Global Gridded Precipitation Datasets Research Articles

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.

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.

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.

Evaluating the performance of eight global gridded precipitation datasets across Iran

Precipitation is one of the crucial variables in hydrological and water resources studies. Runoff, soil moisture, groundwater recharge, and other hydrological variables change with the amount of precipitation in a region. Thus, accurate precipitation estimation is a major issue in the field of water resources and hydrological modeling. Most hydrological studies and water management decisions in Iran are established using in situ observation of precipitation. However, due to limited spatial distribution and lack of long-term high-quality data in many stations, this information usually does not fully meet the information requirements of a given study. In this study, the accuracies of eight global gridded precipitation datasets including Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), PERSIANN-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Project (GPCP), Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and ECMWF Reanalysis version 5 (ERA5) were evaluated to identify the strengths and weaknesses of each dataset in different regions and main catchments of Iran. The precipitation of 96 synoptic stations during the years 1987–2016 was used as the basis for the evaluations. The evaluations were made at monthly, seasonal, and annual scales using grid-based evaluation and areal average evaluation at the catchments scale. In addition to the amount of precipitation, the datasets were evaluated using three statistical performance indices (i.e., correlation coefficient, RMSE, and relative bias) and other metrics such as probability distribution similarity with observation data (using Anderson-darling test), empirical cumulative distribution function (ECDF), and Taylor diagrams. The results indicated that dataset performance varied by catchment. Except for central parts, GPCC was the best dataset in 44% of the evaluated grids in terms of time series pattern recognition. It showed correlation coefficients of 0.71, 0.80, and 0.85, in monthly, seasonal, and annual scales, respectively. In central areas, MERRA2 estimated the average annual precipitation in this catchment with + 2 mm difference, and CRU had a correlation coefficient greater than 0.90. Moreover, CHIRPS was the best dataset in terms of relative bias in the south and southwestern catchments, so that the relative bias was negligible in these catchments in most months. This study provides rankings and recommendations for selecting an appropriate alternative precipitation dataset, which, in turn, provides the knowledge required in monitoring systems and hydrological and water resources modeling that need high-resolution input data.

Comprehensive evaluation of precipitation datasets over Iran

Global gridded precipitation datasets have been developed using rain gauges, satellite observations, and data assimilation techniques to fulfill the need in regions with a limited contribution of ground observations like Iran. This study presents a comprehensive evaluation of currently available precipitation datasets over Iran at monthly (44 datasets) and daily (34 datasets) time scales. To include the maximum number of datasets and in situ data, we consider two periods for the evaluations, namely 2003–2010 for the daily and monthly assessment and 2014–2018 for the daily. For the assessment, a network of more than 1500 rain gauges is utilized within 2003–2010 and 370 rain gauges within 2014–2018. Moreover, we compare the pixel-to-pixel (interpolated in situ data v.s. gridded datasets), and point-to-pixel (in situ data as a point v.s. gridded datasets) approaches in assessing datasets performances. In terms of the Kling-Gupta efficiency (KGE) parameters, the datasets perform worse in bias at monthly time scales and correlation at daily time scales. However, considering in situ precipitation above 5 mm/day, all datasets perform poorly in estimating precipitation variability. We find that, in general, reanalysis products have a higher KGE, ranging between 0.41 (0.21) and 0.91 (0.71), than satellite-based products with a KGE ranging from 0.14 (-0.57) to 0.92 (0.57) over Iran at monthly (daily) scale. Moreover, GPCC overall matches the validation dataset better than other products over Iran’s basins, whereas CPC, ERA5, and IMERG-Final are more suitable for near-real-time studies. Also, if latency is a top criterion, PERSIANN-PDIR will be the first option. Indeed, PERSIANN-PDIR with a KGE value of 0.69 (0.33) at monthly (daily) time scale within 2003–2010 performs remarkably well, as a non-adjusted real-time satellite-based product. The comparison between the point-to-pixel and the pixel-to-pixel approaches shows that the point-to-pixel approach underestimates the quality of the datasets but does not change the ranking of the datasets.

