CHIRPWeb, an online tool for providing a bias corrected CHIRP grid dataset using field measurements

CHIRPWeb, an online tool for providing a bias corrected CHIRP grid dataset using field measurements | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CHIRPWeb, an online tool for providing a bias corrected CHIRP grid dataset using field measurements Julio Pérez-Sánchez, Patricia Jimeno-Sáez, Adrián López-Ballesteros, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7137530/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Precipitation data play a crucial role in hydrological modeling. Although rain ground stations data have traditionally been used, their uneven distribution and numerous gaps raise some doubts about their reliability. As a result, satellite rainfall data sets are increasingly used in hydrological assessments. However, these estimates are prone to inaccuracies due to instrumental problems or theoretical simplifications, and it is essential to eliminate systematic errors before using them in hydrological applications. This paper presents an online tool to select a CHIRP grid in a region and correct its bias derived from field measurements. Furthermore, the tool is designed to generate SWAT-compatible rainfall data input. As an example of the application, the performance of the SWAT model in the Spanish Oskotz river basin has been evaluated. In general, better results are achieved with the corrected grids, obtaining improvements of around 30% in Nash-Sutcliffe efficiency and decreasing PBIAS by around 15%. CHIRP precipitation data hydrological modelling SWAT downscaling techniques Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Precipitation data are the key factors in hydrological modelling and all the studies therefrom, such as water resources management or flood and drought disaster risk strategies (Prakash, 2019 ). Rain ground stations have been traditionally considered as the main sources of information in rainfall-runoff models (Berndt et al., 2014 ; Rogelis & Werner, 2013 ). However, their uneven distribution and their numerous gaps in historical records in some areas (Gummadi et al., 2022 ) leave serious doubts about their validity, reliability and accuracy (Kidd et al., 2017 ). Indeed, these conditions are the greatest hindrances for water resources managers, farmers or hydroclimatic research (Nogueira, 2020 ). Therefore, satellite rainfall datasets are becoming more frequent in hydrological assessments, largely because of the substantial improvement of remote-sensing techniques and automatic data-processing technology (Sharifi, 2020 ; West et al., 2019 ; Zhang & Ma, 2018 ). The development of satellite-based meteorological products has made spectacular progress in recent decades. The Global Precipitation Climatology Project (GPCP) combines since 1980 rainfall gauge data from more than 6,000 stations with rainfall estimates from infrared and microwave imagery from geostationary satellites such as GOES, GMS and GOES, GMS and METEOSAT and NOAA polar satellites. GPCP rainfall data are available at daily temporal resolution and 1° spatial resolution since 1993. Likewise, NOAA's Climate Prediction Center (CPC) developed the Morphing technique (Xie et al., 2017; Joyce et al., 2007) to combine information from different satellite sensors, thus new data from microwave sensors such as SSM/I (DMSP-13, 14 and 15), AMSU-B (NOAA-15, 16, 17 and 18), AMSR-E (Aqua) or TMI (TRMM) have been successfully incorporated. In fact, satellite-based methods for estimating precipitation experienced significant enhancements following the deployment of the Tropical Rainfall Measuring Mission (TRMM) satellite in 1997. Initially, it was a space mission between NASA (USA) and the Japan Aerospace Exploration Agency (JAXA) to monitor and study tropical rainfall. Following the success of the TRMM, both organisations deployed the Global Precipitation Measurement (GPM) mission in 2014 and began publishing the Integrated Multi-satellite Retrievals for GPM (IMERG) new-generation global precipitation products in the same year. Moreover, the Centre for Hydrometeorology and Remote Sensing at the University of California at Irvine (CHRS-UCI) developed the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithm, which estimates rainfall from cloud texture information obtained from multiple geosynchronous (GOOS) satellites provided by the CPC-NOAA. These data cover 50° S to 50° N, with a spatial resolution of 0.25° and a temporal resolution of 6 h (Sorooshian et al., 2000 ). More recently, other products such as reanalysis are also serving as alternative databases of global precipitation data. These systems amalgamate existing observations with background model forecasts, employing physical principles to generate consistent gridded datasets (Gebregiorgis et al., 2022 ). ERA5 (Hersbach et al., 2020 ) and ERA-Interim (Dee et al., 2011 ), both generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), stand out for their reliability in numerous studies (Li et al., 2022 ; Steinkopf & Engelbrecht, 2022 ; Rakhmatova et al., 2021 ; Nogueira, 2020 ; Albergel et al., 2018 ). Nevertheless, the lack of gridded rainfall products with both extended historical records and minimal latency poses challenges for scientists. In fact, many existing operational satellite precipitation products fail to meet the practical needs of certain users, as they do not offer sufficiently long periods of record while maintaining acceptable latency levels (Shen et al., 2020 ). To address this shortcoming, researchers at the University of California-Santa Barbara and the US Geological Survey (USGS) have developed two new near-global satellite precipitation datasets (covering latitudes from 50°S to 50°N): the satellite-only Climate Hazards Group Infrared Precipitation (CHIRP) and the gauge-adjusted Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Among the satellite-derived datasets, these two products stand out for their unparalleled spatial and temporal coverage, minimal latency, and resolution (Funk et al., 2015 ). These two high resolution datasets (0.05° × 0.05°) include daily, pentadal, and monthly precipitation data spanning from 1981 to almost the present, ensuring a substantial data record of approximately 40 years. Both products were generated using spatially diverse regression models that relied on pentadal cold cloud duration (CCD) values and TRMM V7 training data. The CCD time series were obtained from the CPC and NOAA B1 datasets. The satellite-only products (CHIRP) become accessible almost instantly, released shortly after the conclusion of each pentad: on the 2nd, 7th, 12th, 17th, 22nd, and 27th. However, the complete CHIRPS product experiences a delay, becoming available only after the 15th of the subsequent month. CHIRPS was expected more dependable in representing the distribution of rainfall due to its integration of ground-based measurements (Baez-Villanueva et al., 2018 ; Bai et al., 2018 ; Aadhar & Mishra, 2017 ). This improvement might stem from the mean bias adjustment, which mitigates the primary disparities between actual measurements and satellite-derived products (Dinku et al., 2018 ). Nevertheless, CHIRP was found to better perform specifically after 1992 and at lower elevation regions in Nepal. (Shrestha et al., 2017 ). Similarly, (Khandu et al., 2016) noted that CHIRP showed comparatively stronger performance in flat regions over the Eastern Himalayan region characterized by elevation ranges between 150 and 1,500 meters above sea level (m.a.s.l.). Likewise, (Gummadi et al., 2022 ) found notably high probability of detection (POD) scores in the context of Vietnam with this product and (Beyene et al., 2023 ) showed that the bias-corrected CHIRP outperformed the estimates of CHIRPS in an Ethiopian watershed. Therefore, it was decided that the CHIRP product would be the most suitable to use as an input to the watershed hydrological modelling. Despite the considerable progress made, satellite-derived rainfall estimates are susceptible to numerous inaccuracies stemming from instrumental issues, the inherent nature of measurement systems, theoretical simplifications, or the complex non-linear relationship between observed variables and rainfall (Fu et al., 2021 ; Saha et al., 2020 ). Therefore, it is imperative to eliminate systematic errors (bias) from the products prior to their utilization in hydrological and water resources applications. There are various methods to correct these biases of rainfall and temperatures, which can generally be categorized into three groups based on the level of correction they apply (Ghimire et al., 2021 ). While delta change (Middelkoop et al., 2001 ; Räty et al., 2014 ; Shabalova et al., 2003 ) and linear scaling (Lenderink et al., 2007 ) adjust the mean of rainfall and temperature to match observed values, the power transformation method (Leander et al., 2008 ; Leander & Buishand, 2007 ) focuses on correcting the variance. Nevertheless, there is a lack of conclusive evidence in the scientific literature to inform the optimal choice of a bias correction method (Goshime et al., 2020 ). The study carried out by (Soo et al., 2020 ) in Malaysia highlighted the power transformation (PT) method compared to the linear scaling (LS) and the local intensity scaling (LOCI), since it demonstrated significant enhancements across various statistical metrics. However, LS method proved, in general, to be the most effective scheme compared to LOCI or quantile mapping (QM) in India (Jaiswal et al., 2022 ). By contrast, the latter methods (LOCI and QM) exhibited superior performance compared to the LS and PT methods in the context of the Yarlung Tsangpo–Brahmaputra River (Luo et al., 2020 ). Likewise, according to (Rahimi et al., 2021 ), LOCI demonstrated superior effectiveness in stations experiencing wet summers, whereas PT exhibited strong performance in stations with minimal or no summer precipitation. Moreover, (Fang et al., 2015 ) found out that PT and QM showed very good performance regarding precipitation data in China, while for temperature all correction methods used (LS, QM and variance scaling) exhibited equally effective statistical results. The work described in this paper consists of an online tool that provides a bias corrected CHIRP grid dataset using field measurements. The outputs of the open-source web are configured to be used directly in SWAT (Soil and Water Assessment Tool, (Arnold et al., 1998 )), the world's