Global Precipitation Datasets Research Articles

Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate

Increasing flood risk due to urbanization and climate change poses a significant challenge to societies at global scale. Hydrologic information that is required for understanding flood processes and for developing effective warning procedures is currently lacking in most parts of the world. Procedures that can combine global climate dataset from satellite and reanalysis with fast and low computational cost prediction systems, are attractive solutions for addressing flood predictions in ungauged areas. This work develops and tests a prediction framework that relies on two fundamental components. First, meteorological data from global datasets (IMERG and ERA5-Land) provide key input variables and second, ML models trained in the data-rich contiguous US, are applied in climatically similar regions in other parts of the world. Catchments in Australia, Brazil, Chile, Switzerland, and Great Britain were used as pseudo-ungauged regions for testing. Results indicate acceptable performance for both IMERG and ERA5-Land forced models with relative difference in flood peak prediction within 30% and similar overall performance to locally trained ML models. Specific climate regions for which ML models have revealed good performance include Mediterranean climates like the US West Coast, subtropical areas like the Southern Atlantic Gulf, and mild temperate regions like the Mid-Atlantic Basin. This work highlights the potential of combining global precipitation dataset with pre-trained ML models in data-rich areas, for flood prediction in ungauged areas with similar climate.

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.

Meteorological Drivers of North American Monsoon Extreme Precipitation Events

AbstractIn this paper the meteorological drivers of North American Monsoon (NAM) extreme precipitation events (EPEs) are identified and analyzed. First, the NAM area and its subregions are distinguished using self‐organizing maps applied to the Climate Prediction Center global precipitation data set. This reveals distinct subregions, shaped by the inhomogeneous geographic features of the NAM area, with distinct extreme precipitation character and drivers. Next, defining EPEs as days when subregion‐mean precipitation exceeds the 95th percentile of rainy days, five synoptic features and one mesoscale feature are investigated as potential drivers of EPEs. Essentially all EPEs can be associated with at least one selected driver, with only one event remaining unclassified. This analysis shows the dominant role of Gulf of California moisture surges, mesoscale convective systems and frontal systems in generating NAM extreme precipitation. Finally, a frequency and probability analysis is conducted to contrast precipitation distributions conditioned on the associated meteorological drivers. The findings demonstrate that the co‐occurrence of multiple features does not necessarily enhance the EPE probability.

A ten time smaller version of CPC global daily precipitation dataset for parallel distributed processing in Matlab and R

A ten times smaller version of CPC Global Unified Gauge-Based Daily Precipitation Dataset (1979–2021) is released and described in this paper. Its usability is tested and proved, firstly, by illustrating that the transition to the derived smaller dataset is a case of Pearson correlation transitivity starting with the scale of global yearly data and, secondly, by using the original correlation performance criterion that the original dataset satisfies relative to the set of global actual measurements on the record. Subsequently, the daily, weekly, and monthly data cases are considered and discussed. The dataset is (re)structured for parallel processing in Matlab and R from the level of global daily data. Considering the above arguments and its reduced size, the derived dataset is appropriate to be used with Matlab and R as a replacement for the original dataset, especially for the case when much faster exploratory master-slave parallel and distributed Matlab and/or R tasks will run over locally distributed data on the slaves.

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models.

Efficiency of global precipitation datasets in tropical and subtropical catchments revealed by large sampling hydrological modelling

Satellite-based and reanalysis precipitation products are widely adopted as complementary information to in situ measurements for estimating river discharge using hydrological modelling. However, there is still a notable research gap in the literature associated with assessing the accuracy of satellite-based or reanalysis products in different tropical and sub-tropical catchments at large-sampling hydrological modelling with sensitivity analysis. We investigated the accuracy of precipitation, hydrological model performance and parameter sensitivity related to seven precipitation data sets based on satellite and reanalysis products, i.e., CHIRPS, TRMM, GLDAS, IMERG, MERRA-2, PERSIANN-CDR, and ERA5 over 714 contrasting tropical and subtropical catchments located in Brazil. We used the Génie Rural Journalier 4 (GR4J) hydrological model to simulate the hydrological processes of the different catchments with two approaches for the calibration: using measured ground-based precipitation data (approach I) or using each individual satellite/reanalysis precipitation products (approach II) to calibrate the models. The results showed that the precipitation products tend to overestimate precipitation, with the exception of ERA5 and MERRA-2. CHIRPS is the only product that produces unbiased precipitation estimates for most catchments. The model calibration using each precipitation product individually improved the hydrological model performance. CHIRPS, IMERG, and MERRA-2 showed good accuracy in terms of both, precipitation estimation and hydrological simulation performance in the calibration period. In the validation period, the best products in terms of KGE were CHIRPS, IMERG and TRMM (KGE > 0.64). The errors in precipitation products are better compensated via hydrological modelling in wet regions. The model parameter sensitivity varies according to precipitation input, climate, and catchment aridity. Overall, all seven products exhibited their worst hydrological performance in arid regions. This study helps to improve our understanding of the catchment response in tropical and subtropical regions while also providing key insights into the reliability of satellite/reanalysis rainfall products for streamflow simulation. This study is valuable in hydrometeorological applications, climate change assessment, water resources and disaster management, especially in regions with a relatively sparse density of precipitation stations.

