scholarly journals The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015

2021 ◽  
Vol 13 (15) ◽  
pp. 2931
Author(s):  
Jhonatan Ureña ◽  
Oliver Saavedra ◽  
Takuji Kubota
Keyword(s):  
Amazon Basin ◽  
Correlation Coefficients ◽  
Rain Gauge ◽  
Correction Factors ◽  
The Public ◽  
Rain Gauges ◽  
Basin Scale ◽  
Surface Measurements ◽  
Rain Gauge Network ◽  
Water Assessment

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia.

scholarly journals Assessment of rain-gauge networks using a probabilistic GIS based approach

2013 ◽  
Vol 45 (4-5) ◽  
pp. 551-562 ◽  
Author(s):  
Mojtaba Shafiei ◽  
Bijan Ghahraman ◽  
Bahram Saghafian ◽  
Saket Pande ◽  
Shervan Gharari ◽  
...  
Keyword(s):  
Probabilistic Approach ◽  
Rain Gauge ◽  
Rainfall Estimation ◽  
Acceptance Probability ◽  
Rain Gauges ◽  
Basin Scale ◽  
Gis Environment ◽  
Areal Rainfall ◽  
Rain Gauge Network ◽  
Hydrological Applications

Rain-gauge networks provide estimates of areal rainfall as a crucial input for hydrological applications. Hence, it is important to quantify the performance of a rain-gauge network and evaluate the contribution of each rain-gauge to the overall accuracy of areal rainfall estimation at basin scale. This paper evaluates the performance and augmentation of a rain-gauge network in a large basin in Iran. A probabilistic approach combined with a geographic information system (GIS) framework is applied, in order to assess the accuracy of point rainfall in terms of acceptance probability. A simple equation for calculating the acceptance probability is presented which facilitates the application of the probabilistic approach in a GIS environment. This approach analyzes the number and location of rain-gauges and quantifies each gauge's contribution to the accuracy of rainfall estimation over the study area. Results show that among 33 existing gauges, only 21 have significant effect on areal rainfall estimation while other 12 gauges have marginal contribution to the accuracy of the network. Also, by applying an augmentation algorithm, an optimal rain-gauge network with 28 gauges is formed.

scholarly journals Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR

2012 ◽  
Vol 13 (5) ◽  
pp. 1579-1588 ◽  
Author(s):  
Eyal Amitai ◽  
Carl L. Unkrich ◽  
David C. Goodrich ◽  
Emad Habib ◽  
Bryson Thill
Keyword(s):  
Rain Rate ◽  
Correlation Coefficients ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Field Of View ◽  
High Temporal Resolution ◽  
Coverage Area ◽  
Rain Gauges ◽  
Walnut Gulch ◽  
Rain Gauge Network

Abstract The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km2. It consists of 88 weighing rain gauges within a 149-km2 area. On average, approximately 10 gauges can be found in each PR field of view (~5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m × 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999–2010). The results indicate very good agreement between the fields with low bias values (<10%) and high correlation coefficients, especially for the near-nadir cases (>0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael ◽  
Vahid Nourani
Keyword(s):  
Temporal Dynamics ◽  
Disease Outbreaks ◽  
Rain Gauge ◽  
Global Forecast System ◽  
Forecast System ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Basin Scale ◽  
Medium Range

Abstract Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.

scholarly journals Influence of the rain gauge network on the performance of a hydrological lumped model applied at different basin scales

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Stefany Correia de Paula ◽  
Rutineia Tassi ◽  
Daniel Gustavo Allasia Piccilli ◽  
Francisco Lorenzini Neto
Keyword(s):  
Spatial Distribution ◽  
Monitoring Network ◽  
Rain Gauge ◽  
Network Density ◽  
Low Density ◽  
Rainfall Runoff ◽  
Lumped Model ◽  
Rain Gauges ◽  
The Poor ◽  
Rain Gauge Network

