scholarly journals Hydrological Evaluation of Satellite and Reanalysis-based Rainfall Estimates Over the Upper Tekeze Basin, Ethiopia

2021 ◽  
Author(s):  
Kidane Reda ◽  
Xingcai Liu ◽  
Gebremedhin Haile ◽  
Siao Sun ◽  
Qiuhong Tang
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Swat Model ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
High Flow ◽  
Low Flow ◽  
Northern Ethiopia ◽  
Monthly Streamflow ◽  
Rainfall Estimates

Spatial rainfall data is an essential input to physically based, parametrically distributed hydrological models, and a main contributor to hydrological model uncertainty. Two important issues should be addressed before use of satellite and reanalysis rainfall product at basin level: 1) how useful are these rainfall estimates as forcing data for regional hydrological modeling? 2) which should be preferred for hydrological modelling at high flow and low flow seasons? To this end, rainfall estimates from a satellite-based product, CHIRPSv8, and reanalysis data, EWEMBI, were used as input to SWAT model, and mode performances were evaluated against streamflow measured at three gauge stations in the Upper Tekeze River basin, northern Ethiopia for the period of 2006-2015. Results showed that (I) the daily rainfall from both CHIRPSv8 and EWEMBI are close to the rain gauge data, with relative errors 2.12% and 3.85%, respectively; (II) the monthly streamflow simulated by the SWAT model driven by the CHIRPSv8 and EWEMBI had a Kling-Gupta Efficiency value of 0.6-0.79 and 0.58-0.64, respectively; (III) the SWAT model calibrated with the CHIRPSv8 and EWEMBI rainfall estimates has shown an improvement in hydrological performance compared with that calibrated with interpolated ground observations; (IV) the hydrological performance during high flow seasons is superior to low flow seasons for both CHIRPSv8 and EWEMBI, thus promoting the use of the products for applications focusing on the high flow conditions. In particular, CHIRPSv8 showed relatively better hydrologic performance than EWEMBI. This study provides insight on the usefulness of the gridded rainfall products for hydrological modeling and under which conditions they can be used to generate a plausible level of adequacy and reliability over the Upper Tekeze River basin.

scholarly journals Baseflow simulation using SWAT model in an inland river basin in Tianshan Mountains, Northwest China

2012 ◽  
Vol 16 (4) ◽  
pp. 1259-1267 ◽  
Author(s):  
Y. Luo ◽  
J. Arnold ◽  
P. Allen ◽  
X. Chen
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Northwest China ◽  
Tianshan Mountains ◽  
Soil Freezing ◽  
Low Flow ◽  
Model Based ◽  
The One

Abstract. Baseflow is an important component in hydrological modeling. The complex streamflow recession process complicates the baseflow simulation. In order to simulate the snow and/or glacier melt dominated streamflow receding quickly during the high-flow period but very slowly during the low-flow period in rivers in arid and cold northwest China, the current one-reservoir baseflow approach in SWAT (Soil Water Assessment Tool) model was extended by adding a slow- reacting reservoir and applying it to the Manas River basin in the Tianshan Mountains. Meanwhile, a digital filter program was employed to separate baseflow from streamflow records for comparisons. Results indicated that the two-reservoir method yielded much better results than the one-reservoir one in reproducing streamflow processes, and the low-flow estimation was improved markedly. Nash-Sutcliff efficiency values at the calibration and validation stages are 0.68 and 0.62 for the one-reservoir case, and 0.76 and 0.69 for the two-reservoir case. The filter-based method estimated the baseflow index as 0.60, while the model-based as 0.45. The filter-based baseflow responded almost immediately to surface runoff occurrence at onset of rising limb, while the model-based responded with a delay. In consideration of watershed surface storage retention and soil freezing/thawing effects on infiltration and recharge during initial snowmelt season, a delay response is considered to be more reasonable. However, a more detailed description of freezing/thawing processes should be included in soil modules so as to determine recharge to aquifer during these processes, and thus an accurate onset point of rising limb of the simulated baseflow.

scholarly journals Comparison Study of Multiple Precipitation Forcing Data on Hydrological Modeling and Projection in the Qujiang River Basin

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2626 ◽  
Author(s):  
Yongyu Song ◽  
Jing Zhang ◽  
Xianyong Meng ◽  
Yuyan Zhou ◽  
Yuequn Lai ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Water Cycle ◽  
Swat Model ◽  
Precipitation Data ◽  
Simulation Performance ◽  
Surface Water Resources ◽  
Monthly Streamflow ◽  
Key Factor

