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Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model's robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model's superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min. [ABSTRACT FROM AUTHOR]

This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49-76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50-76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784-1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731-1269 mm), while the valley gauge adjustments were 􀀀4% to 1% (􀀀39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites. [ABSTRACT FROM AUTHOR]

The Brazilian savannah-like Cerrado is classified as a fire-dependent biome. Human activities have altered the fire regimes in the region, and as a result, not all fires have ecological benefits. The indigenous lands (ILs) of the Brazilian Cerrado have registered the recurrence of forest fires. Thus, the diagnosis of these events is fundamental to understanding the burning regimes and their consequences. The main objective of this paper is to evaluate the fire regimes in Cerrado's indigenous lands from 2008 to 2017. We used the Landsat time series, at 30 m spatial resolution, available in the Google Earth Engine platform to delineate the burned areas. We used precipitation data from a meteorological station to define the rainy season (RS), early dry season (EDS), middle dry season (MDS), and late dry season (LDS) periods. During 2008-2017, our results show that the total burned area in the indigenous lands and surrounding area was 2,289,562 hectares, distributed in 14,653 scars. Most fires took place between June and November, and the annual burned area was quite different in the years studied. It was also possible to identify areas with high fire recurrence. The fire regime patterns described here are the first step towards understanding the fire regimes in the region and establishing directions to improve management strategies and guide public policies. [ABSTRACT FROM AUTHOR]

This paper analyzes the potential of a nu-support vector regression (nu-SVR) model for the reconstruction of missing data of hydrological time series from a sensor network. Sensor networks are currently experiencing rapid growth of applications in experimental research and monitoring and provide an opportunity to study the dynamics of hydrological processes in previously ungauged or remote areas. Due to physical vulnerability or limited maintenance, networks are prone to data outages, which can devaluate the unique data sources. This paper analyzes the potential of a nu-SVR model to simulate water levels in a network of sensors in four nested experimental catchments in a mid-latitude montane environment. The model was applied to a range of typical runoff situations, including a single event storm, multi-peak flood event, snowmelt, rain on snow and a low flow period. The simulations based on daily values proved the high efficiency of the nu-SVR modeling approach to simulate the hydrological processes in a network of monitoring stations. The model proved its ability to reliably reconstruct and simulate typical runoff situations, including complex events, such as rain on snow or flooding from recurrent regional rain. The worst model performance was observed at low flow periods and for single peak flows, especially in the high-altitude catchments. [ABSTRACT FROM AUTHOR]

The aim of this study is to present the spatial and temporal variability of the frequency of dry and wet days and dry and wet spells against the background of changes in precipitation and atmospheric circulation. The study is based on daily precipitation totals from 46 meteorological stations in Poland from 1966 to 2023. Additionally, seven circulation indices were used, namely GBI, NAO, AO, EA, EA/WR, SCAND, and AMO. Dry days are defined as days without precipitation. Wet days are days with at least 1 mm of precipitation. It was shown that dry spells are much more common than wet spells, are longer, and cover larger areas. Long-term changes in the annual characteristics of dry and wet days and spells are not statistically significant. Only the length of the most extended dry spell in the year increases. However, there are significant changes in their annual cycles. Spring is drier; in summer, precipitation decreases in the south and increases in the north; November and December, symbols of gloomy rainy weather, are increasingly drier; and rainy weather has shifted to January and February. The impact of circulation varies according to the season, with the NAO, AO, SCAND, and GBI indices having the greatest influence. [ABSTRACT FROM AUTHOR]

The objective of this paper was to analyze the temporal and spatial trends in annual and seasonal precipitation in Slovakia utilizing 487 gauging station data collected statewide in the period from 1981 to 2013. The nonparametric Mann-Kendall (MK) statistical test, which has been widely used to assess the significance of trends in hydrological time series, was applied to detect the significant trends. The Theil-Sen (TS) approach was applied to quantify the trend magnitude, and the Sequential Mann-Kendall (SQMK) test was used to detect abrupt temporal trend shifts. In general, precipitation data in the study area have not changed during the last 33 years, and there are no big gaps. However, predominantly increasing trends in precipitation time series were found at most of the gauging stations in Slovakia. The results corroborate previous climatic studies in the area of central Europe. In Slovakia, there is evidence of different rain distribution from the monthly point of view. Decreasing trends were detected in December in the northern part of Slovakia, while the central and southern parts revealed increasing trends. Most of the stations showed increasing summer precipitation trends, especially in July. It is expected that the findings of this comprehensive study of precipitation trends over Slovakia will produce more insights for the understanding of the regional hydrological behavior in Slovakia over the last several decades. [ABSTRACT FROM AUTHOR]

Baseflow is an essential component of river runoff. Accurate measurements and analyses of baseflow change are challenging in permafrost-covered regions. In this paper, the upper reaches of the Shule River were selected as the study area, in which to study the baseflow change regulation and causes. The variable infiltration capacity (VIC) model, based on the ARNO baseflow formulation, was used to simulate the baseflow. Simulated baseflow was validated by the isotopic baseflow separation results and measured runoff in the recession periods throughout an entire year. It was found that approximately 63.1% of the river runoff was sourced by baseflow in the study region; the baseflow change was relatively smooth throughout the year, and it lagged a few days behind the river runoff. Approximately 80% of the total baseflow was generated in the 3500-4500 m alpine regions, with mainly low-temperature and mid-temperature permafrost. Based on the climate, runoff, land use, soil temperature and moisture data of the permafrost active layer, the mechanism of baseflow change in the permafrost zone was analysed. Precipitation and temperature positively enhanced the baseflow in the permafrost region throughout a year, but the baseflow was more influenced by the temperature than precipitation. In the study area, the cold desert and alpine grassland had the largest regulation capacity for baseflow. Affected by the permafrost freeze-thaw process, a baseflow peak occurred in the spring and the baseflow recession slowed in the autumn. This lead to a more uniform distribution of baseflow and runoff throughout the year. [ABSTRACT FROM AUTHOR]