Investigating twelve mainstream global precipitation datasets: Which one performs better on the Tibetan Plateau?

Acquiring precipitation information is a critical yet challenging task for the Tibetan Plateau (TP) with sparse gauges and high altitudes. Presently, there is a multitude of operational precipitation products merged from different data sources. A major concern within the TP science community is which one of these available precipitation datasets performs best on the TP. To address this concern, we selected and compared twelve widely used global precipitation datasets, including six satellite-based precipitation estimates (GSMaP-Gauge, IMERG-Final, TMPA-v7, CMORPH-BLD, PERSIANN-CDR, CHIRPS), five reanalysis data (ERA5, JRA-55, MERRA2, GLDAS, NCEP2), and one multi-source fusion product (MSWEP). Benchmarked against both automatic weather stations and independent field observations, we comprehensively investigate the spatiotemporal accuracy and regional applicability of these global precipitation products on the TP. The evaluation results indicate that almost all the datasets have a consistent tendency to overestimate lower precipitation rates and underestimate higher ones. However, four out of the twelve studied datasets (i.e., GSMaP-Gauge, IMERG-Final, ERA5, and MSWEP) significantly outperform others. Specifically, the two GPM-based satellite precipitation products, GSMaP-Gauge and IMERG-Final, can better reflect the difference between different rain intensities and perform better at moderate rain rates. In contrast, the reanalysis-based ERA5 exhibits a more clustered data series, with relatively higher accuracy at low precipitation rates. Moreover, ERA5 demonstrates a stable superiority in the mountainous areas characterized by complex local terrains. Furthermore, we find that the applicability of different precipitation datasets has an obvious region-related feature. Thus, a regional distribution map was made to clearly outline the optimal choice of current mainstream global precipitation datasets over different climate zones within the TP. This study can offer valuable insights for TP researchers, especially aiding them to select the appropriate precipitation data for their specific studies.

A new approach to ensemble precipitation Estimation: Coupling satellite hydrological products with backward water balance models in Large-Scale

The estimation accuracy of global precipitation datasets has been enhanced using a committee of various sources. However, the existing merging methods mainly rely on gauge-based measurements, which may have significant uncertainty across data-scarce regions. The unique approach of Triple Collocation (TC) has been widely used in various geographical information systems since it allows quantifying the uncertainties of three datasets with independent errors. Recent studies have confirmed the use of the TC for consolidating disparate triple datasets. However, merging techniques based on Modified Collocation (MC), which can combine more than three datasets, have not yet been applied to precipitation data. This study develops the backward water balance as a new approach to ensemble precipitation estimation via large-scale hydrological products without access to ground information. The proposed methodology is validated using monthly observations from more than 580 rain gauge stations in the Central Plateau watershed of Iran. In the proposed method, the error of each water balance component is statistically estimated using different methods, like MC. Furthermore, the residuals of the water balance were minimized using three techniques with different complexity levels, such as the Constrained Kalman Filter, to enforce water balance closure; hence, the error is smoothed. The precipitation products are optimally merged by employing various methods in the ensemble precipitation estimation framework. As stated in the metrics, the best-recorded estimated ensemble precipitation data improved the accuracy of the used raw precipitation products over the study region compared with the benchmark values in 10.8%, 2.3%, and 0.2% greater KGE, NSE, and TS values. Additionally, this data resulted in a 31.2% smaller RMSE. The proposed modification in the CKF method (CKF2) showed better results to close the water balance.

