Number Of Rain Gauge Stations Research Articles

Downscaling TRMM Monthly Precipitation in Cloudy and Rainy Regions and Analyzing Spatiotemporal Variations: A Case Study in the Dongting Lake Basin

High-resolution and accurate precipitation data are essential for hydrological, meteorological, and ecological research at the watershed scale. However, in regions with complex terrain and significant rainfall variability, the limited number of rain gauge stations (RGS) is insufficient, and the spatial resolution of existing satellite precipitation data is too low to capture detailed precipitation patterns at the watershed scale. To address this issue, the downscaling of satellite precipitation products has become an effective method to obtain high-resolution precipitation data. This study proposes a monthly downscaling method based on a random forest model, aiming to improve the resolution of precipitation data in cloudy and rainy regions at mid-to-low latitudes. We combined the Google Earth Engine (GEE) platform with a local Python environment, introducing cloud cover characteristics into traditional downscaling variables (latitude, longitude, topography, and vegetation index). The TRMM data were downscaled from 25 km to 1 km, generating high-resolution monthly precipitation data for the Dongting Lake Basin from 2001 to 2019. Furthermore, we analyzed the spatiotemporal variation characteristics of precipitation in the study area. The results show the following: (1) In cloudy and rainy regions, our method improves resolution and detail while maintaining the accuracy of precipitation data; (2) The response of monthly precipitation to environmental variables varies, with cloud cover characteristics contributing more to the downscaling model than vegetation characteristics, helping to overcome the lag effect of vegetation characteristics; and (3) Over the past 20 years, there have been significant seasonal trends in precipitation changes in the study area, with a decreasing trend in winter and spring (January–May) and an increasing trend in summer and autumn (June–December). These results indicate that the proposed method is suitable for downscaling monthly precipitation data in cloudy and rainy regions of the Dongting Lake Basin.

Modeling run-off flow hydrographs using remote sensing data: an application to the Bashar basin, Iran

Abstract Precipitation is hard to access in countries like Iran, due to inadequate number of rain gauge stations. Remote sensing provides an alternative source of rainfall estimation. In this study, the effectiveness of the HEC-HMS model was evaluated using GPM (Global Precipitation Measurement Mission) satellite and rain gauge station data. The model was calibrated and validated using 5 flood events' data of a hydrometric station at the outlet of Bashar basin. Important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model, using various statistical indices. The accuracy of QP and V estimations using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was about 1% (9.9% and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1% and 10.2% for GPM and rain gauge data, respectively). Such results imply the sole utilization of satellite precipitation data is reliable for modeling hydrological key parameters, which can be helpful in areas with limited ground station coverage.

Evaluation of the RF-MEP Method for Merging Multiple Gridded Precipitation Products in the Chongqing City, China

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products.

Optimization of rain gauge network using multi-criteria decision analysis and entropy approaches: case of Tekeze River basin, northwestern Ethiopia

Rainfall is an important hydro-meteorological variable that has various applications in water resource planning and development. Adequate rain gauge network gives immediate and precise rainfall data that are crucial for effective and economical water resource management. This study describes a multi-criteria decision analysis (MCDA) in combination with kriging and entropy methods to optimize rain gauge networks in the Tekeze River basin, northwestern Ethiopia. The Tekeze River basin is a trans-boundary river basin in the northwestern part of Ethiopia having a drainage area 86 510 km2. The MCDA was used for selecting suitable locations for additional rain gauges in the basin. The kriging and entropy methods were used to determine the optimal number of rain gauge stations in the basin. Initial analysis showed that the existing 63 rain gauge stations in the basin are not adequate and result in about 12% rainfall estimation error. Optimization of rain gauge network in the Tekeze River basin resulted in 216 additional gauging stations. The total optimal number of rain gauges for the Tekeze River would thus be 279. The results of the study revealed that the combined use of the MCDA, kriging, and entropy methods is useful to optimize the spatial distribution and number of rain gauge stations in a basin.

Analysis of monthly and annual rainfall variability using linear models in the state of Mato Grosso do Sul, Midwest of Brazil

AbstractThis study aims at fitting polynomial models based on latitude, longitude and altitude coordinates to estimate the monthly and annual mean rainfall in Mato Grosso do Sul state, Midwest region of Brazil. Furthermore, its target is to verify whether hydrologically similar regions provide statistical improvement in the regression fit for rainfall estimation. To create the monthly and annual rainfall for 32 rain gauge stations, there were used at least 15 years long data records for analysis with a percentage of gaps at most 10%. Generally, the monthly and annual rainfall models present suitable statistical validation coefficients. The number of predictor variables enhances the performance of the regression method when estimating monthly and annual rainfall. Fitting regressions in hydrologically similar groups through cluster analysis tends to increase regression performance; however, the limited number of rain gauge stations in Brazil makes this technique difficult to apply because the number of parameters of the regression models may be greater than the number of rain gauge stations in the cluster. The first degree polynomial regression proved to be the most adequate to represent the mean monthly and annual rainfall because of the equivalence between observed and predicted values of rainfall and because of the statistical analysis. Fitting polynomial models presents suitable method for practical applications, forming an important tool for environmental management in the Mato Grasso do Sul State.

Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward.

Resolving regional frequency analysis of precipitation at large and complex scales using a bottom-up approach: The Latin America and the Caribbean Drought Atlas

Summary Hydrologic frequency analysis is a statistical technique for the assessment of natural hazards, particularly the so-called water hazards caused exclusively by extreme hydrologic events. In particular, L-moments based regional frequency analysis (RFA-LM) has being adopted as the standard method for hydrologic frequency analysis in many parts of the world and for many other applications relating to hydrological extremes. However, despite the widespread use of RFA-LM, its application at large network and high and complex spatial scale conditions (LNHCSSC) has been poorly studied. The lack of studies about RFA-LM under such conditions and its use preferentially at subnational scales and areas with low number of rain gauge stations, or based on grid data, may be explained by the fact that the most difficult, less robust, and most subjective stage of RFA-LM is the delineation of homogeneous regions. This work proposes an integral procedure for the application of RFA-LM under LNHCSSC. The proposed method is applied to the study of drought event frequency in three case studies from Latin America, and incorporates innovating aspects compared to the state-of-the-art RFA-LM. These aspects are specifically (a) the decoupling of the cause of homogeneity from the regionalization stage; (b) the proposal of regionalization efficiency metrics; (c) the development of a regionalization algorithm; and (d) the development of a frequency estimation and mapping method for ungauged sites.

Determining the Adequacy of CFSR Data for Rainfall-Runoff Modeling Using SWAT

Stream discharge data are the essential information required to plan and design any watershed related project. In most of the cases, these data may not be available/ collected as there is no prior information regarding the commencement of a new project at the particular site. For a concerned watershed, number of stream gauge stations may be limited. But, due to the availability of large number of rain gauge stations, it is better to have rainfall – runoff model. However, land – based rain gauge stations do not always adequately represent the weather status of a watershed because they are point measurements, can be far from the characteristics of watershed of interest and can have gaps in their data series. Hence in the present study, the adequacy of CFSR (Climate Forecast System Reanalysis) data is determined to solve the problems of data deficiency in watersheds. Karuvannur watershed in Thrissur district is taken as the study area and is modeled using SWAT (Soil And Water Assessment Tool). Initially, the model was run for the available number of rain gauge stations. Then the model was executed by using CFSR data to find the accuracy of CFSR data in rainfall runoff modeling.

Evaluation of Rain Gauge Network Using Entropy Theory: Comparison of Mixed and Continuous Distribution Function Applications

In this study we compared applications of mixed and continuous distribution functions to the theory of entropy for the evaluation of rain gauge networks. The use of a mixed distribution function to evaluate rain gauge networks has an important advantage of considering rainfall intermittency in both time and space. Parameters of both mixed and continuous distribution functions were estimated using the daily rainfall data collected in the Choongju Dam Basin, Korea. The optimal number of rain gauge stations is, then, decided by using the entropy theory. The optimal number of rain gauge stations estimated by applying the mixed distribution function was found to be much smaller, but still reasonable, than that estimated by applying the continuous distribution function; mostly due to the small wet probability and the high coincidence of daily rainfall between rain gauge stations.

Rainfall network design using kriging and entropy

AbstractThe spatial distribution of rainfall is related to meteorological and topographical factors. An understanding of the weather and topography is required to select the locations of the rain gauge stations in the catchment to obtain the optimum information. In theory, a well‐designed rainfall network can accurately represent and provide the needed information of rainfall in the catchment. However, the available rainfall data are rarely adequate in the mountainous area of Taiwan. In order to provide enough rainfall data to assure the success of water projects, the rainfall network based on the existing rain gauge stations has to be redesigned.A method composed of kriging and entropy that can determine the optimum number and spatial distribution of rain gauge stations in catchments is proposed. Kriging as an interpolator, which performs linear averaging to reconstruct the rainfall over the catchment on the basis of the observed rainfall, is used to compute the spatial variations of rainfall. Thus, the rainfall data at the locations of the candidate rain gauge stations can be reconstructed. The information entropy reveals the rainfall information of the each rain gauge station in the catchment. By calculating the joint entropy and the transmitted information, the candidate rain gauge stations are prioritized. In addition, the saturation of rainfall information can be used to add or remove the rain gauge stations. Thus, the optimum spatial distribution and the minimum number of rain gauge stations in the network can be determined. The catchment of the Shimen Reservoir in Taiwan is used to illustrate the method. The result shows that only seven rain gauge stations are needed to provide the necessary information. Copyright © 2007 John Wiley & Sons, Ltd.

Progress in the use of the method of optimum interpolation for the redesign of the Zambian raingauge network

The Zambian Second National Development Plan calls for an increase in the number of raingauge stations from the present 500 to 1000 by 1976, to bring the national network density to above the WMO recommended minimum figure of one gauge per 900 km 2. The present network is examined for adequacy according to the criterion of maximum admissible error of the estimate of rainfall at any ungauged point, using the method of optimum interpolation. Areas which are inadequately gauged are delimited. Several variations of the maximum admissible error criterion are considered as a basis of network design, and it is concluded that a combination of criteria provides the most suitable basis for network design. The practical constraints in implementing the design, however, restricts the usefulness of detailed planning.