Precipitation Datasets Research Articles

EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset

Abstract. The European climatological high-resolution gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub)daily precipitation product covering 78 % of Europe at a high spatial resolution. A climatological dataset of 1 and 24 h precipitation accumulations on a 2 km grid is derived for the period 2013 through 2020. The starting point is the European Meteorological Network (EUMETNET) Operational Program on the Exchange of Weather Radar Information (OPERA) gridded radar dataset of 15 min instantaneous surface rain rates, which is based on data from, on average, 138 ground-based weather radars. First, methods are applied to further remove non-meteorological echoes from these composites by applying two statistical methods and a satellite-based cloud-type mask. Second, the radar composites are merged with the European Climate Assessment & Dataset (ECA&D) with potentially ∼ 7700 rain gauges from National Meteorological and Hydrological Services (NMHSs) in order to substantially improve its quality. Characteristics of the radar, rain gauge and satellite datasets are presented, as well as a detailed account of the applied algorithms. The clutter-removal algorithms are effective while removing few precipitation echoes. The usefulness of EURADCLIM for quantitative precipitation estimation (QPE) is confirmed by comparison against rain gauge accumulations employing scatter density plots, statistical metrics and a spatial verification. These show a strong improvement with respect to the original OPERA product. The potential of EURADCLIM to derive pan-European precipitation climatologies and to evaluate extreme precipitation events is shown. Specific attention is given to the remaining artifacts in and limitations of EURADCLIM. Finally, it is recommended to further improve EURADCLIM by applying algorithms to 3D instead of 2D radar data and by obtaining more rain gauge data for the radar–gauge merging. The EURADCLIM 1 and 24 h precipitation datasets are publicly available at https://doi.org/10.21944/7ypj-wn68 (Overeem et al., 2022a) and https://doi.org/10.21944/1a54-gg96 (Overeem et al., 2022b).

Toward a better understanding of uncertainty for satellite precipitation products over complex terrain with sparse rain gauge data

Satellite precipitation product quality is critical for various meteorological and hydrological applications. Products are subjected to more uncertainties in sparsely-gauged regions than in densely-gauged regions due to insufficient error correction. However, ground-truth-based validation cannot accurately quantify the spatially distributed uncertainty of satellite precipitation products over complex terrain with sparse rain gauge data. In this study, we explored a gauge-independent method, called the Three-Cornered Hat (TCH), to overcome the quantification issue and better understand the uncertainty of satellite precipitation products in such situations. Firstly, the reliability of the TCH method was explored over a topographically complex area. Then, the performance of the Soil Moisture TO RAIN-Advanced SCATterometer (SM2RAIN-ASCAT) and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) in the Chuan-Yu region was evaluated using TCH, with the help of the China Merged Precipitation Analysis (CMPA) precipitation product as the third input. We found that: (1) The high consistency between error standard deviation (ESD) estimated by TCH and root mean square error (RMSE) estimated by ground-truth-based validation demonstrated that TCH is reliable for evaluating precipitation datasets over complex terrain; (2) The ESDs of SM2RAIN-ASCAT and IMERG had different spatial distributions, and the quality of IMERG was overall higher than that of SM2RAIN-ASCAT over a mountainous area; (3) IMERG performed better than SM2RAIN-ASCAT both at low (under 3000 m) and high elevations (over 3000 m) in most years, as well as for most main land cover types (woody savannas, mixed forests, and grasslands) in the study area. This method and these findings provide a new insight for better use of satellite precipitation products over complex terrain.

Representativeness of Two Global Gridded Precipitation Data Sets in the Intensity of Surface Short-Term Precipitation over China