Evaluation of three gauge-based global gridded precipitation datasets for drought monitoring over Iran

ABSTRACT Gridded precipitation products (GPPs) can provide precipitation data at high spatial coverage and resolution; however, no assessment has been conducted on the performance of GPPs in drought monitoring for Iran’s major basins. In this study, the accuracy of three GPPs, i.e. the Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), and Climate Prediction Centre (CPC), was evaluated to derive the nonparametric Standardized Precipitation Index (SPI) for the period 1987–2016. Six common statistical metrics and typical drought event characteristic criteria, including drought area and drought class identification, were used to measure the performance of the GPPs. The results indicate that GPCC was the best dataset, with high correlation, small errors, and high detectability. The results also revealed a slight difference between the GPPs regarding the correct diagnosis of the drought classes. The findings of this study can help decision makers to effectively use GPPs in drought monitoring for Iran.

Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran

Accurate estimation of the precipitation characteristics, including the value, temporal pattern, and spatial distribution, plays a significant role in the input uncertainty reduction for rainfall-runoff models. In many basins, the improper spatial distribution of rain gauge stations or their limited historical recorded data causes many challenges, especially in heterogeneous catchments which due to the impact of the drastic geographical alterations on the rainfall distribution pattern, the cover of the ground stations cannot estimate the actual precipitation rate. This challenge can be potentially solved by adopting rainfall products as alternative or complementary data sources. In this research, three rainfall products (PERSIANN-CCS, CMORPH and ERA-Interim), were compared against rain gauge stations for calibration of a daily conceptual lumped rainfall-runoff model (CRFM) in a data-scarce and heterogeneous basin located in southwestern Iran. The results indicated that ERA-Interim has the best performance among other datasets. Better performance of this dataset compared to the in-situ data also suggests a better estimation of the basin average as well as the temporal pattern of precipitation. The KGE value was obtained as 0.8 and 0.74, respectively, for a rainfall-runoff model that utilized the ERA-Interim as input in the calibration and validation periods. The results showed that the performance of satellite-based data of CMORPH and PERSIANN-CCS is not acceptable in simulating the daily flow. Also, the seasonal assessment showed that ERA-Interim has a better performance compared to other datasets, during fall and winter. However, in the spring, the performance of all datasets significantly reduces, and the range of BIAS variation increases. Generally, all datasets were shown to perform better in simulating the flow in terms of the transition from dry to wet periods, rather than wet to dry periods.

Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure

AbstractEvaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-sample testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-sample testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.

Potential hydropower estimation for the Mindanao River Basin in the Philippines based on watershed modelling using the soil and water assessment tool

This study aims to identify potential hydropower sites and calculate the theoretical potential hydropower capacity based on watershed modelling of the Mindanao River Basin (MRB) in the Philippines for the sustainable development of a previously unstudied region. The Soil and Water Assessment Tool (SWAT) was applied to delineate the watershed of the MRB and simulate the river discharges with inputs from observed precipitation and global gridded precipitation datasets. Observed weather data, such as temperature, humidity, and solar radiation, from four meteorological stations in the Philippines were also used as inputs for SWAT modelling. Simulated discharges were calibrated at three river gauges on the Nituan, Libungan and Pulangi Rivers. However, due to limited river discharge records, model validations were conducted in proxy basins: the calibrated model parameters in river A were used in the watershed modelling of proxy river B. Of the delineated 107 sub-basins in the MRB watershed, only 33 were identified as having potential sites for hydropower development. These potential sub-basins hosted a total of 154 potential sites with an estimated monthly average power capacity of 5,551.35 MW for all 33 sub-basins. The estimated theoretical power capacity of 15,266.22 MW for all sites in the MRB is approximately equivalent to the Philippines’ total available power capacity in 2017 of 15,393 MW. These sites were classified into 16 mini-scale hydropower sites, 114 small-scale hydropower sites and 24 medium-scale hydropower sites based on the simulated river discharges and potential power capacities. Based on these results, hydropower development could be an alternative to strengthen the exploration of renewable energy resources and improve the energy situation in Mindanao; hydropower development could also have mitigation effects on frequent floods in flat, low-lying downstream areas.