The paper explores the impact of El Niño-Southern Oscillation (ENSO) on rainy season precipitation properties over the Yangtze River Basin. The multi-scale moving t-test was used to determine the onset and withdrawal of the rainy season. Results showed the following: (1) Onset and withdrawal of the averaged rainy season over the Yangtze River Basin showed a similar distribution and both progressed northwards and westwards. There is a trend for delayed onset of rainy season precipitation over the Yangtze River Basin. A similar trend in the withdrawal of rainy season precipitation was not found. (2) The decaying Central and Eastern PacificWarming are indicative of wet signals in the Yangtze River Basin, with precipitation anomalies up to 25% above normal. The decaying Eastern Pacific Cooling and developing Eastern PacificWarming are indicative of dry signals over the Yangtze River Basin, with 5–20% lower than average precipitation (3) ENSO Modoki in the developing phase is more important than the developing ENSO when it comes to present wetter or drier signals. Conversely, the decaying ENSO shows more prominent wet or dry signals than the decaying ENSO Modoki. [ABSTRACT FROM AUTHOR]

Global warming substantially intensifies hydrologic cycles, causing increasing frequency and magnitude of catastrophic floods and droughts. Understanding the patterns and mechanisms of precipitation in historical periods is pivotal for regional disaster prevention and mitigation. Here, we analyzed the daily precipitation of six stations at the Shimantan Reservoir from 1952 to 2013 to examine precipitation characteristics at different time scales. The Mann–Kendall test, moving t-test, and Innovative Polygon Trend Analysis (IPTA) were employed to detect the trends and change points in total precipitation amount, frequency, and duration. Influences of atmospheric circulations on precipitation were then explored via cross-wavelet analysis. Our results showed increased average precipitation and decreased precipitation days annually at the Shimantan Reservoir in the past decades. Specifically, increased seasonal precipitation was only detected in summer, while precipitation days were mainly reduced in winter. There was a noticeable increasing to decreasing transition trend in precipitation from July to August, and a transition from decreasing to increasing from June to July in precipitation days. Summer rainfall was predominantly moderate and light, accompanied by shortening and highly fluctuating rainstorm durations. July exhibited the highest precipitation frequency and always experienced rainstorms. The Arctic Oscillation and East Asian summer monsoon showed positive and negative correlations, respectively, with the changes in precipitation at the Shimantan Reservoir. Our analyses provide a fine-scale portrait of precipitation patterns and mechanisms under a changing climate and benefit regional flood control and sustainable development. [ABSTRACT FROM AUTHOR]

The Amazon Basin is the largest rainforest in the world, and studying the rainfall in this region is crucial for understanding the functioning of the entire rainforest ecosystem and its role in regulating the regional and global climate. This work is part of the application of complex networks, which refer to a network modeled by graphs and are characterized by their high versatility, as well as the extraction of key information from the system under study. The main objective of this article is to examine the precipitation system in the Amazon basin during the austral summer. The networks are defined by nodes and connections, where each node represents a precipitation time series, while the connections can be represented by different similarity functions. For this study, three rainfall networks were created, which differ based on the correlation function used (Pearson, Spearman, and Kendall). By comparing these networks, we can identify the most effective method for analyzing the data and gain a better understanding of rainfall's spatial structure, thereby enhancing our knowledge of its impact on different Amazon basin regions. The results reveal the presence of three important regions in the Amazon basin. Two areas were identified in the northeast and northwest, showing incursions of warm and humid winds from the oceans and favoring the occurrence of large mesoscale systems, such as squall lines. Additionally, the eastern part of the central Andes may indicate an outflow region from the basin with winds directed toward subtropical latitudes. The networks showed a high level of activity and participation in the center of the Amazon basin and east of the Andes. Regarding information transmission, the betweenness centrality identified the main pathways within a basin, and some of these are directly related to certain rivers, such as the Amazon, Purus, and Madeira. Indicating the relationship between rainfall and the presence of water bodies. Finally, it suggests that the Spearman and Kendall correlation produced the most promising results. Although they showed similar spatial patterns, the major difference was found in the identification of communities, this is due to the meridional differences in the network's response. Overall, these findings highlight the importance of carefully selecting appropriate techniques and methods when analyzing complex networks. [ABSTRACT FROM AUTHOR]