This study evaluates the representativeness of two widely used next-generation global satellite precipitation estimates data for short-term precipitation over China, namely the satellite data from the Climate Prediction Center morphing (CMORPH) and the satellite data from the Global Precipitation Measurement (GPM) mission. These two satellite precipitation data sets were compared with the hourly liquid in-situ precipitation from China national surface stations from 2016 to 2020. The results showed that the GPM precipitation data has better representativeness of surface short-term precipitation than that of the CMORPH data, and these two quantitative precipitation estimate (QPE) data sets underestimated extreme precipitation. Moreover, we analyzed the influence of the error between two QPE data sets and the in-situ precipitation on the classification of short-term precipitation intensity. China uses 8.1–16 mm/h as the definition of heavy precipitation, but the accuracy of the satellite QPE product was different due to the different lowest threshold of heavy rain (more than 8.1 mm/h or more than 16 mm/h). Increasing the threshold value of the QPE data for short-term strong precipitation resulted in lower accuracy for detecting such events, but higher accuracy for detecting moderate intensity rainfall. When studying short-term strong precipitation over China using precipitation grade, selecting an appropriate threshold was important to ensure accurate judgments. Additionally, it is important to account for errors caused by QPE data, which can significantly affect the accuracy of precipitation grading.

Investigation of the drivers and atmospheric impacts of energetic electron precipitation

The drivers and atmospheric impacts of energetic electron precipitation are not yet well understood. Further, electron precipitation is often poorly represented in atmospheric modeling. Additional investigations of the drivers and impacts of electron precipitation are needed to improve models and space weather forecasting requirements. To accurately represent the troposphere through the ionosphere in model simulations, it is vital to account for the chemistry accurately. Electron precipitation is a frequent, yet often ignored middle to high latitude forcing that can have dramatic effects on the middle and upper atmosphere. Over the past decade, several electron precipitation data sets have been developed, however, validation has been difficult due to the lack of independent observations of electron fluxes. Additionally, the limited number of satellites making measurements of global magnetospheric wave activity in concert with the resulting electron precipitation restricts our ability to accurately capture the drivers simultaneously with the precipitation. Accurate characterization of the drivers is needed for physics-based magnetosphere modeling. Likewise, accurate precipitating electron fluxes and relative energies are needed to improve our atmospheric modeling studies. Finally, in order to properly validate and improve our current modeling efforts, observations of atmospheric composition are necessary.

Standardized precipitation evapotranspiration index (SPEI) for Canada: assessment of probability distributions

Candidate probability distributions for the Standardized Precipitation Evapotranspiration Index (SPEI) for Canada were examined using the Canadian Gridded (CANGRD) temperature and precipitation dataset and CMIP5 projections. The probability distribution is a core component to the calculation of standardized values. For SPEI, a continuous probability distribution is fitted to the water balance time series so that the resulting transformed index follows a standard normal distribution. Selection of an appropriate distribution is therefore important as an inappropriate distribution may lead to biased values and subsequently affect the interpretation of SPEI results. Candidate distributions considered for SPEI included the generalized logistic (GLO), generalized extreme value (GEV), Pearson Type III (PE3) and normal (NOR) distributions. A range of goodness of fit tests were used to assess how well the distributions fit SPEI. Differences in CANGRD SPEI time series between three pairs of distributions, GLO-GEV, GLO-PE3, and GEV-PE3, showed that there were larger differences in SPEI values between GLO and the other two distributions than the difference between GEV and PE3. SPEI results fitted with GLO resulted in lower threshold exceedances of extreme SPEI (-/+2) than GEV and GLO. A comparison between CMIP5 SPEI values fitted with GLO (SPEI-GLO) and PE3 (SPEI-PE3) showed that there were seasonal and spatial variations in results, although the use of multi-model ensembles reduced these differences. As was the case for CMIP5 SPEI-GLO projections, projected annual changes in CMIP5 SPEI-PE3 show an increase in drying at the surface in central/southern Canada. Overall, the study recommends using PE3 or GEV for SPEI analysis for Canada.

Evaluation of multiple gridded precipitation datasets using gauge observations over Indonesia during the Asian-Australian monsoon period