Watershed Modelling of the Mindanao River Basin in the Philippines Using the SWAT for Water Resource Management

This study aims to simulate the watershed of the Mindanao River Basin (MRB) to enhance water resource management for potential hydropower applications to meet the power demand in Mindanao with an average growth of 3.8% annually. The soil and water assessment tool (SWAT) model was used with inputs for geospatial datasets and weather records at four meteorological stations from DOST-PAGASA. To overcome the lack of precipitation data in the MRB, the precipitation records were investigated by comparing the records with the global gridded precipitation datasets from the NCDC-CPC and the GPCC. Then, the SWAT simulated discharges with the three precipitation data were calibrated with river discharge records at three stations in the Nituan, Libungan and Pulangi rivers. Due to limited records for the river discharges, the model results were, then, validated using the proxy basin principle along the same rivers in the Nituan, Libungan, and Pulangi areas. The R2 values from the validation are 0.61, 0.50 and 0.33, respectively, with the DOST-PAGASA precipitation; 0.64, 0.46 and 0.40, respectively, with the NCDC-CPC precipitation; and 0.57, 0.48 and 0.21, respectively, with the GPCC precipitation. The relatively low model performances in Libungan and Pulangi rivers are mainly due to the lack of datasets on the dam and water withdrawal in the MRB. Therefore, this study also addresses the issue of data quality for precipitation and data scarcity for river discharge, dam, and water withdrawal for water resource management in the MRB and show how to overcome the data quality and scarcity.

Assessment of the reliability of three gauged-based global gridded precipitation datasets for drought monitoring

This study evaluates the reliability on three global gridded precipitation dataset including CRU-TS-V.4.01, GPCC-V.7 and UDEL-V.4.01 to drought monitoring over Lake Urmia basin in Iran using standardised precipitation index (SPI). Assessing the precipitation data of global datasets indicate that the major error in precipitation of global datasets is bias error which means that they can recognise the behaviour of precipitation time series but there is a difference in estimation of precipitation amount. To diminish the bias error, bias correction (BC) was done before evaluating the datasets in drought monitoring. The results indicated that GPCC is the most suitable dataset over the study area for drought studies. In addition, results revealed that there is no significant difference between original and BC data in drought monitoring. For example, in nine-month SPI, the critical success index (CSI) for original CRU and CRU-BC were equal to 71 and 76%, respectively, and the GPCC and UDEL in both original and BC versions were equal to 88 and 55%, respectively. Also, it can be claim that the bias error is not important in drought studies due to the fact that during calculation of SPI, standardisation process will remove bias significantly.

Assessment of the reliability of three gauged-based global gridded precipitation datasets for drought monitoring

This study evaluates the reliability on three global gridded precipitation dataset including CRU-TS-V.4.01, GPCC-V.7 and UDEL-V.4.01 to drought monitoring over Lake Urmia basin in Iran using standardised precipitation index (SPI). Assessing the precipitation data of global datasets indicate that the major error in precipitation of global datasets is bias error which means that they can recognise the behaviour of precipitation time series but there is a difference in estimation of precipitation amount. To diminish the bias error, bias correction (BC) was done before evaluating the datasets in drought monitoring. The results indicated that GPCC is the most suitable dataset over the study area for drought studies. In addition, results revealed that there is no significant difference between original and BC data in drought monitoring. For example, in nine-month SPI, the critical success index (CSI) for original CRU and CRU-BC were equal to 71 and 76%, respectively, and the GPCC and UDEL in both original and BC versions were equal to 88 and 55%, respectively. Also, it can be claim that the bias error is not important in drought studies due to the fact that during calculation of SPI, standardisation process will remove bias significantly.

Evaluation of three global gridded precipitation data sets in central Asia based on rain gauge observations

The accuracies of gridded precipitation data sets are important for regional climate studies and hydrological models. In this study, the performances of Global Precipitation Climatology Centre (GPCC) V7, Climatic Research Unit (CRU) TS 3.22 and Willmott and Matsuura (WM) precipitation data sets were examined over central Asia by comparing them against observed precipitation records (OBS) from 586 meteorological stations during 1901–2010. The results show that all the three gridded data sets underestimated the observed precipitation at annual and monthly scales, especially in mountainous areas. Both GPCC and WM underestimated seasonal precipitation, especially for spring precipitation. Among the three gridded data sets, GPCC had the highest correlation and lowest bias compared with CRU and WM when against the OBS. WM had a higher correlation than that of CRU, and its bias was larger than that of CRU. In terms of the drought and heavy rainfall events, CRU had the best performance in capturing drought events, and GPCC was best at representing heavy rainfall events. These differences in the performances between the three gridded data sets were primarily induced by their different interpolation methods and the numbers of available meteorological stations used in the interpolations of the three gridded data sets. Therefore, compared to the other two data sets, GPCC is more suitable for studies of long‐term precipitation variations over central Asia.

Case Study of Moisture and Heat Budgets within Atmospheric Rivers

Abstract This work studies moisture and heat budgets within two atmospheric rivers (ARs) that made landfall on the west coast of North America during January 2009. Three-dimensional kinematic and thermodynamic fields were constructed using ECMWF Year of Tropical Convection data and global gridded precipitation datasets. Differences between the two ARs are observed, even though both had embedded precipitating convective organizations of the same spatial scale. AR1 extended from 20° to 50°N in an almost west–east orientation. It had excessive warm and moist near-surface conditions. Its precipitating systems were mainly distributed on the southwest and northeast sides of the AR, and tended to exhibit stratiform-type vertical heat and moisture transports. In contrast, AR2 spanned latitudes between 20° and 60°N in a north–south orientation. It was narrower and shorter than AR1, and was mostly covered by pronounced precipitating systems, dominated by a deep convection type of heating throughout the troposphere. In association with these distinctions, the atmosphere over the northeastern Pacific on average experienced episodic cooling and drying despite the occurrence of AR1, yet underwent heating and drying during AR2, when latent heating was strong. Downward sensible heat flux and weak upward surface latent heat flux were observed particularly in AR1. In addition, cloud radiative forcing (CRF) was very weak in AR1, whereas it was strongly negative in AR2. In short, it is found that the oceanic convection in ARs both impacts the moisture transport of ARs, as well as modifies the heat balance in the midlatitudes through latent heat release, convective heat transport, surface heat fluxes, and CRF.

The ENSO–Precipitation Teleconnection and Its Modulation by the Interdecadal Pacific Oscillation

Abstract This study evaluates the role of the interdecadal Pacific oscillation (IPO) in modulating the El Niño–Southern Oscillation (ENSO)–precipitation relationship. The standard IPO index is described together with several alternatives that were derived using a low-frequency ENSO filter, demonstrating that an equivalent IPO index can be obtained as a low-frequency version of ENSO. Several statistical artifacts that arise from using a combination of raw and smoothed ENSO indices in modeling the ENSO–precipitation teleconnection are then described. These artifacts include the potentially spurious identification of low-frequency variability in a response variable resulting from the use of smoothed predictors and the potentially spurious modulation of a predictor–response relationship by the low-frequency version of the predictor under model misspecification. The role of the IPO index in modulating the ENSO–precipitation relationship is evaluated using a global gridded precipitation dataset, based on three alternative statistical models: stratified, linear, and piecewise linear. In general, the information brought by the IPO index, beyond that already contained in the Niño-3.4 index, is limited and not statistically significant. An exception is in northeastern Australia using annual precipitation data, and only for the linear model. Stratification by the IPO index induces a nonlinear ENSO–precipitation relationship, suggesting that the apparent modulation by the IPO is likely to be spurious and attributable to the combination of sample stratification and model misspecification. Caution is therefore required when using smoothed climate indices to model or explain low-frequency variability in precipitation.

