Rain Gauge Network Research Articles

Effect of mountainous rainfall on uncertainty in flood model parameter estimation

Abstract Explaining the significant variability of rainfall in orographically complex mountainous regions remains a challenging task even for modern raingauge networks. To address this issue, a real-time spatial rainfall field estimation model, called WREPN (WRF Rainfall-Elevation Parameterized Nowcasting), has been developed, incorporating the influence of mountain effect based on ground raingauge networks. In this study, we examined the effect of mountainous rainfall estimates on the uncertainty of flood model parameter estimation. As a comparison, an inverse distance weighting technique was applied to ground raingauge data to estimate the spatial rainfall field. To convert the spatial rainfall fields into flood volumes, we employed the ModClark model, a conceptual rainfall–runoff model with distributed rainfall input. Bayesian theory was applied for parameter estimation to incorporate uncertainty analysis. The ModClark model demonstrated good flood reproducibility regardless of the estimation method for spatial rainfall fields. Parameter estimation results indicated that the WREPN spatial rainfall field, which accounted for the influence of the mountain effect, led to lower curve numbers due to higher estimated rainfall compared to the IDW spatial rainfall field, while the concentration time and storage coefficient showed minimal differences.

Estimation of Rainfall via IMERG-FR and Its Relationship with the Records of a Rain Gauge Network with Spatio-Temporal Variation, Case of Study: Mexican Semi-Arid Region

In the last few years, Satellite Precipitation Estimates (SPE) have been increasingly used for rainfall estimation applications. Their validity and accuracy are influenced by several factors related to the location where the SPEs are applied. The objective of this study is to evaluate the performance of the Integrated Multisatellite Retrievals for Global Precipitation Measurement Version 06 Half-Hour Temporal Resolution (IMERG-FR V06 HH) for rainfall estimation, as well as to determine its relationships with the hourly and daily rain gauge network data in a semiarid region during 2019–2021. The methodology contemplates the temporality, elevation, rainfall intensity, and rain gauge density variables, carrying out a point-to-pixel analysis using continuous, (Bias, r, ME, and RMSE), categorical (POD, FAR, and CSI), and volumetric (VHI, VFAR, and VCSI) statistical metrics to understand the different behaviors between the rain gauge and IMERG-FR V06 HH data. IMERG-FR greatly underestimated the heavy rainfall events in values of −63.54 to −23.58 mm/day and −25.29 to −11.74 mm/30 min; however, it overestimates the frequency of moderate rain events (1 to 25 mm/day). At making the correlation (r) between the temporal scales, the monthly temporal resolution was the one that better relates the measured and estimated data, as well as reported r values of 0.83 and 0.85, where records at shorter durations in IMERG-FR do not detect them. The weakness of this system, according to the literature and confirmed by the research findings, in the case of hydrological phenomena, is that recording or estimating short durations is essential for the water project, and therefore, the placement of rain gauges. The 1902–2101 m.a.s.l. range elevation has the best behavior between the data with the lowest error and best detection ability, of which IMERG-FR tended to overestimate the rain at higher altitudes. Considering that the r for two automated rain gauges per IMERG-FR pixel density was 0.74, this indicates that the automated rain gauges versus IMERG-FR have a better data fit than the rain gauges versus IMERG-FR. The distance to centroid and climatic evaluations did not show distinctive differences in the performance of IMERG. These findings are useful to improve the IMERG-FR algorithms, guide users about its performance at semiarid plateau regions, and assist in the recording of data for hydrological projects.

