Evaluation of ten satellite-based and reanalysis precipitation datasets on a daily basis for Czechia (2001–2021)

The consistency of hydrological process modeling depends on reliable parameters and available long-term gauge data, which are frequently restricted within the Dead Sea/Jordan regions. This paper proposes a novel method of utilizing six satellite-based and reanalysis precipitation datasets, which are assessed, evaluated, and corrected, particularly for the cases of ungauged basins and poorly monitored regions, for the first time. Due to natural processes, catchments fluctuate dramatically annually and seasonally, making this a challenge. This variability, which is significantly impacted by topo-geomorphological and climatic variables within the basins themselves, leads to increased uncertainty in models and significant restrictions in terms of runoff forecasting. However, quality evaluations and bias corrections should be conducted before the application of satellite data. Moreover, the hydrological HEC-HMS model was utilized to predict the runoff under different loss methods. Furthermore, this loss method was used with an integrated model that might be efficiently employed when designing hydraulic structures requiring high reliability in predicting peak flows. The models’ performance was evaluated using R-squared (R2), the root mean square error (RMSE), the mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE). In addition, these statistical metrics were implemented to quantitatively evaluate the data quality based on the observed data collected between 2015 and 2020. The results show that AgERA5 exhibited better agreement with the gauge precipitation data than other reanalysis precipitation and satellite-based datasets. The results demonstrate that the data quality of these products could be affected by observational bias, the spatial scale, and the retrieval method. Moreover, the SC loss method demonstrated satisfactory values for the R2, RMSE, NSE, and bias compared to the IC and GA loss, indicating its effectiveness in predicting peak flows and designing hydraulic structures that require high reliability. Overall, the study suggests that AgERA5 can provide better precipitation estimates for hydrological modeling in the Dead Sea region in Jordan. Moreover, integrating the SC, IC, and GA loss methods in hydraulic structure design can enhance prediction accuracy and reliability.

Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

<p>Evaluation of problems related to water resources development and management require accurate precipitation estimates. Although ground-based stations provide direct physical measurement of precipitation, the accuracy of gauge-based precipitation data in terms of quality and spatial pattern may still be controversial. On the other hand, Gridded Precipitation Datasets (GPDs) provide high spatial and temporal precipitation estimates. GPDs are continuously changing with the improving technology and updating of retrospective algorithms, but they still need to be assessed over different regions both in space and time before being used for hydro-climatic studies. This study attempts to evaluate the spatio-temporal consistency of 13 different GPDs (CPCv1, MSWEPv2.2, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3b42v07, TMPA-3b42RTv07, PERSIANN-CDR, PERSIANN-CCS and PERSIANN) over Turkey which is a country characterized by diverse climate and complex terrain. The evaluation is performed for daily and monthly time scales considering the entire period of 2015-2019 as well as seasonal (spring, summer, autumn and winter) variability. Precipitation data from 130 stations are provided as reference data for point-to-grid comparison of GPDs. The modified Kling Gupta Efficiency (KGE) is selected for qualitative analysis whereas the Hanssen–Kuipers Score (HKS) is used to identify the ability of GPDs for capturing various precipitation events. The Probability Density Function (PDF) is selected to evaluate the intensity frequency of 13 GPDs for individual daily-based precipitation events. The results indicate that all GPDs have a median KGE performance ranging between -0.11 and 0.53 for daily precipitation while their performance increases in the monthly case (median KGE from 0.16 to 0.82). Gauge-corrected GPDs exhibit slightly better results over the uncorrected datasets in comparison with ground observations. GPDs from multi-source merging perform better than only satellite-based and reanalysis precipitation datasets. Among uncorrected GPDs, ERA5 and CHIRPv2.0 perform better while PERSIANN perform worse in all conditions. MSWEPv2.2 suffers from high-altitude conditions during winter and CHIRPSv2.0 shows poor performance during dry seasons. On the overall, MSWEPv2.2 performs better than CHIRPSv2.0 during daily/monthly, while CHIRPv2.0 performs better than CHIRPSv2.0 for daily time scale.</p>