ABSTRACT In this study was evaluated the influence of the rainfall monitoring network density and distribution on the result of rainfall-runoff daily simulations of a lumped model (IPH II) considering basins with different drainage scales: Turvo River (1,540 km2), Ijuí River (9,462 km2), Jacuí River (38,700 km2) and Upper Uruguay (61,900 km2). For this purpose, four rain gauge coverage scenarios were developed: (I) 100%; (II) 75%; (III) 50% and (IV) 25% of the rain gauges of the basin. Additionally, a scenario considering the absence of monitoring was evaluated, in which the rainfall used in the modeling was estimated based on the TRMM satellite. Was verified that, in some situations, the modeling produced better results for scenarios with a lower rain gauges density if the available gauges presented better spatial distribution. Comparatively to the simulations performed with the rainfall estimated by the TRMM, the results obtained using rain gauges’ data were better, even in scenarios with low rain gauges density. However, when the poor spatial distribution of the rain gauges was associated with low density, the satellite’s estimation provided better results. Thus, was conclude that spatial distribution of the rain gauge network is important in the rainfall representation and that estimates obtained by the TRMM can be presented as alternatives for basins with a deficient monitoring network.

scholarly journals Evaluation of the High-Resolution CMORPH Satellite Rainfall Product Using Dense Rain Gauge Observations and Radar-Based Estimates

2012 ◽  
Vol 13 (6) ◽  
pp. 1784-1798 ◽  
Author(s):  
Emad Habib ◽  
Alemseged Tamiru Haile ◽  
Yudong Tian ◽  
Robert J. Joyce
Keyword(s):  
Rain Gauge ◽  
Random Errors ◽  
Event Scale ◽  
Entire Study ◽  
Rain Gauges ◽  
Fine Resolution ◽  
Rain Rates ◽  
Climate Prediction Center ◽  
Rain Gauge Network ◽  
Insight Into

Abstract This study focuses on the evaluation of the NOAA–NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based rainfall product at fine space–time resolutions (1 h and 8 km). The evaluation was conducted during a 28-month period from 2004 to 2006 using a high-quality experimental rain gauge network in southern Louisiana, United States. The dense arrangement of rain gauges allowed for multiple gauges to be located within a single CMORPH pixel and provided a relatively reliable approximation of pixel-average surface rainfall. The results suggest that the CMORPH product has high detection skills: the probability of successful detection is ~80% for surface rain rates &gt;2 mm h−1 and probability of false detection &lt;3%. However, significant and alarming missed-rain and false-rain volumes of 21% and 22%, respectively, were reported. The CMORPH product has a negligible bias when assessed for the entire study period. On an event scale it has significant biases that exceed 100%. The fine-resolution CMORPH estimates have high levels of random errors; however, these errors get reduced rapidly when the estimates are aggregated in time or space. To provide insight into future improvements, the study examines the effect of temporal availability of passive microwave rainfall estimates on the product accuracy. The study also investigates the implications of using a radar-based rainfall product as an evaluation surface reference dataset instead of gauge observations. The findings reported in this study guide future enhancements of rainfall products and increase their informed usage in a variety of research and operational applications.

scholarly journals Estimation of convective precipitation: the meteorological radar versus an automatic rain gauge network

2005 ◽  
Vol 2 ◽  
pp. 103-109 ◽  
Author(s):  
M. C. Llasat ◽  
T. Rigo ◽  
M. Ceperuelo ◽  
A. Barrera
Keyword(s):  
Rain Rate ◽  
Rain Gauge ◽  
Space Distribution ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Convective Rainfall ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Meteorological Radar ◽  
Rain Gauge Network