As a key factor in the water cycle and climate change, the quality of precipitation data directly affects the hydrological processes of the river basin. Although many precipitation products with high spatial and temporal resolutions are now widely used, it is meaningful and necessary to investigate and evaluate their merits and demerits in hydrological applications. In this study, two satellite-based precipitation products (Tropical Rainfall Measurement Mission, TRMM; Integrated Multi-satellite Retrievals for GPM, IMERG) and one reanalysis precipitation product (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model, CMADS) are studied to compare their streamflow simulation performance in the Qujiang River Basin, China, using the SWAT model with gauged rainfall data as a reference. The main conclusions are as follows: (1) CMADS has stronger precipitation detection capabilities compared to gauged rainfall, while TRMM results in the most obvious overestimation in the four sub-basins. (2) In daily and monthly streamflow simulations, CMADS + SWAT mode offers the best performance. CMADS and IMERG can provide high quality precipitation data for data-scarce areas, and IMERG can effectively avoid the overestimation of streamflow caused by TRMM, especially on a daily scale. (3) The runoff projections of the three modes under RCP (Representative Concentration Pathway) 4.5 was higher than that of RCP 8.5 on the whole. IMERG + SWAT overestimates the surface water resources of the basin compared to CMADS + SWAT, while TRMM + SWAT provides the most stable uncertainty. These findings contribute to the comparison of the differences among the three precipitation products and provides a reference for the selection of precipitation data in similar regions.

scholarly journals Evaluation of the CRU TS3.1, APHRODITE_V1101, and CFSR Datasets in Assessing Water Balance Components in the Upper Vakhsh River Basin in Central Asia

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1334
Author(s):  
Aminjon Gulakhmadov ◽  
Xi Chen ◽  
Manuchekhr Gulakhmadov ◽  
Zainalobudin Kobuliev ◽  
Nekruz Gulahmadov ◽  
...  
Keyword(s):  
Water Balance ◽  
Central Asia ◽  
Observational Data ◽  
River Basin ◽  
Hydrological Modeling ◽  
Swat Model ◽  
Coefficient Of Determination ◽  
Climatic Research Unit ◽  
Water Balance Components ◽  
Monthly Streamflow

In this study, the applicability of three gridded datasets was evaluated (Climatic Research Unit (CRU) Time Series (TS) 3.1, “Asian Precipitation—Highly Resolved Observational Data Integration Toward the Evaluation of Water Resources” (APHRODITE)_V1101, and the climate forecast system reanalysis dataset (CFSR)) in different combinations against observational data for predicting the hydrology of the Upper Vakhsh River Basin (UVRB) in Central Asia. Water balance components were computed, the results calibrated with the SUFI-2 approach using the calibration of soil and water assessment tool models (SWAT–CUP) program, and the performance of the model was evaluated. Streamflow simulation using the SWAT model in the UVRB was more sensitive to five parameters (ALPHA_BF, SOL_BD, CN2, CH_K2, and RCHRG_DP). The simulation for calibration, validation, and overall scales showed an acceptable correlation between the observed and simulated monthly streamflow for all combination datasets. The coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) showed “excellent” and “good” values for all datasets. Based on the R2 and NSE from the “excellent” down to “good” datasets, the values were 0.91 and 0.92 using the observational datasets, CRU TS3.1 (0.90 and 0.90), APHRODITE_V1101+CRU TS3.1 (0.74 and 0.76), APHRODITE_V1101+CFSR (0.72 and 0.78), and CFSR (0.67 and 0.74) for the overall scale (1982–2006). The mean annual evapotranspiration values from the UVRB were about 9.93% (APHRODITE_V1101+CFSR), 25.52% (APHRODITE_V1101+CRU TS3.1), 2.9% (CFSR), 21.08% (CRU TS3.1), and 27.28% (observational datasets) of annual precipitation (186.3 mm, 315.7 mm, 72.1 mm, 256.4 mm, and 299.7 mm, out of 1875.9 mm, 1236.9 mm, 2479 mm, 1215.9 mm, and 1098.5 mm). The contributions of the snowmelt to annual runoff were about 81.06% (APHRODITE_V1101+CFSR), 63.12% (APHRODITE_V1101+CRU TS3.1), 82.79% (CFSR), 81.66% (CRU TS3.1), and 67.67% (observational datasets), and the contributions of rain to the annual flow were about 18.94%, 36.88%, 17.21%, 18.34%, and 32.33%, respectively, for the overall scale. We found that gridded climate datasets can be used as an alternative source for hydrological modeling in the Upper Vakhsh River Basin in Central Asia, especially in scarce-observation regions. Water balance components, simulated by the SWAT model, provided a baseline understanding of the hydrological processes through which water management issues can be dealt with in the basin.

