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Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya

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Abstract

Study region 19 flood prone catchments in Kenya, Eastern Africa Study focus Flooding is a major natural hazard especially in developing countries, and the need for timely, reliable, and actionable hydrological forecasts is paramount. Hydrological modelling is essential to produce forecasts but is a challenging task, especially in poorly gauged catchments, because of the inadequate temporal and spatial coverage of hydro-meteorological observations. Open access global meteorological reanalysis datasets can fill in this gap, however they have significant errors. This study assesses the performance of four reanalysis datasets (ERA5, ERA-Interim, CFSR and JRA55) over Kenya for the period 1981–2016 on daily, monthly, seasonal, and annual timescales. We firstly evaluate the reanalysis datasets by comparing them against observations from the Climate Hazards group Infrared Precipitation with Station. Secondly, we evaluate the ability of these reanalysis datasets to simulate streamflow using GR4J model considering both model performance and parameters sensitivity and identifiability. New hydrological insights for the region While ERA5 is the best performing dataset overall, performance varies by season, and catchment and therefore there are marked differences in the suitability of reanalysis products for forcing hydrological models. Overall, wetland catchments in the western regions and highlands of Kenya obtained relatively better scores compared to those in the semi-arid regions, this can inform future applications of reanalysis products for setting up hydrological models that can be used for flood forecasting, early warning, and early action in data scarce regions, such as Kenya.

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Global reanalysis products are extensively used for hydrologic applications in sparse data regions. European Centre for Medium‐Range Weather Forecasts Re‐Analysis version 5 (ERA‐5), among the new‐era reanalysis products, has been significantly improved for horizontal and vertical resolutions and data assimilation. However, the new‐era reanalysis products (ERA‐5, Climate Forecast System Reanalysis, ERA‐Interim, Modern Era Retrospective Analysis for Research and Applications version 2, and Japanese 55‐year Reanalysis Project) have not been evaluated for the hydrologic applications in India specially to understand if ERA‐5 outperforms the other reanalysis products or not. Here we use the five new‐era reanalysis products for the monsoon (June–September) season precipitation, maximum (Tmax) and minimum (Tmin) temperatures, total runoff, evapotranspiration, and soil moisture against the observations from India Meteorological Department in India for 1980–2018. We use a well‐calibrated and evaluated hydrological model (the Variable Infiltration Capacity model) to simulate hydrologic variables using the forcing from India Meteorological Department and reanalysis products. In addition, we evaluated the reanalysis products for streamflow and annual water budget for the two basins located in the diverse climatic settings in India. Our results show that ERA‐5 outperforms the other reanalysis products for the monsoon season precipitation, Tmax, evapotranspiration, and soil moisture. However, Climate Forecast System Reanalysis performs better than ERA‐5 for the monsoon season total runoff in India. Performance for streamflow and annual water budget for ERA‐5 is either better or comparable to the other reanalysis products in the two river basins. Overall, we find that ERA‐5 performs better than the other reanalysis products and can be used for the hydrologic assessments in India.
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Evaluation of different reanalysis precipitation datasets is of great importance to understanding the hydrological processes and water resource management practice in the Qinling-Daba Mountains (QDM), located at the eastern fringe of the Tibetan Plateau. Although the evaluation of satellite precipitation data in this region has been performed, another kind of popular precipitation product-reanalysis dataset has not been assessed in depth. Three popular reanalysis precipitation datasets, including ERA-Interim Reanalysis of European Centre for Medium Forecasts (ERA-Interim), Japanese 55-year Reanalysis (JRA-55), and National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis-1 (NCEP/NCAR-1) were evaluated against rain gauge data over the Qinling-Daba Mountains from 2000 to 2014 on monthly, seasonal, and annual scales. Different statistical measures based on the Correlation Coefficient (CC), relative BIAS (BIAS), Root-Mean-Square Error (RMSE), and Mean Absolute Error (MAE) were adopted to determine the performance of the above reanalysis datasets. Results show that ERA-Interim and JRA-55 have good performance on a monthly scale and annual scale. However, the NCEP/NCAR-1 has the least BIAS with the observed precipitation in annual scale in QDM. All reanalysis datasets performed better in spring, summer, and autumn than in winter. The advantages of involving more precipitation observation stations was probably the main reason of the different performance of three precipitation reanalysis products, and the benefit of a four-dimensional variational analysis model over a three-dimensional variational analysis model may be another reason. The evaluation suggested that ERA-Interim is more suitable for study the precipitation and water cycles in the QDM.
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Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non‐existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite‐based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite‐based rainfall products with relatively high spatial and temporal resolutions and quasi‐global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10‐day) and monthly time scales. The evaluation was done by comparing the satellite products with rain gauge data from about 1200 stations. The CHIRP and CHIRPS products were also compared with two similar operation satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time scales, while TAMSAT performed better at daily time scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.
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We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized (2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gaugebased CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed those indirectly incorporating gauge data through other multi-source datasets (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence, the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite- and reanalysis-based P estimates.
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The Turkana Low-Level Jet (LLJ) was discovered in the early 1980s, yet there are still questions about the primary forcing mechanisms that drive and sustain the jet throughout the year. A few studies have addressed these questions, but most focus on numerical simulations of mechanical forcing mechanisms, such as orography, channeling flow, and monsoon background flow. No studies have shown the effects of thermal forcing from differential heating in the regions in and around the Turkana Channel. This paper uses National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data and the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) data in order to analyze and find relationships between temperature gradients and the strength of the Turkana LLJ. In addition to temperature, potential temperature, divergence, wind magnitude, wind fields, and vertical motion are also examined. This analysis attempts to show that thermal forcing is one of the most important factors, if not the primary factor, in the initiation and maintenance of the jet and propose that more research and model simulations should be implemented to determine the contributions from thermal forcing.
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Precipitation forcing is critical for hydrological modeling as it has a strong impact on the accuracy of simulated river flows. In general, precipitation data used in hydrological modeling are provided by weather stations. However, in regions with sparse weather station coverage, the spatial interpolation of the individual weather stations provides a rough approximation of the real precipitation fields. In such regions, precipitation from interpolated weather stations is generally considered unreliable for hydrological mod-eling. Precipitation estimates from reanalyses could represent an interesting alternative in regions where the weather station density is low. This article compares the performances of river flows simulated by a watershed model using precipitation and temperature estimates from reanalyses and gridded observations. The comparison was carried out based on the density of surface weather stations for 316 Canadian watersheds located in three climatic regions. Three state-of-the-art atmospheric reanalyses—ERA-Interim, CFSR, and MERRA—and one gridded observations database over Canada—Natural Resources Canada (NRCan)—were used. Results showed that the Nash–Sutcliffe values of simulated river flows using precipitation and temperature data from CFSR and NRCan were generally equivalent regardless of the weather station density. ERA-Interim and MERRA performed significantly better than NRCan for watersheds with weather station densities of less than 1 station per 1000 km 2 in the mountainous region. Overall, these results indicate that for hydrological modeling in regions with high spatial variability of precipitation such as mountainous regions, reanalyses perform better than gridded observations when the weather station density is low.
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Study region The Danube River Basin. Study focus Hydrological modelling of large, heterogeneous watersheds requires appropriate meteorological forcing data. The global meteorological reanalysis ERA5 and the global forcing dataset WFDE5 were evaluated for driving an uncalibrated setup of the mechanistic hydrological model PROMET (0.00833333°/1 h resolution) for the period 1980–2016. Different climatologies were used for linear bias correction of ERA5: the global WorldClim 2 temperature and precipitation climatologies and the regional GLOWA and PRISM Alpine precipitation climatologies. Simulations driven with the uncorrected ERA5 reanalysis, the WFDE5 forcing dataset, ERA5 bias-corrected with WorldClim 2 and ERA5 bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic were evaluated regarding percent bias of discharge and model efficiency. New hydrological insights for the region Simulations yielded good model efficiencies and low percent biases of discharge at selected gauges. Uncalibrated model efficiencies corresponded with previous hydrological modelling studies. ERA5 and WFDE5 were well suited to drive PROMET in the hydrologically complex Danube basin, but bias correction of precipitation was essential for ERA5. The ERA5-driven simulation bias-corrected with a GLOWA-PRISM-WorldClim 2 mosaic performed best. Bias correction with GLOWA and PRISM outperformed WorldClim 2 in the Alps due to more realistic small-scale Alpine precipitation patterns resulting from higher station densities. In mountainous terrain, we emphasize the need for regional high-resolution precipitation climatologies and recommend them for bias correction of precipitation rather than global datasets.
