Satellite-based Rainfall Estimates Research Articles

Evaluation of Satellite-Based Rainfall Estimates against Rain Gauge Observations across Agro-Climatic Zones of Nigeria, West Africa

Satellite rainfall estimates (SREs) play a crucial role in weather monitoring, forecasting and modeling, particularly in regions where ground-based observations may be limited. This study presents a comprehensive evaluation of three commonly used SREs—African Rainfall Climatology version 2 (ARC2), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) and Tropical Application of Meteorology using SATellite data and ground-based observation (TAMSAT)— with respect to their performance in detecting rainfall patterns in Nigeria at daily scales from 2002 to 2022. Observed data obtained from the Nigeria Meteorological Agency (NiMet) are used as reference data. Evaluation metrics such as correlation coefficient, root mean square error, mean error, bias, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are employed to assess the performance of the SREs. The results show that all the SREs exhibit low bias during the major rainfall season from May to October, and the products significantly overestimate observed rainfall during the dry period from November to March in the Sahel and Savannah Zones. Similarly, over the Guinea Zone, all the products indicate overestimation in the dry season. The underperformance of SREs in dry seasons could be attributed to the rainfall retrieval algorithms, intensity of rainfall occurrence and spatial-temporal resolution. These factors could potentially lead to the accuracy of the rainfall retrieval being reduced due to intense stratiform clouds. However, all the SREs indicated better detection capabilities and less false alarms during the wet season than in dry periods. CHIRPS and TAMSAT exhibited high POD and CSI values with the least FAR across agro-climatic zones during dry periods. Generally, CHIRPS turned out to be the best SRE and, as such, would provide a useful dataset for research and operational use in Nigeria.

Evaluation of satellite-based rainfall estimates in the upper Blue Nile basin

Evaluation of satellite-based rainfall estimates in the upper Blue Nile basin

Analysis of Extreme Rainfall in the Mt. Merapi Area

The slopes of Mount Merapi (Mt. Merapi) are an area prone to hydrological disasters due to elevation and orography. Hydrological disasters that have the potential to occur include floods, erosion, landslides, and drought which are closely related to extreme rainfall. Spatial and temporal variability of rainfall in mountainous areas requires rainfall data that can represent rainfall events. Therefore, this research aims to obtain the reliability of satellite rainfall data in the extreme rainfall indices. The CHIRPS, GPM-IMERG FINAL (IMERG-F) and GPM-IMERG LATE (IMERG-L) will be used in the reliability analysis of satellite-based rainfall compared to observed rainfall station. To validate satellite rain data, statistical criteria are utilized with parameters such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Satellite-based rainfall estimates have a weak to moderate correlation (0.19 – 0.55), the RMSE value is relatively good (12.18 – 31.35 mm) and the observed bias tends to underestimate the estimated values. The capabilities of the IMERG-F, IMERG-L and CHIRPS satellites as alternative rainfall data in the Mt. Merapi area are quite good where IMERG-L has the best performance in capturing rainfall above 50 mm (R50mm), Consecutive Dry Days (CDD) indices, max 1–day and 5-day precipitation (Rx1day and Rx5day). The potential for extreme rainfall that is most prone to trigger lava floods occurs in the western region of Mt. Merapi at Ngandong Station (Sta. Ngandong). In this region, there is a high occurrence of extreme rainfall events. For instance, there were 501 instances of R50mm with an intensity of 77 mm day-1, Total Precipitation (PRCPTOT) reaches 3385 mm, Rx5day reaches 393 mm, and Consecutive Wet Days (CWD) lasts for 30 days. The results of this analysis can assist in climate understanding and modeling of extreme rainfall relevant to the region and support water resource management and disaster risk mitigation.

Performance evaluation of satellite-based rainfall estimation across climatic zones in Burkina Faso

Performance evaluation of satellite-based rainfall estimation across climatic zones in Burkina Faso

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.