Inter-comparison of global precipitation data products at the river basin scale

Abstract While many studies have compared global precipitation datasets at national, continental, and global scales, few have evaluated these data at river basin scales. This study explored differences in precipitation estimates and trends of 12 widely applied precipitation datasets, including gauge-, satellite-, and reanalysis-based products, for the world's 6,292 river basins. Results showed that disparities between 12 precipitation datasets were considerable. A total of 3,125 river basins, with a land area of 5,989.1×104 km2, had differences in estimated annual average precipitation exceeding 500 mm yr−1, and these basins were mainly distributed in Greenland, Africa, Oceania, and West Asia. Disparities between the precipitation datasets were particularly large during the dry season when the percentage difference between the highest and lowest precipitation estimates exceeded 500% in 1,390 river basins (4,839.7×104 km2) expected due to numerical reasons. Differences in rainfall trends also varied markedly between data sources. The data products do not agree on precipitation trends for all river basins. These findings illustrate the importance of accurate precipitation data to ensure effective policy and planning in term of hydropower generation, domestic water supply, flood protection, and drought relief at river basin scales and highlight the uncertainty that exists in current global precipitation data.

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

AbstractPrecipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance.

An innovative hydrological model for the sparsely-gauged Essequibo River basin, northern Amazonia

ABSTRACT Tropical river basins – crucial components of global water and carbon cycles – are threatened by logging, mining, agricultural conversion, and climate change. Thus, decision-makers require hydrological impact assessments to sustainably manage threatened basins, such as the ∼68,000 km2 Essequibo River basin in Guyana. Emerging global data products offer the potential to better understand sparsely-gauged basins. We combined new global meteorological and soils data with established in situ observations to build the first physically-based spatially-distributed hydrological model of the Essequibo. We developed new, open source, methods to translate global data (ERA5-Land, WFDE5, MSWEP, and IMERG) into a grid-based SHETRAN model. Comparing the performance of several global and local precipitation and evaporation datasets showed that WFDE5 precipitation, combined with ERA5-Land evaporation, yielded the best daily discharge simulations from 2000 to 2009, with close water balances (PBIAS = −3%) and good discharge peaks (NSE = 0.65). Finally, we tested model sensitivity to key parameters to show the importance of actual to potential evapotranspiration ratios, Strickler runoff coefficients, and subsurface saturated hydraulic conductivities. Our data translation methods can now be used to drive hydrological models nearly anywhere in the world, fostering the sustainable management of the Earth’s sparsely-gauged river basins.

Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa

Study regionData-scarce basins located in West Africa and northern Central Africa. Study focusMultiple studies have shown that global gridded precipitation datasets could provide an alternative to the lack of observed data in Sub-Saharan Africa. This work evaluated 15 precipitation datasets based on satellite rainfall (ARC v.2, CHIRP v.2, CHIRPS v.2, PERSIANN-CDR, MSWEP v2.2 and TAMSAT v3), reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU and WFDEI-GPCC) and ground measurements (CPC v.1, CRU TS v.4.00 and GPCC v.7), as well as a regional estimation method, based on spatial proximity, for the parameters of a simple monthly water balance model, GR2M. The regional simulations of the GR2M model were evaluated based on a Kling-Gupta Efficiency score in a split-sample spatiotemporal validation scheme. New hydrological insights for the regionThe results show that among all the precipitation products, CHIRPS is the most effective for hydrological modeling in West and Central Africa at a monthly timestep. Also, among the top five products are WFDEI-CRU, CRU, WFDEI-GPCC and GPCC. Overall, regional hydrological modeling is more effective for basins smaller than 80,000 km2. The method of regionalization by spatial proximity causes an overall drop in the ability of the various precipitation products to reproduce discharge, most notably with WFDEI-GPCC and GPCC. CHIRPS remains the best product in terms of KGE2 values in regionalization.

Evaluation of CMIP6 Models and Multi-Model Ensemble for Extreme Precipitation over Arid Central Asia

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method.

Potential Predictability of Seasonal Global Precipitation Associated with ENSO and MJO

A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potentially predictable component related to slowly varying boundary forcing and low-frequency internal dynamics. The “potential predictability” is then defined as the fraction of the total interannual variance accounted for by the latter component. In tropical oceans (30° E–0° W, 30° S–30° N), the consensus is that the El Nino-Southern Oscillation (ENSO, with 4–8 year cycles) is a dominant driver of the potentially predictable component, while the Madden-Julian Oscillation (MJO, with 30–90 days cycles) is a dominant driver of the unpredictable component. In this study, the consensus is verified by using the Nino3-4 SST index and a popular MJO index. It is confirmed that Nino3-4 SST does indeed explain a significant part of the potential predictable component, but only limited variability of the unpredictable component is explained by the MJO index. This raises the question of whether the MJO is dominant in the variability of the unpredictable component of the precipitation, or the current MJO indexes do not represent MJO variability well.