most widely used hydrological model (Mannschatz et al., 2016 ). The use of this software allows decision-makers and stakeholders to obtain a grid of precipitation data based on CHIRP in watersheds with scarce information or gaps in the existing meteorological stations which prevent the development of a reliable hydrological model. This indicates that the application offered and demonstrated in this study are capable of being duplicated, tailored, and utilized in any watershed in the range 50°S to 50°N latitudes. The paper is organized as follows: Section 2 describes the software structure, and programming languages used in the development of the tool, as well as the web interface and the steps to follow to obtain the selected grid and the format of the output files. Section 3 presents, analyses and discusses the performance of the SWAT model in a Spanish watershed using CHIRP dataset compared to CHIRP bias-corrected meteorological information, both provided by the online tool presented and, finally, Section 4 provides the main conclusions of the study carried out. 2. Materials and methods 2.1. Model structure of CHIRPweb The web application, developed in Python 3.8, employs Flask as its web service framework. It is designed to interact with NetCDF files, which are obtained from the CHIRP website ( https://data.chc.ucsb.edu/products/CHIRP/ ). These files serve as a pseudo-database from which the application retrieves information to adjust time series data accordingly. When a user inputs a specific region defined by its latitude and longitude, along with a date range, the application meticulously searches through the NetCDF files for the given coordinates, year by year, loading all pertinent CHIRP data points for that region for each year. Furthermore, when users upload their measurement files and request bias-correction, they must select bias-correction technique from the provided. This selection dictates the correction applied to the user's time series, ultimately allowing for the download of these corrected files. This process ensures that the application not only provides accurate data adjustment based on user specifications but also enhances the usability and effectiveness of climate data analysis by leveraging comprehensive CHIRP data. The use of the tool provided is very easy and intuitive (Fig. 1 ). It mainly consists of five main blocks (Fig. 2 ): definition of the region and period, uploading rain ground stations data, choice of bias-correction technique, results of bias correction and interpolation, and downloading the rainfall grid generated. 2.2. Grid selection Firstly, once the perimeter of the basin to be analysed is known, the coordinates (latitude and longitude) delimiting the region, as well as the temporal period of rainfall data to be obtained, must be entered in Load CHIRP region box (Fig. 3 a). After selecting the Load Region option, all the information related to the data contained in the CHIRP dataset matching the previously defined search will be displayed in CHIRP point loaded box (Fig. 3 b). The information displayed in the summary box will contain the total number of CHIRP points including the selected region, as well as the area delimitation and the selected period. The same box allows the download of the daily precipitation data contained in the CHIRP points through the Download CHIRP option. The download format will be a zip file containing each of these points in comma-separated text files (csv) with the daily precipitation in the selected period. In addition, another reference file will also be obtained with the geolocation of all these points, as well as the assigned name. The files are ready to be loaded directly into SWAT without any additional transformation. 2.3. Input of observed data The following step will be to upload the rainfall gauge data, just clicking on the button “Upload files” after having selected the file in the computer with the button “Choose file”. This file will be in zip format and will contain the SWAT data entry files, all in csv format: a file defining the name given to the rainfall gauge station, as well as its location (longitude, latitude and elevation) and as many files as there are rainfall gauge stations. The first line of the latter files shall always indicate the first day of the precipitation series it contains, followed by the daily precipitation, according to the SWAT format. After uploading the zip file, a list will appear in the corresponding box (Fig. 4 ) showing the number of stations included in the file, as well as their names, location (latitude and longitude) and the range of dates with recorded rainfall data. 2.4. Selection of the bias correction method Once uploaded a zip archive with the ground-based precipitation data, a new drop-down menu button will appear to choose the bias correction technique: linear scaling (LS) (Lenderink et al., 2007 ), local intensity scaling (LOCI) (Schmidli et al., 2006 ) and power transformation (PT) (Teutschbein & Seibert, 2012 ). The three methods (Table 1 ) correct the bias multiplying the raw daily precipitation provided by the CHIRP grid by a parameter which depends on bias in mean precipitation and its variance. Thus, the objective of the LS method is to precisely align the monthly mean of the simulated data with the observed monthly mean. This method utilizes monthly correction values derived from the mean between observed and CHIRP nearest grid dataset point. All daily precipitation values in the CHIRP grid dataset will be multiplied by the correction factor. Since even small amounts of precipitation can introduce biases, the LOCI method seeks to mitigate these biases by setting a minimum threshold above which the relevant statistics will be carried out. This bias-technique consists of two steps: first, a wet day threshold for the m-th month (P thres,m ) is set using the CHIRP precipitation series, so that exceeding this threshold coincides with the observed wet day frequency; second, a scaling factor (s m ) is determined and applied to ensure that the corrected mean precipitation is consistent with the observed mean precipitation. As in the previous two methods only the mean precipitation is taken into account, PT also considers the difference in variance between the observed data and the CHIRP data. To avoid biases in drizzled days, the LOCI-corrected precipitations (P CHIRP ) will be used instead of the ones provided by the CHIRP dataset. On the other hand, not only will we have a multiplicative correction coefficient, but we also consider a correction exponent for each m-month (b m ) that will be obtained from the minimisation of a function (f(b m )) that depends on the standard deviation of the observed and LOCI-corrected precipitation and the standard deviation of the latter. Table 1 Bias correction techniques. Method Description Equations Remarks LS Linear scaling \(\:{\varvec{P}}_{\varvec{c}\varvec{o}\varvec{r},\varvec{m},\varvec{d}}={\varvec{P}}_{\varvec{C}\varvec{H}\varvec{I}\varvec{R}\varvec{P},\varvec{m},\varvec{d}}·\frac{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{o}\varvec{b}\varvec{s},\varvec{m}}\right)}{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{C}\varvec{H}\varvec{I}\varvec{R}\varvec{P},\varvec{m}}\right)}\) P corr : corrected precipitation P CHIRP : CHIRP precipitation P obs : observed precipitation in ground station P thres : threshold precipitation P LOCI : corrected precipitation with LOCI m: monthly d: daily µ: mean operator σ: standard deviation operator LOCI Local intensity scaling \(\:{\varvec{P}}_{\varvec{c}\varvec{o}\varvec{r},\varvec{m},\varvec{d}}=\:\:\:\:0\:\:\:\:\:\:\:\varvec{i}\varvec{f}\:\:\:\:{\varvec{P}}_{\varvec{r}\varvec{a}\varvec{w},\varvec{m},\varvec{d}}<{\varvec{P}}_{\varvec{t}\varvec{h}\varvec{r}\varvec{e}\varvec{s},\varvec{m}}\) \(\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:{\:\:\:\varvec{P}}_{\varvec{C}\varvec{H}\varvec{I}\varvec{R}\varvec{P},\varvec{m},\varvec{d}}·\:{\varvec{S}}_{\varvec{m}}\:\:\:\:\:\:\:\:\:\:\varvec{o}\varvec{t}\varvec{h}\varvec{e}\varvec{r}\varvec{w}\varvec{i}\varvec{s}\varvec{e}\) \(\:{\varvec{S}}_{\varvec{m}}=\:\frac{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{o}\varvec{b}\varvec{s},\varvec{m},\varvec{d}}|{\varvec{P}}_{\varvec{o}\varvec{b}\varvec{s},\varvec{m},\varvec{d}}>0\right)}{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{C}\varvec{H}\varvec{I}\varvec{R}\varvec{P},\varvec{m},\varvec{d}}|{\varvec{P}}_{\varvec{C}\varvec{H}\varvec{I}\varvec{R}\varvec{P},\varvec{m},\varvec{d}}>{\varvec{P}}_{\varvec{t}\varvec{h}\varvec{r}\varvec{e}\varvec{s},\varvec{m}}\right)}\:\) PT Power transformation \(\:{\varvec{P}}_{\varvec{c}\varvec{o}\varvec{r},\varvec{m},\varvec{d}}={\varvec{s}}_{\varvec{m}}·{\varvec{P}}_{\varvec{L}\varvec{O}\varvec{C}\varvec{I},\varvec{m},\varvec{d}}^{{\varvec{b}}_{\varvec{m}}}\) \(\:{\varvec{s}}_{\varvec{m}}=\:\frac{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{o}\varvec{b}\varvec{s},\varvec{m}}\right)}{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{L}\varvec{O}\varvec{C}\varvec{I},\varvec{m}}^{{\varvec{b}}_{\varvec{m}}}\right)}\) \(\:{\varvec{f}(\varvec{b}}_{\varvec{m}})=\:\frac{\varvec{\sigma\:}\left({\varvec{P}}_{\varvec{o}\varvec{b}\varvec{s},\varvec{m}}\right)}{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{L}\varvec{O}\varvec{C}\varvec{I},\varvec{m}}^{{\varvec{b}}_{\varvec{m}}}\right)}-\frac{\varvec{\sigma\:}\left({\varvec{P}}_{\varvec{L}\varvec{O}\varvec{C}\varvec{I},\varvec{m}}^{{\varvec{b}}_{\varvec{m}}}\right)}{\varvec{\mu\:}\left({\varvec{P}}_{\varvec{L}\varvec{O}\varvec{C}\varvec{I},\varvec{m}}^{{\varvec{b}}_{\varvec{m}}}\right)}\) 2.5. Corrected grid Having selected the bias technique, a correction of the CHIRP grid dataset rainfall will be made according to the rain gauge closest to the grid point considered if the weather station is located inside the grid cell with which it is compared. Otherwise, all m-monthly station parameters will be used (depending on the bias chosen bias technique), and the daily rainfall will be corrected using the inverse distance weighted (IDW) method. The box at the bottom of the interface (Fig. 4 ) will show the information related to the CHIRP grid points that are part of the selected region, as well as the rain gauge with which it has been corrected (or all of them if there was not one close) and the method used, which will vary between the one chosen in the previous step (LS, LOCI o PT) or IDW if all the stations have been selected. Finally, the corrected chirp files can be downloaded in a zip file clicking on the Download button in the top left corner of the last menu (Fig. 5 ). This file contains the corrected CHIRP grid in the format required for direct use in the SWAT hydrological model. 