Investigating Spatial Downscaling of Satellite Rainfall Data for Streamflow Simulation in a Medium-Sized Basin

Abstract The objective of this study was to investigate spatial downscaling of satellite rainfall data for streamflow prediction in a medium-sized (970 km2) river basin prone to flooding. The spatial downscaling scheme used in the study was based on the principle of scale invariance. It reproduced the rainfall variability at finer scales while being conditioned on the large-scale rainfall. Two Tropical Rainfall Measuring Mission (TRMM)-based real-time global satellite rainfall products were analyzed: 1) the infrared (IR)-based 3B41RT product available at 1 hourly and 0.25° scales and 2) the combined passive microwave (PMW) and IR-based 3B42RT product available at 3 hourly and 0.25° scales. The conceptual Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was used for the simulation of streamflow. It was found that propagation of spatially downscaled satellite rainfall in the hydrologic model increased simulation uncertainty in streamflow as rainfall grid scales became smaller than 0.25°. The streamflow simulation uncertainty for satellite downscaling was found to be very similar to that for ground validation Next Generation Weather Radar (NEXRAD) downscaling at any given scale, indicating that the effectiveness of the spatial downscaling scheme is not influenced by rainfall data type. Closer inspection at the subbasin level revealed that the limitation of the selected spatial downscaling scheme to preserve the mean rainfall intensity for irregularly sized drainage units was responsible for the increase in simulation uncertainty as scales became smaller. Although the findings should not be construed as a generalization for spatial downscaling schemes, there is a need for more rigorous hydrometeorological assessment of downscaled satellite rainfall data prior to institutionalizing its use for real-time streamflow simulation over ungauged basins.

Download Full-text

Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-074.1 ◽

2013 ◽

Vol 14 (4) ◽

pp. 1194-1211 ◽

Cited By ~ 37

Author(s):

Viviana Maggioni ◽

Humberto J. Vergara ◽

Emmanouil N. Anagnostou ◽

Jonathan J. Gourley ◽

Yang Hong ◽

...

Keyword(s):

Error Propagation ◽

River Flow ◽

Tropical Rainfall Measuring Mission ◽

Global Scale ◽

Hydrologic Model ◽

Runoff Simulation ◽

Stochastic Error ◽

Precipitation Estimation ◽

Satellite Rainfall ◽

The Impact

Abstract This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.

Download Full-text

Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia

Journal of Hydrometeorology ◽

10.1175/2011jhm1292.1 ◽

2012 ◽

Vol 13 (1) ◽

pp. 338-350 ◽

Cited By ~ 96

Author(s):

Menberu M. Bitew ◽

Mekonnen Gebremichael ◽

Lula T. Ghebremichael ◽

Yared A. Bayissa

Keyword(s):

High Resolution ◽

Real Time ◽

Bias Correction ◽

Hydrological Modeling ◽

Rain Gauge ◽

Hydrologic Model ◽

Model Simulations ◽

Mountainous Watershed ◽

Satellite Rainfall ◽

Rainfall Estimates

Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.

Download Full-text

Application of Remotely Sensed Rainfall Data in Rainfall-Runoff Modelling. A Case of Pangani River Basin, Tanzania

Tanzania Journal of Engineering and Technology ◽

10.52339/tjet.v35i1.465 ◽

2014 ◽

Vol 35 (1) ◽

pp. 1-14

Author(s):

Joel Nobert ◽

Patric Kibasa

Keyword(s):