Gridded precipitation datasets are widely available from satellite observations and reanalysis model outputs. However, its performance in specific regions in the world may vary and depends on several factors, such as grid data spatial resolution, rainfall estimation algorithms, geographical location, elevation and regional climate conditions. This study aims to report on 13 gridded precipitation datasets' performance over Indonesia through direct comparisons with rain gauge measurements at various time scales over a 12-year period (2001-2012). The results show that, at daily timescales, the MERRA2 and CPC outperformed other datasets but tended to underestimate the rain gauge data in Indonesia, followed by GPCC. However, MERRA2 has smaller variation and bias than CPC. On monthly and annually timescales, CPC was found to be the best-performing dataset, followed by MERRA2, GPM-IMERG, GPCC and TRMM (TMPA), while JRA55 registered the worst performance at all timescales, followed by ERA-Interim. The performance of all datasets was better during JJA and SON than during DJF and MAM. The best performances were found in the southern (S) region of Indonesia, while the worst were in the northeast (NE) region for all months and datasets. The best performances during DJF (Asian Winter Monsoon) and JJA/SON (Australian Winter Monsoon) were found in the northwest (NW) and southern (S) regions, respectively. Most datasets overestimate the rain gauge data over Indonesia, except for GSMaP, MERRA2, CPC and CMORPH.

Seasonal dynamics of canopy interception loss within a deciduous and a coniferous forest

AbstractCanopy interception loss is a key process in forest hydrology and the role of interception loss in relation to the forest water budgets and future impact of afforestation on water resources is important to quantify. Based on high frequency in situ monitoring, the effect of species‐ and leaf‐cover‐specific canopy structure metrics for interception loss estimation is examined at the two most typical forest types in Denmark. First, interception loss is estimated from precipitation and throughfall data collected in two even‐aged (40–60 years) temperate oak (deciduous) and Norway spruce (coniferous) forests over 13 and 11 months, respectively. Second, based on these observations we estimated canopy structure parameters relevant for interception loss; direct throughfall (ρ), the precipitation necessary to saturate the canopy (P'), the canopy evaporation as a fraction of precipitation (EC/P) and canopy storage capacity (S). Third, we compare observation‐based interception loss with predictions obtained by the analytical Gash interception model using the derived canopy structure parameters. Lastly, the effect of species‐ and leaf‐cover on the interception loss is quantified by applying the canopy structure parameters of the deciduous and coniferous forest on the same daily precipitation data set of approximately 1 year in the Gash model. We found that the derived canopy structure parameters reflect the seasonal change in the leaf cover of the deciduous forest and different parameters are identified for the two forest types. In the deciduous forest, improved agreement between observation‐based and predicted interception loss is obtained using canopy structure parameters for the leafless and full‐foliage periods instead of annual average values. The share of throughfall and interception loss to precipitation (total sum of precipitation = 526 mm) is 65% (340 mm) and 35% (186 mm) for the deciduous forest, respectively, and 49% (260 mm) and 51% (266 mm) for the coniferous forest, respectively. A seasonal variation of interception loss is observed in the deciduous forest where the share of interception loss to precipitation is 40% during the period with leaves on the trees (June to November) and 22% for the leafless periods (December to May). Thus, species‐ and leaf‐cover‐specific canopy structure metrics enhance model performance of throughfall and interception loss dynamics in forests.

Distinct characteristics of western Pacific atmospheric rivers affecting Southeast Asia

The dynamic characteristics of atmospheric rivers (ARs) have been researched over the western North Pacific and East Asia due to their close linkage to disastrous precipitation extremes, while very little attention has been paid to the AR features from the western Pacific to Southeast Asia. This study aims to quantify the climatology, long-term trends and variability of different AR properties from the western Pacific to Southeast Asia using an objective identification algorithm, the ERA5 reanalysis dataset and the APHRODITE precipitation dataset for the period 1951-2015. The results indicate a belt of frequent AR activities from the western Pacific to the Andaman Sea during the boreal autumn-winter season. The long-term trend analyses show a significantly increasing trend in AR frequency and an eastward shift of AR plumes. These dynamic changes contribute to the increasing trend of extreme precipitation amounts in the coastal areas surrounding the South China Sea. The intraseasonal variability of the AR associated with the Madden-Julian oscillation (MJO) shows a pronounced enhancement of AR activity in the MJO phase-2 to phase-3 due to the steeper gradient of low-level geopotential height between the Northwestern Pacific and the tropical Indian Ocean. The modulation is partly explained by the enhanced MJO convection and the adiabatic heating in the vicinity of the trough of the 200-500 hPa geopotential thickness of the region. This study shows that ARs are important mechanisms behind the climatology, trends and variability of the regional precipitation in Southeast Asia. This study implies that more attention is required toward the dynamics of these tropical weather systems, particularly for their interactions with other synoptic processes and their response to future climate warming.

