Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin

Liechti, T. Cohen; Matos, José Pedro; Boillat, Jean-Louis; Schleiss, Anton

doi:10.5194/hess-16-489-2012

Cited by 114 publications

(78 citation statements)

References 22 publications

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“…A study by Duan et al, (2016) Table 2). The correspondence of all products at a daily time scale and in all the validation areas was found comparably weak and the findings are in agreement with earlier studies (Cohen Liechti et al, 2012;Dembélé and Zwart, 2016).…”

supporting

confidence: 82%

“…Therefore, in this study we used point to pixel, point to area grid cell average, and stations average to area grid cell average to evaluate the accuracy of each 20 product. The most commonly used statistical methods such as the Pearson correlation coefficient (CC), bias, relative bias (Rbias), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Index of Agreement (IA) (Cohen Liechti et al, 2012;Daren Harmel and Smith, 2007;Moazami et al, 2013) are used. CC (Eq.…”

Section: Comparing Ground Data With Satellite Observational Reanalysmentioning

confidence: 99%

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Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa

Gebrechorkos¹,

Hülsmann²,

Bernhofer³

2017

Preprint

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Abstract. Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observation by remote sensing, and regional 15 climate models. The accuracy of the Africa Rainfall Climatology version 2 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis hybrid (ORH), and Regional Climate Models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by relating point to pixel, point to area grid cell average, and stations average to area grid cell average over 21 regions of 20 East Africa: 17 in Ethiopia, two in Kenya and two in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analyzed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (T-max and T-min) covering the period of 1983-2005. At daily time scale, CHIRPS, followed by ARC2 and CHIRP are the best performing rainfall products compared to ORH, RCM, and RCMS. CHIRPS 25 captures well the daily rainfall characteristics such as rainfall intensity, amount of wet days, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total rainfall (-30 %) and daily intensity (-14 %). CHIRP on the other hand, showed higher underestimation of the daily intensity 1 (-53 %) and duration of dry days (-29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly time scale, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products while ORH, individual RCM, and RCMs are the least performing products.Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-558 Manuscript under review for journal Hydrol. Earth Syst. Sci.For T-max and T-min, ORH was identified as the most suitable product compared to RCM and RCMs. 5Our results indicate that CHIRPS (rainfall) and ORH (T-max and T-min), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas where station data are not accessible. 2Hydrol. Earth Syst. Sci. Discuss., https://doi

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supporting

confidence: 82%

Section: Comparing Ground Data With Satellite Observational Reanalysmentioning

confidence: 99%

Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa

Gebrechorkos¹,

Hülsmann²,

Bernhofer³

2017

Preprint

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“…Earlier studies have shown the usefulness of such analysis in gauge-satellite comparisons, hydrological and meteorological modeling and setting up gauge networks (Habib et al, 2001;Krajewski et al, 2003;Ciach and Krajewski, 2006;Villarini et al, 2008;Liechti et al, 2012;Luini and Capsoni, 2012;Mandapaka and Qin, 2013;Li et al, 2014;Chen et al, 2015). Spearman correlation coefficients have been computed between each pair of rain gauge locations for different rain accumulation periods.…”

Section: Spatial Correlationmentioning

confidence: 99%

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Sunilkumar

Rao

Satheeshkumar

2016

Hydrol. Earth Syst. Sci.

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Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and subdaily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons.Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insign...

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“…Because of the crucial role of precipitation in driving the 10 land-surface water balance, several precipitation datasets are often compared, if possible to in-situ observations, and evaluated through their performance as model input prior to selecting a specific product (e.g. Awange et al (2016); Milzow et al (2011);Cohen Liechti et al (2012); Stisen and Sandholt (2010)). …”

mentioning

confidence: 99%

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

Kittel¹,

Bauer‐Gottwein²,

Nielsen³

et al. 2017

Preprint

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Abstract. Remote sensing provides a unique opportunity to inform and constrain a hydrological model and to increase its value as a decision-support tool. In this study, we applied a multi-mission approach to force, calibrate and validate a hydrological model of the ungauged Ogooué river basin in Africa with publicly available and free remote sensing observations. We used a rainfall-runoff model based on the Budyko framework coupled with a Muskingum routing approach. We parametrized the model using the SRTM DEM, and forced it using precipitation from two satellite-based rainfall estimates, FEWS-RFE and 5 TRMM 3B42 v.7, and temperature from ECMWF ERA-Interim. We combined three different datasets to calibrate the model using an aggregated objective function with contributions from: (1) historical in-situ discharge observations from the period 1953-1984 at six locations in the basin, (2) radar altimetry measurements of river stages by Envisat and Jason-2 at 12 locations in the basin and (3) GRACE total water storage change. Additionally, we extracted CryoSat-2 observations throughout the basin using a Sentinel-1 SAR imagery water mask and used the observations for validation of the model. The use of new satel-10 lite missions, including Sentinel-1 and CryoSat-2, increased the spatial characterization of river stage. Throughout the basin, we achieved good agreement between observed and simulated discharge and river stage, with a RMSD between simulated and observed water amplitudes at virtual stations of 0.74 m for the TRMM forced model and 0.87 m for the FEWS-RFE forced model. The hydrological model also captures overall total water storage change patterns, although the amplitude of storage change is generally underestimated. By combining hydrological modelling with multi-mission remote sensing from ten dif-15 ferent satellite missions, we obtain new information on an otherwise unstudied basin. The proposed model is the best current baseline characterization of hydrological conditions in the Ogooué in light of the available observations.

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Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin

Cited by 114 publications

References 22 publications

Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa

Evaluation of Multiple Climate Data Sources for Managing Environmental Resources in East Africa

Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Informing a hydrological model of the Ogooué with multi-mission remote sensing data

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