River Basin ◽

Average Method ◽

River Flow ◽

Tropical Rainfall Measuring Mission ◽

Rainfall Data ◽

Coefficient Of Determination ◽

Rainfall Runoff ◽

Data Set ◽

Satellite Rainfall ◽

Areal Rainfall

Rainfall runoff modelling in a river basin is vital for number of hydrologic applicationincluding water resources assessment. However, rainfall data from sparse gauging stationsare usually inadequate for modelling which is a major concern in Tanzania. This studypresents the results of comparison of Tropical Rainfall Measuring Mission (TRMM)satellite rainfall products at daily and monthly time-steps with ground stations rainfalldata; and explores the possibility of using satellite rainfall data for rainfall runoffmodelling in Pangani River Basin, Tanzania. Statistical analysis was carried out to find thecorrelation between the ground stations data and TRMM estimates. It was found thatTRMM estimates at monthly scale compare reasonably well with ground stations data.Time series comparison was also done at daily and annual time scales. Monthly and annualtime series compared well with coefficient of determination of 0.68 and 0.70, respectively.It was also found that areal rainfall comparison in the northern parts of the study area hadpoor results compared to the rest of areas. On the other hand, rainfall runoff modellingwith ground stations data alone and TRMM data set alone was carried out using five Real-Time River Flow Forecasting System models and then outputs combined by Models OutputsCombination Techniques. The results showed that ground stations data performed betterduring calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% forSimple Average Method, Weight Average Method and Neural Network Method respectively.Simulation results using TRMM data were 59.8%, 73.5% and 76.8%. It can therefore beconcluded that TRMM data are adequate and promising in hydrological modelling.

Download Full-text

Evaluating TMPA Rainfall over the Sparsely Gauged East African Rift

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0103.1 ◽

2018 ◽

Vol 19 (9) ◽

pp. 1507-1528 ◽

Cited By ~ 15

Author(s):

Elise Monsieurs ◽

Dalia Bach Kirschbaum ◽

Jackson Tan ◽

Jean-Claude Maki Mateso ◽

Liesbet Jacobs ◽

...

Keyword(s):

Real Time ◽

Spatial Scales ◽

Rainfall Variability ◽

Tropical Rainfall Measuring Mission ◽

East African Rift ◽

East African ◽

High Rainfall ◽

Two Factors ◽

African Rift ◽

Rainfall Estimates

Abstract Accurate precipitation data are fundamental for understanding and mitigating the disastrous effects of many natural hazards in mountainous areas. Floods and landslides, in particular, are potentially deadly events that can be mitigated with advanced warning, but accurate forecasts require timely estimation of precipitation, which is problematic in regions such as tropical Africa with limited gauge measurements. Satellite rainfall estimates (SREs) are of great value in such areas, but rigorous validation is required to identify the uncertainties linked to SREs for hazard applications. This paper presents results of an unprecedented record of gauge data in the western branch of the East African Rift, with temporal resolutions ranging from 30 min to 24 h and records from 1998 to 2018. These data were used to validate the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research version and near-real-time products for 3-hourly, daily, and monthly rainfall accumulations, over multiple spatial scales. Results indicate that there are at least two factors that led to the underestimation of TMPA at the regional level: complex topography and high rainfall intensities. The TMPA near-real-time product shows overall stronger rainfall underestimations but lower absolute errors and a better performance at higher rainfall intensities compared to the research version. We found area-averaged TMPA rainfall estimates relatively more suitable in order to move toward regional hazard assessment, compared to data from scarcely distributed gauges with limited representativeness in the context of high rainfall variability.

Download Full-text

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

Journal of Hydrometeorology ◽

10.1175/2010jhm1353.1 ◽

2011 ◽

Vol 12 (3) ◽

pp. 444-455 ◽

Cited By ~ 41

Author(s):

Fengge Su ◽

Huilin Gao ◽

George J. Huffman ◽

Dennis P. Lettenmaier

Keyword(s):

Real Time ◽

Tropical Rainfall Measuring Mission ◽

Potential Utility ◽

Streamflow Prediction ◽

The Real ◽

Earth Observing System ◽

Precipitation Estimates ◽

Microwave Sounding ◽

Hydrologic Prediction

Abstract The potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time product 3B42RT (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product and through evaluation of streamflow simulations over four tributaries of La Plata basin (LPB) in South America using the two precipitation products, is investigated. Assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February 2005, which include use of additional microwave sensors [Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and Advanced Microwave Sounding Unit-B (AMSU-B)] and implementation of different calibration schemes. This study suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

Download Full-text

Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia

Water ◽

10.3390/w13060800 ◽

2021 ◽

Vol 13 (6) ◽

pp. 800 ◽

Cited By ~ 1

Author(s):

Girma Berhe Adane ◽

Birtukan Abebe Hirpa ◽

Belay Manjur Gebru ◽

Cholho Song ◽

Woo-Kyun Lee

Keyword(s):

Water Resources ◽

Water Balance ◽

River Basin ◽

Pearson Correlation ◽

Hydrologic Model ◽

Water Balance Model ◽

Rainfall Data ◽

Balance Model ◽

Awash River Basin ◽

Satellite Rainfall

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.