Long-term and high-resolution reanalysis precipitation datasets provide important support for research on climate change, hydrological forecasting, etc. The comprehensive evaluation of the error performances of the newly released ERA5-Land and CRA40-Land reanalysis precipitation datasets over the Yongding River Basin in North China was based on the two error decomposition schemes, namely, decomposition of the total mean square error into systematic and random errors and decomposition of the total precipitation bias into hit bias, missed precipitation, and false precipitation. Then, the error features of the two datasets and precipitation intensity and terrain effects against error features were analyzed in this study. The results indicated the following: (1) Based on the decomposition approach of systematic and random errors, the total error of ERA5-Land is generally greater than that of CRA40-Land. Additionally, the proportion of random errors was higher in summer and over mountainous areas, specifically, the ERA5-Land accounts for more than 75%, while the other was less than 70%; (2) Considering the decomposition method of hit, missed, and false bias, the total precipitation bias of ERA5-Land and CRA40-Land was consistent with the hit bias. The magnitude of missed precipitation and false precipitation was less than the hit bias. (3) When the precipitation intensity is less than 38 mm/d, the random errors of ERA5-Land and CRA40-Land are larger than the systematic error. The relationship between precipitation intensity and hit, missed, and false precipitation is complicated, for the hit bias of ERA5-L is always smaller than that of CRA40-L, and the missed precipitation and false precipitation are larger than those ofCRA40-L when the precipitation is small. The error of ERA5-Land and CRA40-Land was significantly correlated with elevation. A comprehensive understanding of the error features of the two reanalysis precipitation datasets is valuable for error correction and the construction of a multi-source fusion model with gauge-based and satellite-based precipitation datasets.

This study investigates the rainfall asymmetry of tropical cyclones (TCs) during landfall in Guangdong (GD), South China, using satellite-based, gauge-satellite merged, and reanalysis precipitation datasets. We examine the characteristics of TC rainfall asymmetry, its primary controlling factors, its evolution during landfall, and potential variations across different El Niño–Southern Oscillation (ENSO) phases.The results demonstrate that vertical wind shear (VWS) dominates TC rainfall asymmetry in GD, with the rainfall maximum consistently located in the downshear left of VWS. Since most TCs are associated with southwesterly VWS, the peak rainfall typically occurs in the south to southwest sector relative to the TC center. This feature persists across all summer months (June–September) and is consistent among TCs of varying intensities, including tropical depressions (TD), tropical storms (TS), severe tropical storms (STS), Typhoons (TY), and super typhoons (STY). In contrast, storm motion shows no significant influence on rainfall asymmetry in GD.Despite a reduction in rain rate during landfall, the TC rainfall asymmetry remains remarkably stable, with no substantial changes in either the phase or amplitude from 24 hours before to 12 hours after landfall. The rainfall maximum persistently aligns with the downshear left of VWS, and the asymmetry magnitude remains approximately 50%, indicating that asymmetric rainfall accounts for nearly half of the total TC rainfall. Furthermore, no statistically significant differences in rainfall asymmetry are found among El Niño, La Niña, and neutral ENSO phases.These findings enhance the understanding of TC rainfall distribution and provide valuable insights for improving rainfall forecasts in GD, particularly for extreme precipitation events associated with landfalling TCs.

Evaluating satellite-based products is vital for precipitation estimation for sustainable water resources management. The current study evaluates the accuracy of predicting precipitation using four remotely sensed rainfall datasets—Tropical Rainfall Measuring Mission products (TRMM-3B42V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN-CDR), Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)—over the Haraz-Gharehsoo basin during 2008–2016. The benchmark values for the assessment are gauge-observed data gathered without missing precipitation data at nine ground-based measuring stations over the basin. The results indicate that the TRMM and CCS-CDR satellites provide more robust precipitation estimations in 75% of high-altitude stations at daily, monthly, and annual time scales. Furthermore, the comparative analysis reveals some precipitation underestimations for each satellite. The underestimation values obtained by TRMM CDR, CCS-CDR, and CFSR are 8.93 mm, 20.34 mm, 9.77 mm, and 17.23 mm annually, respectively. The results obtained are compared to previous studies conducted over other basins. It is concluded that considering the accuracy of each satellite product for estimating remotely sensed precipitation is valuable and essential for sustainable hydrological modelling.

Precipitation downscaling with the integration of multiple precipitation products, land surface data and gauge stations using explainable machine learning algorithms: A case study in the Mediterranean region of Turkiye.

Several satellite-based and reanalysis products with a high spatial and temporal resolution have become available in recent decades, making it worthwhile to study the performance of multiple precipitation forcing data on hydrological modeling. This study aims to examine the veracity of five precipitation products employing a semi-distributed hydrological model, i.e., the Soil and Water Assessment Tool (SWAT) to simulate streamflow over the Chenab River Basin (CRB). The performance indices such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and percentage bias (PBIAS) were used to compare observed and simulated streamflow at daily and monthly scales during calibration (2015–2018) and validation (2019–2020). The hydrologic performance of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land (ERA5) was very good at daily (calibration R2=0.83, NSE=0.81, PBIAS=−6%; validation R2=0.75, NSE=0.74, PBIAS=−9.6%) and monthly ( calibration R2=0.94, NSE=0.94, PBIAS=−3.3%; validation R2=0.91, NSE=0.89, PBIAS=−3.2%) scales. This study suggests that the ERA5 precipitation product was the most reliable of the five precipitation products, while the CHIRPS performance was the worst. These findings contribute to highlighting the performance of five precipitation products and reference in the selection of precipitation data as input data to the SWAT model in similar regions.

This study aims to investigate the applicability of Satellite Based Precipitation (SBP) combined with rain gauges as input to a distributed hydrological model (DHM) for flood forecast. SBP was evaluated at different scales in upper Chao Phraya basin against available local gauge network in Thailand. The procedure includes the usage of Root Mean Square Error (RMSE) to assess the accuracy of SBP and Relative Error (RE) to evaluate the degree of estimation. Furthermore, RE values were utilized to obtain correction factors at each rain gauge per season. DHM was run during 2007-2010 to validate spatial and temporal accuracy of improved SBP. The river discharge simulations using corrected SBP could reduce the overestimation gaps when compared to observed discharge in target period. It was noticed that SBP can enhance precipitation’s pattern by using local gauge network. The obtained results show possibility to apply this procedure to other humid vegetated basins and also using other SBP dataset. We believe that this method can be useful not only for flooding risk assessment but also to support enhanced dam operation.

Hydrological application and accuracy evaluation of PERSIANN satellite-based precipitation estimates over a humid continental climate catchment

This study evaluates a machine learning–based precipitation ensemble technique (MLPET) over three mountainous tropical regions. The technique, based on quantile regression forests, integrates global satellite precipitation datasets from CMORPH, PERSIANN, GSMaP (V6), and 3B42 (V7) and an atmospheric reanalysis precipitation product (EI_GPCC) with daily soil moisture, specific humidity, and terrain elevation datasets. The complex terrain study areas include the Peruvian and Colombian Andes in South America and the Blue Nile in East Africa. Evaluation is performed at a daily time scale and 0.25° spatial resolution based on 13 years (2000–12) of reference rainfall data derived from dense in situ rain gauge networks. The technique is evaluated using K-fold, separately in each region, and leave-one-region-out validation experiments. Comparison of MLPET with the individual satellite and reanalysis precipitation datasets used for the blending and the recent Multi-Source Weighted-Ensemble Precipitation (MSWEP) global precipitation product exhibited improved systematic and random error statistics for all regions. In addition, it is shown that observations are encapsulated well within the ensemble envelope generated by the blending technique.

Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets.

Comprehensive evaluation of satellite and reanalysis precipitation products over the eastern Tibetan plateau characterized by a high diversity of topographies

Accurate precipitation estimates are crucial for various hydrological and environmental applications. This study presents a comprehensive evaluation of three widely used satellite-based precipitation datasets (SPDs)—PERSIANN, CHIRPS, and MERRA—and a monthly reanalysis dataset—TERRA—that include data from across the contiguous United States (CONUS) and Hawaii, at daily, monthly, and yearly timescales. We present the performance of these SPDs using ground-based observations maintained by the USGS (United States Geological Survey). We employ evaluation metrics, such as the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), to identify optimal SPDs. Our findings reveal that MERRA outperforms PERSIANN and CHIRPS on a daily scale, while CHIRPS is the best-performing dataset on a monthly scale. However, all datasets show limitations in accurately estimating absolute amount of precipitation totals. The spatial analysis highlights regional variations in the datasets’ performance, with MERRA consistently performing well across most regions, while CHIRPS and PERSIANN show strengths in specific areas and months. We also observe a consistent seasonal pattern in the performance of all datasets. This study contributes to the growing body of knowledge on satellite precipitation estimates and their applications, guiding the selection of suitable datasets based on the required temporal resolution and regional context. As such SPDs continue to evolve, ongoing evaluation and improvement efforts are crucial to enhance their reliability and support informed decision-making in various fields, including water resource management, agricultural planning, and climate studies.

BackgroundFor understanding and evaluating a more realistic and accurate assessment of ecosystem carbon balance related with environmental change or difference, it is necessary to analyze the various interrelationships between soil respiration and environmental factors. However, the soil temperature is mainly used for gap filling and estimation of soil respiration (Rs) under environmental change. Under the fact that changes in precipitation patterns due to climate change are expected, the effects of soil moisture content (SMC) on soil respiration have not been well studied relative to soil temperature. In this study, we attempt to analyze relationship between precipitation and soil respiration in temperate deciduous broad-leaved forest for 2 years in Gwangneung.ResultsThe average soil temperature (Ts) measured at a depth of 5 cm during the full study period was 12.0 °C. The minimum value for monthly Ts was − 0.4 °C in February 2015 and 2.0 °C in January 2016. The maximum monthly Ts was 23.6 °C in August in both years. In 2015, annual precipitation was 823.4 mm and it was 1003.8 mm in 2016. The amount of precipitation increased by 21.9% in 2016 compared to 2015, but in 2015, it rained for 8 days more than in 2016. In 2015, the pattern of low precipitation was continuously shown, and there was a long dry period as well as a period of concentrated precipitation in 2016. 473.7 mm of precipitation, which accounted for about 51.8% of the precipitation during study period, was concentrated during summer (June to August) in 2016. The maximum values of daily Rs in both years were observed on the day when precipitation of 20 mm or more. From this, the maximum Rs value in 2015 was 784.3 mg CO2 m−2 h−1 in July when 26.8 mm of daily precipitation was measured. The maximum was 913.6 mg CO2 m−2 h−1 in August in 2016, when 23.8 mm of daily precipitation was measured. Rs on a rainy day was 1.5~1.6 times higher than it without precipitation. Consequently, the annual Rs in 2016 was about 12% higher than it was in 2015. It was shown a result of a 14% increase in summer precipitation from 2015.ConclusionsIn this study, it was concluded that the precipitation pattern has a great effect on soil respiration. We confirmed that short-term but intense precipitation suppressed soil respiration due to a rapid increase in soil moisture, while sustained and adequate precipitation activated Rs. In especially, it is very important role on Rs in potential activating period such as summer high temperature season. Therefore, the accuracy of the calculated values by functional equation can be improved by considering the precipitation in addition to the soil temperature applied as the main factor for long-term prediction of soil respiration. In addition to this, we believe that the accuracy can be further improved by introducing an estimation equation based on seasonal temperature and soil moisture.