The dry-wet abrupt transition (DWAT) is a typical anomaly of precipitation at the subseasonal scale and may result in a severer and greater hydro-meteorological hazard. This paper identified and examined the DWAT events in terms of precipitation in Poyang Lake basin, including both from dry to wet (DTW) and from wet to dry (WTD), during the period of 1960-2010 and analyzed its characteristics of temporal and spatial distribution and intra- and inter-annual tendencies based on the Mann-Kendall (M-K) test. The results revealed that the DTW was inclined to occur in March, but WTD in July and September; the inter-annual variation of the DWAT index showed a feeble long-term increasing trend in July and a decreasing trend in September with a Z-statistic of 1.81 and -1.44, respectively, although none of these trends achieved the statistical significance level (a = 0.05); the north parts of Poyang Lake basin experienced more DWAT events during the last 50 years in which the occurrence of WTD is more frequent than that of DTW. The outcomes of the study will help mitigate and regulate the flood and drought in Poyang Lake basin, as well as in other regions. [ABSTRACT FROM AUTHOR]

In this paper, thermomechanical processing of niobium microalloyed steel was performed with the purpose of determining the interaction between niobium precipitates and dislocations, as well as determining the influence of the temperature of final deformation on the degree of precipitation and dislocation density. Two variants of thermomechanical processing with different final rolling temperatures were carried out. Samples were studied using electrochemical isolation with an atomic absorption spectrometer, transmission electron microscopy, X-ray diffraction analysis, and universal tensile testing with a thermographic camera. The results show that the increase in the density of dislocations before the onset of intense precipitation is insignificant because the recrystallization process takes place simultaneously. It increases with the onset of strain-induced precipitation. In this paper, it is shown that niobium precipitates determine the density of dislocations. The appearance of Lüders bands was noticed as a consequence of the interaction between niobium precipitates and dislocations during the subsequent cold deformation. In both variants of the industrial process performed on the cold deformed strip, Lüders bands appeared. [ABSTRACT FROM AUTHOR]

In Italy, most of the destructive landslides are triggered by rainfall, particularly in central Italy. Therefore, effective monitoring of rainfall is crucial in hazard management and ecosystem assessment. Global precipitation measurement (GPM) is the next-generation satellite mission, which provides the precipitation measurements worldwide. In this research, we employed the available monthly GPM data to estimate the monthly precipitation for the twenty administrative regions of Italy from June 2000 to June 2021. For each region, we applied the non-parametric Mann–Kendall test and its associated Sen's slope to estimate the precipitation trend for each calendar month. In addition, for each region, we estimated a linear trend and the seasonal cycles of precipitation with the antileakage least-squares spectral analysis (ALLSSA) and showed the annual precipitation variations using box plots. Lastly, we compared machine-learning models based on the auto-regressive moving average for monthly precipitation forecasting and showed that ALLSSA outperformed them. The findings of this research provide a significant insight into processing climate data, both in terms of trend-season estimates and forecasting, and can potentially be used in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

In 2009–2018, the isotopic composition of oxygen and hydrogen in the atmospheric precipitation, groundwater and river and lake water of Lake Onega basin was studied. The weighted annual isotope composition of precipitation at Petrozavodsk was δ18O = −11.7‰ and δ2H = −84‰ and varied from −30.9 to −4.1‰ for δ18O and from −23 to −22‰ for δ2H. The isotopic composition of the water in Lake Onega was relatively uniform from −11.5 to −9.3‰ for δ18O and from −85 to −71‰ for δ2H. In the bays, the isotopic composition of the water varied more substantially than in the central part of the lake due to the river runoff during springtime flooding. In late summer, the concentrations of deuterium and oxygen-18 increased in the lake water, and figurative points on the δ2H vs. δ18O diagram shifted above the meteoric line. The absorption of the isotopically heavy summer precipitation and disequilibrium isotope fractionation during evaporation led to the enrichment of the lake water by heavy isotopes. Experiments were conducted to estimate the evaporation influence on the isotope enrichment of the residual water, and a comparison of the obtained isotope data with the experimental function showed that commonly, about 4% and up to 12% of water was lost during the spring and summer, respectively. In the water of the tributaries, the abundance of the deuterium and oxygen-18 varied in a wider scale than in the lakes, from −14.4 to −9.1‰ for δ18O and from −102 to −73‰ for δ2H. An evaporation loss of up to 35% was found for the rivers in late summer, and this value was proportional to the area of lakes and wetlands in the elementary watershed. The initial isotope composition of the water in the tributaries prior to evaporation was estimated to be δ18O ≈ −14.1‰ and δ2H ≈ −103‰ on average and crossed the approximation and meteoric lines. This estimation was close to the average composition of the groundwater, i.e., δ18O ≈ −13.4‰ and δ2H ≈ −94‰ on the Lake Onega catchment. The slightly increased isotope depletion of the calculated composition in the initial river water in comparison with the groundwater was the result of the contribution of the spring snowmelt water, which had a significant influence on the lake water balance. [ABSTRACT FROM AUTHOR]

Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] 3BV42, PERSIANN [Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks] CDR, GSMaP [Global Satellite Mapping of Precipitation] RENALYSIS, CMORPH [Climate Prediction Center's morphing technique] BLD and CMORPH_RAW) were compared with in situ measurements over China for the period of 2005 to 2013. To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAWunderestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer. [ABSTRACT FROM AUTHOR]