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Reanalysis precipitation products (RPPs) are frequently used for studying the water cycle changes from short to long-term scale globally. In the current study, ERA-5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japanese 55-year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), and the Climate Forecast System version 2 (CFS-2) precipitation products were evaluated with the rain-gauge data as a reference during 1981–2019 over Pakistan. The performance was assessed using statistical error metrics on daily, monthly, and annual timescales. The reanalysis precipitation products (RPPs) captured the precipitation intensities and the extreme precipitation events (75th to 99th percentile) across climatic classes. On a daily scale, the ERA-5 follows rain-gauges very closely (RC: 0.67, R: 0.81, RMSE: 1.69 mm), consistently capturing the precipitation intensities (light to violent) and extreme precipitation events (95th percentile), followed by CFS-2. The MERRA-2 captured precipitation intensity but did not detect extreme precipitation events in some regions. The JRA-55 produced good results in the central area while overestimated the precipitation in the northern and southern parts of the study area. On a monthly time scale, ERA-5 performed well as compared to the rest of RPPs, with regression coefficient values of 0.91, correlation coefficient (0.96), and a lower value of RMSE (11.09 mm), followed by JRA-55, MERRA-2, and CFS-2. All the RPPs performed better in winter, pre-monsoon, and post-monsoon seasons with slight deviations/differences, but in monsoon season, the ERA-5 and JRA-55 (MERRA-2, CFS-2) overestimated (underestimated) precipitation mean. The findings can help the researchers select reliable datasets for bias correction of the projections and real-time application in flood, drought estimation, and prediction.
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The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products.
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Water resources reanalysis (WRR) can be used as a numerical tool to advance our understanding of hydrological processes where in situ observations are limited. However, WRR products are associated with uncertainty that needs to be quantified to improve usability of such products in water resources applications. In this study, we evaluate estimates of water cycle components from 18 state-of-the-art WRR datasets derived from different land surface/hydrological models, meteorological forcing, and precipitation datasets. The evaluation was conducted at three spatial scales in the upper Blue Nile basin in Ethiopia. Precipitation, streamflow, evapotranspiration (ET), and terrestrial water storage (TWS) were evaluated against in situ daily precipitation and streamflow measurements, remote sensing–derived ET, and the NASA Gravity Recovery and Climate Experiment (GRACE) product, respectively. Our results highlight the current strengths and limitations of the available WRR datasets in analyzing the hydrological cycle and dynamics of the study basins, showing an overall underestimation of ET and TWS and overestimation of streamflow. While calibration improves streamflow simulation, it results in a relatively poorer performance in terms of ET. Additionally, we show that the differences in the schemes used in the various land surface models resulted in significant differences in the estimation of the water cycle components from the respective WRR products, while we noted small differences among the products related to precipitation forcing. We did not identify a single product that consistently outperformed others; however, we found that there are specific WRR products that provided accurate representation of a single component of the water cycle (e.g., only runoff) in the area.
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Consistent hydrological models for multiple time steps are needed to respond to the operational requirement of using a model for different objectives and with varied data sets. In this paper, we propose a rationale for multi-time step model development based on the temporal consistency of the fluxes modelled. The new methodology is applied for the temporal downscaling of a lumped rainfall-runoff model (GR4J) from daily to sub-hourly time steps, within a coherent modelling framework. The modelling tests are performed at eight time steps from daily to 6-min, for a set of 240 French catchments, for which 2400 flood events were selected. To identify an improved multi-time step model structure, a two-step approach is followed. First, we propose a model diagnosis to evaluate the internal behaviour of the baseline model across time steps, whereby a significant inconsistency was found in the simulated water fluxes, especially interception. Second, we recommend a prognosis to improve the scale invariance of the modelled water balance by matching the model fluxes across time scales. The new model structure retained for sub-daily time steps is derived from the daily baseline model by refining the representation of the interception process, so that the flux-matching condition is satisfied. A complementary modification of the groundwater exchange function is also resumed following a previous study. The structural changes are motivated by the improvement of model flux consistency across time steps, while also improving model performance. This new dual paradigm for model identification, based on both flux coherence and output accuracy, also results in more robust model parameters across time steps.