Satellite-based rainfall estimates to simulate daily streamflow using a hydrological model over Gambia watershed

ABSTRACT Satellite rainfall products (SRPs) have the potential to overcome the limitations of ground-based rainfall observations and provide an alternative to inadequately or ungauged watersheds. However, due to the relatively poor accuracy and associated uncertainties to SRPs, it is necessary to evaluate their quality and applicability for each investigated watershed. This paper evaluates the usefulness of SRPs as forcing data for hydrological modeling under different scenarios and assesses their applicability for the Kedougou, Mako and Simenti sub-basins of the Gambia River. To achieve this, the “Génie Rural à 4 paramètres Journalier model” (GR4J) hydrological model was employed to simulate the streamflow considering four different scenarios: i) non-calibrated GR4J model run with uncorrected SRPs (Scenario 1); ii) non-calibrated model run with corrected SRPs (Scenario 2); GR4J model was calibrated and validated using uncorrected SRPs, and then they were utilized to drive the model (Scenario 3); GR4J model was calibrated and validated and then run using forcing inputs from corrected SRPs (Scenario 4). Results revealed that under Scenario 1 the SRPs performed poorly over the three sub-basins, while under scenario 2, the simulated daily streamflows showed relative improvement when run using corrected SRPs with 6 or 10 rainfall stations. Under the scenarios 3 and 4, the calibrated model provides significant improvement of the simulated streamflow with both the corrected and non-corrected SRPs. Finally, the SRPs demonstrate potential for use in watersheds where there are no rain gauges. The performance loss from scenario 4 (considered as the reference) to scenario 3 does not exceed 20%. Similarly, the performance loss from scenario 4 to scenario 2 does not exceed 50% when the SRPs are corrected using 3 and 6 rainfall stations (e.g., in the Kedougou sub-basin). Thus, they can be considered acceptable for hydrological simulations when the hydrological model is calibrated with measured streamflow.

Evaluation of long-term satellite-based precipitation products for developing intensity-frequency (IF) curves of daily precipitation

Iran is one of the most flood-prone countries in the world. It experiences much damage from floods, leading to economic challenges in the country. One of the most important strategies for flood risk management is an accurate estimation of design flood for water infrastructures construction. It is estimated based on the intensity-duration-frequency (IDF) curves. Traditionally, the IDF curves are generated by the gauged-based rainfall data. But, recently, the application of satellite-based rainfall estimates is evaluating by a few researchers around the world. In this study, four satellite-based precipitation products (MSWEP, CHIRPS, PERSIANN-CDR, and PERSIANN-CCS-CDR) were used to evaluate the performance of the individual and ensemble of the products for developing the Intensity-Frequency (IF) curves for daily precipitation in Iran. The investigation was conducted in 14 stations with different climatic conditions. The evaluation metrics (ME (mean error), RMSE, PCC (Pearson correlation coefficient), and RE (relative error)) show that CHIRPS (ME<-1 mm/h, RMSE<1.5 mm/h, PCC > 0.85, RE < 30%) performs better than the other products for all return periods. However, the other products may provide reasonable IF curves if the bias of the data is corrected. The ensemble of the bias-corrected products leads to the most accurate estimation of the IF curve (ME<0.1 mm/h, RMSE<0.5 mm/h, PCC > 0.85, RE < 7%); however, the ensemble of the unadjusted products provides a reliable estimation (ME<-0.5 mm/h, RMSE<1.7 mm/h, PCC > 0.5, RE < 10%). Accordingly, for the ungauged regions, where the measured data is not available for bias correction, the unadjusted CHIRPS or the ensemble of the unadjusted products can be applied.