Research progress on the water vapor channel within the Yarlung Zsangbo Grand Canyon, China

The Second Tibetan Plateau Scientific Expedition and Research Program tasked a research team with the “Investigation of the water vapor channel of the Yarlung Zsangbo Grand Canyon (INVC)” in the southeastern Tibetan Plateau (TP). This paper summarizes the scientific achievements obtained from the data collected by the INVC observation network and highlights the progress in investigating the development of heavy rainfall events associated with water vapor changes. The rain gauge network of the INVC can represent the impacts of the Yarlung Zsangbo Grand Canyon (YGC) topography on precipitation at the hourly scale. The microphysical characteristics of the precipitation in the YGC are different than those in the lowland area. The GPM-IMERG (Integrated Multi-satellitE Retrievals for Global Precipitation Measurement) satellite precipitation data for the YGC region should be calibrated before they are used. The meridional water vapor flux through the YGC is more important than the zonal flux for the precipitation over the southeastern TP. The decreased precipitation around the YGC region is partly due to the decreased meridional water vapor flux passing through the YGC. High-resolution numerical models can benefit precipitation forecasting in this region by using a combination of specific schemes that capture the valley wind and water vapor flux along the valley floor.摘要第二次青藏高原科学考察研究在青藏高原东南部组建了雅鲁藏布大峡谷水汽通道科学考察分队, 本文主要总结了该科考分队近几年开展的观测研究以及利用该分队建立的观测网收集的观测数据所取得的科学成果, 重点介绍了与大峡谷水汽输送相关的强降雨过程的研究进展; 研究主要发现科考分队在大峡谷建立的雨量筒观测网可以代表该地区地形对小时降水量的空间影响; 藏东南降水的微物理特征与低海拔地区有明显差异; GPM卫星降水数据在大峡谷地区存在干偏差的问题, 使用前需进行校准; 穿越大峡谷的经向水汽输送对青藏高原东南部的降水有重要影响, 大峡谷周边区域降水量的减少可能是由于穿越大峡谷经向水汽通量的减少造成; 使用特定云降水方案的高分辨率数值模型可以较好的捕捉大峡谷内的风场和水汽输送时, 该模型能对该地区夜间强降水做出准确预报.

Entropy based approach for precipitation monitoring network in Bihar, India

Study regionBihar State, located in India’s eastern region, displays significant spatial and temporal variation in rainfall during the Indian Summer Monsoon period with subsequent flooding problems. Study focusRecent severe flooding problems highlight the need for improved spatial precipitation monitoring to enable effective flood management and reduce water-related disasters. To address this challenge, we employed Shannon entropy theory to assess the spatial distribution of precipitation and identify critical areas for rain gauge network improvements. We used Principal of Maximum Entropy (POME) to compute entropy measures and Value of Monitoring (VOM) with Thiessen polygons, and Adjacent Station Groups (ASGs). New hydrological insights for the regionThe results showed that the Marginal Entropy (ME) values lie between 0.039 and 0.048. The maximum values of ME are in the northeast area of the study region, exhibiting larger complexity and variability in the environmental conditions typical for northeast Bihar. The VOM was in the range of − 1 to + 1 suggesting strategic placement of additional 12 rain gauge stations to improve the existing monitoring network. The new locations were in the south mountainous area, the east, and the northwest, enhancing network coverage and addressing spatial and temporal precipitation variability. These findings support the design of a more effective monitoring network and have significant implications in hydrological modelling, flood prediction, and water resources management.

Intercomparison of Different Sources of Precipitation Data in the Brazilian Legal Amazon

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.

Evaluation of Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand.

Improving flood forecasting in Narmada river basin using hierarchical clustering and hydrological modelling

The purpose of the study was to use hierarchical clustering and Thiessen polygon algorithms to identify the significant rain gauge stations for flood forecasting at Sardar Sarovar Dam. Rainfall data from 2010 to 2018 was utilized to analyze the catchment region between Omkareshwar Dam and Sardar Sarovar Dam. The study identified two clusters of rain gauge stations with similar rainfall patterns and divided the study area into seventeen regions using Thiessen polygons. The land use map showed that the study area was mostly covered by crop lands, and the soil map divided the area into three types of sedimentary claystone soil. A hydrological model, Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS), was used for rainfall-runoff modeling, and the computed runoff was compared with observed gauge discharge and inflow of Sardar Sarovar Dam. Regression analysis was performed to assess the performance of the model, and the results showed a good correlation between observed rainfall and estimated runoff values for 2012 and 2016. The study concludes that the existing rain gauge network is sufficient for flood forecasting, and the developed model along with the Thiessen polygon method can provide more accurate predictions of flow. The study highlights the importance of selecting suitable rain gauge stations for reliable flood forecasting in flood-prone basins. The study findings can be useful for future runoff prediction and flood forecasting in the study area.