Abstract. The estimation of convective precipitation and its contribution to total precipitation is an important issue both in hydrometeorology and radio links. The greatest part of this kind of precipitation is related with high intensity values that can produce floods and/or damage and disturb radio propagation. This contribution proposes two approaches for the estimation of convective precipitation, using the β parameter that is related with the greater or lesser convective character of the precipitation event, and its time and space distribution throughout the entire series of the samples. The first approach was applied to 126 rain gauges of the Automatic System of Hydrologic Information of the Internal Basins of Catalonia (NE Spain). Data are series of 5-min rain rate, for the period 1996-2002, and a long series of 1-min rain rate starting in 1927. Rainfall events were classified according to this parameter. The second approach involved using information obtained by the meteorological radar located near Barcelona. A modified version of the SCIT method for the 3-D analysis and a combination of different methods for the 2-D analysis were applied. Convective rainfall charts and β charts were reported. Results obtained by the rain gauge network and by the radar were compared. The application of the β parameter to improve the rainfall regionalisation was demonstrated.

scholarly journals Multifractal Comparison of Reflectivity and Polarimetric Rainfall Data from C- and X-Band Radars and Respective Hydrological Responses of a Complex Catchment Model

2017 ◽  
Author(s):  
Igor Paz ◽  
Bernard Willinger ◽  
Auguste Gires ◽  
Laurent Monier ◽  
Christophe Zobrist ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Radar Data ◽  
Rain Gauge ◽  
Distributed Hydrological Model ◽  
Space Resolution ◽  
Rain Gauges ◽  
X Band ◽  
Distributed Simulations ◽  
Rain Gauge Network ◽  
Catchment Model

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the M&eacute;t&eacute;o-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

scholarly journals Rainfall estimation over the Wadi Dhuliel arid catchment, Jordan from GSMaP_MVK+

2011 ◽  
Vol 8 (1) ◽  
pp. 1665-1704 ◽  
Author(s):  
E. Abushandi ◽  
B. Merkel
Keyword(s):  
Rain Gauge ◽  
Rainfall Distribution ◽  
Temporal And Spatial Distribution ◽  
Rain Gauges ◽  
Daily Data ◽  
Independent Form ◽  
The Relationship ◽  
Rain Gauge Network ◽  
Annual Time ◽  
Mlr Model

Abstract. The GSMaP_MVK+ (Global Satellite Mapping of Precipitation) dataset was used to evaluate the precipitation rates over the Wadi Dhuliel arid catchment in Northeast Jordan for the period of January 2003 to March 2008. The scarcity of the ground rain gauge network alone did not adequately show the detailed structure of the rainfall distribution, independent form interpolation techniques used. This study combines GSMaP_MVK+ and ground rain gauges to produce accurate, high-resolution datasets. Three meteorological stations and six rain gauges were used to adjust and compare GSMaP_MVK+ estimates. Comparisons between GSMaP_MVK+ measurements and ground rain gauges records showed distinct regions where they correlate, as well as areas where GSMaP_MVK+ systematically over- and underestimated ground rain gauge records. A multiple linear regression (MLR) model was used to derive the relationship between rainfall and GSMaP_MVK+ in conjunction with temperature, relative humidity, and wind speed. The MLR equations were defined for the three meteorological stations. The "best" fit of MLR model for each station was chosen and used to interpolate a multiscale temporal and spatial distribution. Results show that the rainfall distribution over the Wadi Dhuliel is characterized by clear west-east and north-south gradients. Estimates from the monthly MLR model were more reasonable than estimates obtained using daily data. The adjusted GSMaP_MVK+ performed well in capturing the spatial patterns of the rainfall at monthly and annual time scales while daily estimation showed some weakness in light and moderate storms.

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

2021 ◽  
Vol 108 (september) ◽  
pp. 1-6
Author(s):  
Venkadesh Samykannu ◽  
◽  
Pazhanivelan S ◽  
Keyword(s):  
High Altitude ◽  
Tamil Nadu ◽  
Rain Gauge ◽  
Northeast Monsoon ◽  
Data Set ◽  
Climatic Zones ◽  
Rain Gauges ◽  
Precipitation Estimation ◽  
Cauvery Delta ◽  
Rain Gauge Network

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

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