Rain Gauge Precipitation Research Articles

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

AbstractSatellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

Evaluation of Precipitation Frequency and Intensity as Estimated by the GPM IMERG Precipitation Product at Daily and Hourly Scales over the Tibetan Plateau

The IMERG V06 hourly rainfall product at daily and hourly scales was evaluated during the warm season (May to September) from 2015 to 2020 using 651 high-quality rain-gauge stations over the Tibetan Plateau (TP). Based on hourly observed rain-gauge precipitation, four categories were classified: light rainfall (0–12 mm·d−1), moderate rainfall (12–20.1 mm·d−1), torrential rainfall (20.1–32.2 mm·d−1), and extreme torrential rainfall (>32.2 mm·d−1). Precipitation frequency and intensity were calculated to further validate the accuracy and suitability of the IMERG estimated-precipitation product. At the daily scale, IMERG underestimated the number of days with less than moderate rainfall, but overestimated the frequency of torrential and extreme torrential rainfall. IMERG estimated the main characteristics of precipitation frequency at different daily precipitation amount levels better than the precipitation intensity, but its best estimate was at the moderate rainfall level, with the highest correlation coefficient (0.69) and the lowest root mean square error (0.17). At the hourly scale, IMERG underestimated the hourly precipitation amount mainly between the early morning and midday (the average deviation was 0.019 mm·h−1) and overestimated it between the afternoon and late night (the average deviation was 0.047 mm·h−1). IMERG overestimated precipitation frequency and underestimated precipitation intensity between the afternoon and the evening, which means that this analysis shows that IMERG estimated more precipitation hours than the observation and underestimated precipitation intensity. These results further our understanding of the suitability of the IMERG precipitation products over the TP and further improve the IMERG retrieval algorithm to better apply the corresponding precipitation product to light and extreme rainfall over regions with complicated topography.

Reinforcing the Effect of Warm Ocean Anomalies in the South China Sea on the Extended Tropical-Depression-Induced Heavy Rainfall Event in Hainan Island

An unusually persistent and heavy rainfall event occurred in Hainan Island from 1 to 9 October 2010, in association with one extended tropical depression (TD) over the South China Sea (SCS). Based on rain-gauge precipitation, satellite altimetry, in situ Argo profile, air–sea enthalpy flux, and reanalysis data, this study investigates the impact of warm ocean anomalies in the SCS on the formation and intensification of the extended TD, and their reinforcing effect on TD-related heavy rainfall. The TD intensified and migrated northward to the vicinity of Hainan Island. A thicker-than-normal warm subsurface layer that was present beneath the positive sea surface temperature (SST) anomalies contained a sufficient upper-ocean heat content to effectively restrain the TD’s self-induced SST cooling effect, and available enthalpy fluxes were therefore sufficient to support the maintenance of the TD. The composite analyses confirm the reinforcing effect of warm oceanic anomalies in the central SCS off the south-central coast of Vietnam on heavy rainfall in Hainan Island, with the composite precipitation of “Warm eddy” cases being significantly larger in Hainan Island and northern Vietnam than that of the “Normal” cases, using reanalysis and remote sensing precipitation data over 29 years (1993–2021).

Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada

Water percolation in snow plays a crucial role in the avalanche risk assessment. Liquid water content and wetting front are hard to measure in the field; hence, accurate simulation of the phenomena can be of great help to forecasters. This study was the first to evaluate water percolation simulations with the SNOWPACK model using Richards’ scheme on Mount Fidelity, Glacier National Park, Canada. The study highlights that, at this site, an updated configuration on precipitation phase transition and new snow density can significantly improve simulations of the snow cover, and water percolation in particular, which can be relevant in an era of an increased occurrence of rain-on-snow (ROS) events. More specifically, emphasis was put on the quality of the input data and parameters. The analysis of the precipitation phase temperature threshold showed that a value of 1.4°C was the best suited to track the rain/snow transition on site. A 10-year analysis of 24-h precipitation measured using the rain gauge and 24-h new snow water equivalent showed an excellent correlation. New snow density sub-models were evaluated using the 24-h new snow density values taken by the park technicians. The BELLAIRE model performed best and was used to drive the snow simulations. Two SNOWPACK snow simulations were evaluated using 1) rain gauge precipitation amount (PCPM) and 2) automatic snow height measurement (HS) at the same site. Both runs simulated the main snowpack layers observed during the dry season (i.e., before spring percolation was observed), and both simulated the snow properties with good accuracy. The water equivalent of snow cover, used as a proxy for a first-order characterization of the simulations generated by both simulations, was slightly underestimated compared with four manual measurements taken on-site during the winter. Nevertheless, the comparison of both measured density and modeled bulk density showed great correspondence. The percolation timing and wetting front depth were evaluated using field measurements from field campaigns and continuous observations from on-site instruments. The main percolation events were correctly simulated and were coincident with the observed wet avalanche cycles. The results highlight the need for accurate input data on valid simulation of the wetting front and percolation timing on site. Good percolation information generated using the SNOWPACK model and Richards’ scheme could be used to assess the snowpack stability by forecasters in areas where such data are available.

Evaluation of IMERG Over CONUS Complex Terrain Using Environmental Variables

AbstractSatellite‐based precipitation products (SPPs) provide extensive spatial and temporal coverage globally but are challenged in complex terrain. This study aims at understanding uncertainties of SPP estimation due to topographically induced rainfall. Environmental and physical parameters, that is, orographically forced upward motion and horizontal moisture flux convergence, are considered to condition precipitation mechanisms for a detailed uncertainty analysis. The Global Precipitation Measurement (GPM) precipitation product Integrated Multi‐Satellite Retrievals for GPM (IMERG) is compared at its native resolution against a high quality and accurate radar and rain gauge precipitation reference Ground Validation Multi‐Radar Multi‐Sensor (GV‐MRMS). Conditional analysis of IMERG using environmental parameters is conducted over mountainous terrain. The highest GV‐MRMS mean rainfall rate is found to be associated with positive high Q and moderate w values, confirming that vigorous moisture flux convergence is a strong condition for heavy rainfall. IMERG shows over‐ and underestimation for positive w and Q when GV‐MRMS rainfall magnitudes between 0.8 and 13 mm h−1.

A GIS-Based Methodology to Combine Rain Gauge and Radar Rainfall Estimates of Precipitation Using the Conditional Merging Technique for High-Resolution Quantitative Precipitation Forecasts in Țibleș and Rodnei Mountains

Rain gauges provide accurate rainfall amount data; however, the interpolation of their data is difficult, especially because of the high spatial and temporal variability. On the other hand, a high-resolution type of information is highly required in hydrological modeling for discharge calculations in small catchments. This problem is partially solved by meteorological radars, which provide precipitation data with high spatial and temporal distributions over large areas. The purpose of this study is to validate a conditional merging technique (CMT) for 15 rainfall events that occurred on the southern slope of the Tibleș and Rodnei Mountains (Northern Romania). A Geographic Information System (GIS) methodology, based on three interpolation techniques—simple kriging, ordinary kriging, and cokriging—were utilized to derive continuous precipitation fields based on discrete rain gauge precipitation data and to derive interpolated radar data at rain gauge locations, and spatial analysis tools were developed to extract and analyze the optimal information content from both radar data and measurements. The dataset contains rainfall events that occurred in the period of 2015–2018, having 24 h temporal resolution. The model performance accuracy was carried out by using three validation metrics: mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). The validation stage showed that our model, based on conditional merging technique, performed very well in 11 out of 15 rainfall events (approximate 78%), with an MAE under 0.4 mm and RMSE under 0.7 mm.

