scholarly journals Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

2016 ◽  
Vol 20 (2) ◽  
pp. 903-920 ◽  
Author(s):  
W. Qi ◽  
C. Zhang ◽  
G. Fu ◽  
C. Sweetapple ◽  
H. Zhou
Keyword(s):  
Hydrological Model ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Precipitation Product ◽  
Physically Based ◽  
Precipitation Estimation ◽  
Discharge Simulation ◽  
Fine Resolution

Abstract. The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash–Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

scholarly journals Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration

2014 ◽  
Vol 18 (9) ◽  
pp. 3511-3538 ◽  
Author(s):  
H. Müller Schmied ◽  
S. Eisner ◽  
D. Franz ◽  
M. Wattenbach ◽  
F. T. Portmann ◽  
...  
Keyword(s):  
Land Cover ◽  
Water Use ◽  
River Discharge ◽  
Hydrological Model ◽  
Grid Cell ◽  
Global Scale ◽  
Climate Forcing ◽  
Actual Evapotranspiration ◽  
Climate Data ◽  
Freshwater Fluxes

Abstract. Global-scale assessments of freshwater fluxes and storages by hydrological models under historic climate conditions are subject to a variety of uncertainties. Using the global hydrological model WaterGAP (Water – Global Assessment and Prognosis) 2.2, we investigated the sensitivity of simulated freshwater fluxes and water storage variations to five major sources of uncertainty: climate forcing, land cover input, model structure/refinements, consideration of human water use and calibration (or no calibration) against observed mean river discharge. In a modeling experiment, five variants of the standard version of WaterGAP 2.2 were generated that differed from the standard version only regarding the investigated source of uncertainty. The basin-specific calibration approach for WaterGAP was found to have the largest effect on grid cell fluxes as well as on global AET (actual evapotranspiration) and discharge into oceans for the period 1971–2000. Regarding grid cell fluxes, climate forcing ranks second before land cover input. Global water storage trends are most sensitive to model refinements (mainly modeling of groundwater depletion) and consideration of human water use. The best fit to observed time series of monthly river discharge or discharge seasonality is obtained with the standard WaterGAP 2.2 model version which is calibrated and driven by daily reanalysis-based WFD/WFDEI (combination of Watch Forcing Data based on ERA40 and Watch Forcing Data based on ERA-Interim) climate data. Discharge computed by a calibrated model version using monthly CRU TS (Climate Research Unit time-series) 3.2 and GPCC (Global Precipitation Climatology Center) v6 climate input reduced the fit to observed discharge for most stations. Taking into account uncertainties of climate and land cover data, global 1971–2000 discharge into oceans and inland sinks ranges between 40 000 and 42 000 km3 yr−1. Global actual evapotranspiration, with 70 000 km3 yr−1, is rather unaffected by climate and land cover uncertainties. Human water use reduced river discharge by 1000 km3 yr−1, such that global renewable water resources are estimated to range between 41 000 and 43 000 km3 yr−1. The climate data sets WFD (available until 2001) and WFDEI (starting in 1979) were found to be inconsistent with respect to shortwave radiation data, resulting in strongly different actual evapotranspiration. Global assessments of freshwater fluxes and storages would therefore benefit from the development of a global data set of consistent daily climate forcing from 1900 to present.

Calibration of a semi-distributed hydrological model adding constrains from remotely sensed snow cover and soil moisture products

2020 ◽  
Author(s):  
Rui Tong ◽  
Juraj Parajka ◽  
Jürgen Komma ◽  
Günter Blöschl
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Snow Melt ◽  
Distributed Hydrological Model ◽  
Calibration Methods ◽  
Discharge Simulation ◽  
Normalized Difference Snow Index ◽  
Discharge Modeling

<p>Remote sensing products have been widely applied in hydrological modeling for more realistic representations of hydrological processes. In this study, in addition to gauged discharge, the combined MODIS snow cover maps and ERS scatterometer based soil moisture products were added to constrain a semi-distributed conceptual hydrological model. The latest version of MODIS snow cover images provides a daily Normalized Difference Snow Index (NDSI) in a 500-meter resolution. We derived the snow cover maps by using a new NDSI thresholding method from the MODIS Aqua (MYD10A1) and Terra (MOD10A1) daily snow cover products. Furthermore, the newest ERS soil moisture product also provided a finer spatial resolution of 500-meter over Austria. The semi-distributed TUW-model was tested in 213 catchments using both single and multiple object calibration methods. We found that the semi-distributed TUW-model performed well in discharge modeling. Moreover, applying the MODIS snow cover maps improved the accuracy in the snow-melt season, while the soil moisture product helped the discharge simulation in the no-snow period.</p>

scholarly journals Influence of Rainfall Data with Different Spatial Resolutions on Flood Forecasting Reliability

10.29007/74bp ◽  
2018 ◽  
Author(s):  
Mamoru Miyamoto ◽  
Kazuhiro Matsumoto
Keyword(s):  
River Discharge ◽  
Hydrological Model ◽  
Radar Data ◽  
Flood Forecasting ◽  
Rainfall Data ◽  
Error Assessment ◽  
Distributed Hydrological Model ◽  
Radar Rainfall ◽  
Convergence Results ◽  
Gauge Data