2.6. Goodness-of-fit indicators The rainfall data obtained through the application will be compared with the data downloaded from the CHIRP grid without any correction. For this purpose, the PBIAS (Table 2 ) of both data sources will be assessed with respect to the values of the observed rainfall series of the weather station closest to the grid cell considered. In addition, in order to evaluate the accuracy of the corrected grid in the modelling of the hydrological response in a basin, the SWAT model will be used, taking as input both the non-corrected CHIRP rainfall grid and the corrected ones with the methods shown in Table 1 . The performance metrics to be used for comparison with the observed streamflow will be the usual ones for hydrological modelling: Nash-Sutcliffe efficiency (NSE), PBIAS and coefficient of determination (R 2 ). Their expressions and optimal values are shown in Table 2 . Table 2 Performance metrics ( \(\:{X}_{obs,i}\) and \(\:{X}_{sim,i}\) are the observed and simulated values, respectively, and \(\:\overline{{X}_{obs}}\) and \(\:\overline{{X}_{sim}}\:\) are the average observed and simulated values). Statistic Description Equations Range Optimal value NSE Nash-Sutcliffe efficiency \(\:NSE=\frac{{\sum\:}_{i=1}^{n}{\left({X}_{obs,i}-{X}_{sim,i}\right)}^{2}}{{\sum\:}_{i=1}^{n}{(X}_{obs,i}-{\overline{{X}_{obs}})}^{2}}\) -∞ − 1 1 PBIAS Percent bias \(\:PBIAS=\frac{{\sum\:}_{i=1}^{n}\left({X}_{obs,i}-{X}_{sim,i}\right)}{{\sum\:}_{i=1}^{n}{X}_{obs,i}}*100\:\) −100% - +100% 0 R 2 Coefficient of determination \(\:{R}^{2}=\frac{{\sum\:}_{i=1}^{n}\left({X}_{obs,i}-\overline{{X}_{obs}}\right)·({X}_{sim,i}-\overline{{X}_{sim}}}{\sqrt{{\sum\:}_{i=1}^{n}{({X}_{obs,i}-\overline{{X}_{obs}})}^{2}}\sqrt{{\sum\:}_{i=1}^{n}{({X}_{sim,i}-\overline{{X}_{sim}})}^{2}}}\) 0–1 1 3. Results and discussion Initially, a statistical analysis was made between corrected and uncorrected data to assess the accuracy of both against the recorded data. Furthermore, this section presents, analyses and discusses the performance of the SWAT model in the Spanish Oskotz river basin using CHIRP dataset compared to CHIRP bias-corrected rainfall data, both provided by the online tool presented. Furthermore, 3.1. Study area. The Oskotz river basin has been selected as an example of the application of the tool presented. It is located in the region of Navarra, northern Spain, between the coordinates 1°47′-1°44′ West longitude and 42°55′-42°58′ North latitude (Fig. 6 ). This experimental basin covers an area of 16.74 km 2 . The altitude of the basin varies between 531 and 918 m above sea level. According to Casali et al. (2010) the average annual rainfall and temperature are 1200 mm and 12°C, respectively, and therefore its climate can be considered sub-Atlantic (Jimeno-Sáez et al., 2022 ). Despite a wet season during autumn and winter, summer rainfall represents around 11% of the annual total. The predominant soil type varies according to the landscape: the accumulation slopes are mostly composed of Ustochrepts Typic, while the eroded slopes exhibit Ustochrepts Lythic and Typic soils, and the valley plain is characterised by Ustochrepts Fluventic. The thickness of soils is around 1 m, except on eroded slopes, where they tend to be shallower. As can be seen in the Fig. 5 d. forests are the predominant land use in the basin, covering nearly 70% of its area. These forests include native forests and reforestation areas and are home to a wide variety of plant species. Furthermore, these forests play a crucial role in soil retention, water regulation and the provision of wildlife habitat in the basin. A significant part of the land is also used for animal grazing, either for livestock rearing or traditional livestock activity. These rangelands can be either natural, which have evolved spontaneously, or areas of cultivated grassland, maintained for livestock grazing. Agricultural land occupies a smaller proportion compared to forests and pasture. Crops vary according to the season and the preferences of local farmers, and can include cereals, vegetables, fruit trees, among others. 3.2. Databases. The basin has an automatic weather station and a hydrological station that records climatic variables and water, respectively. Data collected are available on http://cuencasagrarias.navarra.es/ and include information on rainfall, maximum and minimum temperatures, as well as observed data on flow in the 2002–2020 period. The rainfall data recorded at the meteorological stations will be used for bias correction of the CHIRP rainfall grid in the study catchment. The digital elevation model (DEM) with a resolution of 25 × 25 metres was obtained from the Instituto Geográfico Nacional (IGN) of Spain. The land use mapping at 1:100,000 scale was extracted from Corine Land Cover (2012), while the soil map, with a resolution of 1 km, was obtained from the Harmonised World Soil Database (HWSD). 3.3. Bias correction of CHIRP grid. First of all, an analysis of the different precipitation products obtained was carried out. Table 3 shows the main statistics of the series themselves and the comparison of the observed rainfall (Station) with respect to the CHIRP products that estimate it. While the uncorrected CHIRP (CHIRP_ORG) estimated the amount of rainfall to be 20% higher than actual rainfall, LS and PT corrections reduced this amount to less than 9%, demonstrating greater accuracy in the total amount of rainfall generated. The average precipitation values over the study period showed similar conclusions: an overestimation for all products but LOCI, which reduced the observed value by one third. In the rest of the cases, the percentages remained practically the same as those obtained with the sum of total precipitation. As far as extreme values are concerned, the number of days that CHIRP and its corrections consider that no precipitation occurs is higher than those recorded at the ground station. For the 75th percentile the rain gauge record gives values of 3 mm, while the CHIRP products (original and corrected) do not give positive values until the 79th percentile. However, for values above the 87th percentile the estimated values exceed the observed value, so that for the 95th percentile values above the observed value are again obtained which are usually lower in the LS and PT corrections (18.5%) with respect to the CHIRP without corrections (30%). As expected, the PBIAS values are negative in all series except LOCI, where a value close to + 60% is obtained, indicating a high underestimation of rainfall. The original CHIRP grid exceeds − 20% and the corrections made with LS and PT do not reach − 9%. As expected, the PBIAS values are negative in all series except LOCI, where a value close to + 60% is obtained, indicating a high underestimation of rainfall. The original CHIRP grid exceeds − 20% and the corrections made with LS and PT do not reach − 9%, proving a satisfactory performance in both cases to the observed records. Table 3 Comparison of rainfall statistics 2002–2020 period in mm (the suffix of each CHIRP grid refers to whether the rainfall data used are uncorrected, original-ORG, or the method of bias correction, LS, PT or LOCI). STATION CHIRP_ORG CHIRP_LS CHIRP_PT CHIRP_LOCI SUM 22834.40 27452.65 24798.75 24801.86 9493.20 AVERAGE 3.25 3.91 3.53 3.53 1.35 MAXIMUM 115.40 119.89 150.47 143.32 55.35 PERCENTILE-75 3 0 0 0 0 PERCENTILE-95 19.3 25.07 22.88 23.57 8.80 PBIAS (%) - -20.22% -8.60% -8.62% 58.43% 3.1. Hydrological model. The rainfall series obtained with the CHIRP grid, uncorrected and corrected with the three methods in Table 1 , have been used to develop four hydrological models with SWAT, obtaining the statistics shown in Table 4 according to the comparison with the observed streamflow in the Oskotz river basin in the period 2002–2020. Table 4 SWAT model performance statistics (the suffix of each CHIRP grid refers to whether the rainfall data used are uncorrected, original-ORG, or the method of bias correction, LS, PT or LOCI). CHIRP_ORG CHIRP_LS CHIRP_PT CHIRP_LOCI R 2 0.65 0.76 0.77 0.46 NSE 0.54 0.70 0.70 0.52 PBIAS -44.82% -30.00% -31.09% 46.72% In general, better results are achieved with the corrected grids than with the use of the CHIRP raw grid, with the exception of the LOCI method, where the results do not improve and even worsen significantly, therefore, this method has not been considered in the rest of the study. This difficulty of the LS method has already been highlighted in other basins (Jaiswal et al. 2022 ). As for the other two correction methods (LS and PT) the results were very similar and quite satisfactory in both cases. In fact, LS and PT increase R 2 with regard to the raw grid up to 0.11 and 0.12, respectively. Moreover, NSE improves around 30% and PBIAS decreases around 15% (from − 45% to -30%). However, according to (Moriasi et al., 2007 ), the values provided by PBIAS are unsatisfactory as they are greater (in absolute value) than 20%. Moreover, they are always negative, so it could be considered that the simulation overestimates the observed values, i.e. the model's predictions tend to be higher than the real observed values. Although this will be analysed thoroughly below with the flow-duration curves, it should be remembered that the parameters of the hydrological model have not been calibrated given the focus of this study. These good results are in line with previous studies (Goshime et al., 2019 ) showing improvements in hydrological modelling with CHIRP rainfall data corrected on the basis of rainfall series from existing stations. All these indicate the significant improvement of estimates with LS and PT bias-correction. Referring to Fig. 7 , none of the models was able to simulate the highest peaks of the observed streamflow, although the corrected-bias CHIRP (LS and PT) outperformed both in high and low flows. Overall, CHIRP_ORG overestimates base flow and this fact is corroborated in the flow-duration curves (Fig. 8 ) in which CHIRP_ORG dataset is above observed streamflow for exceedance probability higher than 0.2. The gap is reduced with bias-corrected CHIRP (LS and PT) grid in low flow and the differences with observed data in exceedance probability higher