Performance evaluation of satellite-based rainfall estimates for hydrological modeling over Bilate river basin, Ethiopia

PurposeThe purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.Design/methodology/approachSelected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.FindingsThe spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R2 = 0.72) is better as compared to MSWEP (NSE = 0.69; R2 = 0.7), TRMM (NSE = 0.67, R2 = 0.68) and CHIRPS (NSE = 0.58 and R2 = 0.62).Practical implicationsCalibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.Originality/valueThis research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Smallholder Knowledge of Local Climate Conditions Predicts Positive On-Farm Outcomes

Abstract People’s observations of climate change and its impacts, mediated by cultures and capacities, shape adaptive responses. Adaptation is critical in regions of rainfed smallholder agriculture where changing rainfall patterns have disproportionate impacts on livelihoods, yet scientific climate data to inform responses are often sparse. Despite calls for better integration of local knowledge into adaptation frameworks, there is a lack of empirical evidence linking both smallholder climate observations and scientific data to on-farm outcomes. We combine smallholder observations of past seasonal rainfall timing with satellite-based rainfall estimates in Uganda to explore whether farmers’ ability to track climate patterns is associated with higher crop yields. We show that high-fidelity tracking, or alignment of farmer recall with recent rainfall patterns, predicts higher yields in the present year, suggesting that farmers may translate their cumulative record of environmental knowledge into productive on-farm decisions, such as crop selection and timing of planting. However, tracking of less-recent rainfall (i.e., 1–2 decades in the past) does not predict higher yields in the present, while climate data indicate significant trends over this period toward warmer and wetter seasons. Our findings demonstrate the value of smallholder knowledge systems in filling information gaps in climate science while suggesting ways to improve adaptive capacity to climate change.

Bias Adjustment of Four Satellite-Based Rainfall Products Using Ground-Based Measurements over Sudan

Satellite-based rainfall estimates (SREs) represent a promising alternative dataset for climate and hydrological studies, where gauge observations are insufficient. However, these datasets are accompanied by significant uncertainties. Therefore, this study aims to minimize the systematic bias of Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Global Precipitation Climatology Project (GPCP) rainfall estimates using a quantile mapping (QM) method with climatic zones (CZs). The adjusted rainfall estimates were evaluated for the period from 2003–2017; data from 2003 to 2016 were used for calibration, and data from 2017 were used for validation. The results revealed significant improvements for the adjusted PERSIANN-CCS, PERSIANN-CDR, CHIRPS, and GPCP monthly time series in terms of all statistical measures and evaluation of overall CZs. In terms of Root Mean Square Errors (RMSEs), the adjusted CHIRPS did not show an improvement. This method successfully removed the mean bias of the daily time series for all SREs. The findings suggest that this method can be applied to correct the systematic bias of all SREs in the monthly time series in the future without the need for further gauge measurements over Sudan.

Satellite-based rainfall estimates evaluation using a parsimonious hydrological model in the complex climate and topography of the Nile River Catchments

Satellite-based rainfall products with high spatial and temporal resolutions can provide an excellent data source for regions where rain-gauge networks are missing or unevenly and sparsely distributed. The objective of this study was to examine the suitability of six high-resolution satellite-based rainfall products, Climate Hazards Group InfraRed Precipitation with Stations Version 8 (CHIRPSv8), African Rainfall Estimation Algorithm Version 2 (RFEv2), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis version 7 (TMPA3B42v7), Integrated Multi-SatelliteE Retrievals for Global Precipitation Measurement version 6 (IMERGv6), Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEPv2.8) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), for the complex topography and climate of the Nile headwater catchments. The reliability of these products was assessed using rainfall estimates as an input to force a parsimonious and high-resolution hydrological model. A spatially distributed and parsimonious hydrological model was developed in Wflow-PCRaster/Python modelling framework to simulate streamflow using rain-gauge and satellite-based rainfall inputs. The model was calibrated and validated at three locations for each rainfall product and ground rainfall measurement. The accuracy of satellite-based rainfall products as input to the hydrological model for daily streamflow forecasting was dependent upon the product type. CHIRPSv8, IMERGv6 and RFEv2 demonstrated a reliable capability to simulate the streamflow at different spatial scales. In contrast, the TMPA3B42v7 and PERSIANN products failed to capture the ground streamflow measurements. All products performed inconsistently when simulating large floods and low flows. While TMPA3B42v7 and MSWEPv2.8 underestimated the streamflow with a large magnitude of variation in peak flow, the remaining products underestimated it throughout the study period. CHIRPSv8 and IMERGv6 product outperformed all other products consistently across all periods and locations followed by RFEv2. PERSIANN failed to capture the observed streamflow with lower performance indices. A better agreement of simulated streamflow with ground streamflow measurements was attained from CHIRPSv8 and IMERGv6 compared to the gauge-driven rainfall inputs. Performance of streamflow simulations improved when catchment area was increased from 481 to 46,000 km2. Overall, this study demonstrated the potential of CHIRPSv8 and IMERGv6 rainfall products as an alternate source of rainfall data for hydrological applications in the Nile basin catchments and the potential for its use in other regions which lack direct rainfall measurement capacity.

Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal

AbstractHeavy precipitation events are recurrently occurring in Nepal, affecting lives and properties every year, especially in the summer monsoon season (i.e., June‐September). We investigated an extreme (heavy) precipitation event of August 2014 over the West Rapti River (WRR) Basin, Nepal. First, we forced a rainfall‐runoff model with ground‐based (gauge) hourly rainfall data of nine stations. Second, we validated against hourly water level at an outlet of the WRR Basin. This study then evaluated the performance of different satellite‐based rainfall estimates (SREs) in capturing an extreme precipitation event. We considered the use of half‐hourly data of Integrated Multi‐satellite Retrievals for GPM (IMERG) (Early, Late, and Final versions), spatial resolution (10 km), and hourly data of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), spatial resolution (25 km), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS), spatial resolution (4 km). Also, we used 3 h data of Tropical Multi‐satellite Precipitation Analysis (TMPA) product real‐time (3B42RT), spatial resolution (25 km). In general, we find that all selected SREs depicted a similar pattern of extreme precipitation as shown by the gauge data on a daily scale. However, we find these products could not replicate precisely on a sub‐daily scale. Overall, IMERG and TMPA showed a better performance than PERSIANN and PERSIANN‐CCS. Finally, we corrected poor‐performed SREs with respect to gauge data and also filled data gaps of gauge rainfall using the information of good‐performed SREs. Our study reveals that there is a great challenge in local flood simulation employing SREs at high‐temporal resolution in Nepal.

Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia

Abstract. Precipitation is a crucial driver of hydrological processes. Ironically, a reliable characterization of its spatiotemporal variability is challenging. Ground-based rainfall measurement using rain gauges is more accurate. However, installing a dense gauging network to capture rainfall variability can be impractical. Satellite-based rainfall estimates (SREs) could be good alternatives, especially for data-scarce basins like in Ethiopia. However, SRE rainfall is plagued with uncertainties arising from many sources. The objective of this study was to evaluate the performance of the latest versions of several SRE products (i.e., CHIRPS2, IMERG6, TAMSAT3 and 3B42/3) for the Dhidhessa River Basin (DRB). Both statistical and hydrological modeling approaches were used for the performance evaluation. The Soil and Water Analysis Tool (SWAT) was used for hydrological simulations. The results showed that whereas all four SRE products are promising to estimate and detect rainfall for the DRB, the CHIRPS2 dataset performed the best at annual, seasonal and monthly timescales. The hydrological simulation-based evaluation showed that SWAT's calibration results are sensitive to the rainfall dataset. The hydrological response of the basin is found to be dominated by the subsurface processes, primarily by the groundwater flux. Overall, the study showed that both CHIRPS2 and IMERG6 products could be reliable rainfall data sources for the hydrological analysis of the DRB. Moreover, the climatic season in the DRB influences rainfall and streamflow estimation. Such information is important for rainfall estimation algorithm developers.