Recent advancements in precipitation observation technology make it possible to precisely describe the intensity and temporal-spatial distribution of heavy rainfall, which can cause severe floods and inundations. Such technologies have also increased the accuracy of flood forecasting. However, error factors in flood forecasting remain to be solved, originating in not only input data but also model structure and calibration. Thus, this study focused on convergence results of errors in parameter optimization of the PWRI Distributed Hydrological Model and the reproducibility of river discharge. The reliability of ground-gauge and C-band-radar rainfall is compared in terms of flood forecasting under the condition of the minimum error due to calibration. Although the convergence results showed that C-band radar rainfall was superior to ground gauge rainfall, both were equally effective in reproducing river discharge with a high NSE of 0.9 at a station with error assessment. On the other hand, the reproducibility of river discharge with C-band radar data was highly superior to that with ground gauge data at a station without error assessment. This indicates that grid-based high resolution rainfall data is necessary for basin-wide flood forecasting.

scholarly journals Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

2015 ◽  
Vol 12 (9) ◽  
pp. 9337-9391 ◽  
Author(s):  
W. Qi ◽  
C. Zhang ◽  
G. T. Fu ◽  
C. Sweetapple ◽  
H. C. Zhou
Keyword(s):  
Hydrological Model ◽  
Heavy Precipitation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Precipitation Product ◽  
Precipitation Measurement ◽  
Physically Based ◽  
Discharge Simulation ◽  
Fine Resolution ◽  
Global Precipitation

Abstract. The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products using different precipitation computation recipes, is comprehensively evaluated using statistical and hydrological methods in a usually-neglected area (northeastern China), and a framework quantifying uncertainty contributions of precipitation products, hydrological models and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products include TRMM3B42, TRMM3B42RT, GLDAS/Noah, APHRODITE, PERSIANN and GSMAP-MVK+. Two hydrological models of different complexities, i.e., a water and energy budget-based distributed hydrological model and a physically-based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and the cloud motion vectors used by GSMAP-MVK+ show huge advantage. These findings could be very useful for validation, refinement and future development of satellite-based products (e.g., NASA Global Precipitation Measurement). Although significant uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models contribute significantly to uncertainty in discharge simulations and a better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based product, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

scholarly journals Which rainfall score is more informative about the performance in river discharge simulation? A comprehensive assessment on 1318 basins over Europe

2020 ◽  
Vol 24 (10) ◽  
pp. 4869-4885 ◽  
Author(s):  
Stefania Camici ◽  
Christian Massari ◽  
Luca Ciabatta ◽  
Ivan Marchesini ◽  
Luca Brocca
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
River Discharge ◽  
Hydrological Model ◽  
Efficiency Index ◽  
Hydrological Modelling ◽  
Mean Square ◽  
Discharge Simulation

Abstract. The global availability of satellite rainfall products (SRPs) at an increasingly high temporal and spatial resolution has made their exploitation in hydrological applications possible, especially in data-scarce regions. In this context, understanding how uncertainties transfer from SRPs to river discharge simulations, through the hydrological model, is a main research question. SRPs' accuracy is normally characterized by comparing them with ground observations via the calculation of categorical (e.g. threat score, false alarm ratio and probability of detection) and/or continuous (e.g. bias, root mean square error, Nash–Sutcliffe index, Kling–Gupta efficiency index and correlation coefficient) performance scores. However, whether these scores are informative about the associated performance in river discharge simulations (when the SRP is used as input to a hydrological model) is an under-discussed research topic. This study aims to relate the accuracy of different SRPs both in terms of rainfall and in terms of river discharge simulation. That is, the following research questions are addressed: is there any performance score that can be used to select the best performing rainfall product for river discharge simulation? Are multiple scores needed? And, which are these scores? To answer these questions, three SRPs, namely the Tropical Rainfall Measurement Mission (TRRM) Multi-satellite Precipitation Analysis (TMPA), the Climate Prediction Center MORPHing (CMORPH) algorithm and the SM2RAIN algorithm applied to the Advanced SCATterometer (ASCAT) soil moisture product (SM2RAIN–ASCAT) have been used as input into a lumped hydrologic model, “Modello Idrologico Semi-Distribuito in continuo” (MISDc), for 1318 basins over Europe with different physiographic characteristics. Results suggest that, among the continuous scores, the correlation coefficient and Kling–Gupta efficiency index are not reliable indices to select the best performing rainfall product for hydrological modelling, whereas bias and root mean square error seem more appropriate. In particular, by constraining the relative bias to absolute values lower than 0.2 and the relative root mean square error to values lower than 2, good hydrological performances (Kling–Gupta efficiency index on river discharge greater than 0.5) are ensured for almost 75 % of the basins fulfilling these criteria. Conversely, the categorical scores have not provided suitable information for addressing the SRP selection for hydrological modelling.

scholarly journals High resolution satellite products improve hydrological modeling in northern Italy

2021 ◽  
Author(s):  
Lorenzo Alfieri ◽  
Francesco Avanzi ◽  
Fabio Delogu ◽  
Simone Gabellani ◽  
Giulia Bruno ◽  
...  
Keyword(s):  
High Resolution ◽  
River Discharge ◽  
Model Calibration ◽  
Environmental Science ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Observation Data ◽  
Distributed Hydrological Model ◽  
Po River ◽  
Hydrological Applications

Abstract. Satellite Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolution, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based Earth observation data in hydrological modelling. In a set of experiments, the distributed hydrological model Continuum is set up for the Po River Basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling-Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean = 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite dataset on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.

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