than 0.7 are virtually non-existent. Furthermore, PT provides a better performance for high Flow. The underestimation of peak flows by SWAT has already been highlighted in several previous studies (Senent-Aparicio et al., 2019 ; Bieger et al., 2014 ). Furthermore, in line with the findings of (Castellanos-Osorio et al., 2023 ) and (Franco et al., 2020 ), there was a slight overestimation of baseflow, and the streamflow recession appeared to be slower than observed. However, it is again recalled that since the aim of this study is to assess the improvement of the presented tool over the use of the original CHIRP grid, no calibration of the model has been carried out. Therefore, it is the comparison between models that we have used to evaluate the greater accuracy of the rainfall data. 4. Conclusions The use of satellite-based rainfall products can enable the development of accurate and reliable hydrological models in basins with scarce information or significant gaps in the recorded information. However, its use is subject to the previous bias-correction derived from field measurements. This paper describes the setup and use of an online tool to download the CHIRP rainfall grid in a region and the correction of its bias using various methods (LS, LOCI, and PT). Additionally, the provided dataset is delivered for direct use in SWAT. This tool has been applied to a Spanish watershed, confirming that the precipitation series obtained with raw CHIRP grid estimate the total rainfall to be over 20% higher than the amount observed in the existing meteorological stations, whereas corrections made with LS and PT reduced this amount to less than 9%, demonstrating greater accuracy in the volume of generated rainfall. The comparison of the simulated streamflow by the hydrological modeling carried out with SWAT demonstrates that the corrected CHIRP grids with LS and PT provide better results than those derived from using the raw CHIRP rainfall dataset, achieving an improvement in PBIAS and NSE of 15% and 30%, respectively. This tool enables decision-makers to obtain CHIRP-corrected precipitation data in river basins where meteorological station coverage is limited, thus aiding in the creation of dependable hydrological models. Declarations CRediT author statement: Julio Pérez-Sánchez : Conceptualization, Methodology, Writing- Original draft preparation, Writing- Reviewing and Editing. Patricia Jimeno-Sáez : Data curation, Visualization, Writing- Reviewing. Adrián López-Ballesteros : Methodology, Investigation, Resources. José Ginés Giménez: Software. José M. Cecilia: Software, Validation. Javier Senent-Aparicio : Supervision, Validation, Writing- Reviewing and Editing. Software and data availability: The code used for the creation of the CHIRPWeb tool is hosted in the following public repository: https://bitbucket.org/Jgines/chirpweb/src/master/ Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Statement This research was funded by Spanish Ministry of Science and Innovation under grant PID2021-128126OA-I00. References Aadhar, S., & Mishra, V. 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Spatiotemporal variability of snow cover and snow water equivalent in the last three decades over Eurasia. Journal of Hydrology , 559 , 238–251. https://doi.org/10.1016/j.jhydrol.2018.02.031 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7137530","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501867717,"identity":"70057d92-c3e4-4697-bf6d-858015deef4e","order_by":0,"name":"Julio Pérez-Sánchez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACxgbmhgMQJg8QVzDIEKGFEVnLGTBJWBMDXAtjGxFamGckNh74wFAnb85+9uDDn/MO85jzL37A8OEPHjtmJDYcnMFw2HBnT16ygeS2wzyWM54ZMM5sw6/lMA/DAcYNN3jMJAyBWgxunGFg5m0gqKXOHqjF/EfiHKiWPwQcBtTCnAiyheEgkG1wvoeBmYENj5aeh0C/GBxO3nAmx1iy4Vg60C9sBgd78fjFsD358IcPFXW2G46fMfz4o8Zazpz/8MMHP/A4zBDsTwMkEQOJBIYDuDUwMMhjiBjw49UwCkbBKBgFIxAAAIWoV6FIQgc5AAAAAElFTkSuQmCC","orcid":"","institution":"Universidad de Las Palmas de Gran Canaria","correspondingAuthor":true,"prefix":"","firstName":"Julio","middleName":"","lastName":"Pérez-Sánchez","suffix":""},{"id":501867718,"identity":"62364a53-4f11-41be-b8bd-4e9376639f32","order_by":1,"name":"Patricia Jimeno-Sáez","email":"","orcid":"","institution":"Universidad Católica San Antonio de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Jimeno-Sáez","suffix":""},{"id":501867719,"identity":"3cc8f849-fe6c-41e9-8f15-9a82aa0cedf1","order_by":2,"name":"Adrián López-Ballesteros","email":"","orcid":"","institution":"Universidad Católica San Antonio de Murcia","correspondingAuthor":false,"prefix":"","firstName":"Adrián","middleName":"","lastName":"López-Ballesteros","suffix":""},{"id":501867720,"identity":"f47408aa-4710-42bf-9727-7923726189f7","order_by":3,"name":"José Ginés Giménez","email":"","orcid":"","institution":"Universidad Católica San Antonio de Murcia","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Ginés","lastName":"Giménez","suffix":""},{"id":501867721,"identity":"fd9eead2-9707-4184-ac7c-328dd0d88ef4","order_by":4,"name":"José M. 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3","display":"","copyAsset":false,"role":"figure","size":98637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDelimitation of the region (a) and CHIRP information related (b).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/14aa0eb2d94fc80bf44b781e.png"},{"id":89482111,"identity":"c8731f7e-8249-4d2d-bb2b-b5ae42a40e0f","added_by":"auto","created_at":"2025-08-20 12:07:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRainfall data observations uploading.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/b80d766eb462fc1c447f6e14.png"},{"id":89481806,"identity":"caad7799-52b6-40d9-a803-db7fcd0e07f2","added_by":"auto","created_at":"2025-08-20 11:59:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":210709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRainfall data observations uploading.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/cd7cc9284c967b8229599b65.png"},{"id":89483182,"identity":"1ff090f8-fe68-44dd-91fa-b78f06021fc6","added_by":"auto","created_at":"2025-08-20 12:15:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":776253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) and (b) Location (c) topography and hydrography, and (d) land use of the Oskotz river basin.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/05fe12afcaebfe2a421ce2e0.png"},{"id":89481812,"identity":"7799d279-19ad-4f3d-a0ab-99dba49b6535","added_by":"auto","created_at":"2025-08-20 11:59:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":112971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of observed hydrograph with simulated hydrographs \u003c/strong\u003e(the suffix of each CHIRP grid refers to whether the rainfall data used are uncorrected, original-ORG, or the method of bias correction, LS, PT or LOCI).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/1652f70bb389b362c025f010.png"},{"id":89482115,"identity":"5ac56697-7700-42e0-87b4-eaa8b844b30b","added_by":"auto","created_at":"2025-08-20 12:07:28","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":55949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of observed flow-duration curve with simulated ones \u003c/strong\u003e(the suffix of each CHIRP grid refers to whether the rainfall data used are uncorrected, original-ORG, or the method of bias correction, LS, PT or LOCI).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/4d561bad80c5452bee7382a7.png"},{"id":99787966,"identity":"28e58561-1fa0-456e-a0a2-8549b8ea98b0","added_by":"auto","created_at":"2026-01-08 12:42:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2695644,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7137530/v1/4b82eaca-01ed-4a26-8138-fa5e212092e1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CHIRPWeb, an online tool for providing a bias corrected CHIRP grid dataset using field measurements","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePrecipitation data are the key factors in hydrological modelling and all the studies therefrom, such as water resources management or flood and drought disaster risk strategies (Prakash, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rain ground stations have been traditionally considered as the main sources of information in rainfall-runoff models (Berndt et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rogelis \u0026amp; Werner, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, their uneven distribution and their numerous gaps in historical records in some areas (Gummadi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) leave serious doubts about their validity, reliability and accuracy (Kidd et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Indeed, these conditions are the greatest hindrances for water resources managers, farmers or hydroclimatic research (Nogueira, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, satellite rainfall datasets are becoming more frequent in hydrological assessments, largely because of the substantial improvement of remote-sensing techniques and automatic data-processing technology (Sharifi, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; West et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang \u0026amp; Ma, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe development of satellite-based meteorological products has made spectacular progress in recent decades. The Global Precipitation Climatology Project (GPCP) combines since 1980 rainfall gauge data from more than 6,000 stations with rainfall estimates from infrared and microwave imagery from geostationary satellites such as GOES, GMS and GOES, GMS and METEOSAT and NOAA polar satellites. GPCP rainfall data are available at daily temporal resolution and 1\u0026deg; spatial resolution since 1993. Likewise, NOAA's Climate Prediction Center (CPC) developed the Morphing technique (Xie et al., 2017; Joyce et al., 2007) to combine information from different satellite sensors, thus new data from microwave sensors such as SSM/I (DMSP-13, 14 and 15), AMSU-B (NOAA-15, 16, 17 and 18), AMSR-E (Aqua) or TMI (TRMM) have been successfully incorporated. In fact, satellite-based methods for estimating precipitation experienced significant enhancements following the deployment of the Tropical Rainfall Measuring Mission (TRMM) satellite in 1997. Initially, it was a space mission between NASA (USA) and the Japan Aerospace Exploration Agency (JAXA) to monitor and study tropical