Download Full-text

Floods in Southern Thailand in December 2016 and January 2017

Journal of Disaster Research ◽

10.20965/jdr.2018.p0793 ◽

2018 ◽

Vol 13 (4) ◽

pp. 793-803

Author(s):

Taichi Tebakari ◽

Sanit Wongsa ◽

Yoshiaki Hayashi ◽

◽

...

Keyword(s):

Large Scale ◽

Field Survey ◽

Rainfall Data ◽

Precipitation Event ◽

Rain Gauges ◽

Southern Thailand ◽

Satellite Rainfall ◽

Precipitation Phenomena ◽

Precipitation Events

A large scale flood disaster occurred in Southern Thailand in December, 2016 and January, 2017, resulting in 95 deaths. The majority of the 15 provinces in Southern Thailand suffered from the disaster and extensive, long-term damage was caused which distinguished this flood event from previous flood disasters. This paper reports the findings of a field survey conducted in February, 2017 and analyzes the precipitation phenomena by using ground rainfall data as well as satellite rainfall data because there were not enough ground rain gauges set in this region. Results revealed that this precipitation event had the highest intensity out of all precipitation events occurring over the last 11 years.

Download Full-text

Uncertainty Assessments of Satellite Derived Rainfall Products

10.20944/preprints201611.0019.v1 ◽

2016 ◽

Cited By ~ 1

Author(s):

Margaret Kimani ◽

Joost Hoedjes ◽

Zhongbo Su

Keyword(s):

Large Scale ◽

Rainfall Variability ◽

Tropical Rainfall Measuring Mission ◽

Global Precipitation Climatology Project ◽

Rain Gauge ◽

Climate Data ◽

Error Distributions ◽

Precipitation Estimation ◽

Scatter Plots ◽

Global Precipitation

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products’ error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the ‘long’ (March-May) and ‘short’ (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products’ performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products’ errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during ‘short’ rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products’ errors before applications.

Download Full-text

Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

Applied Sciences ◽

10.3390/app11031087 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1087

Author(s):

Li Zhou ◽

Mohamed Rasmy ◽

Kuniyoshi Takeuchi ◽

Toshio Koike ◽

Hemakanth Selvarajah ◽

...

Keyword(s):

Real Time ◽

River Basin ◽

Bias Correction ◽

Large Scale ◽

River Flow ◽

Hydrologic Model ◽

Satellite Precipitation ◽

Flood Discharge ◽

Discharge Simulation ◽

The Impact

Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

Download Full-text

A Correlation–Scale–Threshold Method for Spatial Variability of Rainfall

Hydrology ◽

10.3390/hydrology6010011 ◽

2019 ◽

Vol 6 (1) ◽

pp. 11 ◽

Cited By ~ 1

Author(s):

Bellie Sivakumar ◽

Fitsum M. Woldemeskel ◽

Rajendran Vignesh ◽

Vinayakam Jothiprakash

Keyword(s):

Rainfall Variability ◽

Monitoring Network ◽

Tropical Rainfall Measuring Mission ◽

Rain Gauge ◽

Rainfall Data ◽

Spatial Correlations ◽

Threshold Method ◽

Southeastern Australia ◽

Correlation Threshold ◽

Hydrology And Water Resources

Rainfall data at fine spatial resolutions are often required for various studies in hydrology and water resources. However, such data are not widely available, as their collection is normally expensive and time-consuming. A common practice to obtain fine-spatial-resolution rainfall data is to employ interpolation schemes to derive them based on data available at nearby locations. Such interpolation schemes are generally based on rainfall correlation or distance between stations. The present study proposes a combined rainfall correlation-spatial scale-correlation threshold method for representing spatial rainfall variability. The method is applied to monthly rainfall data at a resolution of 0.25 × 0.25 latitude/longitude across Australia, available from the Tropical Rainfall Measuring Mission (TRMM 3B43 version). The results indicate that rainfall dynamics in northern and northeastern Australia have far greater spatial correlations when compared to the other regions, especially in southern and southeastern Australia, suggesting that tropical climates generally have greater spatial rainfall correlations when compared to temperate, oceanic, and continental climates, subject to other influencing factors. The implications of the outcomes for rainfall data interpolation and the rain gauge monitoring network are also discussed, especially based on results obtained for ten major cities in Australia.

Download Full-text