Spatiotemporal variability in extreme precipitation and associated large-scale climate mechanisms in Central Asia from 1950 to 2019

Increased sea surface temperature, evaporation, air temperature, and moisture holding capacity associated with climate change may the enhance transport of moisture to Central Asia (CA), resulting in increased extreme precipitation events (EPEs). Changes in EPEs may affect the socioeconomic and ecological environment in typical arid and semi-arid regions of CA. Therefore, based on meteorological station data and four reanalyzed precipitation datasets, we used the Taylor diagram, Geo-Detector method, and other methods to examine 10 EPE indices in CA from 1950 to 2019, analyzed their spatial-temporal trends, and quantified the influence of large-scale climate factors on EPEs. The results showed that GSWP3_W5E5 was the optimal option with better performance in terms of monthly and annual precipitation in CA. Overall, EPEs in CA showed an increasing trend, but an abrupt point was found in 1986, when very wet day precipitation (R95p) increased from 36.64 to 47.84 mm; the increasing rate also changed from 0.8 to 5.06 mm/10a. The changes in the Northern Hemisphere Subtropical High Intensity Index (NSI), NINO B Area Sea Surface Temperature Anomaly Index (NINOB), Asian Zonal Circulation Index, and Atlantic Multidecadal Oscillation Index are the main reasons for the increase in EPEs; the NSI and NINOB had the largest contributions to EPEs, with at 44.32 % and 43.80 %, respectively. Additionally, two-factor interactions had a more significant influence. Further analysis revealed that the abnormal warming of sea surface temperature and others led to the strengthen of water vapor transport in CA caused by a series of cyclones and anticyclonic anomalies in the westerly circulation zone, leading to the increase of EPEs. The results obtained in this study can contribute to a more comprehensive understanding of the changes in EPEs and provide a reference for water resource management and disaster prevention in CA.

Downscaling and Merging of Daily Scale Satellite Precipitation Data in the Three River Headwaters Region Fused with Cloud Attributes and Rain Gauge Data

Complex terrain, the sparse distribution of rain gauges, and the poor resolution and quality of satellite data in remote areas severely restrict the development of watershed hydrological modeling, meteorology, and ecological research. In this study, based on the relationship between cloud optical and physical properties and precipitation, a daily geographically weighted regression (GWR) precipitation downscaling model was constructed for the Three Rivers Source region, China, for the period from 2010 to 2014. The GWR precipitation downscaling model combined three different satellite precipitation datasets (CMORPH, IMERG, and ERA5) which were downscaled from a coarse resolution (0.25° and 0.1°) to a fine resolution (1 km). At the same time, the preliminary downscaling results were calibrated and verified by employing the geographic difference analysis (GDA) and geographic ratio analysis (GRA) methods combined with rainfall data. Finally, the analytical hierarchy process (AHP) and the entropy weight method (EW) were adopted to fuse the three downscaled and calibrated satellite precipitation datasets into the merged satellite precipitation dataset (MSP), which provides a higher quality of data (CC = 0.790, RMSE = 2.189 mm/day, and BIAS = 0.142 mm). In summary, the downscaling calibration and precipitation fusion scheme proposed in this study is suitable for obtaining high-resolution daily precipitation data in the Three Rivers Source region with a complex climate and topography.

Assessment of Meteorological Drought under the Climate Change in the Kabul River Basin, Afghanistan