On the Spatial Scale Dependence of Long‐Term Persistence in Global Annual Precipitation Data and the Hurst Phenomenon

AbstractPrecipitation deficits are the main physical drivers of droughts across the globe, and their level of persistence can be characterized by the Hurst coefficient H (0.5 < H < 1), with high H indicating strong long‐term persistence (LTP). Previous analyses of point and gridded annual global precipitation datasets have concluded that LTP in precipitation is weak (H ∼ 0.6) which is inconsistent with higher values of H for large river basins, for example, the Nile. Based on an analysis of gridded annual precipitation data for eight selected regions distributed across the globe, an important new finding is that H increases with the spatial scale of averaging, with mean H values at the grid and regional scale of 0.66 and 0.83, respectively. The discovery of enhanced LTP at the regional scale of averaging of precipitation has important implications for characterizing the severity of regional droughts, as well as LTP in the annual flows of large rivers and recharge to major aquifers. Teleconnections with known modes of low frequency variability in the global climate system are demonstrated using correlation analysis and stepwise regression. Despite having several constituent regions exhibiting LTP, the Northern Hemisphere surprisingly has no LTP; this is shown to result from different modes of low frequency climatic variability canceling each other out. LTP for the Southern Hemisphere is moderate, and weak for Global average precipitation. LTP in Blue Nile basin scale precipitation is shown to explain the Hurst Phenomenon in naturalized annual flows for the River Nile, more than 70 years after its discovery by Hurst.

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.

Applicability comparison of various precipitation products of long-term hydrological simulations and their impact on parameter sensitivity

Precipitation is an important component of water circulation and an essential input for various hydrological models. A high quality, high spatial resolution, and long-term precipitation dataset would benefit hydrological investigations, particularly for regions having insufficient precipitation records. The upper Huaihe River Basin (UHRB) was selected as the research location in this study, and the accuracies of three precipitation products (PPs: a high-resolution daily gridded precipitation dataset for China (HRLT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Global (CPC) precipitation dataset) were assessed at multiple spatio-temporal scales comparing with the gauge precipitation (GP) for 2000–2019. Subsequently, the applicability of the three PPs on streamflow (Q) and sediment yield (SY) simulations, as well as the impact on parameter sensitivity, were compared using the Soil and Water Assessment Tool (SWAT) model. The results showed that the accuracy of the three PPs were ranked as CPC > HRLT > PERSIANN-CDR on the watershed average scale, HRLT would underestimate the extreme precipitation; and PERSIANN-CDR would overestimate the annual precipitation. On the grid-to-point scale, PERSIANN-CDR was found to be the most stable with high accuracy, followed by CPC and HRLT on all temporal scales. The ability of these PPs to detect rainfall events was ranked as CPC > HRLT > PERSIANN-CDR. The sensitivity of the Q parameters changed with the variation in the precipitation input. The sensitive parameters for GP were distributed on average for almost all processes, while the sensitive parameters for PPs mainly controlled the groundwater and evapotranspiration processes. Among all the PPs, the performance of CPC in the Q and SY simulations was found to be the best, followed by HRLT and PERSIANN-CDR, and all the PPs could simulate SY better than Q in spatial distribution. HRLT has the potential to be used in long-term hydrological simulations in ungauged or small watersheds based on its high spatial resolution compared to other products.

Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.

Mapping rainfall erosivity over India using multiple precipitation datasets

Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.

Using InSAR Data to Improve the Water Vapor Distribution Downstream of the Core of the South American Low‐Level Jet

AbstractTwo and half years of Interferometric Synthetic Aperture Radar (InSAR) images obtained by Sentinel‐1 near Santa Cruz de la Sierra, Bolivia, before August 2019 are here analyzed and assimilated in the Weather Research and Forecast model (WRF) to assess the quality of the water vapor field at the core of the South American Low‐level Jet, and the downstream propagation of the implied water vapor anomalies into a large sector of South America. Due to the topographic locking of the Jet near the edge of the Andes cordillera at Santa Cruz, this experiment allows an assessment of the extension of the spatial impact of InSAR data assimilation in varying circulation conditions. That data improves the model skill at varying locations thousands of kilometers downstream, in both the distribution of water vapor assessed at a large number of well‐distributed GNSS observations and precipitation assessed against the Global Precipitation dataset and ERA5 reanalysis. Gains in the precipitation forecast skill are found to have a larger impact on the forecast of light rain, and lead to an almost cancellation of forecasts of no rain in cases with moderate to heavy rain. Because the distribution of water vapor is a main driver of weather, and InSAR is one of the few sources of data that can look down to mesoscale resolution regardless of daytime or weather, it is suggested that it may have a significant positive impact on short‐range weather forecasting and, maybe more importantly, on the quality of the climate of the forecast model.