Sub-daily rainfall patterns in the mountainous regions of the Island of Tahiti: Insights from a one-year rain gauge network expansion

Study regionThe Island of Tahiti in French Polynesia (17.5°S,149.5°W), with a focus on the mountainous regions of the island interior. Study focusThe high topography of Tahiti creates an obstacle for wet air masses traveling along the surface of the South Tropical Pacific Ocean, which forces air flow uplift and triggers orographic rainfall. Although the main physical processes behind orographic rain enhancement on tropical islands were identified, the ways they shape Tahitian rain fields are still not fully understood, particularly due to a lack of observations. To address the limitations posed by this shortage of rain data, this study describes the temporary expansion of the rain gauge network of Tahiti towards the mountainous island interior, and uses the resultant dataset to investigate the spatial and temporal patterns of orographic rainfall at sub-daily resolution. New hydrological insights for the regionThe analysis of the rain gauge observations on daily and shorter time-scales leads to the identification of three main types of rainfall patterns: (1) a Dry Season Trade Winds type linked to a persistent Trade Winds Inversion (TWI) and characterized by moderate orographic rainfall in windward slopes, dry conditions in most leeward areas, and localized mid-afternoon showers in the mountains downwind of the main summit of the island; (2) a Transition Season Trade Winds type linked to atmospheric disturbances weakening the TWI, and characterized by intense orographic rainfall in windward slopes and mountains, and frequent showers in leeward areas by overshoot of the main crest; and (3) a SPCZ-related type occurring when the South Pacific Convergence Zone (SPCZ) crosses Tahiti during the wet season, and characterized by intense and widespread rainfall throughout the island with clear diurnal cycle and peak intensities recorded in the mid-afternoon.

CAIPEEX: Indian Cloud Seeding Scientific Experiment

Abstract The demand for effective methods to augment precipitation over arid regions of India has been increasing over the past several decades as the changing climate brings warmer average temperatures. In the fourth phase of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX IV), a scientific investigation was conducted over a rain-shadow region of the Western Ghats mountains in India. The primary objective was to investigate the efficacy of hygroscopic seeding in convective clouds and to develop a cloud seeding protocol. CAIPEEX IV followed the World Meteorological Organization (WMO) recommendations in a peer-reviewed report with physical, statistical, and numerical investigations. The initial results of the campaign in the monsoon period of 2018 and 2019 with two instrumented aircraft, a ground-based dual-polarization C-band radar, a network of rain gauges, radiosondes, and surface aerosol measurements are reported here. The hygroscopic seeding material was detected in cloud droplets and key cloud microphysical processes in the seeding hypothesis were tracked. The formidable challenges of assessing seeding impacts in convective clouds and the results from 150 seed and 122 no-seed samples of randomized experiments are illustrated. Over 5,000 cloud passes from the airborne campaign provided details about the convective cloud properties as the key indicators for a seeding strategy and the evaluation protocol. The experimental results suggest that cloud seeding can be approached scientifically to reduce uncertainty. The results from this study should interest the scientific community and policymakers concerned with climate change’s impact on precipitation and how to mitigate rainfall deficiencies.

Assessing the Improvement of a Sparse Rain Gauge Network in a Landslide Hotspot in Kerala, India—A Multi-Criteria Approach

Assessing the Improvement of a Sparse Rain Gauge Network in a Landslide Hotspot in Kerala, India—A Multi-Criteria Approach

Machine learning models to complete rainfall time series databases affected by missing or anomalous data

