent decades. A multidecadal variability is seen to modulate the precipitation changes, which is in phase with the AMO cycle. CMIP6 detection and attribution experiments showed that both natural and anthropogenic factors can play a role in the recent changes observed in northern Brazil river basins. [ABSTRACT FROM AUTHOR]

Examining historical variations of hydroclimatic variables can provide crucial information about changes of water resources in a water cycle. In this study, the Mann–Kendall (MK) and Innovative Polygon Trend Analysis (IPTA) methods were applied to 56-year precipitation data collected at 8 measuring stations. These stations are located in Eastern Black Sea Basin (EBSB), which has a significant amount of annual precipitation and hydroelectric potential in Turkey. This study has two objectives: (1) investigating possible changes in the monthly precipitation and (2) comparing the results achieved by a classical (MK) and one of the latest trend analysis methods presented in the literature (IPTA). Based on the results, MK achieved no trend for most of months, while it reached an increasing trend for March at most of the stations. Likewise, IPTA determined an increasing trend for March precipitation. However, an increasing/decreasing trend was obtained by IPTA for most of the months and stations. In other words, comparing the trend analysis results obtained by IPTA and MK indicates a significant discrepancy between the numbers of months with detected trends primarily because the former is relatively more sensitive in trend identification. To be more precise, IPTA and MK determined trends in approximately 81.25% and 12.5% of all months, respectively. Furthermore, the former identified quite the same trends in every month which the latter reported a trend. Moreover, the polygon of the mean and standard deviation graphs developed by IPTA provides a year cycle, which brings about useful information for water utility sectors and decision makers of the study area. Finally, the findings of this study contribute to a large amount of research that attempts to explore spatio-temporal variations of hydroclimatic variables around the globe not only to enhance humans' knowledge about changes in a water cycle but also assess climate change impacts. [ABSTRACT FROM AUTHOR]

The Amazon Basin is the region with the highest continental water density in the world and plays an important role in the global hydroclimatic system and the carbon cycle. In recent decades, the Amazon Basin has seen intense land use and land cover change (LULCC), specifically in terms of the conversion of rainforest into crop and livestock areas. The feedback mechanisms between the surface and atmosphere have been modified and biodiversity has also been endangered. In order to establish the long-term hydrological trends across the Amazon Basin and possible relationships with LULCC, monthly data on streamflow and precipitation obtained from surface stations and remote sensing of deforestation information from 1976 to 2010 were analyzed. Homogeneous streamflow sub-regions were determined, and assessments of these sub-regions show distinct patterns: while the trend for both rainfall and streamflow is positive in the northern basin, in the North and Amazon-Mouth sub-regions, the trend is negative for both variables in the southern basin, the West, South, and Central sub-regions. In the regions with a negative trend, the yearly deforestation rate presented high values, indicating the possible influence of LULCC on the Amazon's hydrological cycle. Seasonal analysis shows that the reductions in streamflow and rainfall rates in the southern Amazon Basin were more frequently registered at the end of the dry season, indicating a prolonging of this season. Analysis with Global Precipitation Climatology Centre (GPCC) linear trend data relating the positive peak of Pacific decadal oscillation (PDO) to the negative period shows that the negative trend for rainfall and streamflow in the South sub-region is more intense than the influence of PDO. This reinforces the hypothesis that deforestation may be influencing the hydrological cycle in the Amazon Basin. [ABSTRACT FROM AUTHOR]

Water is an important factor that affects local ecological environments, especially in drylands. The hydrological cycle and vegetation dynamics in Central Asia (CA) have been severely affected by climate change. In this study, we employed data from Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model, and Climate Research Unit to analyze the spatiotemporal changes in hydrological factors (terrestrial water storage (TWS), evapotranspiration, precipitation, and groundwater) in CA from 2003 to 2015. Additionally, the spatiotemporal changes in vegetation dynamics and the influence of hydrological variables on vegetation were analyzed. The results showed that the declining rates of precipitation, evapotranspiration, GRACE-TWS change, GLDAS-TWS change and GW change were 0.40 mm/year, 0.11 mm/year, 50.46 mm/year (p < 0.05), 8.38 mm/year, and 41.18 mm/year (p < 0.05), respectively. Human activity (e.g., groundwater pumping) was the dominant in determining the GW decline in CA. Precipitation dominated the changes in evapotranspiration, GRACE-TWS and GLDAS-TWS (p < 0.05). The 2- to 3-month lagging signal has to do with the transportation from the ground surface to groundwater. The change in the normalized difference vegetation index (NDVI) from 2003 to 2015 indicated the slight vegetation degradation in CA. The results highlighted that precipitation, terrestrial water storage, and soil moisture make important contributions to the vegetation dynamics changes in CA. The effect of precipitation on vegetation growth in spring was significant (p < 0.05), while the soil moisture effect on vegetation in summer and autumn was higher than that of precipitation. [ABSTRACT FROM AUTHOR]

The variability of air temperature extremes exerts a great influence on agricultural production and the global hydrologic cycle. It has been the focus of attention for the past several decades. Using observed surface air temperature from 192 meteorological stations in South China maintained by the China Meteorological Administration, this study computed and analyzed 10 extreme temperature indices and the mean temperature at multiple spatiotemporal scales for the period from 1961 to 2018. These indices were analyzed with particular reference to the growing season of rice. Results showed that the variation trends of all annual indices exhibited different north–south patterns across decades, and the most recent 20-year period experienced greater warming than previous periods. The regional averaged rates of the annual mean maximum temperature, the annual mean minimum temperature, summer days, and tropical nights were 0.163 °C decade−1, 0.197 °C decade−1, 1.2 days decade−1, and 5.4 days decade−1, respectively. Except for the month of April, the southern region mostly experienced stronger warming than the northern region, especially in summer and autumn. Nighttime warming was usually greater than daytime warming, especially in June and October. Most temperature indices showed very weak correlations with large-scale atmospheric oscillations. [ABSTRACT FROM AUTHOR]