Comparison of monthly precipitation derived from high-resolution gridded datasets in arid Xinjiang, central Asia

To investigate the reliability of global gridded precipitation datasets with horizontal resolution of 0.5° × 0.5° in arid Xinjiang, central Asia, the monthly precipitation datasets released by the Climatic Research Unit (CRU TS, version 3.21), the University of Delaware (Terrestrial Precipitation: 1900–2010 Gridded Monthly Time Series, version 3.01), the Global Precipitation Climatology Centre (GPCC Full Data Reanalysis Product, version 6) and the National Oceanic and Atmospheric Administration (NOAA's Precipitation Reconstruction over Land) during 1979–2010 were selected. A precipitation dataset released by the National Meteorological Information Center, China Meteorological Administration is applied as the observation series. The result indicates that changes in seasonal and annual precipitation can be simulated by the four gridded datasets in most regions of Xinjiang, but there are seasonal and regional discrepancies in the correlation coefficients (R), mean bias error (MBE) and root-mean-square error (RMSE). Generally speaking, the gridded datasets can be more effective in simulating precipitation in low-lying basins, that is, the elevation is less than 1500 m (mainly desert and scattered oasis), while RMSE in mountainous areas is much larger than that in low-lying areas. In Altay, Tianshan and Kunlun Mountains, the precipitation simulated by the gridded datasets is less than that of observation series. The accuracy of gridded precipitation datasets generally decreases with elevation. On a seasonal basis, the errors of gridded precipitation datasets in summer months are generally higher than those in other seasons, and RMSE usually shows low values during wintertime. Among the four gridded precipitation datasets, RMSE of CRU is slightly larger than other three datasets in most areas of Xinjiang. The wetting trend magnitude in this study area from 1979 to 2010 is generally underrated by all the gridded datasets, indicating that the long-term precipitation trend using gridded datasets should be treated with caution.

Evaluation of reanalysis and satellite-based precipitation datasets in driving hydrological models in a humid region of Southern China

This paper compares the original and bias corrected global gridded precipitation datasets, tropical rainfall measuring mission (TRMM) and water and global change forcing data (WFD) with gauged precipitation and evaluates the usefulness of gridded precipitation datasets for hydrological simulations using the distributed soil and water assessment tool (SWAT) and lumped Xinanjiang model in Xiangjiang River basin, Southern China. The results show that the differences in areal mean rainfalls of original TRMM and WFD datasets and gauged dataset are in acceptable limits of less than 10 %, while larger differences exist in maximal 5-day rainfalls, dry spells and Frechet distance. The bias correction methods are able to significantly improve the biases in the mean values of TRMM and WFD datasets. The nonlinear bias correction method gives good results in correcting the standard deviations of TRMM/WFD data. The hydrological modelling results show that the WFD datasets perform relatively better than TRMM datasets even though the results are poor as compared with using gauged rainfall as input in daily step hydrological models. At monthly time step, both TRMM and WFD data produce acceptable model simulation results in terms of Nash–Sutcliffe efficiency (E ns > 0.7 for original TRMM/WFD data and E ns > 0.8 for linearly corrected TRMM/WFD data) and relative error (|R E | < 10 %).

Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins

Abstract This study quantifies the uncertainty in discharge calculations caused by uncertainty in precipitation input for 294 river basins worldwide. Seven global gridded precipitation datasets are compared at river basin scale in terms of mean annual and seasonal precipitation. The representation of seasonality is similar in all datasets, but the uncertainty in mean annual precipitation is large, especially in mountainous, arctic, and small basins. The average precipitation uncertainty in a basin is 30%, but there are strong differences between basins. The effect of this precipitation uncertainty on mean annual and seasonal discharge was assessed using the uncalibrated dynamic global vegetation and hydrology model Lund–Potsdam–Jena managed land (LPJmL), yielding even larger uncertainties in discharge (average 90%). For 95 basins (out of 213 basins for which measurements were available) calibration of model parameters is problematic because the observed discharge falls within the uncertainty of the simulated discharge. A method is presented to account for precipitation uncertainty in discharge simulations.