In recent years, artificial intelligence in geosciences is spreading more and more, thanks to the availability of a large amount of data. In particular, the development of automatic raingauges networks allows to get rainfall data and makes these techniques effective, even if the performance of artificial intelligence models is a consequence of the coherency and quality of the input data. In this work, we intended to provide machine learning models capable of predicting rainfall data starting from the values of the nearest raingauges at one historic time point. Moreover, we investigated the influence of the anomalous input data on the prediction of rainfall data. We pursued these goals by applying machine learning models based on Linear Regression, LSTM and CNN architectures to several raingauges in Tuscany (central Italy). More than 75% of the cases show an R2 higher than 0.65 and a MAE lower than 4 mm. As expected, we emphasized a strong influence of the input data on the prediction capacity of the models. We quantified the model inaccuracy using the Pearson's correlation. Measurement anomalies in time series cause major errors in deep learning models. These anomalous data may be due to several factors such as temporary malfunctions of raingauges or weather conditions. We showed that, in both cases, the data-driven model features could highlight these situations, allowing a better management of the raingauges network and rainfall databases.

Optimal selection of daily satellite precipitation product based on structural similarity index at 1 km resolution for the Pra catchment, Ghana

Thirteen satellite precipitation products (SPPs), re-gridded to 1 km resolution, were evaluated in terms of the structural similarity index (SSI) over the Pra catchment in Ghana. Three SPP scenarios were considered: Scenario one (S1) was the original SPPs; Scenario two (S2) was bias-corrected SPPs; and Scenario three (S3) was the better of S1 and S2 for each wet day. For each scenario, the best SPP was selected to constitute the 14th SPP referred to as the BEST SPP. Each SPP was evaluated in terms of SSI against the rain gauge rainfield for each wet day. For S1, the top three SPPs were TMPA, GSMAP and CMORPH; for S2, CMORPH, PERCCS and MSWEP were the top three; and for S3, CMORPH, PERCCS and TMPA came out on top in order of decreasing performance. Bias correction led to improvement in the overall SSI measure (SSIM) for 73% of wet days. The BEST SPP increased the SSIM of the best individual SPP by over 50% for S1, and over 30% for both S2 and S3. Comparing the BEST SPP of the three scenarios, S2 increased the SSIM statistic by 20% over that for S1, and SSIM was further improved by 4% for S3. It is highly recommended to use BEST SPP (S3) to generate the required 1 km × 1 km rainfields for the Pra, or other catchments around the world with a sparse rain gauge network, through conditional merging with rain gauge data as demonstrated.

Daily rainfall assimilation based on satellite and weather radar precipitation products along with rain gauge networks

Abstract The analysis of the spatial and temporal distribution of storm events contributes to a better use of water resources, for example, the supply of drinking water, irrigation practices, electricity generation and management of extreme events to control floods and mitigate droughts, among others. The traditional observation of rainfall fields in Mexico has been carried out using rain gauge network data, but their spatial representativeness is unsatisfactory. Therefore, this study reviewed the possibility of obtaining better estimates of the spatial distribution of daily rainfall considering information from three different databases, which include rain gauge measurements and remotely sensed precipitation products of satellite systems and weather radars. In order to determine a two-dimensional rainfall distribution, the information has been merged with a sequential data assimilation scheme up to the diagnostic stage, paying attention to the benefit that the rain gauge network density has on the estimation. With the application of the Barnes method, historical events in the Mexican territory were analyzed using statistical parameters for the validation of the estimates, with satisfactory results because the assimilated rainfalls turned out to be better approximations than the values calculated with the individual databases, even for a not very low density of surface observations.

Implementation of HydroBID Model with Satellite-Based Precipitation Products in Guadalquivir Basin, Bolivia