rainfall. Following the success of the TRMM, both organisations deployed the Global Precipitation Measurement (GPM) mission in 2014 and began publishing the Integrated Multi-satellite Retrievals for GPM (IMERG) new-generation global precipitation products in the same year. Moreover, the Centre for Hydrometeorology and Remote Sensing at the University of California at Irvine (CHRS-UCI) developed the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithm, which estimates rainfall from cloud texture information obtained from multiple geosynchronous (GOOS) satellites provided by the CPC-NOAA. These data cover 50\u0026deg; S to 50\u0026deg; N, with a spatial resolution of 0.25\u0026deg; and a temporal resolution of 6 h (Sorooshian et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). More recently, other products such as reanalysis are also serving as alternative databases of global precipitation data. These systems amalgamate existing observations with background model forecasts, employing physical principles to generate consistent gridded datasets (Gebregiorgis et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ERA5 (Hersbach et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and ERA-Interim (Dee et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), both generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), stand out for their reliability in numerous studies (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Steinkopf \u0026amp; Engelbrecht, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rakhmatova et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nogueira, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Albergel et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNevertheless, the lack of gridded rainfall products with both extended historical records and minimal latency poses challenges for scientists. In fact, many existing operational satellite precipitation products fail to meet the practical needs of certain users, as they do not offer sufficiently long periods of record while maintaining acceptable latency levels (Shen et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To address this shortcoming, researchers at the University of California-Santa Barbara and the US Geological Survey (USGS) have developed two new near-global satellite precipitation datasets (covering latitudes from 50\u0026deg;S to 50\u0026deg;N): the satellite-only Climate Hazards Group Infrared Precipitation (CHIRP) and the gauge-adjusted Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Among the satellite-derived datasets, these two products stand out for their unparalleled spatial and temporal coverage, minimal latency, and resolution (Funk et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These two high resolution datasets (0.05\u0026deg; \u0026times; 0.05\u0026deg;) include daily, pentadal, and monthly precipitation data spanning from 1981 to almost the present, ensuring a substantial data record of approximately 40 years. Both products were generated using spatially diverse regression models that relied on pentadal cold cloud duration (CCD) values and TRMM V7 training data. The CCD time series were obtained from the CPC and NOAA B1 datasets. The satellite-only products (CHIRP) become accessible almost instantly, released shortly after the conclusion of each pentad: on the 2nd, 7th, 12th, 17th, 22nd, and 27th. However, the complete CHIRPS product experiences a delay, becoming available only after the 15th of the subsequent month. CHIRPS was expected more dependable in representing the distribution of rainfall due to its integration of ground-based measurements (Baez-Villanueva et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Aadhar \u0026amp; Mishra, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This improvement might stem from the mean bias adjustment, which mitigates the primary disparities between actual measurements and satellite-derived products (Dinku et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, CHIRP was found to better perform specifically after 1992 and at lower elevation regions in Nepal. (Shrestha et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, (Khandu et al., 2016) noted that CHIRP showed comparatively stronger performance in flat regions over the Eastern Himalayan region characterized by elevation ranges between 150 and 1,500 meters above sea level (m.a.s.l.). Likewise, (Gummadi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found notably high probability of detection (POD) scores in the context of Vietnam with this product and (Beyene et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) showed that the bias-corrected CHIRP outperformed the estimates of CHIRPS in an Ethiopian watershed. Therefore, it was decided that the CHIRP product would be the most suitable to use as an input to the watershed hydrological modelling.\u003c/p\u003e\u003cp\u003eDespite the considerable progress made, satellite-derived rainfall estimates are susceptible to numerous inaccuracies stemming from instrumental issues, the inherent nature of measurement systems, theoretical simplifications, or the complex non-linear relationship between observed variables and rainfall (Fu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Saha et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is imperative to eliminate systematic errors (bias) from the products prior to their utilization in hydrological and water resources applications. There are various methods to correct these biases of rainfall and temperatures, which can generally be categorized into three groups based on the level of correction they apply (Ghimire et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While delta change (Middelkoop et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; R\u0026auml;ty et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shabalova et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and linear scaling (Lenderink et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) adjust the mean of rainfall and temperature to match observed values, the power transformation method (Leander et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Leander \u0026amp; Buishand, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) focuses on correcting the variance. Nevertheless, there is a lack of conclusive evidence in the scientific literature to inform the optimal choice of a bias correction method (Goshime et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study carried out by (Soo et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in Malaysia highlighted the power transformation (PT) method compared to the linear scaling (LS) and the local intensity scaling (LOCI), since it demonstrated significant enhancements across various statistical metrics. However, LS method proved, in general, to be the most effective scheme compared to LOCI or quantile mapping (QM) in India (Jaiswal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By contrast, the latter methods (LOCI and QM) exhibited superior performance compared to the LS and PT methods in the context of the Yarlung Tsangpo\u0026ndash;Brahmaputra River (Luo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Likewise, according to (Rahimi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), LOCI demonstrated superior effectiveness in stations experiencing wet summers, whereas PT exhibited strong performance in stations with minimal or no summer precipitation. Moreover, (Fang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found out that PT and QM showed very good performance regarding precipitation data in China, while for temperature all correction methods used (LS, QM and variance scaling) exhibited equally effective statistical results.\u003c/p\u003e\u003cp\u003eThe work described in this paper consists of an online tool that provides a bias corrected CHIRP grid dataset using field measurements. The outputs of the open-source web are configured to be used directly in SWAT (Soil and Water Assessment Tool, (Arnold et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)), the world's most widely used hydrological model (Mannschatz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The use of this software allows decision-makers and stakeholders to obtain a grid of precipitation data based on CHIRP in watersheds with scarce information or gaps in the existing meteorological stations which prevent the development of a reliable hydrological model. This indicates that the application offered and demonstrated in this study are capable of being duplicated, tailored, and utilized in any watershed in the range 50\u0026deg;S to 50\u0026deg;N latitudes.\u003c/p\u003e\u003cp\u003eThe paper is organized as follows: Section 2 describes the software structure, and programming languages used in the development of the tool, as well as the web interface and the steps to follow to obtain the selected grid and the format of the output files. Section 3 presents, analyses and discusses the performance of the SWAT model in a Spanish watershed using CHIRP dataset compared to CHIRP bias-corrected meteorological information, both provided by the online tool presented and, finally, Section 4 provides the main conclusions of the study carried out.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Model structure of CHIRPweb\u003c/h2\u003e\n \u003cp\u003eThe web application, developed in Python 3.8, employs Flask as its web service framework. It is designed to interact with NetCDF files, which are obtained from the CHIRP website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.chc.ucsb.edu/products/CHIRP/\u003c/span\u003e\u003c/span\u003e). These files serve as a pseudo-database from which the application retrieves information to adjust time series data accordingly. When a user inputs a specific region defined by its latitude and longitude, along with a date range, the application meticulously searches through the NetCDF files for the given coordinates, year by year, loading all pertinent CHIRP data points for that region for each year. Furthermore, when users upload their measurement files and request bias-correction, they must select bias-correction technique from the provided. This selection dictates the correction applied to the user\u0026apos;s time series, ultimately allowing for the download of these corrected files. This process ensures that the application not only provides accurate data adjustment based on user specifications but also enhances the usability and effectiveness of climate data analysis by leveraging comprehensive CHIRP data.