Article
This paper evaluates nine satellite rainfall products and the Global Precipitation Centre Climatology (GPCC) gauge dataset over the Congo basin. For the evaluation the reference dataset is a newly created, gridded gauge dataset based on a gauge network that is more complete than that of GPCC in recent years. It is termed NIC131-gridded. Gridding was achieved via a climatic reconstruction method based on principal components, so that reliable estimates of rainfall are available even in the data-sparse central basin. The satellite products were evaluated for two locations, the Congo basin and areas on its eastern and western periphery (termed the ''east plus west'' sector). The station density was notably higher in the latter region. Two time periods were also considered: 1983-94, when station density was relatively high, and 1998-2010, when station density was much lower than during the earlier period. Several products show excellent agreement with the NIC131-gridded reference dataset. These include CHIRPS2, PERSIANN-CDR, GPCP 2.3,TRMM3B43, and, to a lesser extent, GPCC V7. RMSE for the period 1983-94 in the east plus west sector is on the order of 20mmmonth ⁻¹ for GPCC V7 and 20-30mmmonth ⁻¹ for the other products. The compares with 40-60mm month ⁻¹ for the most poorly performing products, African Rainfall Climatology version 2 (ARCv2) and CMAP. Over the Congo basin, RMSE for those two products is about the same as in the east plus west sector but is on the order of 30-40mm month ⁻¹ for the better-performing products. In all cases, the performance of the 10 products evaluated is notably poorer in recent years (1998-2010), when the station network is sparse, than during the period 1983-94, when the dense station network provides reliable estimates of rainfall. For the more recent period RMSE is on the order of 30-40mm month21 for the best-performing products in the east plus west sector but only slightly higher over the Congo basin. All products do reasonably well in reproducing the seasonal cycle and the latitudinal gradients of rainfall. Estimates of interannual variability show more scatter among the various products and are less reliable. Overall, the most important results of the study are to demonstrate the strong impact that actual gauge data have on the various products and the need to have access to such gauge data, in order to produce reliable rainfall estimates from satellites.
Article
We describe a fourth version of the Global Historical Climatology Network (GHCN)-monthly (GHCNm) temperature dataset. Version 4 (v4) fulfills the goal of aligning GHCNmtemperature values with the GHCNdaily dataset and makes use of data from previous versions of GHCNm as well as data collated under the auspices of the International Surface Temperature Initiative. GHCNm v4 has many thousands of additional stations compared to version 3 (v3) both historically and with short time-delay updates. The greater number of stations as well as the use of records with incomplete data during the base period provides for greater global coverage throughout the record compared to earlier versions. Like v3, the monthly averages are screened for random errors and homogenized to address systematic errors. New to v4, uncertainties are calculated for each station series, and regional uncertainties scale directly from the station uncertainties. Correlated errors in the station series are quantified by running the homogenization algorithm as an ensemble. Additional uncertainties associated with incomplete homogenization and use of anomalies are then incorporated into the station ensemble. Further uncertainties are quantified at the regional level, the most important of which is for incomplete spatial coverage. Overall, homogenization has a smaller impact on the v4 global trend compared to v3, though adjustments lead to much greater consistency than between the unadjusted versions. The adjusted v3 global mean therefore falls within the range of uncertainty for v4 adjusted data. Likewise, annual anomaly uncertainties for the other major independent land surface air temperature datasets overlap with GHCNm v4 uncertainties.
