Long-term Precipitation Data Research Articles

Grid-Based Precipitation Quantile Estimation Considering Homogeneity Using ERA5-Land Data for the Korean Peninsula

In this study, a grid-based precipitation quantile was estimated using long-term reanalysis precipitation data, considering the homogeneity of the annual maximum series (AMS) for the Korean Peninsula. For regions where significant changes in homogeneity were observed, the precipitation quantile was estimated using only the AMS from after the change point, and these results were compared with those from the original AMS. The examination of homogeneity revealed a significant increasing trend in homogeneity variability in the southeastern region of Korea. This change was particularly pronounced in the location parameter of the Gumbel distribution, resulting in an improved model fit. The change in precipitation quantile was most noticeable for a 2-year return period with a 36 h duration, with an average increase of approximately 11.5%. The results obtained from this study are anticipated to offer crucial foundational data for the design of hydraulic structures in regions with insufficient long-term ground observation data.

Spatial and temporal variability of meteorological droughts including atmospheric circulation in Central Europe

Drought is one of the natural phenomena influencing many aspects of human activities like water scarcity, food production, agriculture, industry and ecological conditions of the environment. For decades, drought has caused huge financial losses in Europe and around the world. In the area of the Polish Carpathians, there are periods with a deficit of rainwater and increasing frequency of dry months especially in the cold half-year. However, only a limited number of studies exist about the spatial and temporal variability of meteorological drought and the main mechanisms determining its performance in Central Europe. The aim of this work is to perform the spatial and temporal analysis of drought, expressed as SPI, in a heterogenous area of the Polish Carpathian and the highland region in the East-Central part of Europe based on long-term precipitation data. Moreover, for the first time in this work drought characteristics assessed by means of the SPI were discussed considering the atmospheric circulation calendar. In this work monthly precipitation from 55 rainfall stations were analysed from 1961 to 2022. The 3-, 6-, 9-, and 12-month Standardized Precipitation Index (SPI) was used as indicators of meteorological drought. For the 3-month SPI, the main climatic mechanisms determining extreme droughts were defined based on the calendar of synoptic circulations. The Mann-Kendall test was used to detect the trend of extreme droughts. Statistically significant trends of SPI were observed on 52.7% of all analyzed stations and, in most cases, a positive trend was observed, indicating an increase in water resources in the Upper Vistula Basin. Statistically significant trends were more frequently observed in stations located in the western part of the analyzed region. Long-term droughts, represented by the 12-month SPI, occurred in all stations but not in all years. Short-term droughts (3-month SPI) were most frequent in the winter season, 6- and 9-month SPI in winter and spring, and 12-month SPI in winter and autumn. The spatial distribution of droughts was highly diverse. The most intensive drought occurred in 1984, with the 6-month SPI covering 98% of the analyzed region, and the 9- and 12-month SPI covering 90% of the entire region. Droughts exhibit a seasonal pattern, with a dominant 10-year periodicity for all analyzed variants of SPI. Additionally, Fourier analysis revealed a 2-year periodicity for the 3-, 6-, and 9-month SPI and a 31-year periodicity for the 12-month SPI. The results provide insights into the typical climatic conditions in Poland, with strong seasonality in precipitation. The study highlighted that short-term extreme droughts, represented by the 3-month SPI, are often caused by anticyclonic situations with high-pressure wedges Ka and Wa, as observed in 52.3% of cases. These findings are crucial for understanding the spatial and temporal variability of short and long-term extreme droughts in Central Europe, particularly for the agriculture sector which is dominant in the northern part of the analyzed region, where drought frequency is highest.

Effect of NO2 on SO2–SO42- conversion in atmosphere: Evidence from long-term precipitation

Sulfate aerosol (SO42−) is an important factor leading to acid rain and aggravating atmospheric particulate pollution. SO42− is mainly converted from gaseous SO2, in which the oxidant gas NO2 may play a key role. In real atmosphere with complexity and nonlinearity, the impact of NO2 on SO2–SO42- conversion exhibits scale dependence due to the combined effect of meteorology, atmospheric chemistry, and pollution emission. Here, based on the long-term atmospheric precipitation data from 2014 to 2021 of Nanchong City in the Sichuan Basin, the Extended Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) methods have been employed to analyze the nonlinear correlations between gaseous SO2 and SO42− in rainwater at different time scales. A multifractal parameter Δh has been described as the scaling features of SO2–SO42- conversion efficiency in the real atmosphere. Then, the impact of NO2 on SO2–SO42- conversion in cold and warm seasons has been quantified, using sliding window analysis and Pearson correlation analysis. The results show that NO2 significantly promoted SO2–SO42- conversion in the cold season, but had little effect on it in the warm season. As direct evidence from atmospheric precipitation analysis, this finding has provided a new theoretical basis for evaluating the impact of NO2 on secondary aerosol generation.

Evaluation of five gridded precipitation products for estimating precipitation and drought over Yobe, Nigeria

ABSTRACT Ground observations are often considered as the most reliable and precise source of precipitation data. However, long-term precipitation data from ground observations are lacking in many parts of the world. Gridded precipitation products (GPPs) therefore have emerged as crucial alternatives to ground observations, but it is essential to assess their capability to accurately replicate precipitation patterns. This study aims to evaluate the performance of five GPPs, NASA POWER, TerraClimate, Climate Hazards Group Infrared Precipitation with Climate Data (CHIRPS), GPCC, and Climate Research Unit (CRU), in capturing precipitation and drought patterns from 1981 to 2021 in Yobe, Nigeria. The results indicate that GPCC had good performance at both monthly and annual scales, with high correlation coefficients and low error values. However, it tends to underestimate precipitation amounts in certain areas. Other products also exhibit satisfactory performance with moderate correlations with ground observations. Drought analysis indicates that GPCC outperforms other products in standardised precipitation index-6 calculations, while NASA POWER demonstrates inconsistencies with ground observations, particularly during the early 1980s and mid-2000s. In conclusion, GPCC is the most preferable GPP for precipitation and drought analysis in the Yobe State in Nigeria.

Morphometric Analysis Using Geographical Information System and the Relationship with Precipitation Quantiles of Major Dam Basins in South Korea

Geographic information system (GIS) and remote sensing (RS) technologies are potent tools for evaluating various aspects of basin hydrology. This study conducted a morphological analysis using GIS tools on seven major dam basins in Korea. Additionally, long-term RS-based precipitation data were obtained, the probability of precipitation was estimated, and their relationship was examined. The findings are summarized as follows. It was observed that most major dam basins in Korea, which were the focus of this study, had a broad radial shape, and due to the mountainous topography, there was a notable presence of numerous river branches. Through the estimation of probability precipitation and its comparison with morphological indices, it was noted that wider basins tend to have higher rainfall amounts and a relatively uniform spatial distribution. Furthermore, it was found that the more uniform the spatial distribution, the simpler the river network. This trend becomes more pronounced in relation to basin size for longer durations, and in spatial dispersion for shorter durations. However, it is crucial to acknowledge that the complexity of these relationships is also affected by other factors such as climate, altitude, and local geographical conditions.

Drought Assessment of Yeşilırmak Basin Using Long-term Data

Drought is a prolonged period of inadequate rainfall, such as one season, one year or several years, on a statistical multi-year average for a region. Drought is a natural disaster effective on several socio-economic activities from agriculture to public health and leads to deterioration of the environment sustainability. The drought starts with meteorological drought, continues with agricultural and hydrological drought, and when it is in the socioeconomic dimension, the effects begin to be observed. Generally, drought studies are based on drought indices in the literature. This study applied long-term precipitation, temperature, and evaporation data from Samsun, Tokat, Merzifon, Çorum and Amasya meteorological stations from 1961 to 2022 to investigate the drought in the Yeşilırmak basin of Turkey. The present study applied Standardized Precipitation Index (SPI), and Effective Drought Index (EDI), China Z- Index (CZI) and Standardized Precipitation Evapotranspiration Index (SPEI) based on daily, monthly, seasonal, and annual time periods to evaluate drought. The Sen slope and Mann-Kendall test were employed for data analysis. The results revealed that the monthly drought indices for the study area were almost identical for the study area. Although dry and wet periods were observed.

Trend Analysis of Drought Severity in Southeast Region of Türkiye

The study of drought, one of the many problems caused by climate change, enables the planning of water use and water resources. The increase or decrease in drought severity, which is one of the drought parameters, is an important source of information in terms of predetermining the consequences that may arise due to drought. In this study, drought analysis was conducted for 1-, 3-, 6-, 9- and 12-month time periods with the Standard Precipitation Index (SPI) using long-term precipitation data of four stations in the Tigris River basin from 1960 to 2022. Drought duration and severity series were obtained from drought index values, and the trends of drought severity series were calculated with Mann-Kendall (MK) and Innovative Trend Analysis (ITA) tests. According to the results, the ITA test was found to be more sensitive in detecting trends in drought severity series compared to the MK test. According to the MK test, drought did not show a significant increase or decrease. On the other hand, according to the ITA test, drought increased at 1- and 12-month scales at Siirt and Batman stations, respectively, and at 1- and 12-month scales at Diyarbakır station. Drought increased in all time periods at Mardin station.

Meteorological Drought Assessment for Jhelum River Basin, Jammu and Kashmir\'s Sangam and Ram Munshi Bagh Stations

Abstract: Drought is a severe natural calamity that harms the global ecology. To examine drought conditions including intensity and duration, a variety of drought indicators (DIs) are commonly employed. This study assesses the occurrences of drought in Kashmir's Jhelum River Basin (JRB) at the Sangam and Ram Munshi Bagh stations. Several DI approaches, such as Deciles, Percent of Normal (PN), and Standardized Precipitation Index (Normal-, Log-, and Gamma-SPI), are used to analyse 20 years of monthly recorded precipitation data from 2002 to 2023. Every technique is used on the annual long-term precipitation data set. The findings indicate that nearly identical outcomes are obtained for the stations by the DI approaches. While the normalSPI indicates rainy and less droughty conditions, the log-SPI and gamma-SPI forecast moderate dry conditions. The results emphasize that the PN method predicts more moderate drought years in comparison with SPI method, however, Deciles method shows longer period of extreme and severe drought than other methods. As a result, the five methods indicate various drought intensities in 2017, 2018,2001, and 2002 with a peak moderate drought in 2017 in the Sangam station. The Ram Munshi Bagh station area experienced the less drought conditions from 2002 to 2023 with a moderate drought in 2017 and 2018 as well. Therefore, moderate drought conditions happened in 2017 -2018 in both stations confirm to the recorded drought reports for the same region.

Evaluation of GPM IMERG and error sources for tropical cyclone precipitation over eastern China

Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product.

Evaluating Markov chains and Bayesian networks as probabilistic meteorological drought forecasting tools in the seasonally dry tropics of Costa Rica

Meteorological drought is a climatic phenomenon that affects all global climates with social, political, and economic impacts. Consequently, it is essential to develop drought forecasting tools to minimize the impacts on communities. Here, probabilistic models based on Markov chains (first and second order) and Bayesian networks (first and second order) were explored to generate forecasts of meteorological drought events. A Ranked Probability Score (RPS) metric selected the best-performing model. Long-term precipitation data from Liberia Airport in Guanacaste, Costa Rica, from 1937 to 2020 were used to estimate the 1-month Standardized Precipitation Index (SPI-1) characterizing four meteorological drought states (no drought, moderate drought, severe drought, and extreme drought). The validation results showed that both models could reflect the climatic seasonality of the dry and rainy seasons without mistaking 4–5 months of the rain-free dry season for a drought. Bayesian networks outperformed Markov chains in terms of the RPS at both reproducing probabilities of drought states in the rainy season and when compared to the months in which a drought state was observed. Considering the forecasting capability of the latter method, we conclude that these models can help predict meteorological drought with a 1-month lead time in an operational early warning system.

Distribution areas and monthly dynamic distribution changes of three Aedes species in China: Aedes aegypti, Aedes albopictus and Aedes vexans.

Mosquitoes play an absolute role in the spread of epidemic arbovirus diseases. Worldwide, Aedes aegypti and Aedes albopictus are the main vectors responsible for the spread of these mosquito-borne diseases. Aedes vexans, a mosquito species native to China, also carries mosquito-borne viruses, such as dengue fever virus and Japanese encephalitis virus, but research on this mosquito has been inadequate. Mapping the potential distribution range of and monthly change in the distribution of these three Aedes species is of particular importance for mosquito surveillance, eradication and disease control. Monitoring data were collected for the three Aedes species in China. Long-term temperature and precipitation data (2001-2021) and land cover data were used to represent various climate and environmental conditions. An ecological niche model was developed using a maximum entropy modeling method to predict the current optimum habitat areas for the three Aedes species and to obtain important variables influencing their monthly distribution. The distribution model for the three Aedes species performed well, with an area under the receiver operating characteristic curve value of 0.991 for Ae. aegypti, 0.928 for Ae. albopictus and 0.940 for Ae. vexans. Analysis of the distribution change and mapping of the optimum habitat range for each Aedes species for each month demonstrated that temperature, precipitation and construction land were important factors influencing the distribution of these three Aedes species. In China, Aedes aegypti is mainly concentrated in a few tropical regions and along the Yunnan border; Aedes albopictus is widely distributed throughout most of the country, except for the arid and semi-arid regions of northwest China; and Aedes vexans is mainly found in the northern regions. Our results provide a basis for the timing and location of surveillance efforts for high-priority mosquitoes.

Utility of Open-Access Long-Term Precipitation Data Products for Correcting Climate Model Projection in South China

Insufficient precipitation observations hinder the bias-correction of Global Climate Model (GCM) precipitation outputs in ungauged and remote areas. As a result, the reliability of future precipitation and water resource projections is restricted for these areas. Open-access quantitative precipitation estimation (QPE) products offer a potential solution to this challenge. This study assesses the effectiveness of three widely used, long-term QPEs, including ERA5, PERSIANN-CDR, and CHIRPS, in bias-correcting precipitation outputs from the CMIP6 GCMs. The evaluation involves the reproduction of precipitation distribution, streamflow simulation utility based on a hydrological model, and the accuracy of extreme indices associated with rainstorm/flood/drought events. This study selects the Beijiang basin located in the subtropical monsoon area of South China as the case study area. The results demonstrate that bias-correction using QPEs improves the performance of GCM precipitation outputs in reproducing precipitation/streamflow distribution and extreme indices, with a few exceptions. PCDR generally exhibits the most effective bias-correction utility, consistently delivering reasonable performance across various cases, making it a suitable alternative to gauge data for bias-correction in ungauged areas. However, GCM outputs corrected by ERA5 tend to overestimate overall precipitation and streamflow (by up to about 25% to 30%), while the correction results of CHIRPS significantly overestimate certain extreme indices (by up to about 50% to 100%). Based on the revealed performance of QPEs in correcting GCM outputs, this study provides references for selecting QPEs in GCM-based water resource projections in remote and ungauged areas.

Analysis of Dry-Wet Changes and the Driving Factors in Mainland China under Climate Change

Evaluation of changes in dry-wet climate is crucial in the context of global climate change to ensure regional water resources, ecosystem stability, and socio-economic development. Long-term daily meteorological data, including temperature, precipitation, relative humidity, wind speed, sunshine duration, and air pressure data from 1680 stations across mainland China from 1971 to 2019, were collected to investigate the temporal and spatial variations in aridity index (AI), precipitation (P), reference evapotranspiration (ET0), and the underlying driving climatic factors. Results indicated that the Northwest, Northeast, and Huang-Huai regions were undergoing significant wetting processes, while the Southwest and Southeast China were undergoing significant drying processes. The changing AI was mainly decided by the changing trends of ET0. For most regions, ET0 has undergone significant increases. The average increasing rate over mainland China was 3.76 mm/10a. Stations with decreasing trends were mainly located in the Tibet Plateau, Huang-Huai, and northern Northeast China. Trends in ET0 were negatively affected by the increasing changes in relative humidity and positively affected by the decreasing changes in wind speed and sunshine duration and the increasing changes in air temperature. Wind speed and relative humidity were found to be the main dominant factors driving the changes in ET0, and their contribution varied with regions. Huang-Huai and northern Northeast China showed a significant downward trend in ET0, mainly driven by the decrease in wind speed, while the increase in relative humidity was the primary contributor to the significant upward trends in ET0 across all other regions in China.

Assigning harvested waterfowl to geographic origin using feather δ2H isoscapes: What is the best analytical approach?

Establishing links between breeding, stopover, and wintering sites for migratory species is important for their effective conservation and management. Isotopic assignment methods used to create these connections rely on the use of predictable, established relationships between the isotopic composition of environmental hydrogen and that of the non-exchangeable hydrogen in animal tissues, often in the form of a calibration equation relating feather (δ2Hf) values derived from known-origin individuals and amount-weighted long-term precipitation (δ2Hp) data. The efficacy of assigning waterfowl to moult origin using stable isotopes depends on the accuracy of these relationships and their statistical uncertainty. Most current calibrations for terrestrial species in North America are done using amount-weighted mean growing-season δ2Hp values, but the calibration relationship is less clear for aquatic and semi-aquatic species. Our objective was to critically evaluate current methods used to calibrate δ2Hp isoscapes to predicted δ2Hf values for waterfowl. Specifically, we evaluated the strength of the relationships between δ2Hp values from three commonly used isoscapes and known-origin δ2Hf values three published datasets and one collected as part of this study, also grouping these data into foraging guilds (dabbling vs diving ducks). We then evaluated the performance of assignments using these calibrations by applying a cross-validation procedure. It remains unclear if any of the tested δ2Hp isoscapes better predict surface water inputs into food webs for foraging waterfowl. We found only marginal differences in the performance of the tested known-origin datasets, where the combined foraging-guild-specific datasets showed lower assignment precision and model fit compared to data for individual species. We recommend the use of the more conservative combined foraging-guild-specific datasets to assign geographic origin for all dabbling duck species. Refining these relationships is important for improved waterfowl management and contributes to a better understanding of the limitations of assignment methods when using the isotope approach.

Response patterns of simulated corn yield and soil nitrous oxide emission to precipitation change

BackgroundPrecipitation plays an important role in crop production and soil greenhouse gas emissions. However, how crop yield and soil nitrous oxide (N2O) emission respond to precipitation change, particularly with different background precipitations (dry, normal, and wet years), has not been well investigated. In this study, we examined the impacts of precipitation changes on corn yield and soil N2O emission using a long-term (1981–2020, 40 years) climate dataset as well as seven manipulated precipitation treatments with different background precipitations using the DeNitrification-DeComposition (DNDC) model.ResultsResults showed large variations of corn yield and precipitation but small variation of soil N2O emission among 40 years. Both corn yield and soil N2O emission showed near linear relationships with precipitation based on the long-term precipitation data, but with different response patters of corn yield and soil N2O emission to precipitation manipulations. Corn yield showed a positive linear response to precipitation manipulations in the dry year, but no response to increases in precipitation in the normal year, and a trend of decrease in the wet year. The extreme drought treatments reduced corn yield sharply in both normal and wet years. In contrast, soil N2O emission mostly responded linearly to precipitation manipulations. Decreases in precipitation in the dry year reduced more soil N2O emission than those in the normal and wet years, while increases in precipitation increased more soil N2O emission in the normal and wet years than in the dry year.ConclusionsThis study revealed different response patterns of corn yield and soil N2O emission to precipitation and highlights that mitigation strategy for soil N2O emission reduction should consider different background climate conditions.

Spatio-temporal analysis of slope-type debris flow activity in Horlachtal, Austria, based on orthophotos and lidar data since 1947

Abstract. In order to get a better understanding of the future development of alpine slope-type debris flows in the frame of climate change, complete and gapless records of the last century for this type of geomorphologic process are necessary. However, up to now such records have been scarce. Here, the slope-type debris flow activity in Horlachtal, Austria, has been investigated since 1947 with the help of historic and recent area-wide remote sensing data. Using geomorphological mapping, both spatial and temporal variabilities in debris flow dynamics can be shown. The results indicate short-term variations rather than consistent increasing or decreasing trends of slope-type debris flow activity in Horlachtal. Specifically, three active periods between 1954 and 1973, 1990 and 2009, as well as 2015 and 2018, can be registered. Analyses of the deposited debris flow volumes show that for parts of the study area the largest volumes appeared in the early 1990s, which might have even influenced the dynamics in the following years. Studies on the spatial variabilities revealed differences of slope-type debris flow activity within the study area and point to local rainfall events as triggers. However, long-term precipitation data of high temporal resolution of two alpine meteorological stations do not reveal increasing or decreasing trends in the occurrence of such events.

Spatiotemporal Trends and Variation of Precipitation over China’s Loess Plateau across 1957–2018

Better understanding of the spatiotemporal characteristics of precipitation is essential in developing the best management practices for ecological restoration and soil erosion control on China’s Loess Plateau, an arid and semiarid region with severe soil erosion. This study aimed to explore the spatiotemporal trends of precipitation using long-term precipitation data from 1957 to 2018 from the 100 national standard meteorological stations across the Loess Plateau. The nonparametric Mann–Kendall statistical test and geospatial interpolation were used to detect the trends and to analyze spatial patterns of annual and seasonal precipitation. Wavelet analysis was applied to examine periodical variation across the plateau. The results reveal that regional annual precipitation over the Loess Plateau decreased during the 62 years of the study at a mean rate of −1.05 mm/10 years (p > 0.1). The changes in annual precipitation showed a periodical fluctuation with an increasing trend from 1957 to 1969, a decreasing trend from 1970 to 1999, and an increasing trend again in the first 18 years of the 21st century. The annual precipitation decreased in all eight sub-annual periods except for winter. Spatially, a decreasing trend occurred in the southern and eastern parts of the Loess Plateau, whereas a slight increase existed in the northwest in all periods. The decrease in six stations was statistically significant (p < 0.05), and a significant increase occurred in four stations (p < 0.1). These changes can be explained by an evident southward shift of the precipitation isohyets especially for 350 mm, 450 mm and 550 mm from the 1960s to the 1990s, and a clearly northward shift after the 1990s. Findings from this study facilitate an understanding of the spatial temporal trends of precipitation so appropriate countermeasures can be developed for effective vegetation restoration and soil erosion control across the Loess Plateau.

Hydrological and Meteorological Drought Monitoring and Trend Analysis in Abbay River Basin, Ethiopia

The definition of drought is very controversial due to its multi-dimensional impact and slow propagation in onset and end. Predicting the accurate occurrence of drought remains a challenging task for researchers. The study focused on hydrological and meteorological drought monitoring and trend analysis in the Abbay river basin, using the streamflow drought index (SDI), standardized precipitation index (SPI), and reconnaissance drought index (RDI), respectively, to fill this research gap. The study also looked into the interrelationships between the two drought indicators. The SDI, SPI, and RDI were calculated using long-term streamflow, precipitation, and temperature data collected from 1973 to 2014. The data were collected from eight streamflow stations and fifteen meteorological gauge stations. DrinC software (Drought Indices Calculator) was used to calculate the SDI, SPI, and RDI values. The result from meteorological drought using SPI12 and RDI12 shows that 1975, 1981, 1984, 1986, 1991, 1994, and 2010 were extreme drought years, whereas 1983, 1984, 2001, and 2010 were the most extreme hydrological drought years based on the SDI12 result. Except for Bahir Dar and Gondar, a severe drought occurs at least once a decade in all stations considered in this study. In general, the SPI, RDI, and SDI results indicated that the study area was exposed to the most prolonged severe and extreme drought from 1981 to 1991. The findings of this study also demonstrated that the occurrence of hydrometeorological droughts in the Abbay river basin has a positive correlation at long time scales of 6 and 12 months. The trend analysis using the Mann–Kendall test implied that there was a significant meteorological drought trend in two stations (Debre Berhan and Fiche) at SPI12 and RDI12 time scale, but for the remaining thirteen stations, there is no trend in all time scales. The hydrological drought trend analysis in the basin on a seasonal (SDI3) and yearly (SDI12) time scale also revealed that three streamflow stations have a positive trend (Kessie, Gummera, and Border). This implies that water resource management is still a vital tool for the sustainable development of the Abbay river basin in the future.

Towards improving the hydrologic design of permeable pavements

Abstract The common approach to the hydrologic design of permeable pavements (PPs) uses synthetic rainfall events. This study assessed the validity of the design approach using synthetic rainfall events for undrained PP. Synthetic rainfall events (25-year return period) were used to design undrained pavements for five Norwegian cities. The effectiveness of these pavements was tested using long-term simulation (12–30 years) with high temporal resolution (1 min). The Storm Water Management Model (SWMM) was used to generate time series of surface runoff for PPs and flow duration curves were applied to analyse the hydrological performances. Designing PP using synthetic rainfall events was found to underestimate the storage layer depth of the permeable pavements leading to the frequent occurrence of surface runoff, which is considered a failure of the hydrologic design of undrained pavements. Long-term simulation of surface runoff was found to provide valuable information for the hydrologic design of PP and can be used as a basis for the PP hydrologic design. In the future, it is recommended to use long-term precipitation data generated from climate change models to incorporate the effect of climate change in the design of PP.

Corrected ERA5 Precipitation by Machine Learning Significantly Improved Flow Simulations for the Third Pole Basins

Abstract Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region. Significance Statement The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.