Kabul River Basin is one of the most significant river basins in Afghanistan from a socio-economic perspective. Since the country is located in an arid climate zone with drastically varying climatic behavior, an effective assessment of meteorological drought is very essential to managing the limited availability of water resources. For this endeavor, the outputs of three general circulation models under two representative concentration pathways (RCP 4.5 and RCP 8.5) were used against the baseline period of 1961–1980. Different bias correction methods were applied, and the results show that the delta change method, quantile mapping method, and empirical quantile mapping all performed better for the precipitation, maximum temperature, and minimum temperature datasets, respectively. The ERA5-Land datasets and WorldClim Version 2 are used to validate the bias-corrected precipitation and temperature datasets, respectively, to determine their dependability, and the results were found to be promising. Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Deciles Index (DI), and New Drought Index (NDI) were used to assess the drought condition in the past and forecast for the future periods of the 2020s, 2050s, and 2080s. The spatial distribution of assessed drought indices was mapped using the inverse distance weighting (IDW) method. Our results revealed that moderate to extreme droughts are consistent across the entire basin. This might be because the projected annual precipitation in the river basin shows a decline of 53–65% up to the end of this century (2100), and the average annual temperature is projected to increase by 1.8 °C, 3.5 °C, and 4.8 °C, respectively, for the three future periods of the 2020s, 2050s, and 2080s. Furthermore, the results show that the drought estimated by SPI and RDI for future climate scenarios is almost the same, whereas NDI estimates frequent drought events after the 2050s. However, for moderate drought, RDI, which includes the effects of evapotranspiration, was found to be far greater than SPI under both scenarios, and NDI considering temperature and precipitation also estimates a larger number of drought years, strengthening the possibility of its occurrence in the basin. A regional comparison of drought also indicates a decrease in precipitation in future periods, predominantly in high altitudes.

Interpretation of spatio-temporal variation of precipitation from spatially sparse measurements using Bayesian compressive sensing (BCS)

ABSTRACT Precipitation might change rapidly and vary spatially, therefore, knowledge on spatio-temporal variation of precipitation plays a pivotal role in water resources management, hydrogeological hazard and risk assessment, and city resilience enhancement. However, precipitation monitoring data are collected through a limited number of precipitation stations in practice, and they are often sparse and discontinuous, particularly in spatial domain. Furthermore, regional precipitation data exhibits characteristics of seasonality, periodicity and highly non-stationarity on a long-time scale. Therefore, it is challenging to obtain a spatio-temporal variation of precipitation with high spatial resolution from monitoring data measured at a limited number of precipitation stations. To address these challenges, this study develops a non-parametric spatio-temporal Bayesian compressive sensing (ST-BCS) method for interpolation of spatio-temporally varying, but sparsely measured precipitation data in the spatial domain. The proposed method is able to not only provide precipitation interpolation results with high spatial resolution from a limited number of monitoring stations, but also quantify the associated interpolation uncertainty simultaneously. In addition, ST-BCS is directly applicable to the non-stationary spatio-temporal meteorological data. Furthermore, real precipitation datasets are established to benchmark different spatio-temporal interpolation methods. The benchmarking results show that the proposed ST-BCS method performs well and outperforms the spatial BCS method.

Correction of precipitation measurement for weighing precipitation gauges in a glacierized basin in the Tianshan Mountains

Precipitation is one of the most important climatological data for global hydrothermal cycle and climate change. The accuracy of precipitation data not only directly affects the hydrological processes, but also plays an important role in the climate and hydrology at regional and global scales. According to the in situ datasets, the precipitation measurement in automatic weather stations for Geonor T-200B was corrected by the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE) transfer functions. The parameters of transfer functions were tested and recalibrated by the local datasets. The results showed that the transfer functions showed better performance after recalibrating parameters by the local datasets. The root-mean-square error (RMSE) and mean bias decreased by an average of 34% and 42%, respectively. The corrected snowfall increased by 7% (14 mm) at the test station. Then, the new parameters were used in other automatic weather stations to correct precipitation, and it was found that solid precipitation was underestimated by 13% on the glacier surface affected by wind speed. Moreover, according to the corrected precipitation datasets observed in automatic weather stations and national meteorological stations, the precipitation–altitude relationship in the Urumqi River Basin was analyzed. The annual precipitation gradient was 115 mm km-1, and the maximum seasonal altitude occurred in summer with a value of 35 mm km-1 and in autumn with the lowest value of 1 mm km-1. When considering precipitation on the glacier surface, the yearly precipitation gradient was increased with the value of 158 mm km -1 in 2019.

A fusion-based data assimilation framework for runoff prediction considering multiple sources of precipitation

ABSTRACT A fusion-based framework, in which a particle filter Markov chain Monte Carlo (PFMCMC) data assimilation method was coupled with the hydrological Sacramento Soil Moisture Accounting Model (SAC-SMA), was developed to improve the model’s capacity to predict one-day-ahead runoff. A case study was applied where mean daily precipitation from multiple sources served as forcing data in the data assimilation procedure, while ground station and multiple bias-corrected satellite-based precipitation datasets served as precipitation input datasets. The model training period used six years (2002–2007) of data to determine optimal weights through a genetic algorithm optimization model, while two years (2008–2009) were used to test the model. The proposed framework, applied to a real case study, improved SAC-SMA runoff prediction accuracy by incorporating precipitation datasets from multiple sources in the data assimilation procedure. On average, the PFMCMC-based data assimilation procedure led to a 13.7% improvement in SAC-SMA model performance metrics (NSE, MAB, RMSE, RMSRE, RMRE).

A Multiplicative-Exponential function to correct precipitation for distributed hydrological modeling in Poorly-gauged basins

Given the greater spatial and temporal variability in precipitation and the absence of rain gauges at mountainous regions, it is challenging to accurately estimate precipitation, consequently impeding the predictive capability of hydrological models. Here, we developed a nonlinear precipitation correction method in the SWAT (Soil and Water Assessment Tool) model to correct gridded (remote sensing or reanalysis) precipitation data for better distributed hydrological modeling. The precipitation correction module is featured by a multiplicative-exponential equation with two parameters, a multiplicative coefficient (PCP_COE) and an exponent (PCP_EXP). We proposed three configurations to determine PCP_COE and PCP_EXP, i.e., curve fitting between gridded and gauge precipitation data pairs (Configuration C1) or using gridded precipitation from multi-grids within an elevation range of the gauge (Configuration C2), and the calibration of the SWAT model against observed streamflow (Configuration C3). We applied the proposed method to a typical poorly-gauged alpine inland basin, the Xiangride River Basin of Northwest China, with only one rain gauge and streamflow station. Results show that Configuration C1 outperformed C2 and C3 by significantly improving streamflow modeling in both model calibration and validation, particularly when the GPM (Global Precipitation Measurement) precipitation datasets were used. We also argue that Configuration C3 is promising in ungauged basins with only streamflow available, since this configuration does not require gauge precipitation. This study puts forward a feasible method to derive more accurate spatiotemporal precipitation estimate based on gridded precipitation products, hence improving distributed hydrological modeling in poorly-gauged and ungauged basins.

Orographic Effect and the Opposite Trend of Rainfall in Central Vietnam

Central Vietnam is characterized by severe flooding associated with heavy rainfall events caused by interactions between multiscale atmospheric circulations and the complex local terrain. Previous studies believed rainfall in central Vietnam is closely related to the cold surge; however, it fails to explain the cause of the early rainfall occurrence in August in the subregion. For the first time, this study investigates the detailed atmospheric mechanisms associated with rainfall variations in central Vietnam using the empirical orthogonal function (EOF) applied to the recently developed high-resolution Vietnam gridded precipitation (VnGP) dataset. Reanalysis data NCEP/NCAR is used to associate the rainfall changes with respective atmospheric mechanisms. EOF analysis detected two dominant rainfall modes. The primary mode explains the rainfall variation from October to November over the central and is directly related to the interaction of cold surges and tropical disturbances. The second mode accounts for rainfall occurring in north central from September to mid-October, which is attributed to the westerly summer monsoon activities. Also, we revealed that, while the first mode exhibits a significant correlation with El Niño-southern oscillation, the second depends highly on the contrast of sea surface temperature in the northern and southern Hemispheres. This different oceanic forcing and the local topological effect of Truong Son mountain range reasonably explain the opposite rainfall pattern in central Vietnam in early fall.