The use of distributed precipitation data in hydrological models is critically important to simulate processes at a micro-basin scale. However, aerial precipitation at a high resolution is required to run these models. This study aimed to set up the HydroBID tool in the Guadalquivir River basin using satellite-based precipitation products. The employed products included GSMaP gauge version 6, interpolated rain gauges using Kriging, the combined GS product for Bolivia, and the proposed combined product for the Guadalquivir basin. The GS Guadalquivir was generated by combining the satellite-based product GSMaP gauge version 6 with the local rain gauge network. The main difference with GS Bolivia is the improvement of the resolution from 5 km to 250 m. An iteration scheme using 230 micro-basins was employed, reaching a correlation of 0.98 compared to the control dataset. By using the hydrological model with the precipitation products, the daily river discharge was obtained, showing a high correlation of 0.99 and efficiency of 0.96 in relation to observed data between 2000 and 2016 at Obrajes station. Simulated flows with Kriging and GS Guadalquivir products presented similarly high correlations compared to the observed flows. In the case of GSMaP and GS Bolivia, these products showed general underestimations of the simulated flows, reaching correlations between 0.28 and 0.91, respectively. Moreover, annual volumes were analyzed, where the overestimation of GSMaP, Kriging, and GS Guadalquivir showed similar characteristics concerning the distribution of specific river discharges and volumes. Therefore, HydroBID appeared to be a feasible tool with enough adaptability to use distributed precipitation and simulate flows at a micro-basin scale. Therefore, we recommend applying this scheme to other basins to carry out analysis of events, water balance, and floods and similar studies.

Verification of satellite and model products against a dense rain gauge network for a severe flooding event in Kumasi, Ghana

AbstractFloods as a result of severe storms cause significant impacts on lives and properties. Therefore, timely and accurate forecasts of the storms will reduce the associated risks. In this study, we look at the characteristics of a storm on 28 June, 2018 in Kumasi from a rain gauge network and satellite data, and reanalysis data. The storm claimed at least 8 lives and displaced 293 people in Kumasi, Ghana. The ability of satellite and reanalysis data to capture the temporal variations of the storm was assessed using a high temporal resolution (accumulation per minute) rain gauge data. We employed the observation data from the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) rain gauges to assess the storm's onset, duration, and cessation. Subsequently, the performance of the ERA5 reanalysis and Global Precipitation Measurement (GPM) satellite precipitation estimates in capturing the rainfall is assessed. Both GPM and the ERA5 had difficulty reproducing the hourly pattern of the rain. However, the GPM produced variability that is similar to the observed. Generally, the region of maximum rainfall was located in the southern parts of the study domain in ERA5, while GPM placed it in the northern parts. The study contributes a verification measure to improve weather forecasting in Ghana as part of the objectives of the GCRF African Science for Weather Information and Forecasting Techniques (SWIFT) project.

Utilizing Entropy-Based Method for Rainfall Network Design in Huaihe River Basin, China

The nonstationary characteristics caused by significant variation in hydrometeorological series in the context of climate change inevitably have a certain impact on the selection of an optimal gauging network. This study proposes an entropy-based, multi-objective, rain gauge network optimization method to facilitate the design of a 43 stations-based network in Huaihe River Basin (HRB), China. The first goal of this study is to improve the accuracy of gauge-related information estimation through the selection and comparison of discretization methods. The second goal of this study is to quantify the impact of trend-caused nonstationarity on optimal network design using the sliding window method. This study compares the divergence of three kinds of discretization methods, including the floor function-based approach, Scott’s equal bin width histogram (EWH-Sc) approach, and Sturges’s equal bin width histogram (EWH-St) approach. The matching degree of the variance and marginal entropy of the observed series is computed to select the most suitable of the above three discretization methods. The trend-caused nonstationarity in 75% of all stations in the HRB could definitely influence the final results of the optimal rain-gauge network design using the sliding window method. Therefore, in future studies of rain-gauge network optimization, it is necessary to carry out uncertainty research according to local conditions in view of climate change and human activities.

Assessing applicability of two gridded precipitation datasets for hydrological simulation in a poorly gauged river basin towards supporting sustainable water resources management