Spatial Analysis of Rainfall Return Period and Probable Maximum Precipitation over Central Java - Indonesia

Most of the lowlands and coastal area in Indonesia are highly affected by flood hazards, particularly Central Java Area. The flood hazards are often associated with extreme precipitation that occur in long duration. This research aims to identify the spatial distribution of extreme precipitations based on daily rain gauge precipitation data over Central Java. Daily precipitation data from 1998-2010 were obtained from rain gauge stations from Indonesian Bureau of Meteorology, Climatology, and Geophysics (BMKG). Extreme precipitation identification taken by precipitation threshold, return period rainfall, and probable maximum precipitation (PMP). The results indicate that extreme precipitation occurs mainly in the lowlands and coastal areas over the northern and southern part of Central Java. The distribution of extreme precipitation could be utilized to map the susceptible area affected by floods, such as Tegal, Pekalongan, Kendal, Semarang, Demak, Purworejo, Kebumen, Kutoarjo, Gombong, and Cilacap.

Comparative Analysis of Rain Gauge and Radar Precipitation Estimates towards Rainfall-Runoff Modelling in a Peri-Urban Basin in Attica, Greece

In this research work, an analysis is conducted concerning the impact on rainfall-runoff simulations of utilizing rain gauge precipitation measurements against weather radar quantitative precipitation estimates. The study area is the Sarantapotamos river basin, a peri-urban basin located in the greater area of Athens, and measurements from a newly installed X-Band weather radar system, referred to as rainscanner, along with ground rain gauge stations were used. Rainscanner, in contrast to rain gauges, is able to provide with higher resolution surface precipitation datasets, but due to signal errors, uncertainty is involved, and thus proper calibration and evaluation of these estimates must be first performed. In this context, this research work evaluates the impact of adopting different precipitation datasets and interpolation methods for generating runoff, through the use of a lumped based rainfall-runoff model. Initially, the analysis focuses on the correlation between the rain gauge and the rainscanner estimations for each station, as well as for the calculated mean areal precipitation. The results of the rainfall-runoff simulations show that even though a different spatial and temporal variability of the rainfall field is calculated through the two datasets, in a lumped-based scheme, the most important factor that dictates the runoff generation is the amount of total precipitation.

Sediment fate and transport: Influence of sediment source and rainfall

The capability of hydrologic models to spatially simulate the changes in hydrologic processes, like precipitation, is an important consideration in capturing the impacts of these processes on sediment prediction across the domain. Radar-derived precipitation (RDP) provides an enhanced detail of rainfall characteristics in time and space compared to estimates from rain-gauge precipitation (RGP) commonly used in hydrologic modeling. However, the impacts of these datasets on sediment fate and transport depend on how sediment sources were conceptualized in the model. This paper developed a modeling framework to simulate sediment transport from upland to the stream and to the outlet of the watershed based on a gridded conceptualization and to examine the impacts of RGP and RDP with different types of sediment sources on sediment prediction. The Water Erosion Prediction Project (WEPP) model was used to estimate daily sediment sources in a semi-distributed and fully distributed manner using the hydrologic model, MIKE SHE and MIKE 11. Model comparison was performed in a watershed in Illinois characterized by a dominant agricultural landscape. The results indicated that the use of RDP only ensured better model performance for sediment yield with the fully distributed sediment source. That is, combining both the ability of the RDP to capture the spatial variability of rainfall across the watershed and assessing sediment production at higher resolution improved the accuracy of predictions in sediment yield while decreasing uncertainties associated with sediment simulations. Advancing modeling capabilities will require the development of new modeling platforms that aim to seamlessly integrate large-scale distributed simulations and environmental input data at finer spatial resolutions.

Validation of PERSIANN Precipitation Product Using TAWN Rain Gauge Network Over Different Agro-climatic Zones in Tamil Nadu

Currently, several satellite-precipitation products were developed using multiple algorithms to estimate rainfall. This study carried out using Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product over seven agro-climatic zones of Tamil Nadu during the northeast monsoon (NEM) season of October to December for 2015-2017 (three years) against 118 rain-gauges data of Tamil Nadu Agricultural Weather Network (TAWN). The performance compares aggregated seasonal scale of rainfall using continuous (CC, RMSE, and NRMSE) statistical approaches. It was observed that PERSIANN is accurate in the high-altitude hilly zone and the Cauvery delta zone. For 2015, 2016, and 2017, the correlation values were 0.77, 0.52, and 0.71, respectively. The highest RMSE value was measured for northeast zone (NEZ) during 2015 (222.17 mm), and the lowest was determined for 22.63 in the High-altitude hilly zone (HAHZ) during 2016 and NRMSE had less errors during all three seasons. The study concluded that the PERSIANN data set could be useful substitute for rain-gauge precipitation data.

Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China.

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing-based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000-2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was - 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of - 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

Comparative evaluation of statistically downscaling Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) mission precipitation data: evidence from a typical semi-arid to arid environment

This study aimed at comparing the performance of statistically downscaling Tropical Rainfall Measuring Mission (TRMM) 3B43 Version 7 and Global Precipitation Measurement (GPM) 3IMERGM Version 6 precipitation data from 25 km and 10 km resolutions, respectively, to 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13A3 Version 6 Normalized Difference Vegetation Index (NDVI) data. Simple linear regression (SLR) and geographically weighted regression (GWR) were applied in a typical semi-arid to arid country—Jordan—using data for the year 2015. It was observed that all statistically downscaled outputs exhibited almost similar spatial distribution patterns. These patterns captured the typical decreasing behavior of precipitation in the country from northwest to south and east. They also provided more spatial detail of precipitation information than their raw counterparts. Unscaled and scaled validation measures of bias, precision, and accuracy relied on precipitation data for the year 2015 obtained from 137 rain gauges distributed all over the country revealed that: (1) GWR produced better results than SLR, (2) statistically downscaling GPM 3IMERGM Version 6 precipitation data outperformed statistically downscaling TRMM 3B43 Version 7 precipitation data, and (3) although, all statistically downscaled precipitation outputs overestimated rain gauge precipitation data, this overestimation was larger in the statistically downscaled TRMM 3B43 Version 7 precipitation outputs than in the statistically downscaled GPM 3IMERGM Version 6 precipitation outputs.

Evaluation of Multi-Satellite Precipitation Products and Their Ability in Capturing the Characteristics of Extreme Climate Events over the Yangtze River Basin, China

Against the background of global climate change and anthropogenic stresses, extreme climate events (ECEs) are projected to increase in both frequency and intensity. Precipitation is one of the main climate parameters for ECE analysis. However, accurate precipitation information for extreme climate events research from dense rain gauges is still difficult to obtain in mountainous or economically disadvantaged regions. Satellite precipitation products (SPPs) with high spatial and temporal resolution offer opportunities to monitor ECE intensities and trends on large spatial scales. In this study, the accuracies of seven SPPs on multiple spatiotemporal scales in the Yangtze River Basin (YRB) during the period of 2003–2017 are evaluated, along with their ability to capture ECE characteristics. The seven products are the Tropical Rainfall Measuring Mission, Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (25), CHIRPS (05), Climate Prediction Center Morphing (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record, PERSIANN-Cloud Classification System, and Global Precipitation Measurement (GPM) IMERG. Rain gauge precipitation data provided by the China Meteorological Administration are adopted as reference data. Various statistical evaluation metrics and different ECE indexes are used to evaluate and compare the performances of the selected products. The results show that CMORPH has the best agreement with the reference data on the daily and annual scales, but GPM IMERG performs relatively well on the monthly scale. With regard to ECE monitoring in the YRB, in general, GPM IMERG and CMORPH provide higher precision. As regards the spatial heterogeneity of the SPP performance in the YRB, most of the examined SPPs have poor accuracy in the mountainous areas of the upper reach. Only CMORPH and GPM IMERG exhibit superior performance; this is because they feature an improved inversion precipitation algorithm for mountainous areas. Furthermore, most SPPs have poor ability to capture extreme precipitation in the estuaries of the lower reach and to monitor drought in the mountainous areas of the upper reach. This study can provide a reference for SPP selection for ECE analysis.