Mediterranean regions are prone to heavy rainfall, flash floods, and erosion issues. Drop size distribution (DSD) is a key element for studying these phenomena through the hydrological variables which can be derived from it (rainfall rates and totals, kinetic energy fluxes). This paper proposes a five-year (2012-2016) DSD climatology, summarized by scaling parameters for concentration, size, and shape. The DSD network is composed of two longitudinal transects of three OTT Parsivel optical disdrometers each, across the Mediterranean Cevennes-Vivarais region. The influence of several factors are analysed: location (distance from the sea, orographic environment), season, daily synoptic weather situation (derived from geopotential heights, at 700 and 1000 hPa), rainfall type (analysed from 5 min radar data), as well as some combinations of these factors. It was found and/or confirmed that the orographic environment, season, weather patterns associated with the exposure to low level atmospheric flows, and rainfall types influenced the microphysical processes, leading to rainfall, measured at the ground. Consequently, the DSD characteristics, as well as the relationships between the rainfall rate and reflectivity factor, are influenced by these factors. [ABSTRACT FROM AUTHOR]

The eastern North American sea level pressure dipole (ENA) pattern is a recently identified teleconnection pattern that has been shown to influence mid-Atlantic United States (U.S.) streamflow variability. Because the pattern was only recently identified, its impacts on U.S. precipitation and estuaries on daily to seasonal timescales is unknown. Thus, this paper presents the first seasonal investigation of ENA relationships with global atmospheric fields, U.S. precipitation, and mid-Atlantic estuarine salinity. We show that the ENA pattern explains up to 25-36% of precipitation variability across Texas and the western U.S. We also show that, for the Northeast U.S., the ENA pattern explains up to 65% of precipitation variability, contrasting with previous work showing how well-known climate indices can only explain a modest amount of precipitation variability. The strongest ENA-precipitation relationships are in the spring and fall. The relationships between the ENA pattern and precipitation across remote regions reflect the upper-atmospheric Rossby wave pattern associated with the ENA pattern that varies seasonally. The El-Nino/Southern Oscillation (ENSO) is related to the spring ENA pattern, indicating that extended outlooks of the ENA pattern may be possible. We also show that the ENA index is strongly correlated with salinity and vertical haline stratification across coastal portions of the mid-Atlantic Bight so that hypoxia forecasts based on the ENA index may be possible. Statistical connections between vertical salinity gradient and ENSO were identified at lags of up two years, further highlighting the potential for extended hypoxia outlooks. The strong connection between anomalies for precipitation and mid-Atlantic Bight salinity suggests that the ENA pattern may be useful at an interdisciplinary level for better understanding historical regional climate variability and future impacts of climate change on regional precipitation and the health of estuaries. [ABSTRACT FROM AUTHOR]

As one of the most important geographical units affected by global climate change, lakes are sensitive to climatic changes and are considered "indicators" of climate and the environment. In this study, changes in the spatial-temporal characteristics of the water levels of 204 global major lakes are systematically analyzed using satellite altimetry data (Hydroweb product) from 2002 to 2010. Additionally, the responses of the major global lake levels to climatic fluctuations are analyzed using Global Land Surface Assimilation System (GLDAS) data (temperature and precipitation). The results show that the change rates of most global lakes exceed 0, which means that the lake levels of these lakes are rising. The change rates of the lake levels are between -0.3~0.3 m/a, which indicates that the rate of change in the water-level of most lakes is not obvious. A few lakes have a particularly sharp change rate, between -5.84~-2 m/a or 0.7~1.87 m/a. Lakes with increasing levels are mainly located in the mountain and plateau regions, and the change rates in the coastal highlands are more evident. The global temperatures rise by a change rate of 0.0058 °C/a, while the global precipitation decreases by a change rate of -0.6697 mm/a. However, there are significant regional differences in both temperature and precipitation. In addition, the impact of precipitation on the water level of lakes is significant and straightforward, while the impact of temperature is more complex. A study of lake levels on a global scale would be quite useful for a better understanding of the impact which climate change has on surface water resources. [ABSTRACT FROM AUTHOR]

Assessment of satellite precipitation products' capability for monitoring drought is relatively new in tropical regions. The purpose of this paper is to evaluate the reliability of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B43 product in estimating the standardized precipitation index (SPI) in the Kelantan River Basin, Malaysia from 1998 to 2014, by comparing it with data from 42 rain gauges. Overall, the TMPA-3B43 performed well in the monthly precipitation estimation, but performed moderately in the seasonal scale. Better performance was found in the northeast monsoon (wet season) than in the southwest monsoon (dry season). The product is more reliable in the northern and north-eastern regions (coastal zone) compared to the central, southern and south-eastern regions (mountainous area). For drought assessment, the correlations between the TMPA-3B43 and ground observations are moderate at various time-scales (one to twelve months), with better performance at shorter time-scales. The TMPA-3B43 shows similar temporal drought behavior by capturing most of the drought events at various time-scales, except for the 2008-2009 drought. These findings show that the TMPA-3B43 is not suitable to be used directly for SPI estimation in this basin. More bias correction and algorithm improvement work are needed to improve the accuracy of the TMPA-3B43 in drought monitoring. [ABSTRACT FROM AUTHOR]

Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended. [ABSTRACT FROM AUTHOR]

Hydroclimate is non-stationary and varies in often unpredictable ways on local, regional and global scales, which can lead to water insecurity. This editorial relates the advances and challenges in our understanding of the spatio-temporal relationship between climate variability and change and the components of the hydrologic cycle through the lens of six articles, which contributed to the Water Special Issue: Hydroclimatic Variability at Local, Regional and Global Scales. The relationship between the El Niño/Southern Oscillation and precipitation, temperature, and evapotranspiration is examined within the Indian Summer monsoon, gauge-based precipitation datasets are intercompared over Pakistan, trends in precipitation, temperature, and streamflow are investigated in Ethiopia and China, alternate configurations of hydroclimate modeling are assessed over Canada, and finally, future limitations in groundwater supply are presented for Italy. [ABSTRACT FROM AUTHOR]