Combining satellite-based rainfall data with rainfall - runoff modelling to simulate low flows in a Southern Andean catchment

Since 2010, Chile has been affected by a long-term drought, often referred to as the Mega-Drought. To adapt to future drought events, we need reliable hydro-climatic information based on robust observations and models. However, reliable hydro-climatic data needed for hydrological modelling of remote Andean catchments are rarely available. Our objective was therefore to identify the most suitable combination of different rainfall estimates with two rainfall-runoff models of varying complexity to reliably simulate low flows related to drought.We forced the widely used SWAT and HBV-light models with four satellite-based rainfall estimates (SREs) (CHIRPSv2, MSWEPv1.2, MSWEPv2.0, TMPA 3B42v7) and inverse distance weighted (IDW) precipitation observations for the main headwater catchment of the Imperial River basin (Araucania, 38°N). Sobol global sensitivity analysis and calibration were carried out using the hydroPSO R package, using two objective functions focused on the performance of low flows (logNSE and low flows KGE).Despite the differing amount and temporal distribution of water, for all four SREs, the Sobol sensitivity analysis resulted in robust identification of key model parameters for all SWAT optimisation runs, with most sensitivity for parameters related to deep groundwater recharge, lateral flow travel time and the SCS runoff curve number. For the HBV-light model, only parameters related to snow-melt and deep groundwater recharge were robustly identified for the wetter SRE products. For both models and the two objective functions, the best efficiencies were obtained with the wetter SREs (MSWEP v1.2 and CHIRPSv2) as well as with IDW.We found that SREs combined with rainfall-runoff modelling provide a valuable instrument to simulate daily discharges for the Upper Imperial River Basin. The resulting models are hence suitable to project low flows and water availability; information that is requested by local decision makers needed to design irrigation systems for the agricultural downstream area and the secure water supply of settlements in the context of future droughts.

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

Spatial-Temporal Assessment of Satellite-Based Rainfall Estimates in Different Precipitation Regimes in Water-Scarce and Data-Sparse Regions

Accurate precipitation measurement is very important for socio-hydrological resilience in the face of frequent extreme weather events such as cyclones. This study evaluates the performance of two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA, hereafter: TRMM) and the Integrated Multi-satellite Retrievals for GPM (IMERG, Final Run V06, hereafter: GPM) in the Sultanate of Oman. Oman is an arid country that generally has few rainy days, but has experienced significant flash floods, tropical storms and cyclones recently, leading to the loss of lives and millions of dollars in damage. Accurate precipitation analysis is crucial in flood monitoring, hydrologic modeling, and the estimation of the water balance of any basin, and the lack of a sufficient weather monitoring network is a barrier to accurate precipitation measurement. Satellite rainfall estimates can be a reliable option in sparse network areas, especially in arid and semi-arid countries. This study evaluated monthly rainfall (hereafter: OBSERVED) levels at 77 meteorological stations from January 2016 to December 2018. The capacity of the TRMM and GPM satellite products to detect monthly rainfall amounts at varying precipitation thresholds was also evaluated. Findings included (1) overall and across the 11 Governorates of Oman, both satellite products show different spatial variability and performance to the OBSERVED at the monthly, seasonal, and annual temporal scales; (2) from the perspective of precipitation detection and frequency bias, GPM showed a similar performance to TRMM at detecting low precipitation (2 mm/month) but was poorer at detecting high precipitation (&gt;30 mm/month) across the entire country as well as in the Northern, Interior, and Dhofar regions; (3) both products show similarities to the OBSERVED through the partitioning of their seasonal time series into a distinct number of homogenous segments; and (4) both products had difficulty reproducing OBSERVED levels in the Dhofar and Interior regions, which is consistent with studies conducted in mountainous and coastal regions. With the aim of reproducing the spatial and temporal structure of OBSERVED in a rugged terrain, the study shows that both satellite products can be used in areas of sparse rain gauges or as additional observation for studies of extreme weather events. Overall, this study suggests that for Oman, both satellite products can be used as proxies for OBSERVED with appropriate bias corrections and GPM is also a reliable replacement for TRMM as a precipitation satellite product. The findings will be useful to the country’s flood resilience and mitigation efforts, especially in areas where there is sparse rain gauge coverage.