Reliable and accurate precipitation estimates are important for hydrological studies and sustainable water resource management. However, networks of rain gauges are often sparsely and unevenly distributed in many large river basins in the world including the Red River basin (RRB). Thus this study aimed to comprehensively evaluate the applicability of two widely used gridded precipitation products, gauge-based APHRODITE and gauge satellite-based GSMaP-Gauge, over the RRB using both statistical and hydrological assessment approaches. The accuracy assessment of the gridded precipitation datasets was performed by comparing with the reference precipitation dataset derived from the local weather stations. The hydrological performance of both gridded products was evaluated through the Variable Infiltration Capacity (VIC) hydrological modelling scheme for simulation of daily streamflow at the hydrological stations in the RRB. The results demonstrated that both gridded products could generally capture the spatiotemporal variation of the reference precipitation over the RRB during the period of 2005–2014, although both underestimated the reference precipitation. Results of statistical analysis showed that the APHRODITE data outperformed the GSMaP-Gauge data in precipitation estimation. The performance of the VIC model driven by the gridded precipitation products in streamflow simulation was satisfactory, although simulations forced with APHRODITE data displayed the better performance. Generally, the APHRODITE product showed its encouraging potential for hydrological studies over the RRB.

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

In the District of Abidjan, flooding typically occurs suddenly during intense rainfall events. The individual rainfall event provides the basic element for the study. Its analysis is required to develop solutions for managing the impact of extreme rainfall occurrences. Our study proposes to identify individual rainfall events that occurred in the District of Abidjan from a densified network and analyze some of their characteristics related to the amount of rainfall they provided, their duration, and their level of intensity. A total of 1240 individual rainfall events were identified between 2018 and 2021 using a network of 21 rain gauges. Rainfall events were identified based on criteria such as a minimum inter-event time without rainfall of 30 min, a detection threshold of 0.02 mm/5 min, a minimum duration of 30 min applicable to the average hyetograph, and a minimum of 1 mm of rainfall in at least one rain gauge. The analysis of characteristics related to accumulation, intensity, and duration showed that the rainfall events were essentially convective, with an average duration of more than 2 h and a rainfall of 11.30 mm/event. For 70% of the rainfall events of a mixed nature, the convective episodes last up to 33.33% of the total duration of the event and produce an average of 80% of the cumulative rainfall. The 30-min peak intensities generally occur in the first half of the event. Less than 13.5% of the events have peaks greater than 50 mm/h. The probability of observing more than two, three, or four events per day is high in June and October, the core of the two rainy seasons.

Implementation of a probability matching method in developing intensity–duration–frequency relationships for sub-daily durations using IMERG satellite-based data

ABSTRACT Reliable estimation of design rainfall in time and space can play a significant role in reducing flood-related losses. However, many parts of the world suffer from a lack of dense raingauge networks. Under this condition, the use of satellite-based data can be an alternative solution. In this research, using data from a dense ground-based raingauge network and Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite-based precipitation product, the performance of a probability matching method for satellite-based extreme values has been investigated. The results indicate that the correction model, especially at shorter durations, is able to reduce the error of extreme values of raw satellite-based estimates, and its performance is not affected significantly by the density of the available raingauge network. In conclusion, implementation of the proposed method in poorly gauged regions can play an effective role in providing a reliable estimate of the design rainfall and eventually reducing the financial losses caused by floods.

Improving Rainfall Fields in Data-Scarce Basins: Influence of the Kernel Bandwidth Value of Merging on Hydrometeorological Modeling

Accurate rainfall fields are important for several hydrological and meteorological applications. In poorly instrumented basins, the lack of rain gauges heavily affects the spatial rainfall estimation, yet neither remote sensed nor climate model estimates are good enough for management applications. To tackle this problem, we investigate the impacts of combining both sources of information by varying the kernel bandwidth value of the double smoothing merging algorithm and analyzing the error of the rainfall fields. We explored the correlation between rain gauge density and bandwidth and compared the results against classical geostatistical interpolation methods based solely on in-situ measurements. Propagation of rainfall error into hydrological modelling is usual, and therefore we evaluated the influence of the bandwidth in streamflow simulations implementing two hydrological models. The hydrological evaluation considered the analysis of hydrological signatures rather than just performance metrics. We found that there is a clear correlation between kernel bandwidth and monitoring network density and that the bandwidth also affects hydrological performance. Simple bilinear downscaling did not produce a significant difference in meteorological or hydrological errors, and rain gauge network configuration also impacts the error of the field. We conclude that merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains.