scholarly journals Predictability of Seasonal Streamflow Forecasting Based on CSM: Case Studies of Top Three Largest Rivers in China

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 162
Author(s):  
Lyuliu Liu ◽  
Ying Wu ◽  
Peiqun Zhang ◽  
Jianqing Zhai ◽  
Li Zhang ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate Models ◽  
Climate System ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Relative Operating Characteristic ◽  
Climate System Model ◽  
Seasonal Streamflow

Accurate seasonal streamflow forecasting is important in reservoir operation, watershed planning, and water resource management, and streamflow forecasting is often based on hydrological models driven by coupled global climate models (CGCMs). To understand streamflow forecasting predictability, this study considered the three largest rivers in China and explored deterministic and probabilistic skill metrics on the monthly scale according to ensemble streamflow hindcasts from the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV) driven by multiple climate forcings from the climate system model by the Beijing Climate Center (BCC_CSM1.1m). The effects of initial conditions (ICs) and meteorological forcings (MFs) on skill were investigated using the conventional ensemble streamflow prediction (ESP) and reverse-ESP (revESP). The results revealed the following: (1) Skill declines as lead time increases, and forecasting is generally the most skillful for lead month 1; (2) skill is higher for dry rivers than wet rivers, and higher for dry target months than wet months for the Yellow and Yangtze Rivers, suggesting greater skill in potential drought forecasting than flood forecasting; (3) the relative operating characteristic (ROC) area is greater for abnormal terciles than the near-normal tercile for all three rivers, greater for the above-normal tercile than the below-normal tercile for the Yellow and Yangtze Rivers, but slightly greater for the below-normal tercile than the above-normal tercile for the Xijiang River; and (4) the influence of ICs outweighs that of MFs in dry months, and the period of influence varies from 1 to 3 months; however, the influence of MFs is dominant in wet target months. These findings will help improve the understanding of both the seasonal streamflow forecasting predictability based on coupled climate system/hydrological models and of streamflow forecasting for variable rivers and seasons.

New airGR developments: semi-distribution and data assimilation

2021 ◽  
Author(s):  
Guillaume Thirel ◽  
Olivier Delaigue ◽  
David Dorchies ◽  
Gaia Piazzi
Keyword(s):  
Data Assimilation ◽  
Water Resource Management ◽  
Transfer Functions ◽  
Initial Conditions ◽  
R Package ◽  
Snow Accumulation ◽  
Sequential Data ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Distributed Models

<p>airGR (Coron et al., 2017, 2020) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea). It allows running seven hydrological models (including GR4J) dedicated to different time steps (hourly to annual) that can be combined to a snow accumulation and melt model (CemaNeige).</p><p>Thanks to the success of the airGR package, that was downloaded 45,000 times so far among 50 countries in the world and was used in dozen of publications since its release[1], its development team carries on its efforts to offer new features and improve the computer codes. This is how after offering a first add-on, the airGRteaching package, expressly developed for educational purposes, the team now offers tools dedicated to semi-distribution and data assimilation.</p><p>Using (semi-)distributed models is often necessary to explicitly represent spatial climatic and physiographic heterogeneities and to allow an analysis of their impact on the watershed response. Consequently, in the latest version of the airGR package, we introduced the semi-distribution of GR models, which are traditionally lumped, on a sub-basin basis. This development will also ultimately enable possibilities of implementing on a modular way different transfer functions as well as integrated water resource management (see package airGRiwrm in Abstract EGU21-2190).</p><p>In addition, a new package, called airGRdatassim, was recently proposed (Piazzi et al., 2021a, b) as an add-on to the airGR package. airGRdatassim enables the user to assimilate discharge observations via both Ensemble Kalman filter (EnKF) and particle filter (PF) schemes. Besides improving the simulations of GR models, this new package extends the potential applications of airGR to forecasting purposes by allowing for a reliable assessment of the initial conditions of streamflow forecasts. </p><p> </p><p>References:</p><p>Coron L., Thirel G., Delaigue O., Perrin C., Andréassian V. (2017). The Suite of Lumped GR Hydrological Models in an R package, Environmental Modelling & Software, 94, 166-171. DOI: 10.1016/j.envsoft.2017.05.002.</p><p>Coron, L., Delaigue, O., Thirel, G., Perrin, C. and Michel, C. (2020). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.4.3.65. URL: https://CRAN.R-project.org/package=airGR.</p><p>Piazzi, G., Delaigue, O. (2021a). airGRdatassim: Suite of Tools to Perform Ensemble-Based Data Assimilation in GR Hydrological Models. R package version 0.0.3.13. URL: https://gitlab.irstea.fr/HYCAR-Hydro/airgrdatassim.</p><p>Piazzi, G., Thirel, G., Perrin, C., Delaigue, O. (2021b, accepted). Sequential data assimilation for streamflow forecasting: assessing the sensitivity to uncertainties and updated variables of a conceptual hydrological model. Water Resources Research.</p><div><br><div> <p>[1] https://hydrogr.github.io/airGR/page_publications.html</p> </div> </div>

A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change

2021 ◽  
Author(s):  
Thedini Asali Peiris ◽  
Petra Döll
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Net Radiation ◽  
Ensemble Mean ◽  
The Impact

<p>Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO<sub>2</sub> concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO<sub>2</sub> and climate. This active vegetation response consists of three components. With higher CO<sub>2</sub> concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.</p><p>Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.</p><p>The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.</p><p>We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).</p><p>Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.</p>

scholarly journals A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

2006 ◽  
Vol 63 (11) ◽  
pp. 2813-2830 ◽  
Author(s):  
Roger Marchand ◽  
Nathaniel Beagley ◽  
Sandra E. Thompson ◽  
Thomas P. Ackerman ◽  
David M. Schultz
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate ◽  
Stable State ◽  
Local Scale ◽  
Global Climate Models ◽  
Scale Model ◽  
Synoptic Scale ◽  
Cloud Properties