A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

A Spatio-Temporal Analysis of Rainfall and Drought Monitoring in the Tharparkar Region of Pakistan

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall.

Simulating Flash Floods Using Geostationary Satellite-Based Rainfall Estimation Coupled with a Land Surface Model

Clarifying hydrologic behavior, especially behavior related to extreme events such as flash floods, is vital for flood mitigation and management. However, discharge and rainfall measurement data are scarce, which is a major obstacle to flood mitigation. This study: (i) simulated flash floods on a regional scale using three types of rainfall forcing implemented in a land surface model; and (ii) evaluated and compared simulated flash floods with the observed discharge. The three types of rainfall forcing were those observed by the Automated Meteorological Data Acquisition System (AMeDAS) (Simulation I), the observed rainfall from the Ministry of Land, Infrastructure and Transportation (MLIT) (Simulation II), and the estimated rainfall from the Multi-purpose Transport Satellite (MTSAT), which was downscaled by AMeDAS rainfall (Simulation III). MLIT rainfall observations have a denser station network over the Ishikari River basin (spacing of approximately 10 km) compared with AMeDAS (spacing of approximately 20 km), so they are expected to capture the rainfall spatial distribution more accurately. A land surface model, the Minimal Advance Treatments of Surface Interaction and Runoff (MATSIRO), was implemented for the flash flood simulation. The river flow simulations were run over the Ishikari river basin at a 1-km grid resolution and a 1-h temporal resolution during August 2010. The statistical performance of the river flow simulations during a flash flood event on 23 and 24 August 2010 demonstrated that Simulation I was reasonable compared with Simulation III. The findings also suggest that the advantages of the MTSAT-based estimated rainfall (i.e., good spatial distribution) can be coupled with the benefit of direct AMeDAS observations (i.e., representation of the true rainfall).

An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China

Accurate gridded precipitation products with both finer tempo-spatial resolutions are critical for various scientific communities (e.g., hydrology, meteorology, climatology, and agriculture). Downscaling on coarse satellite-based rainfall estimates is an optimal approach to obtain such datasets. The Integrated Multi-satellitE Retrivals for Global Precipitation Measurement (GPM) (IMERG) data provides the “best” satellite-based precipitation estimates at half-hourly/0.1° scales, while its spatial resolution is still coarse for certain hydrometeorology research. To acquire hourly downscaled precipitation estimates based on IMERG, there are two great challenges: (1) limited rainfall-related environmental variables (0.01°×0.01°, hourly) used to downscale the IMERG data; and (2) far few rainfall pixels used for regressing traditional relationships between precipitation and environmental variables. In this case, most traditional or commonly-used regression/empirical models and the state-of-art machine learning algorithms are not suitable to cater these requirements. Therefore, we proposed a new strategy to obtain hourly downscaled precipitation estimates based on IMERG called Geographically Moving Window Weight Disaggregation Analysis (GMWWDA). Additionally, we explored multiple cloud properties, including cloud effective radius (CER), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT), as covariates to downscale IMERG using GMWWDA method, and concluded as follows: (1) the downscaled results (0.01° × 0.01°, hourly) based on the above mentioned cloud properties outperformed the original IMERG data; (2) the downscaled results based on CER performed better than those based on CTH, CTT and COT, respectively; (3) the accuracy of satellite-based precipitation products pose significant effects on those of the downscaled results. This study provides a great potential solution for generating satellite-based precipitation dataset with finer spatio-temporal resolutions.