\u003c/p\u003e\n \u003cp\u003eThe use of the tool provided is very easy and intuitive (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It mainly consists of five main blocks (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e): definition of the region and period, uploading rain ground stations data, choice of bias-correction technique, results of bias correction and interpolation, and downloading the rainfall grid generated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Grid selection\u003c/h2\u003e\n \u003cp\u003eFirstly, once the perimeter of the basin to be analysed is known, the coordinates (latitude and longitude) delimiting the region, as well as the temporal period of rainfall data to be obtained, must be entered in Load CHIRP region box (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). After selecting the Load Region option, all the information related to the data contained in the CHIRP dataset matching the previously defined search will be displayed in CHIRP point loaded box (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The information displayed in the summary box will contain the total number of CHIRP points including the selected region, as well as the area delimitation and the selected period. The same box allows the download of the daily precipitation data contained in the CHIRP points through the Download CHIRP option.\u003c/p\u003e\n \u003cp\u003eThe download format will be a zip file containing each of these points in comma-separated text files (csv) with the daily precipitation in the selected period. In addition, another reference file will also be obtained with the geolocation of all these points, as well as the assigned name. The files are ready to be loaded directly into SWAT without any additional transformation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Input of observed data\u003c/h2\u003e\n \u003cp\u003eThe following step will be to upload the rainfall gauge data, just clicking on the button \u0026ldquo;Upload files\u0026rdquo; after having selected the file in the computer with the button \u0026ldquo;Choose file\u0026rdquo;. This file will be in zip format and will contain the SWAT data entry files, all in csv format: a file defining the name given to the rainfall gauge station, as well as its location (longitude, latitude and elevation) and as many files as there are rainfall gauge stations. The first line of the latter files shall always indicate the first day of the precipitation series it contains, followed by the daily precipitation, according to the SWAT format. After uploading the zip file, a list will appear in the corresponding box (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) showing the number of stations included in the file, as well as their names, location (latitude and longitude) and the range of dates with recorded rainfall data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Selection of the bias correction method\u003c/h2\u003e\n \u003cp\u003eOnce uploaded a zip archive with the ground-based precipitation data, a new drop-down menu button will appear to choose the bias correction technique: linear scaling (LS) (Lenderink et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), local intensity scaling (LOCI) (Schmidli et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) and power transformation (PT) (Teutschbein \u0026amp; Seibert, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The three methods (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) correct the bias multiplying the raw daily precipitation provided by the CHIRP grid by a parameter which depends on bias in mean precipitation and its variance. Thus, the objective of the LS method is to precisely align the monthly mean of the simulated data with the observed monthly mean. This method utilizes monthly correction values derived from the mean between observed and CHIRP nearest grid dataset point. All daily precipitation values in the CHIRP grid dataset will be multiplied by the correction factor. Since even small amounts of precipitation can introduce biases, the LOCI method seeks to mitigate these biases by setting a minimum threshold above which the relevant statistics will be carried out. This bias-technique consists of two steps: first, a wet day threshold for the m-th month (P\u003csub\u003ethres,m\u003c/sub\u003e) is set using the CHIRP precipitation series, so that exceeding this threshold coincides with the observed wet day frequency; second, a scaling factor (s\u003csub\u003em\u003c/sub\u003e) is determined and applied to ensure that the corrected mean precipitation is consistent with the observed mean precipitation. As in the previous two methods only the mean precipitation is taken into account, PT also considers the difference in variance between the observed data and the CHIRP data. To avoid biases in drizzled days, the LOCI-corrected precipitations (P\u003csub\u003eCHIRP\u003c/sub\u003e) will be used instead of the ones provided by the CHIRP dataset. On the other hand, not only will we have a multiplicative correction coefficient, but we also consider a correction exponent for each m-month (b\u003csub\u003em\u003c/sub\u003e) that will be obtained from the minimisation of a function (f(b\u003csub\u003em\u003c/sub\u003e)) that depends on the standard deviation of the observed and LOCI-corrected precipitation and the standard deviation of the latter.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBias correction techniques.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEquations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRemarks\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLinear scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}}_{\\varvec{c}\\varvec{o}\\varvec{r},\\varvec{m},\\varvec{d}}={\\varvec{P}}_{\\varvec{C}\\varvec{H}\\varvec{I}\\varvec{R}\\varvec{P},\\varvec{m},\\varvec{d}}\u0026middot;\\frac{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{o}\\varvec{b}\\varvec{s},\\varvec{m}}\\right)}{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{C}\\varvec{H}\\varvec{I}\\varvec{R}\\varvec{P},\\varvec{m}}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eP\u003csub\u003ecorr\u003c/sub\u003e: corrected precipitation\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003eCHIRP\u003c/sub\u003e: CHIRP precipitation\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003eobs\u003c/sub\u003e: observed precipitation in ground station\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003ethres\u003c/sub\u003e: threshold precipitation\u003c/p\u003e\n \u003cp\u003eP\u003csub\u003eLOCI\u003c/sub\u003e: corrected precipitation with LOCI\u003c/p\u003e\n \u003cp\u003em: monthly\u003c/p\u003e\n \u003cp\u003ed: daily\u003c/p\u003e\n \u003cp\u003e\u0026micro;: mean operator\u003c/p\u003e\n \u003cp\u003e\u0026sigma;: standard deviation operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal intensity scaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}}_{\\varvec{c}\\varvec{o}\\varvec{r},\\varvec{m},\\varvec{d}}=\\:\\:\\:\\:0\\:\\:\\:\\:\\:\\:\\:\\varvec{i}\\varvec{f}\\:\\:\\:\\:{\\varvec{P}}_{\\varvec{r}\\varvec{a}\\varvec{w},\\varvec{m},\\varvec{d}}\u0026lt;{\\varvec{P}}_{\\varvec{t}\\varvec{h}\\varvec{r}\\varvec{e}\\varvec{s},\\varvec{m}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:{\\:\\:\\:\\varvec{P}}_{\\varvec{C}\\varvec{H}\\varvec{I}\\varvec{R}\\varvec{P},\\varvec{m},\\varvec{d}}\u0026middot;\\:{\\varvec{S}}_{\\varvec{m}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\varvec{o}\\varvec{t}\\varvec{h}\\varvec{e}\\varvec{r}\\varvec{w}\\varvec{i}\\varvec{s}\\varvec{e}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{S}}_{\\varvec{m}}=\\:\\frac{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{o}\\varvec{b}\\varvec{s},\\varvec{m},\\varvec{d}}|{\\varvec{P}}_{\\varvec{o}\\varvec{b}\\varvec{s},\\varvec{m},\\varvec{d}}\u0026gt;0\\right)}{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{C}\\varvec{H}\\varvec{I}\\varvec{R}\\varvec{P},\\varvec{m},\\varvec{d}}|{\\varvec{P}}_{\\varvec{C}\\varvec{H}\\varvec{I}\\varvec{R}\\varvec{P},\\varvec{m},\\varvec{d}}\u0026gt;{\\varvec{P}}_{\\varvec{t}\\varvec{h}\\varvec{r}\\varvec{e}\\varvec{s},\\varvec{m}}\\right)}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePower transformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}}_{\\varvec{c}\\varvec{o}\\varvec{r},\\varvec{m},\\varvec{d}}={\\varvec{s}}_{\\varvec{m}}\u0026middot;{\\varvec{P}}_{\\varvec{L}\\varvec{O}\\varvec{C}\\varvec{I},\\varvec{m},\\varvec{d}}^{{\\varvec{b}}_{\\varvec{m}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{s}}_{\\varvec{m}}=\\:\\frac{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{o}\\varvec{b}\\varvec{s},\\varvec{m}}\\right)}{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{L}\\varvec{O}\\varvec{C}\\varvec{I},\\varvec{m}}^{{\\varvec{b}}_{\\varvec{m}}}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{f}(\\varvec{b}}_{\\varvec{m}})=\\:\\frac{\\varvec{\\sigma\\:}\\left({\\varvec{P}}_{\\varvec{o}\\varvec{b}\\varvec{s},\\varvec{m}}\\right)}{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{L}\\varvec{O}\\varvec{C}\\varvec{I},\\varvec{m}}^{{\\varvec{b}}_{\\varvec{m}}}\\right)}-\\frac{\\varvec{\\sigma\\:}\\left({\\varvec{P}}_{\\varvec{L}\\varvec{O}\\varvec{C}\\varvec{I},\\varvec{m}}^{{\\varvec{b}}_{\\varvec{m}}}\\right)}{\\varvec{\\mu\\:}\\left({\\varvec{P}}_{\\varvec{L}\\varvec{O}\\varvec{C}\\varvec{I},\\varvec{m}}^{{\\varvec{b}}_{\\varvec{m}}}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Corrected grid\u003c/h2\u003e\n \u003cp\u003eHaving selected the bias technique, a correction of the CHIRP grid dataset rainfall will be made according to the rain gauge closest to the grid point considered if the weather station is located inside the grid cell with which it is compared. Otherwise, all m-monthly station parameters will be used (depending on the bias chosen bias technique), and the daily rainfall will be corrected using the inverse distance weighted (IDW) method. The box at the bottom of the interface (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) will show the information related to the CHIRP grid points that are part of the selected region, as well as the rain gauge with which it has been corrected (or all of them if there was not one close) and the method used, which will vary between the one chosen in the previous step (LS, LOCI o PT) or IDW if all the stations have been selected.