The Shiyang River Basin is located at the edge of the monsoon wind system of South and Southeast Asia. The hydrochemical characteristics of precipitation are influenced by both monsoon and arid regions. The regression analysis method, comparative analysis, neutralization factor (NF), enrichment factor (EF) and HYSPLIT4 were used to analyze the precipitation samples collected from the upper reaches of the Shiyang River from October 2016 to October 2017. In order to study the hydrochemical characteristics and ion sources of precipitation in this basin. The results, as discussed in this paper, show that the precipitation in the upper reaches of the Shiyang River is mildly alkaline all year round while the neutralization ability of Ca2+ and NH4+ in precipitation is strong. The ion concentration was higher in the dry season than that in the wet season, but the concentration of NH4+ was higher in summer. Furthermore, as the altitude increased, the electrical conductivity (EC) of the precipitation decreased gradually. Influenced by precipitation and rainy days, the wet deposition of nitrogen (N) and sulfur (S) was higher in the wet season than that during the dry season, and the wet deposition gradually increased with the elevation. In precipitation, the earth's crust is a major source of Ca2+, K+ and Mg2+, the ocean is a major source of Cl−, the ocean and the earth's crust are the sources of Na+, human activities are the main sources of SO42−, NO3− and NH4+, the amount of F− is very small, its sources are natural and human activities. Water vapor in precipitation mainly comes from westerly air mass circulation and monsoon circulation while the particles come mainly from the earth's crust. [ABSTRACT FROM AUTHOR]

The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (rPearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with rPearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and rPearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world. [ABSTRACT FROM AUTHOR]

There are currently different types of commercial optical disdrometers to measure the rainfall intensity, of which many are commonly used for monitoring road conditions. Having information about the amount of rain, the composition of the precipitation particles and visibility are essential to avoid accidents, which requires intelligent systems that warn drivers and redirect traffic. However, few studies related to Intelligent Transport Systems (ITS) have been performed regarding why these devices are not optimized for this type of applications. Therefore, this paper analyzes and evaluates the operating mode of these equipment through their theoretical model, which will allow the design of prototypes of disdrometers with different characteristics. In addition, this model will be implemented in a simulation program, through which an exhaustive study analyzing how the type of precipitation and its intensity affect the measures provided by the model will be conducted. In this way, the results will help optimize its operation to be thus used in urban settings, which will allow obtaining more accurate real-time information, better traffic management, and a reduction in the number of accidents. [ABSTRACT FROM AUTHOR]

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection. [ABSTRACT FROM AUTHOR]

The influence of urbanization on regional precipitation is one of the most important issues in hydrology. This paper selected the North Haihe Basin as the study area and explored the connection between summer precipitation and urbanization index (URBI) based on satellite precipitation and nighttime lights datasets. A moving spatial percentage anomaly method (MSP) was used to signify the local induced enhancement of precipitation (LIEP). Local indicators of spatial association (LISA) clustering for LIEP and URBI based on Bivariate Moran's I coupled with digital elevation model (DEM) segmentation was used to separate the impacts caused by urbanization and terrain uplifting, and thus, the regions of interest (ROI) where the urban induced enhancement of precipitation (UIEP) plays a dominant role were located. A geographically weighted regression (GWR) model was used to reveal the spatial variation of the sensitivity of UIEP to URBI within the ROI. Pearson correlation and cross-wavelet analyses (XWT) were adopted to investigate the teleconnection between UIEP and climate anomalies, using Niño3.4 and SOI as indices. The results indicated that: (1) Urbanization effects on precipitation spatial variation in the upstream mountainous area would be hidden away by topographic factors. (2) From the perspective of the basin, the highest urbanization level areas have the Lowest LIEP, while the suburban areas have the highest LIEP, and the rural areas are in the middle. (3) The UIEP and the URBI are generally negatively correlated within ROI. (4) UIEP versus Niño3.4 and SOI both show a significant common high-power period at a time scale of 2 years. This work can help comprehensively understand the hydrological response to urbanization. [ABSTRACT FROM AUTHOR]

Atmospheric water vapor plays an important role in the water cycle, especially in arid Central Asia, where precipitation is invaluable to water resources. Understanding and quantifying the relationship between water vapor source regions and precipitation is a key problem in water resource research in typical arid Central Asia, Northern Xinjiang. However, the relationship between precipitation and water vapor sources is still unclear of snow season. This paper aimed at studying the role of water vapor source supply in the Northern Xinjiang precipitation trend, which was investigated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results showed that the total water vapor contributed from Western Eurasia and the North Polar area presented upward trends similar to the precipitation change trend, which indicated that the water vapor contribution from the two previous water vapor source regions supplied abundant water vapor and maintained the upward precipitation trend from 1980 to 2017 in Northern Xinjiang. From the climatology of water vapor transport, the region was controlled by midlatitude westerlies and major water vapor input from the western boundary, and the net water vapor flux of this region also showed an annual increasing trend. Western Eurasia had the largest moisture percentage contribution to Northern Xinjiang (48.11%) over the past 38 years. Northern Xinjiang precipitation was correlated with water vapor from Western Eurasia, the North Polar area, and Siberia, and the correlation coefficients were 0.66, 0.45, and 0.57, respectively. These results could aid in better understanding the water cycle process and climate change in this typical arid region of Central Asia. [ABSTRACT FROM AUTHOR]