Article
Accurate and spatially distributed rainfall data are crucial for a realistic simulation of the hydrological processes in a watershed. However, limited availability of observed hydro-meteorological data often challenges the rainfall–runoff modelling efforts. The main goal of this study is to evaluate the Climate Forecast System Reanalysis (CFSR) and Water and Global Change (WATCH) rainfall by comparing them with gauge observations for different rainfall regimes in the Mara Basin (Kenya/Tanzania). Additionally, the skill of these rainfall datasets to simulate the observed streamflow is assessed using the Soil and Water Assessment Tool (SWAT). The daily CFSR and WATCH rainfall show a poor performance (up to 52% bias and less than 0.3 correlation) when compared with gauge rainfall at grid and basin scale, regardless of the rainfall regime. However, the correlations for both CFSR and WATCH substantially improve at monthly scale. The 95% prediction uncertainty (95PPU) of the simulated daily streamflow, as forced by CFSR and WATCH rainfall, bracketed more than 60% of the observed streamflows. We however note high uncertainty for the high flow regime. Yet, the monthly and annual aggregated CFSR and WATCH rainfall can be a useful surrogate for gauge rainfall data for hydrologic application in the study area.
Article
The vital demand of reliable climatic and hydrologic data of fine spatial and temporal resolution triggered the employment of reanalysis datasets as a surrogate in most of the hydrological modelling exercises. This study examines the performance of four widely used reanalysis datasets: ERA-Interim, NCEP-DOE R2, MERRA and CFSR, in reproducing the spatio-temporal characteristics of observed daily precipitation of different stations spread across Ethiopia, East Africa. The appropriateness of relying on reanalysis datasets for hydrologic modelling, climate change impact assessment and regional modelling studies is assessed using various statistical and non-parametric techniques. ERA-Interim is found to exhibit higher correlation and least root mean square error values with observed daily rainfall, which is followed by CFSR and MERRA in most of the stations. The variability of daily precipitation is better captured by ERA, CFSR and MERRA, while NCEP-DOE R2 overestimated the spread of the precipitation data. While ERA overestimates the probability of moderate rainfall, it is seemingly better in capturing the probability of low rainfall. CFSR captures the overall distribution reasonable well. NCEP-DOE R2 appears to be outperforming others in capturing the probabilities of higher magnitude rainfall. Climatological seasonal cycle and the characteristics of wet and dry spells are compared further, where ERA seemingly replicates the pattern more effectively. However, observed rainfall exhibits higher frequency of short wet spells when compared to that of any reanalysis datasets. MERRA relatively underperforms in simulating the wet spell characteristics of observed daily rainfall. CFSR overestimates the mean wet spell length and mean dry spell length. Spatial trend analysis indicates that the northern and central western Ethiopia show increasing trends, whereas the Central and Eastern Ethiopia as well as the Southern Ethiopia stations show either no trend or decreasing trend. Overall, ERA-Interim and CFSR are better in depicting various characteristics of daily rainfall in Ethiopian region.
Article
Accurate predictions of hydrological models require accurate spatial and temporal distribution of rainfall. In developing countries, the network of observation stations for rainfall is sparse and unevenly distributed. Satellite-based products have the potential to overcome this shortcoming. The objective of this study is to compare the advantages and the limitation of commonly used high-resolution satellite rainfall products (Climate Forecast System Reanalysis, CFSR and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 version 7) as input to hydrological models as compared to sparsely and densely populated network of rain gauges. We used two (semi-distributed) hydrological models that performed well in the Ethiopian highlands: Hydrologiska Byråns Vattenbalansavdelning (HBV) and Parameter Efficient Distributed (PED). The rainfall products were tested in two watersheds: Gilgel Abay with a relatively dense network of rain gauge stations and Main Beles with a relatively scarce network, both are located in the Upper Blue Nile Basin. The results indicated that TMPA 3B42 was not be able to capture the gauged rainfall temporal 28 variation in both watersheds and was not tested further. CFSR over predicted the rainfall pattern slightly. Both the gauged and the CFSR reanalysis data were able to reproduce the streamflow well for both models and both watershed when calibrated separately to the discharge data. Using the calibrated model parameters of gauged rainfall dataset together with the CFSR rainfall, the stream discharge for the Gilgel Abay was reproduced well, but the discharge of the Main Beles was captured poorly partly because of the poor accuracy of the gauged rainfall dataset with none of the rainfall stations located inside the watershed. HBV model performed slightly better than the PED model, but the parameter values of the PED could be identified with the features of the landscape.