\u003c/p\u003e\n \u003cp\u003eFinally, the corrected chirp files can be downloaded in a zip file clicking on the Download button in the top left corner of the last menu (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This file contains the corrected CHIRP grid in the format required for direct use in the SWAT hydrological model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Goodness-of-fit indicators\u003c/h2\u003e\n \u003cp\u003eThe rainfall data obtained through the application will be compared with the data downloaded from the CHIRP grid without any correction. For this purpose, the PBIAS (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) of both data sources will be assessed with respect to the values of the observed rainfall series of the weather station closest to the grid cell considered. In addition, in order to evaluate the accuracy of the corrected grid in the modelling of the hydrological response in a basin, the SWAT model will be used, taking as input both the non-corrected CHIRP rainfall grid and the corrected ones with the methods shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The performance metrics to be used for comparison with the observed streamflow will be the usual ones for hydrological modelling: Nash-Sutcliffe efficiency (NSE), PBIAS and coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e). Their expressions and optimal values are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerformance metrics\u003c/strong\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{obs,i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{sim,i}\\)\u003c/span\u003e\u003c/span\u003e are the observed and simulated values, respectively, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{{X}_{obs}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\overline{{X}_{sim}}\\:\\)\u003c/span\u003e\u003c/span\u003e are the average observed and simulated values).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEquations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOptimal value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNash-Sutcliffe efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NSE=\\frac{{\\sum\\:}_{i=1}^{n}{\\left({X}_{obs,i}-{X}_{sim,i}\\right)}^{2}}{{\\sum\\:}_{i=1}^{n}{(X}_{obs,i}-{\\overline{{X}_{obs}})}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u0026infin; \u0026minus;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePBIAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PBIAS=\\frac{{\\sum\\:}_{i=1}^{n}\\left({X}_{obs,i}-{X}_{sim,i}\\right)}{{\\sum\\:}_{i=1}^{n}{X}_{obs,i}}*100\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;100% - +100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient of determination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}=\\frac{{\\sum\\:}_{i=1}^{n}\\left({X}_{obs,i}-\\overline{{X}_{obs}}\\right)\u0026middot;({X}_{sim,i}-\\overline{{X}_{sim}}}{\\sqrt{{\\sum\\:}_{i=1}^{n}{({X}_{obs,i}-\\overline{{X}_{obs}})}^{2}}\\sqrt{{\\sum\\:}_{i=1}^{n}{({X}_{sim,i}-\\overline{{X}_{sim}})}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eInitially, a statistical analysis was made between corrected and uncorrected data to assess the accuracy of both against the recorded data. Furthermore, this section presents, analyses and discusses the performance of the SWAT model in the Spanish Oskotz river basin using CHIRP dataset compared to CHIRP bias-corrected rainfall data, both provided by the online tool presented. Furthermore,\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Study area.\u003c/h2\u003e\u003cp\u003eThe Oskotz river basin has been selected as an example of the application of the tool presented. It is located in the region of Navarra, northern Spain, between the coordinates 1\u0026deg;47\u0026prime;-1\u0026deg;44\u0026prime; West longitude and 42\u0026deg;55\u0026prime;-42\u0026deg;58\u0026prime; North latitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This experimental basin covers an area of 16.74 km\u003csup\u003e2\u003c/sup\u003e. The altitude of the basin varies between 531 and 918 m above sea level. According to Casali et al. (2010) the average annual rainfall and temperature are 1200 mm and 12\u0026deg;C, respectively, and therefore its climate can be considered sub-Atlantic (Jimeno-S\u0026aacute;ez et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite a wet season during autumn and winter, summer rainfall represents around 11% of the annual total. The predominant soil type varies according to the landscape: the accumulation slopes are mostly composed of Ustochrepts Typic, while the eroded slopes exhibit Ustochrepts Lythic and Typic soils, and the valley plain is characterised by Ustochrepts Fluventic. The thickness of soils is around 1 m, except on eroded slopes, where they tend to be shallower.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs can be seen in the Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed. forests are the predominant land use in the basin, covering nearly 70% of its area. These forests include native forests and reforestation areas and are home to a wide variety of plant species. Furthermore, these forests play a crucial role in soil retention, water regulation and the provision of wildlife habitat in the basin. A significant part of the land is also used for animal grazing, either for livestock rearing or traditional livestock activity. These rangelands can be either natural, which have evolved spontaneously, or areas of cultivated grassland, maintained for livestock grazing. Agricultural land occupies a smaller proportion compared to forests and pasture. Crops vary according to the season and the preferences of local farmers, and can include cereals, vegetables, fruit trees, among others.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Databases.\u003c/h2\u003e\u003cp\u003eThe basin has an automatic weather station and a hydrological station that records climatic variables and water, respectively. Data collected are available on \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cuencasagrarias.navarra.es/\u003c/span\u003e\u003cspan address=\"http://cuencasagrarias.navarra.es/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and include information on rainfall, maximum and minimum temperatures, as well as observed data on flow in the 2002\u0026ndash;2020 period. The rainfall data recorded at the meteorological stations will be used for bias correction of the CHIRP rainfall grid in the study catchment. The digital elevation model (DEM) with a resolution of 25 \u0026times; 25 metres was obtained from the Instituto Geogr\u0026aacute;fico Nacional (IGN) of Spain. The land use mapping at 1:100,000 scale was extracted from Corine Land Cover (2012), while the soil map, with a resolution of 1 km, was obtained from the Harmonised World Soil Database (HWSD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Bias correction of CHIRP grid.\u003c/h2\u003e\u003cp\u003eFirst of all, an analysis of the different precipitation products obtained was carried out. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the main statistics of the series themselves and the comparison of the observed rainfall (Station) with respect to the CHIRP products that estimate it. While the uncorrected CHIRP (CHIRP_ORG) estimated the amount of rainfall to be 20% higher than actual rainfall, LS and PT corrections reduced this amount to less than 9%, demonstrating greater accuracy in the total amount of rainfall generated. The average precipitation values over the study period showed similar conclusions: an overestimation for all products but LOCI, which reduced the observed value by one third. In the rest of the cases, the percentages remained practically the same as those obtained with the sum of total precipitation. As far as extreme values are concerned, the number of days that CHIRP and its corrections consider that no precipitation occurs is higher than those recorded at the ground station. For the 75th percentile the rain gauge record gives values of 3 mm, while the CHIRP products (original and corrected) do not give positive values until the 79th percentile. However, for values above the 87th percentile the estimated values exceed the observed value, so that for the 95th percentile values above the observed value are again obtained which are usually lower in the LS and PT corrections (18.5%) with respect to the CHIRP without corrections (30%). As expected, the PBIAS values are negative in all series except LOCI, where a value close to +\u0026thinsp;60% is obtained, indicating a high underestimation of rainfall. The original CHIRP grid exceeds \u0026minus;\u0026thinsp;20% and the corrections made with LS and PT do not reach \u0026minus;\u0026thinsp;9%. As expected, the PBIAS values are negative in all series except LOCI, where a value close to +\u0026thinsp;60% is obtained, indicating a high underestimation of rainfall. The original CHIRP grid exceeds \u0026minus;\u0026thinsp;20% and the corrections made with LS and PT do not reach \u0026minus;\u0026thinsp;9%, proving a satisfactory performance in both cases to the observed records.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eComparison of rainfall statistics 2002\u0026ndash;2020 period in mm\u003c/b\u003e (the suffix of each CHIRP grid refers to whether the rainfall data used are uncorrected, original-ORG, or the method of bias correction, LS, PT or LOCI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCHIRP_ORG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCHIRP_LS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCHIRP_PT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCHIRP_LOCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSUM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22834.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27452.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24798.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24801.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9493.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAVERAGE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMAXIMUM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e143.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e55.