Improvement of the multi-source weighted-ensemble precipitation dataset and application in the arid area of Tianshan Mountains, central Asia

With high resolution and wide coverage, satellite precipitation products like the Multi-Source Weighted Ensemble Precipitation (MSWEP) could support hydrological research in arid regions, where the spatial heterogeneity of precipitation is high, but rain gauges are sparse, but these products are inevitably subject to errors. In this study, we integrated measured precipitation and topographic data, and improved MSWEP by interpolation and an optimal regression model. The Tianshan Mountain area was taken as a typical arid area, and spatial stratified heterogeneity of precipitation was analyzed. The results show that: (1) The optimal regression model combined the advantages of site interpolation and MSWEP and improved the underestimation of precipitation in mountainous areas; (2) The precipitation decreased from West to East, and from North to South, and the precipitation in West and East subregions increased linearly with the elevation. The maximum precipitation height zone in the North is located at 4300 m. There are two maximum precipitation height zones in the South; (3) The accuracy of spatial interpolation of station data was limited by the uneven distribution of the stations in the East subregion, and the inaccuracy of MSWEP was due to the land cover and topography of the North and South subregions. The improved MSWEP (IMMSWEP) not only resolved the problem of lack of rainfall stations in arid area, but also resulted in an inversion of regional spatial precipitation differentiation with high resolution and high precision, providing a reference for meteorological researchers.

Blue and green water re-distribution dependency on precipitation datasets for a tropical Indian River basin

Study regionThe Damodar River basin, India. Study focusWater resource assessment at the river basin scale is crucial for human well-being and ecosystem health, and it can be performed quantitatively through green water (GW) and blue water (BW) flow evaluation. However, the quantification of BW and GW through land surface models (LSMs) is significantly influenced by the accuracy of precipitation datasets. In this present study, the spatiotemporal variation of blue and green water resources under the influence of six secondary precipitation products (SPPs) [APHRODITE; IMDAA; WFDEI; PRINCETON; CHIRPS, PERSIANN-CDR] are investigated using JULES LSM, considering India Meteorological Department (IMD) precipitation product as an observed precipitation dataset. This will help in examining the dependencies of precipitation datasets on partitioning between BW and GW distribution in a tropical River basin. New hydrological insights for the study regionThe results suggest significant differences in BW and GW simulation with the use of six aforementioned SPPs. In comparison to the reference IMD-based JULES model simulation, the annual average BW and GW estimates varied significantly: from 9.05% to − 40% and 15.24% to − 11.79%, respectively due to changes in the SPPs. Furthermore, the higher correlation between BW and precipitation datasets (R2 = 0.84–0.96) suggested that BW is more sensitive to precipitation changes in comparison to GW. Overall, our study emphasizes the importance of carefully considering the specification of precipitation databases for estimating BW and GW in a tropical River basin.

An appraisal of the NEX-GDDP precipitation dataset across homogeneous precipitation sub-regions of Iran

An appraisal of the NEX-GDDP precipitation dataset across homogeneous precipitation sub-regions of Iran

Performance Analysis of Precipitation Datasets at Multiple Spatio-Temporal Scales over Dense Gauge Network in Mountainous Domain of Tajikistan, Central Asia

Cryospheric and ecological studies become very complicated due to the absence of observed data, particularly in the mountainous regions of Central Asia. Performance analysis of Satellite-Based Precipitation Datasets (SBPD) is very critical before their direct hydro-climatic applications. This study assessed the ground validation of four SBPDs (IMERG, TRMM, PERSIANN-CDR, and PERSIANN-CSS). From January 2000 to December 2013, all SBPD data were analyzed on daily, monthly, seasonal (winter, spring, summer, autumn), and annual scales at the entire spatial domain and point-to-pixel scale. The performance of SBPD was analyzed by using evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (r-Bias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI). Results revealed that: (1) IMERG’s spatiotemporal tracking ability is better as compared to other datasets with appropriate ranges (CC > 0.8 and r-BIAS (±10)). The performance of all SBPDs is more capable on a monthly scale as compared to a daily scale. (2) In terms of POD, the IMERG outperformed all other SBPD on daily and seasonal scales. All SBPD showed underestimations in the summer season, and PERSIANN-CCS showed the most significant underestimation (−70). Moreover, the IMERG signposted the most satisfactory performance in all seasons. (3) All SBPD showed better performance in capturing the light precipitation events as indicated by the Probability Density Function (PDF%). Moreover, the performance of PERSIANN-CDR and TRMM is acceptable at low topography; the performance of PERSIANN-CCS is very poor in diverse topographical and climatic conditions over Tajikistan. Therefore, we advocate the use of daily, monthly, and seasonal estimations of IMERG precipitation product for hydro-climatic applications over the mountainous domain of Central Asia.