scholarly journals Baseflow simulation of SWAT model in an inland river basin in Tianshan Mountains, Northwest China

2011 ◽  
Vol 8 (6) ◽  
pp. 10397-10424 ◽  
Author(s):  
Y. Luo ◽  
J. Arnold ◽  
P. Allen ◽  
X. Chen
Keyword(s):  
River Basin ◽  
Hydrological Modeling ◽  
Assessment Tool ◽  
Swat Model ◽  
Northwest China ◽  
Tianshan Mountains ◽  
Soil Freezing ◽  
Low Flow ◽  
Model Based ◽  
The One

Abstract. Baseflow is an important component in hydrological modeling. Complex streamflow recession process complicates the baseflow simulation. In order to simulate the snow and/or glacier melt dominated streamflow receding quickly during high-flow period but very slowly during the low-flow period in rivers in arid and cold Northwest China, the current one-reservoir baseflow approach in SWAT (Soil Water Assessment Tool) model was extended by adding a slow reacting reservoir and applied to the Manas River basin in Tianshan Mountains. Meanwhile, a digital filter program was employed to separate baseflow from streamflow records for comparisons. Results indicated that the two-reservoir method yielded much better results than the one-reservoir one in reproducing streamflow processes, and the low-flow estimation was improved markedly. Nash-Sutcliff efficiency values at the calibration and validation stages are 0.68 and 0.62 for the one-reservoir case, and 0.76 and 0.69 for the two-reservoir case, respectively. The filter-based method estimated the baseflow index as 0.60, while the model-based as o.45. The filter-based baseflow responds almost immediately to surface runoff occurrence at onset of rising limb, while the model-based with a delay. In consideration of watershed surface storage retention and soil freezing/thawing effects on infiltration and recharge during initial snowmelt season, a delay response is considered to be more reasonable. However, a more detailed description of freezing/thawing processes should be included in soil modules so as to determine recharge to aquifer during these processes, and thus an accurate onset point of rising limb of the simulated baseflow.

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

scholarly journals Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China

2018 ◽  
Vol 10 (12) ◽  
pp. 1881 ◽  
Author(s):  
Yueyuan Zhang ◽  
Yungang Li ◽  
Xuan Ji ◽  
Xian Luo ◽  
Xue Li
Keyword(s):  
River Basin ◽  
Southwest China ◽  
Spatial Scales ◽  
Red River ◽  
Low Flow ◽  
Efficiency Coefficient ◽  
Monthly Streamflow ◽  
Red River Basin ◽  
Hydrological Applications ◽  
Trmm 3B42

Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.

scholarly journals Automatic Prediction of High-Resolution Daily Rainfall Fields for Multiple Extents: The Potential of Operational Radar

2007 ◽  
Vol 8 (6) ◽  
pp. 1204-1224 ◽  
Author(s):  
J. M. Schuurmans ◽  
M. F. P. Bierkens ◽  
E. J. Pebesma ◽  
R. Uijlenhoet
Keyword(s):  
High Resolution ◽  
Hydrological Modeling ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Added Value ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Gauge Data ◽  
Event Based ◽  
Better Than

Abstract This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km2). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km2), medium (10 000 km2), and large (82 875 km2). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

scholarly journals Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia

2014 ◽  
Vol 15 (6) ◽  
pp. 2347-2369 ◽  
Author(s):  
Matthew P. Young ◽  
Charles J. R. Williams ◽  
J. Christine Chiu ◽  
Ross I. Maidment ◽  
Shu-Hua Chen
Keyword(s):  
Daily Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Shallow Convection ◽  
Rain Gauges ◽  
The Mean ◽  
Satellite Rainfall ◽  
Convective Cells ◽  
Rainfall Estimates

Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.

scholarly journals Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China

2020 ◽  
Vol 12 (6) ◽  
pp. 1042 ◽  
Author(s):  
Xiaoying Yang ◽  
Yang Lu ◽  
Mou Leong Tan ◽  
Xiaogang Li ◽  
Guoqing Wang ◽  
...  
Keyword(s):  
River Basin ◽  
Time Scales ◽  
Daily Rainfall ◽  
Central China ◽  
Spatiotemporal Resolution ◽  
Study Region ◽  
East Central ◽  
Hourly Rainfall ◽  
Rainfall Estimates ◽  
Different Time Scales

Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.

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

2012 ◽  
Vol 13 (1) ◽  
pp. 338-350 ◽  
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.

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