Flood projections are still highly uncertain, partly resulting from the limited accuracy of simulated precipitation by climate models. To overcome this limitation, recent studies suggest to use direct linkages between atmospheric processes leading precipitation, often better simulated than precipitation, and the flood occurrence. Such an approach implies, however, that historical flood events mainly result from direct contribution of precipitation only. Consequently, this paper has a twofold objective: (i) To explore to what extent the generation of medium-magnitude flood events in a large mountainous catchment can be explained by the precipitation only, and (ii) to identify what are the best features of flood-inducing precipitation episodes (i.e., duration and accumulation). Taking advantage of centennial-long discharge (gauge stations) and precipitation (ERA-20C reanalysis) data series, this study is based on three-year return period flood events of the upper Rhône River (NW European Alps). Our results suggest that half of the studied floods are triggered by precipitation only, but precipitation indices are mainly good only for high-magnitude events with return period of at least 20 years. Hence, modelling flood occurrence directly from atmospheric processes leading precipitation seems to be possible for events with the highest magnitude (i.e., the ones with the highest potential to impact societies). [ABSTRACT FROM AUTHOR]

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data. [ABSTRACT FROM AUTHOR]

This paper characterizes the influence of synoptic-scale air mass conditions on the spatial and temporal patterns of precipitation in North Carolina over a 16-year period (2003–2018). National Center for Environmental Prediction Stage IV multi-sensor precipitation estimates were used to describe seasonal variations in precipitation in the context of prevailing air mass conditions classified using the spatial synoptic classification system. Spatial analyses identified significant clustering of high daily precipitation amounts distributed along the east side of the Appalachian Mountains and along the Coastal Plains. Significant and heterogeneous clustering was prevalent in summer months and tended to coincide with land cover boundaries and complex terrain. The summer months were dominated by maritime tropical air mass conditions, whereas dry moderate air mass conditions prevailed in the winter, spring, and fall. Between the three geographic regions of North Carolina, the highest precipitation amounts were received in western North Carolina during the winter and spring, and in eastern North Carolina in the summer and fall. Central North Carolina received the least amount of precipitation; however, there was substantial variability between regions due to prevailing air mass conditions. There was an observed shift toward warmer and more humid air mass conditions in the winter, spring, and fall months throughout the study period (2003–2018), indicating a shift toward air mass conditions conducive to higher daily average rain rates in North Carolina. [ABSTRACT FROM AUTHOR]

Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend. [ABSTRACT FROM AUTHOR]

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation. [ABSTRACT FROM AUTHOR]

The latest research indicates that there are time-lag effects between the normalized difference vegetation index (NDVI) and the precipitation variation. It is well known that the time-lags are different from region to region, and there are time-lags for the NDVI itself correlated to the precipitation. In the arid and semi-arid grasslands, the annual NDVI has proved not only to be highly dependent on the precipitation of the concurrent year and previous years, but also the NDVI of previous years. This paper proposes a method using recurrent neural network (RNN) to capture both time-lags of the NDVI with respect to the NDVI itself, and of the NDVI with respect to precipitation. To quantitatively capture these time-lags, 16 years of the NDVI and precipitation data are used to construct the prediction model of the NDVI with respect to precipitation. This study focuses on the arid and semi-arid Hulunbuir grasslands dominated by perennials in northeast China. Using RNN, the time-lag effects are captured at a 1 year time-lag of precipitation and a 2 year time-lag of the NDVI. The successful capture of the time-lag effects provides significant value for the accurate prediction of vegetation variation for arid and semi-arid grasslands. [ABSTRACT FROM AUTHOR]

The processes of isotope fractionation in the hydrological cycle naturally occur during vapor formation, vapor condensation, and moisture transportation. These processes are therefore dependent on local and regional surface and atmospheric physical features such as temperature, pressure, wind speed, and land morphology, and hence on the climate. Because of the strong influence of climate on the isotope fractionation, latitudinal and altitudinal effects on the δ18O and δ2H values of precipitation at a global scale are observed. In this study, we present and compare the processes governing precipitation isotope fractionation from two contrasting climatic regions: Virunga in Central-Eastern Africa and the Central Mediterranean (Stromboli and Sicily, Italy). While Virunga is a forested rainy tropical region located between Central and Eastern Africa, the Mediterranean region is characterized by a rainy mild winter and a dry hot summer. The reported δ18O and δ2H dataset are from precipitation collected on rain gauges sampled either on a monthly or an approximately bimonthly basis and published in previous papers. Both regions show clearly defined temporal and altitudinal variations of δ18O and δ2H, depending on precipitation amounts. The Central Mediterranean shows a clear contribution of local vapor forming at the sea–air interface, and Virunga shows a contribution from both local and regional vapor. The vapor of Virunga is from two competing sources: the first is the continental recycled moisture from soil/plant evaporation that dominates during the rainy season, and the second is from the East African Great Lakes evaporation that dominates during the dry season. [ABSTRACT FROM AUTHOR]