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

scholarly journals Influence of parallel computational uncertainty on simulations of the Coupled General Climate Model

2012 ◽  
Vol 5 (2) ◽  
pp. 313-319 ◽  
Author(s):  
Z. Song ◽  
F. Qiao ◽  
X. Lei ◽  
C. Wang
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Quality Standard ◽  
Ensemble Member ◽  
Climate System ◽  
Climate System Model ◽  
Climate Simulations ◽  
Computational Uncertainty ◽  
The Impact ◽  
General Climate

Abstract. This paper investigates the impact of the parallel computational uncertainty due to the round-off error on climate simulations using the Community Climate System Model Version 3 (CCSM3). A series of sensitivity experiments have been conducted and the analyses are focused on the Global and Nino3.4 average sea surface temperatures (SST). For the monthly time series, it is shown that the amplitude of the deviation induced by the parallel computational uncertainty is the same order as that of the climate system change. However, the ensemble mean method can reduce the influence and the ensemble member number of 15 is enough to ignore the uncertainty. For climatology, the influence can be ignored when the climatological mean is calculated by using more than 30-yr simulations. It is also found that the parallel computational uncertainty has no distinguishable effect on power spectrum analysis of climate variability such as ENSO. Finally, it is suggested that the influence of the parallel computational uncertainty on Coupled General Climate Models (CGCMs) can be a quality standard or a metric for developing CGCMs.

scholarly journals Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

2008 ◽  
Vol 9 (1) ◽  
pp. 132-148 ◽  
Author(s):  
Andrew W. Wood ◽  
John C. Schaake
Keyword(s):  
River Basin ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Summer Period ◽  
Streamflow Forecast ◽  
Ensemble Mean ◽  
Ensemble Streamflow Prediction ◽  
Deterministic Components ◽  
Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

scholarly journals Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2130 ◽  
Author(s):  
Zhu ◽  
Zhang ◽  
Wu ◽  
Qi ◽  
Fu ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Water Resource Management ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Liaoning Province ◽  
Lower Envelope ◽  
The Impact

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts

2015 ◽  
Vol 16 (2) ◽  
pp. 762-780 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Martyn P. Clark ◽  
Naoki Mizukami ◽  
Andrew J. Newman ◽  
Michael Barlage ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Change Scenario ◽  
Hydrologic Models ◽  
Climate System Model

Abstract The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

Two Limits of Initial-Value Decadal Predictability in a CGCM

2010 ◽  
Vol 23 (23) ◽  
pp. 6292-6311 ◽  
Author(s):  
Grant Branstator ◽  
Haiyan Teng
Keyword(s):  
Initial Conditions ◽  
Heat Content ◽  
Forced Response ◽  
Climate System ◽  
Change Scenario ◽  
Model Errors ◽  
Initial Value ◽  
Climate System Model ◽  
Predictive Skill ◽  
Decadal Climate

Abstract When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results of this study are sensitive and insensitive to are also discussed.

Initialization and Potential Predictability of Soil Moisture in the Canadian Seasonal to Interannual Prediction System

2018 ◽  
Vol 31 (13) ◽  
pp. 5205-5224 ◽  
Author(s):  
Reinel Sospedra-Alfonso ◽  
William J. Merryfield
Keyword(s):  
Soil Moisture ◽  
Climate Models ◽  
Initial Conditions ◽  
Soil Depth ◽  
Global Climate ◽  
Global Climate Models ◽  
Prediction System ◽  
Potential Predictability ◽  
Temporal Variance ◽  
The Tropics

The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.

scholarly journals Impact of Global Warming on Extreme Heavy Rainfall in the Present Climate: Case Study of Heavy Rainfall in Kinugawa, Japan (2015)

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 220
Author(s):  
Kenji Taniguchi ◽  
Yuto Minobe
Keyword(s):  
Global Warming ◽  
Heavy Rainfall ◽  
Climate Models ◽  
Initial Conditions ◽  
Atmospheric Temperature ◽  
Global Climate Models ◽  
Extreme Weather Events ◽  
Heavy Rainfall Event ◽  
Simulated Rainfall ◽  
Total Rainfall

Hazardous heavy rainfall and wide-scale inundation occurred in the Kinugawa River basin, north of Tokyo, in 2015. In this study, ensemble hindcast and non-global warming (NGW) simulations of this heavy rainfall event were implemented. In the NGW simulations, initial and boundary conditions were generated by using the outputs of natural forcing historical experiments by twelve different global climate models. The results of the hindcast and NGW simulations indicated the high likelihood of the generation of linear heavy rainfall bands and the intensification of Kinugawa heavy rainfall due to anthropogenic greenhouse gas emissions. However, in some NGW simulations, the total rainfall was greater than in the hindcast. In addition, the maximum total rainfall was greater in many NGW simulations. Lower atmospheric temperature, sea surface temperature (SST), and precipitable water content (PWC) under the initial conditions can cause less rainfall in the NGW simulations. However, some discrepancies were found in the initial conditions and simulated rainfall; less rainfall with higher atmospheric temperature, SST and PWC, and vice versa. A detailed investigation of simulated atmospheric conditions explained the simulated rainfall. These results indicate that it is not sufficient to examine climatological anomalies to understand individual extreme weather events, but that detailed simulations are useful.

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