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePERCENTILE-75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePERCENTILE-95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePBIAS (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-20.22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-8.60%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-8.62%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58.43%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Hydrological model.\u003c/h2\u003e\u003cp\u003eThe rainfall series obtained with the CHIRP grid, uncorrected and corrected with the three methods in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, have been used to develop four hydrological models with SWAT, obtaining the statistics shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e according to the comparison with the observed streamflow in the Oskotz river basin in the period 2002\u0026ndash;2020.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSWAT model performance statistics\u003c/b\u003e (the suffix of each CHIRP grid refers to whether the rainfall data used are uncorrected, original-ORG, or the method of bias correction, LS, PT or LOCI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCHIRP_ORG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCHIRP_LS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCHIRP_PT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCHIRP_LOCI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePBIAS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-44.82%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-30.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-31.09%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn general, better results are achieved with the corrected grids than with the use of the CHIRP raw grid, with the exception of the LOCI method, where the results do not improve and even worsen significantly, therefore, this method has not been considered in the rest of the study. This difficulty of the LS method has already been highlighted in other basins (Jaiswal et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As for the other two correction methods (LS and PT) the results were very similar and quite satisfactory in both cases. In fact, LS and PT increase R\u003csup\u003e2\u003c/sup\u003e with regard to the raw grid up to 0.11 and 0.12, respectively. Moreover, NSE improves around 30% and PBIAS decreases around 15% (from \u0026minus;\u0026thinsp;45% to -30%). However, according to (Moriasi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the values provided by PBIAS are unsatisfactory as they are greater (in absolute value) than 20%. Moreover, they are always negative, so it could be considered that the simulation overestimates the observed values, i.e. the model's predictions tend to be higher than the real observed values. Although this will be analysed thoroughly below with the flow-duration curves, it should be remembered that the parameters of the hydrological model have not been calibrated given the focus of this study.\u003c/p\u003e\u003cp\u003eThese good results are in line with previous studies (Goshime et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) showing improvements in hydrological modelling with CHIRP rainfall data corrected on the basis of rainfall series from existing stations. All these indicate the significant improvement of estimates with LS and PT bias-correction.\u003c/p\u003e\u003cp\u003eReferring to Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, none of the models was able to simulate the highest peaks of the observed streamflow, although the corrected-bias CHIRP (LS and PT) outperformed both in high and low flows. Overall, CHIRP_ORG overestimates base flow and this fact is corroborated in the flow-duration curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) in which CHIRP_ORG dataset is above observed streamflow for exceedance probability higher than 0.2. The gap is reduced with bias-corrected CHIRP (LS and PT) grid in low flow and the differences with observed data in exceedance probability higher than 0.7 are virtually non-existent. Furthermore, PT provides a better performance for high Flow.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe underestimation of peak flows by SWAT has already been highlighted in several previous studies (Senent-Aparicio et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bieger et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, in line with the findings of (Castellanos-Osorio et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and (Franco et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), there was a slight overestimation of baseflow, and the streamflow recession appeared to be slower than observed. However, it is again recalled that since the aim of this study is to assess the improvement of the presented tool over the use of the original CHIRP grid, no calibration of the model has been carried out. Therefore, it is the comparison between models that we have used to evaluate the greater accuracy of the rainfall data.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe use of satellite-based rainfall products can enable the development of accurate and reliable hydrological models in basins with scarce information or significant gaps in the recorded information. However, its use is subject to the previous bias-correction derived from field measurements. This paper describes the setup and use of an online tool to download the CHIRP rainfall grid in a region and the correction of its bias using various methods (LS, LOCI, and PT). Additionally, the provided dataset is delivered for direct use in SWAT. This tool has been applied to a Spanish watershed, confirming that the precipitation series obtained with raw CHIRP grid estimate the total rainfall to be over 20% higher than the amount observed in the existing meteorological stations, whereas corrections made with LS and PT reduced this amount to less than 9%, demonstrating greater accuracy in the volume of generated rainfall. The comparison of the simulated streamflow by the hydrological modeling carried out with SWAT demonstrates that the corrected CHIRP grids with LS and PT provide better results than those derived from using the raw CHIRP rainfall dataset, achieving an improvement in PBIAS and NSE of 15% and 30%, respectively. This tool enables decision-makers to obtain CHIRP-corrected precipitation data in river basins where meteorological station coverage is limited, thus aiding in the creation of dependable hydrological models.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT author statement: Julio P\u0026eacute;rez-S\u0026aacute;nchez\u003c/strong\u003e: Conceptualization, Methodology, Writing- Original draft preparation, Writing- Reviewing and Editing. \u003cstrong\u003ePatricia Jimeno-S\u0026aacute;ez\u003c/strong\u003e: Data curation, Visualization, Writing- Reviewing. \u003cstrong\u003eAdri\u0026aacute;n L\u0026oacute;pez-Ballesteros\u003c/strong\u003e: Methodology, Investigation, Resources. \u003cstrong\u003eJos\u0026eacute; Gin\u0026eacute;s Gim\u0026eacute;nez:\u003c/strong\u003e Software. \u0026nbsp;\u003cstrong\u003eJos\u0026eacute; M. Cecilia:\u0026nbsp;\u003c/strong\u003eSoftware, Validation. \u003cstrong\u003eJavier Senent-Aparicio\u003c/strong\u003e: Supervision, Validation, Writing- Reviewing and Editing. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware and data availability:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used for the creation of the CHIRPWeb tool is hosted in the following public repository: https://bitbucket.org/Jgines/chirpweb/src/master/ \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Spanish Ministry of Science and Innovation under grant PID2021-128126OA-I00.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAadhar, S., \u0026amp; Mishra, V. (2017). High-resolution near real-time drought monitoring in South Asia. \u003cem\u003eScientific Data\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 170145. https://doi.org/10.1038/sdata.2017.145\u003c/li\u003e\n \u003cli\u003eAlbergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., \u0026amp; Balsamo, G. (2018). ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(6), 3515\u0026ndash;3532. https://doi.org/10.5194/hess-22-3515-2018\u003c/li\u003e\n \u003cli\u003eArnold, J. G., Srinivasan, R., Muttiah, R. S., \u0026amp; Williams, J. R. (1998). 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Spatiotemporal variability of snow cover and snow water equivalent in the last three decades over Eurasia. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, \u003cem\u003e559\u003c/em\u003e, 238\u0026ndash;251. https://doi.org/10.1016/j.jhydrol.2018.02.031\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CHIRP, precipitation data, hydrological modelling, SWAT, downscaling techniques","lastPublishedDoi":"10.21203/rs.3.rs-7137530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7137530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrecipitation data play a crucial role in hydrological modeling. Although rain ground stations data have traditionally been used, their uneven distribution and numerous gaps raise some doubts about their reliability. As a result, satellite rainfall data sets are increasingly used in hydrological assessments. However, these estimates are prone to inaccuracies due to instrumental problems or theoretical simplifications, and it is essential to eliminate systematic errors before using them in hydrological applications. This paper presents an online tool to select a CHIRP grid in a region and correct its bias derived from field measurements. Furthermore, the tool is designed to generate SWAT-compatible rainfall data input. As an example of the application, the performance of the SWAT model in the Spanish Oskotz river basin has been evaluated. In general, better results are achieved with the corrected grids, obtaining improvements of around 30% in Nash-Sutcliffe efficiency and decreasing PBIAS by around 15%.\u003c/p\u003e","manuscriptTitle":"CHIRPWeb, an online tool for providing a bias corrected CHIRP grid dataset using field measurements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 11:59:23","doi":"10.21203/rs.3.rs-7137530/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7da73619-fb24-41f1-b000-e9e1bcfca342","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-28T19:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 11:59:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7137530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7137530","identity":"rs-7137530","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}