The source region of the Indus River (SRIR), which is located in the Hindukush, Karakoram and Himalayan (HKH) mountainous range and on the Third Pole (TP), is very sensitive to climate change, especially precipitation changes, because of its multifarious orography and fragile ecosystem. Climate changes in the SRIR also have important impacts on social and economic development, as well as on the ecosystems of the downstream irrigation areas in Pakistan. This paper investigates the changes in precipitation characteristics by dividing the daily precipitation rate into different classes, such as light (0–10 mm), moderate (10.1–25 mm) and heavy precipitation (>25 mm). Daily precipitation data from gauging and non-gauging stations from 1961–2015 are used. The results of the analysis of the annual precipitation and rainy day trends show significant (p < 0.05) increases and decreases, respectively, while light and heavy precipitation show significant decreasing and increasing trends, respectively. The analysis of the precipitation characteristics shows that light precipitation has the highest number of rainy days compared to moderate or heavy precipitation. The analysis of the seasonal precipitation trends shows that only 18 stations have significant increasing trends in winter precipitation, while 27 stations have significant increasing trends in summer precipitation. Both short and long droughts exhibit increasing trends, which indicates that the Indus Basin will suffer from water shortages for agriculture. The results of this study could help policymakers cope with floods and droughts and sustain eco-environmental resources in the study area. [ABSTRACT FROM AUTHOR]

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation. [ABSTRACT FROM AUTHOR]

In this paper, the diurnal variations of various clouds are analyzed using hourly cloud observations at weather stations in China from 1985 to 2011. In combination with merged hourly precipitation data, the relationship between the diurnal variations of clouds and precipitation in the summers from 2008 to 2011 are studied. The results show that the occurrence frequencies of total cloud and various cloud types exhibit significant diurnal variations. The diurnal variations of the occurrence frequencies of altocumulus and stratocumulus show a bimodal pattern, with peaks appearing in the early morning and late afternoon. The early morning peaks of altocumulus and stratocumulus appear earlier in the summer than in the other seasons, while the late afternoon maxima show an opposite trend. The occurrence frequency of nimbostratus peaks in the morning between 07 and 12 LST (local solar time), and the peak value lags 2 to 3 h from west to east along the Yangtze River valley; meanwhile, the diurnal variation shows no clear differences caused by changes in the latitude or seasons. Cumulus shows an afternoon (14 LST) maximum, while cumulonimbus peaks in the late afternoon during 16–20 LST, and both of them present a great diurnal range. Cirrus usually reaches its peak at 17–18 LST, and it differs by 1 to 2 h with a change in the latitude. The results of the study first show that the diurnal variations of precipitation among different regions are dominated by different clouds. The upper reaches of the Yangtze River valley present a midnight precipitation maximum that is mainly dominated by cumulonimbus. For the middle reaches of the Yangtze River valley impacted by nimbostratus, the precipitation peaks in the early morning. In South and Northeast China, the precipitation peaks in the afternoon and is determined by the diurnal variations of convective clouds. In the region between the Yangtze River valley and Yellow River valley, the precipitation peaks in the early morning and afternoon; the early morning peak is mainly determined by stratiform clouds, while the afternoon peak is closely related to convective clouds. [ABSTRACT FROM AUTHOR]

Multifractal detrended fluctuation analysis (MFDFA) method can examine higher-dimensional fractal and multifractal characteristics hidden in time series. However, removal of local trends in MFDFA is based on discontinuous polynomial fitting, resulting in pseudo-fluctuation errors. In this paper, we propose a two-stage modified MFDFA for multifractal analysis. First, an overlap moving window (OMW) algorithm is introduced to divide time series of the classic MFDFA method. Second, detrending by polynomial fitting local trend in traditional MFDFA is replaced by ensemble empirical mode decomposition (EEMD)-based local trends. The modified MFDFA is named OMW-EEMD-MFDFA. Then, the performance of the OMW-EEMD-MFDFA method is assessed by extensive numeric simulation experiments based on a p-model of multiplicative cascading process. The results show that the modified OMW-EEMD-MFDFA method performs better than conventional MFDFA and OMW-MFDFA methods. Lastly, the modified OMW-EEMD-MFDFA method is applied to explore multifractal characteristics and multifractal sources of daily precipitation time series data at the Mapoling and Zhijiang stations in Dongting Lake Basin. Our results showed that the scaling properties of the daily precipitation time series at the two stations presented a long-range correlation, showing a long-term persistence of the previous state. The strong q-dependence of H (q) and τ (q) indicated strong multifractal characteristics in daily precipitation time series data at the two stations. Positive Δ f values demonstrate that precipitation may have a local increasing trend. Comparing the generalized Hurst exponent and the multifractal strength of the original precipitation time series data with its shuffled and surrogate time series data, we found that the multifractal characteristics of the daily precipitation time series data were caused by both long-range correlations between small and large fluctuations and broad probability density function, but the broad probability density function was dominant. This study may be of practical and scientific importance in regional precipitation forecasting, extreme precipitation regulation, and water resource management in Dongting Lake Basin. [ABSTRACT FROM AUTHOR]

