Precipitation Datasets Research Articles

South Asian High Identification and Rainstorm Monitoring Using Fengyun-4-Derived Atmospheric Motion Vectors

Based on atmospheric motion vectors (AMVs) derived from the Fengyun-4 meteorological satellite (FY-4), in this paper, integrated multi-satellite retrievals for GPM precipitation and reanalysis datasets and the vertical distribution characteristics of FY-4 AMVs, their application in the identification of the South Asian high (SAH) anticyclone and their application in the real-time monitoring of rainstorm disasters are studied. The results show that the AMVs’ vertical distribution characteristics are different across regions and seasons. AMVs from 150 to 350 hPa can be chosen as the upper troposphere wind (the total number accounts for about 77.2% on average). The center and shape of the upper tropospheric anticyclone obtained from AMVs are close to or slightly southward compared with those of the SAH at 200 hPa obtained from the ERA5 geopotential height. The SAH ridge line identified using the upper troposphere AMV zonal wind (the zonal wind is equal to zero) is slightly southward by about 1–2 degrees of latitude from that identified using ERA5 at 200 hPa but with a similar seasonal advance. The upper troposphere AMV can be used to monitor the location of the SAH and the evolution of its ridge line. The abnormally strong precipitation in South China is related to the location of the SAH and its ridge line. When the precipitation is abnormally strong/weak, the upper troposphere AMV deviation airflow shows divergence/convergence. During the “Dragon Boat Water” period in South China in 2022, strong precipitation occurred in the strong westerly winds or divergent flow on the northeast side of the upper troposphere anticyclone obtained from AMVs, and the precipitation intensity was the strongest when the divergence reached its peak, but this is not shown clearly in the EAR5 dataset.

Spatial and temporal analysis of drought resistance of different vegetation in the Ta-pieh Mountains based on multi-source data

Abstract This study focuses on the ecological and environmental safety of Ta-pieh Mountain. Drought episodes can lead to ecological problems such as vegetation damage. Therefore, quantifying the response of vegetation to drought is essential for ecological management. The study utilized normalized difference vegetation index (NDVI) and precipitation datasets from 1999 to 2019 to derive seasonal NDVI and standardized precipitation index (SPI) data. Using Theil-Sen median trend analysis and Mann-Kendall significance test analysis, we initially examined the characteristics of vegetation and drought for the 21-year time series. SPI is used to investigate and assess the occurrence and severity of drought in the research region. Then, the strength and variability of cropland, woodland, and grassland drought resistance in the Ta-pieh Mountains were discussed using the ratio of coefficient of variation (RCV). Finally, the cross-spectrum was used to calculate the vegetation lag time to drought. The study found that NDVI increased across all seasons, while SPI increased in spring and autumn and decreased in summer and winter. The spring drought had the most significant impact on vegetation. Cropland showed the highest improvement in drought tolerance and woodland showed the highest drought tolerance. The lagged response periods of cropland, woodlands, and grassland to drought were 1.62 months, 8.94 months, and 2.49 months, respectively. These findings provide a scientific basis for the management and preservation of the ecology of the Ta-pieh Mountains.

A downscaling-calibrating framework for generating gridded daily precipitation estimates with a high spatial resolution

Accurate precipitation datasets with a high spatiotemporal resolution are crucial for regional hydrological simulation and water resources management. Although various global satellite precipitation products (SPPs) are available, they cannot be directly applied to small-scale research due to their low spatial resolution and significant bias. To solve these problems, based on the consideration of spatiotemporal relationships, this paper presented a comprehensive downscaling-calibrating framework that consists of spatial downscaling procedures and calibrating processes. Specifically, a geographically weighted regression (GWR) model, coupled with the environmental variables selected by the Geodetector model, was applied to generate uncorrected daily precipitation data with high-spatial resolution (1 km × 1 km). Then, the preliminary downscaled daily precipitation was calibrated using a bidirectional long short-term memory (Bi-LSTM) network. The proposed framework was applied to downscale and calibrate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product of the Jiulong River Basin in southeast China from 2010 to 2016. The results indicate that: (1) the spatial downscaling procedures improved the spatial resolution of the SPPs while maintaining its accuracy; (2) the calibrating processes can effectively correct the bias of preliminary downscaled daily precipitation. Meanwhile, the accuracy evaluation showed that the proposed GWR_Bi-LSTM framework outperformed the comparative methods, and the generated precipitation dataset exhibited a better performance under various evaluation conditions. Generally, based on the proposed framework, gridded daily precipitation data with high quality and spatial resolution can be generated for hydrological and meteorological research.

Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA

Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Multiscale Evaluation of Gridded Precipitation Datasets across Varied Elevation Zones in Central Asia’s Hilly Region

The lack of observed data makes research on the cryosphere and ecology extremely difficult, especially in Central Asia’s hilly regions. Before their direct hydroclimatic uses, the performance study of gridded precipitation datasets (GPDS) is of utmost importance. This study assessed the multiscale ground evaluation of three reanalysis datasets (ERA5, MEERA2, and APHRO) and five satellite datasets (PERSIANN-PDIR, CHIRPS, GPM-SM2Rain, SM2Rain-ASCAT, and SM2Rain-CCI). Several temporal scales (daily, monthly, seasonal (winter, spring, summer, autumn), and annual) of all the GPDS were analyzed across the complete spatial domain and point-to-pixel scale from January 2000 to December 2013. The validation of GPDS was evaluated using evaluation indices (Root Mean Square Error, correlation coefficient, bias, and relative bias) and categorical indices (False Alarm Ratio, Probability of Detection, success ratio, and Critical Success Index). The performance of all GPDS was also analyzed based on different elevation zones (≤1500, ≤2500, >2500 m). According to the results, the daily estimations of the spatiotemporal tracking abilities of CHIRPS, APHRO, and GPM-SM2Rain are superior to those of the other datasets. All GPDS performed better on a monthly scale than they performed on a daily scale when the ranges were adequate (CC > 0.7 and r-BIAS (10)). Apart from the winter season, the CHIRPS beat all the other GPDS in standings of POD on a daily and seasonal scale. In the summer, all GPDS showed underestimations, but GPM showed the biggest underestimation (−70). Additionally, the CHIRPS indicated the best overall performance across all seasons. As shown by the probability density function (PDF %), all GPDS demonstrated more adequate performance in catching the light precipitation (>2 mm/day) events. APHRO and SM2Rain-CCI typically function moderately at low elevations, whereas all GPDS showed underestimation across the highest elevation >2500 m. As an outcome, we strongly suggest employing the CHIRPS precipitation product’s daily, and monthly estimates for hydroclimatic applications over the hilly region of Tajikistan.

Divergent runoff regime revealed by hydrological simulations with corrected precipitation in the upper Indus

The upper Indus basin (UIB) supplies water for the largest irrigation system in Asia and for the livelihoods of millions of people downstream. Yet a large gap still remains in the current knowledge of the hydrological processes in this region due to data scarcity, especially at sub-basin scales. In this work, we evaluated eight precipitation datasets and screened out an optimized (MERRA-2) to be inversely corrected through the Variable Infiltration Capacity (VIC)-glacier hydrological model across the UIB. Divergent runoff regimes, changes, and their attributions were quantified at sub-basin scales based on corrected MERRA-2 precipitation and the well-constrained VIC-glacier model against observed streamflow and glacier/snow changes. Spatial variations exist in the corrected precipitation over the UIB with the lowest mean annual average of 414 mm in the Kharmong and the highest of 1318 mm in the Astore, and a mean of 646 mm over the entire basin in 1980–2013. Three runoff regimes were identified across the UIB basins, including the glacial regime of Shigar, Hunza, and Shyok with glacier runoff dominating (41.6%–53.4%), nival-pluvial regime of Astore and Gilgit with rainfall (46.9%–51.9%) and snowmelt (33.1%–40.7%) runoff dominating, and pluvial regime of UIB-M and Kharmong with rainfall runoff (56.7%–60.9%) dominating. Total runoff generally exhibited stable or insignificant positive tendencies over the UIB basins during 1980–2013. Runoff increases in the glacial regime basins were mainly controlled by the increased glacier runoff, while runoff changes in the non-glacial regime basins were mostly attributed to the changes of precipitation-induced runoff from non-glacierized areas. The increased glacier runoff served as a buffer against hydrological drought by offsetting the non-glacierized runoff decrease caused by the overall decreased precipitation. This study is expected to further the understanding of divergent hydrological regimes in the UIB, and provide essential information for policy decisions on sustainable water resource management in this water-stressed area.

Microphysics-based rainfall energy estimation using remote sensing and reanalysis data

Rainfall plays a critical role in soil erosion by detaching and transporting soil particles, making it a key factor in soil erosion models. Its potential effect on soil erosion is reflected by rainfall erosivity, which considers the interaction between raindrops and soil, primarily driven by the kinetic energy carried by raindrops. Traditional approaches rely on empirical relationships between rainfall rate and kinetic energy, but they often overlook regional differences in rainfall microphysical characteristics. This study aims to compare the performance of conventional fixed and disdrometer-derived local microphysics-based unit rainfall kinetic energy–rain rate (KE–I) relationships for estimating rainfall energy using the IMERG and ERA5-land precipitation datasets. The findings reveal that conventional method using the fixed KE–I relationship do not account for regional differences in rainfall microphysical characteristics and tend to overestimate KE values for high rainfall intensities in the UK. Disdrometer-derived microphysics-based KE–I relationships improve estimation correlations, with the fully fitting formula exhibiting the highest correlation coefficient. The findings highlight the need to consider local rainfall characteristics for improved estimation accuracy and demonstrate that ERA5-land exhibits higher correlation and lower error compared to IMERG for estimating rainfall energy, but it generally underestimates rainfall intensity. This study will provide valuable insights into the applicability of using gridded precipitation datasets for large-scale soil erosion estimation and emphasizes the significance of accounting for regional variations in rainfall characteristics.

Different maximum precipitation altitudes and precipitation gradient characteristics of different basins in the Third Pole region

Investigation of maximum precipitation altitude (MPA) and precipitation gradients supports the understanding of vertical precipitation distribution in the mountains and obtaining accurate areal precipitation datasets. This study provided the MPAs and precipitation gradients of 524 subbasins based on Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and discussed the dominant factors for the spatial pattern of MPAs and precipitation gradients. The results show that the MPAs across all subbasins in the Third Pole (TP) have noticeable seasonal variation, with the highest mean MPAs in winter (3803.2 m) and lowest mean MPAs in summer (3646.8 m). At the spatial scale, the MPAs are located at lower altitudes in the Himalayas, western Kunlun and Hengduan Mountains (<1500 m); in the Tienshan Mountains and Pamir, the MPAs mainly lie at middle altitudes (2000–3500 m); and in the interior of the TP, Tanggula Mountains and Karakoram Mountains, the MPAs exist at higher altitudes (>4500 m). Additionally, the precipitation gradients had different characteristics above and below the MPAs. Below the MPAs, there is a positive annual mean precipitation gradient of 11.35 mm/100 m, and the larger positive precipitation gradients are mainly located in the Himalayas, Hengduan Mountains, and western Kunlun. Above the MPAs, there is a negative annual mean precipitation gradient of − 8.61 mm/100 m, and smaller negative precipitation gradients are mainly located in the Himalayas and western Tienshan Mountains. Further discussions indicated that MPAs and precipitation gradients have a positive correlation with wind speed but a negative correlation with water vapor flux, the angle between wind direction and the aspect of mountains (AWA). The maximum effect of wind and topography on precipitation occurs when the AWA is close to 0, and the precipitation gradients are the largest.

1 km monthly precipitation and temperatures dataset for China from 1952 to 2019 based on new baseline climatology surfaces

Long-term climate data and high-quality baseline climatology surface with high resolution are essential to investigate climate change and its effect on hydrological processes and ecosystem functioning. However, large uncertainties remain in the climate products in China owing to lacking of high-density distribution network of weather stations. Here, the thin plate spline (TPS) algorithm was used to produce new baseline climatology surfaces (ChinaClim_baseline) using >2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1 km monthly precipitation and temperatures dataset for China during 1952–2019 (ChinaClim_time-series) via superimposing ChinaClim_baseline and monthly anomaly surface. Our finding showed that ChinaClim_baseline performed exceptionally well in four climatic regions, with RMSEs for precipitation and temperature element estimation of 1.276–28.439 mm and 0.310–2.040 °C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but there were clearly spatial differences. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502– 52.307 mm, and 0.461–0.939 °C for all months, respectively. In comparison to Peng's climatic surface and CHELSAcruts, R2 increased by ~7 %, RMSE and MAE dropped by ~17 % for precipitation; R2 hardly increased, while RMSE and MAE decreased by ~50 % for temperature elements. Our findings indicated that ChinaClim_baseline improved the accuracy of time-series climatic elements estimation obviously, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperature estimation. Overall, ChinaClim_baseline as an excellent baseline climatology surface can be used for obtaining high-quality and long-term climate datasets from past to future. Meantime, ChinaClim_time-series of 1 km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.

Ground-based climate data show evidence of warming and intensification of the seasonal rainfall cycle during the 1960–2020 period in Yangambi, central Congo Basin

Meteorological stations are rare in central Africa, which leads to uncertainty in regional climatic trends. This is particularly problematic for the Congo Basin, where station coverage decreased significantly during the last few decades. Here, we present a digitized dataset of daily temperature and precipitation from the Yangambi biosphere reserve, covering the period 1960–2020 (61 years) and located in the heart of the Congo Basin. Our results confirm a long-term increase in temperature and temperature extremes since the 1960s, with strong upward trends since the early 1990s. Our results also indicate a drying trend for the dry season and intensification of the wet season since the early 2000s. Ongoing warming and increasing precipitation seasonality and intensity already have a significant impact on crop yields in Yangambi. This calls for urgent development of climate-smart and dynamic agriculture and agroforestry systems. We conclude that systematic digitization and climate recording in the Congo Basin will be critical to improve much-needed gridded benchmark datasets of climatic variables.

Detecting drought events over a region in Central Europe using a regional and two satellite-based precipitation datasets

In this study, the accuracy of two satellite-based datasets is evaluated. The evaluation includes monthly precipitation estimates, spatial detection of precipitation, and drought monitoring against a regional gridded dataset spanning 2007–2019. A study area covering Poland and parts of the neighboring countries in Central Europe was selected for this evaluation. The Standardized Precipitation Index (SPI) at multi-time scales was employed to monitor meteorological (SPI-3), agricultural (SPI-6, SPI-9), and hydrological (SPI-12) droughts over the study region. This study selected PERSIANNCDR as a top-down precipitation dataset and SM2RAIN-ASCAT as a bottom-up dataset. According to the results, both datasets exhibit good accuracy for precipitation estimations, but PERSIANNCDR shows higher accuracy based on the R (coefficient of correlation) and KGE (Kling-Gupta Efficiency) performance indicators. However, SM2RAIN-ASCAT has a lower bias according to PBIAS(%) (percent bias). The reference dataset indicates that the study area experienced dry conditions over 50% of the months. Specifically, based on the reference dataset, 12 (SPI-6) and 16 (SPI-9) severe agricultural droughts were detected. Twenty-four severe agricultural drought events were identified via SPI-6, while the longer SPI window (SPI-9) demonstrated that PERSIANNCDR assessed 20 severe droughts over the study area. SM2RAIN-ASCAT detected 11 severe agricultural droughts via SPI-6 and SPI-9. Furthermore, based on SPI-12, the reference dataset identified 75 hydrological droughts, while the top-down dataset indicated a lower number of hydrological droughts (67 events) than the reference dataset over the studied period. In contrast, the bottom-up dataset detected 84 hydrological droughts. The spatial distribution of severe meteorological droughts showed a clear pattern with predominant occurrence in eastern parts (Vistula River Basin), as shown by the reference dataset, while this pattern changed for agricultural and hydrological droughts (Odra River Basin). Additionally, the results reveal that meteorological drought does not have a similar spatial distribution to agricultural and hydrological droughts.

Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal

This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.

Spatial Downscaling of GPM Satellite Precipitation Data Using Extreme Random Trees

Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model’s capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions.

Predictive Study on Extreme Precipitation Trends in Henan and Their Impact on Population Exposure

This study employs precipitation data sets from historical trials on 20 CMIP6 global climate models and four shared socioeconomic pathway scenario trials (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to predict trends in extreme precipitation changes in Henan Province quantitatively, while ascertaining the risk of population exposure to extreme precipitation in this area. The capacity of the CMIP6 models to simulate extreme precipitation indices from 1985 to 2014 is assessed using CN05.1 daily precipitation observational data. The correlation coefficients of the multi-model ensemble median’s simulation of the extreme precipitation indices are approximately 0.8, with a standard deviation ratio closer to 1 compared with the single models, demonstrating superior modeling ability. Analyses using the multi-model ensemble median demonstrate an overall increase in the total amount, frequency, and intensity of extreme precipitation in Henan throughout this century, particularly in its southern regions; in the mid-century high-emission scenario (SSP5-8.5), the maximum increase in annual total precipitation exceeds 150 mm, and it can be over 250 mm in the late-century period. For the entire province, the maximum five-day precipitation increase relative to the historical period is nearly 25 mm in the late-century SSP5-8.5 scenario. The spatiotemporal concentration of precipitation will significantly increase, heightening the risk of flood disasters. Comparative analysis reveals that, under the same population prediction, the total population exposure will be higher in high radiative forcing scenarios than in low radiative forcing scenarios, especially in Kaifeng City, where the total population exposure in SSP1 and SSP5-8.5 exceeds that in SSP1-2.6 by 2 million person-days. However, in the same radiative forcing scenario, the total population exposure in the development pathway dominated by traditional fossil fuels (SSP5) will not be significantly higher than that in the sustainable development pathway (SSP1), indicating that population activity in this century will not be the main contributor to changes in total exposure. Overall, for Henan, in the same population forecast scenario, population exposure to extreme precipitation will gradually rise with global warming.

Evolutionary Characteristics of Daytime and Nocturnal Precipitation Heterogeneity in Gansu Province, Northwest China

The diurnal variation in precipitation and its evolution are important foundations for understanding the regional impact of climate change and the parameterization of the model. Based on the daily precipitation data set of 23 national meteorological stations during 1970–2019, the spatial and temporal distribution characteristics of precipitation concentration degree (PCD) and precipitation concentration period (PCP) in Gansu province were evaluated on daytime and nocturnal scales. The results show the following: (1) Annual precipitation ranges from 69.1 ± 24.7 mm to 578.3 ± 96.6 mm, mainly (54.1 ± 2.6%) occurring at night, and the spatial distribution of the nocturnal precipitation rate is positively (r = 0.53, p &lt; 0.01) correlated with annual precipitation; the wetting trend (12.7 mm/10 a, p &lt; 0.01) in the past 50 years is obvious, and is mainly dominated by the frequency of precipitation (r = 0.58, p &lt; 0.001), with both performing better during the day. (2) Most PCD is located between 0.55 and 0.75, showing a basic distribution pattern for daytime greater than nocturnal, higher values, and stronger interannual fluctuations in arid areas; the significant decreasing trend (p &lt; 0.05) of PCD is very clear and highly consistent, especially in the high-altitude area, and the increase in precipitation in the dry season and the improvement in precipitation uniformity in the wet season play a key role. (3) PCP often fluctuates slightly around the 39th–41st pentad, but the general rule that daytime values are smaller than night values and the interannual variability is larger in arid areas also requires special attention; PCP has shown a relatively obvious advance trend in a few regions, but this is because the prominent and complex changes in the monthly precipitation distribution pattern have not been fully reflected. Along with continuous humidification, the decrease in PCD and the advance of PCP are likely to be the priority direction of precipitation evolution in the arid region of Northwest China, especially during the day. These findings provide a new perspective for understanding regional climate change.

Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya

Abstract Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Comprehensive analysis of droughts over the Middle East using IMERG data over the past two decades (2001–2020)

The Middle East is home to approximately 475 million people, most of whom face food insecurity due to severe and extreme droughts that have affected most regions over the past two decades. In this study, we analyzed the spatio-temporal characteristics of different drought intensities during the period 2001–2020 using the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation and ERA5 temperature datasets. As a first step, we compared the consistency of the IMERG-based 1-month Standardized Precipitation Evapotranspiration Index (SPEI1) with the station-based SPEI1. The results showed good agreement between them with R2 ranging from 64% to 84%. The principal component analysis method is used to show the different modes of the monthly 3-month SPEI (SPEI3) and the associated circulation types in this region. Spatially, SPEI3 shows a homogeneous region in the first five leading modes and for SPEI3 below −0.84 there was high spatial variability and inhomogeneity within and between countries. The most significant SPEI3 trend is observed in June–August with up to −0.1 SPEI3/year in some countries. Nine circulation types (CTs) were identified for the eight most severe and extreme drought episodes. The first circulation type in the positive and negative phases could explain the drought variability up to 30.1% and are closely associated with the Arabian thermal low pressure and the Siberian high pressure systems, respectively. In a final step, the moisture transport in each episode is analyzed using the vertical integral moisture flux (VIMF) and its divergence.

A Simple Method of Coupled Merging and Downscaling for Multi-Source Daily Precipitation Data

High accuracy and a high spatiotemporal resolution of precipitation are essential for the hydrological, ecological, and environmental fields. However, the existing daily gridded precipitation datasets, such as remote sensing products, are limited both by the coarse resolution and the low accuracy. Despite considerable efforts having been invested in downscaling or merging, a method of coupled and simultaneously downscaling and merging multiple datasets is currently lacking, which limits the wide application of individual popular satellite precipitation products. For the first time, in this study, we propose a simple coupled merging and downscaling (CMD) method for simultaneously obtaining multiple high-resolution and high-accuracy daily precipitation datasets. A pixel-repeated decomposition method was first proposed, and the random forest (RF) method was then applied to merge multiple daily precipitation datasets. The individual downscaled dataset was obtained by multiplying the result of merging by an explanatory rate obtained by RF. The results showed that the CMD method exhibited significantly better performance compared with the original datasets, with the mean absolute error (MAE) improving by up to 50%, the majority of the values of bias ranging between −1 mm and 1 mm, and the majority of the Kling–Gupta efficiency (KGE) values being greater than 0.7. CMD was more accurate than the widely used dataset, Multi-Source Weighted-Ensemble Precipitation (MSWEP), with a 43% reduction in the MAE and a 245% improvement in the KGE. In addition, the long-term estimation suggested that the proposed method exhibits stable good performance over time.

Development of high resolution daily gridded precipitation and temperature dataset for potohar plateau of indus basin

Development of high resolution daily gridded precipitation and temperature dataset for potohar plateau of indus basin