Enhancement of the summer extreme precipitation over North China by interactions between moisture convergence and topographic settings

This study investigates the roles of atmospheric moisture transport under the influence of topography for summer extreme precipitation over North China (NC) during 1979–2016. Based on rain gauge precipitation data and a reanalysis, 38 extreme precipitation days in NC during the 38 years were selected and associated moisture fluxes estimated. The results show that there is a dominant moisture influx of 311.8 kg m−1 s−1 into NC along its southern boundary from tropical oceans, and a secondary influx of 107.9 kg m−1 s−1 across its western boundary carried by mid-latitude westerlies. The outflux across the eastern boundary is 206.9 kg m−1 s−1 and across the northern boundary is 76.0 kg m−1 s−1, giving a net moisture gain over NC of 136.8 kg m−1 s−1. During extreme precipitation days, the moisture flux convergence (MFC) was much larger, exceeding 4 × 10−5 kg m−1 s−1. The MFC maximum core, the pronounced moisture transport, and the striking extreme precipitation zone over NC are all anchored to the east of the steep slopes of the surrounding topography. Moreover, a remarkably high humidity and strong upward motion also occur near steep slopes, indicating the critical role of the adjacent topography on the extreme precipitations. Simulations with and without the topography in NC using the Weather and Research Forecasting model for six selected out of the 38 extreme precipitation days demonstrate that the surrounding topography reinforces the MFC over NC by 16% relative to the case without terrain, primarily through enhanced wind convergence and higher moisture content, as well as stronger vertical motion induced by diabatic heating. The interactions between moisture convergence and topographic settings strengthen the extreme precipitation over NC.

Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey

Use of the satellite and reanalysis precipitation products, as supplementary data sources, are steadily rising for hydrometeorological applications, especially in data-sparse areas. However, the accuracy of these data sets is often lacking, especially in Turkey. This study evaluates the accuracy of satellite precipitation product (TRMM 3B42V7) and reanalysis precipitation product (NCEP-CFSR) against rain gauge observations for the 1998–2010 periods. Average annual precipitation for the 25 basins in Turkey was calculated using rain gauge precipitation data from 225 stations. The inverse distance weighting (IDW) method was used to calculate areal precipitation for each basin using GIS. According to the results of statistical analysis, the coefficient of determination for the TRMM product gave satisfactory results (R2 &gt; 0.88). However, R2 for the CFSR data set ranges from 0.35 for the Eastern Black Sea basin to 0.93 for the West Mediterranean basin. RMSE was calculated to be 95.679 mm and 128.097 mm for the TRMM and CFSR data, respectively. The NSE results of TRMM data showed very good performance for 6 basins, while the PBias value showed very good performance for 7 basins. The NSE results of CFSR data showed very good performance for 3 basins, while the PBias value showed very good performance for 6 basins.

A method for real‐time temporal disaggregation of blended radar–rain gauge precipitation fields

AbstractA fully automated quasi‐real‐time method is presented to disaggregate hourly to sub‐hourly precipitation information operationally in a blended radar–rain gauge product. The method proposes a fully automated solution to disaggregate precipitation in regions characterized by measurement errors or partial absence of auxiliary information on the temporal precipitation evolution. The solution relies on a combination of low‐pass filtered radar information and stochastically generated noise fields. A comprehensive validation of the new method is provided demonstrating higher skill compared to a uniform disaggregation in time. The method is now an integral part of CombiPrecip, the official operational code of MeteoSwiss for radar–rain gauge merging.

Performance of Remotely Sensed Soil Moisture for Temporal and Spatial Analysis of Rainfall over São Francisco River Basin, Brazil

Variability in precipitation patterns in the northeast and southeast regions of Brazil are complex, and the combined effects of the Tropical Atlantic, Pacific Niños, and local characteristics influence the precipitation rates. This study assesses the performance of multi-satellite precipitation product SM2RAIN-Climate Change Initiative (SM2RAIN-CCI) for the period of 1998–2015 at monthly scale. To accomplish this aim, various statistical analyses and comparison of multi-satellite precipitation analysis products with rain gauge stations are carried out. In addition, we used three values corresponding to extreme events: The total daily precipitation (PRCPTOT) and the number of consecutive dry/wet days (CDD/CWD). Results reveal that monthly rainfall data from SM2RAIN-CCI are compatible with surface observations, showing a seasonal pattern typical of the region. Data correlate well with observations for the selected stations (r ≥ 0.85) but tend to overestimate high rainfall values (&gt;80 mm/month) in the rainy area. There is a significant decrease in rainfall to the indices, especially in PRCPTOT during the occurrence of tropical ocean–atmosphere interactions, reflecting CWD and CDD values. Moreover, our findings also indicate a relationship, at interannual timescales, between the state of El Niño Southern-Oscillation (ENSO) and Tropical Atlantic (TA) annual precipitation variability from 1998 to 2015. The SM2RAIN-CCI could be a useful alternative for rain-gauge precipitation data in the São Francisco River basin.

Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland

Precipitation is one of the essential variables in rainfall-runoff modeling. For hydrological purposes, the most commonly used data sources of precipitation are rain gauges and weather radars. Recently, multi-satellite precipitation estimates have gained importance thanks to the emergence of Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG GPM), a successor of a very successful Tropical Rainfall Measuring Mission (TRMM) mission which has been providing high-quality precipitation estimates for almost two decades. Hydrological modeling of mountainous catchment requires reliable precipitation inputs in both time and space as the hydrological response of such a catchment is very quick. This paper presents an inter-comparison of event-based rainfall-runoff simulations using precipitation data originating from three different sources. For semi-distributed modeling of discharge in the mountainous river, the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) is applied. The model was calibrated and validated for the period 2014–2016 using measurement data from the Upper Skawa catchment a small mountainous catchment in southern Poland. The performance of the model was assessed using the Nash–Sutcliffe efficiency coefficient (NSE), Pearson’s correlation coefficient (r), Percent bias (PBias) and Relative peak flow difference (rPFD). The results show that for the event-based modeling adjusted radar rainfall estimates and IMERG GPM satellite precipitation estimates are the most reliable precipitation data sources. For each source of the precipitation data the model was calibrated separately as the spatial and temporal distributions of rainfall significantly impact the estimated values of model parameters. It has been found that the applied Soil Conservation Service (SCS) Curve Number loss method performs best for flood events having a unimodal time distribution. The analysis of the simulation time-steps indicates that time aggregation of precipitation data from 1 to 2 h (not exceeding the response time of the catchment) provide a significant improvement of flow simulation results for all the models while further aggregation, up to 4 h, seems to be valuable only for model based on rain gauge precipitation data.

Advancing Precipitation Estimation and Streamflow Simulations in Complex Terrain with X-Band Dual-Polarization Radar Observations

In mountain basins, the use of long-range operational weather radars is often associated with poor quantitative precipitation estimation due to a number of challenges posed by the complexity of terrain. As a result, the applicability of radar-based precipitation estimates for hydrological studies is often limited over areas that are in close proximity to the radar. This study evaluates the advantages of using X-band polarimetric (XPOL) radar as a means to fill the coverage gaps and improve complex terrain precipitation estimation and associated hydrological applications based on a field experiment conducted in an area of Northeast Italian Alps characterized by large elevation differences. The corresponding rainfall estimates from two operational C-band weather radar observations are compared to the XPOL rainfall estimates for a near-range (10–35 km) mountainous basin (64 km2). In situ rainfall observations from a dense rain gauge network and two disdrometers (a 2D-video and a Parsivel) are used for ground validation of the radar-rainfall estimates. Ten storm events over a period of two years are used to explore the differences between the locally deployed XPOL vs. longer-range operational radar-rainfall error statistics. Hourly aggregate rainfall estimates by XPOL, corrected for rain-path attenuation and vertical reflectivity profile, exhibited correlations between 0.70 and 0.99 against reference rainfall data and 21% mean relative error for rainfall rates above 0.2 mm h−1. The corresponding metrics from the operational radar-network rainfall products gave a strong underestimation (50–70%) and lower correlations (0.48–0.81). For the two highest flow-peak events, a hydrological model (Kinematic Local Excess Model) was forced with the different radar-rainfall estimations and in situ rain gauge precipitation data at hourly resolution, exhibiting close agreement between the XPOL and gauge-based driven runoff simulations, while the simulations obtained by the operational radar rainfall products resulted in a greatly underestimated runoff response.

Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

AbstractThe accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point‐based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.