Precipitation (P) and evapotranspiration (ET) are the key factors determining water availability for water resource management activities in river basins. While global P and ET data products have become more accessible, their performances in river basins with a diverse climate and landscape remain less discussed. This paper evaluated the performance of four representative global P (CHIRPSP, GLDASP, TRMMP and PersiannP) and ET products (CSIROET, GLDASET, MODET and TerraClimateET) against the reference data provided by the Australian Water Availability Project (AWAP) in the Murray Darling Basin (MDB) of Australia. The disparities among the data products both in the period from 2001 to 2016 and across the 22 catchments of MDB were related to a set of catchment characteristics (climate, terrain, etc.) to explore any possible contributors. The results show that the four global P products presented overall high consistency with AWAPP across the MDB catchments except in southeastern catchments with abundant rainfalls and large terrain variations. The Penman–Monteith algorithm based MODET underestimated ET in the MDB, especially in the arid, less vegetation covered catchments. While the CSIROET, which also estimated with the Penman–Monteith method, presented overall better estimations, which can be attributed to the better parameterization of the landscape in the simulation processes. The hydrological model based TerraClimateET showed overall good consistency with AWAPET except in the arid catchments, which might be attributed to the simplified water balance model it applied, however it did not adequately reflect the intensive ground water uses in these catchments. The findings indicated that basin and catchment characteristics had impacts on the accuracy of global products and therefore provided important implications for choosing appropriate product and/or conducting field calibrations for potential users in large basins characterized with diverse rainfall, terrain variations and land use patterns. [ABSTRACT FROM AUTHOR]

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor. [ABSTRACT FROM AUTHOR]

The aim of ensemble precipitation prediction in this paper was to achieve the best performance via artificial intelligence (AI) based modeling. In this way, ensemble AI based modeling was proposed for prediction of monthly precipitation with three different AI models (feed forward neural network-FFNN, adaptive neural fuzzy inference system-ANFIS and least square support vector machine-LSSVM) for the seven stations located in the Turkish Republic of Northern Cyprus (TRNC). Two scenarios were examined each having specific inputs set. The scenario 1 was developed for predicting each station's precipitation through its own data at previous time steps while in scenario 2, the central station's data were imposed into the models, in addition to each station's data, as exogenous input. Afterwards, the ensemble modeling was generated to improve the performance of the precipitation predictions. To end this aim, two linear and one non-linear ensemble techniques were used and then the obtained outcomes were compared. In terms of efficiency measures, the averaging methods employing scenario 2 and non-linear ensemble method revealed higher prediction efficiency. Also, in terms of Skill score, non-linear neural ensemble method could enhance predicting efficiency up to 44% in the verification step. [ABSTRACT FROM AUTHOR]

The changes in climatic variables in Ethiopia are not entirely understood. This paper investigated the recent trends of precipitation and temperature on two eco-regions of Ethiopia. This study used the observed historical meteorological data from 1980 to 2016 to analyze the trends. Trend detection was done by using the non-parametric Mann-Kendall (MK), Sen's slope estimator test, and Innovative Trend Analysis Method (ITAM). The results showed that a significant increasing trend was observed in the Gondar, Bahir Dar, Gewane, Dembi-Dolo, and Negele stations. However, a slightly decreasing trend was observed in the Sekoru, Degahabur, and Maichew stations regarding precipitation trends. As far as the trend of temperature was concerned, an increasing trend was detected in the Gondar, Bahir Dar, Gewane, Degahabur, Negele, Dembi-Dolo, and Maichew stations. However, the temperature trend in Sekoru station showed a sharp decreasing trend. The effects of precipitation and temperature changes on water resources are significant after 1998. The consistency in the precipitation and temperature trends over the two eco-regions confirms the robustness of the changes. The findings of this study will serve as a reference for climate researchers, policy and decision makers. [ABSTRACT FROM AUTHOR]

Mongolia's Selenga sub-basin of the Lake Baikal basin is spatially extensive, with pronounced environmental gradients driven primarily by precipitation and air temperature on broad scales. Therefore, it is an ideal region to examine the dynamics of the climate and the hydrological system. This study investigated the annual precipitation, air temperature, and river discharge variability at five selected stations of the sub-basin by using Mann-Kendall (MK), Innovative trend analysis method (ITAM), and Sen's slope estimator test. The result showed that the trend of annual precipitation was slightly increasing in Ulaanbaatar (Z = 0.71), Erdenet (Z = 0.13), and Tsetserleg (Z = 0.26) stations. Whereas Murun (Z = 2.45) and Sukhbaatar (Z = 1.06) stations showed a significant increasing trend. And also, the trend of annual air temperature in Ulaanbaatar (Z = 5.88), Erdenet (Z = 3.87), Tsetserleg (Z = 4.38), Murun (Z = 4.77), and Sukhbaatar (Z = 2.85) was sharply increased. The average air temperature has significantly increased by 1.4 °C in the past 38 years. This is very high in the semi-arid zone of central Asia. The river discharge showed a significantly decreasing trend during the study period years. It has been apparent since 1995. The findings of this paper could help researchers to understand the annual variability of precipitation, air temperature, and river discharge over the study region and, therefore, become a foundation for further studies. [ABSTRACT FROM AUTHOR]

This paper presents a consensus estimate of the changes in oceanic precipitation off the coast of Europe under increasing greenhouse gas emissions. An ensemble of regional climate models (RCMs) and three gauge and satellite-derived observational precipitation datasets are compared. While the fit between the RCMs’ simulation of current climate and the observations shows the consistency of the future-climate projections, uncertainties in both the models and the measurements need to be considered to generate a consensus estimate of the potential changes. Since oceanic precipitation is one of the factors affecting the thermohaline circulation, the feedback mechanisms of the changes in the net influx of freshwater from precipitation are relevant not only for improving oceanic-atmospheric coupled models but also to ascertain the climate signal in a global warming scenario. [ABSTRACT FROM AUTHOR]