Journal articles on the topic 'Stochastic hydrological forcing'
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Feng, Xue, Amilcare Porporato, and Ignacio Rodriguez-Iturbe. "Stochastic soil water balance under seasonal climates." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2174 (February 2015): 20140623. http://dx.doi.org/10.1098/rspa.2014.0623.
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The analysis of soil water partitioning in seasonally dry climates necessarily requires careful consideration of the periodic climatic forcing at the intra-annual timescale in addition to daily scale variabilities. Here, we introduce three new extensions to a stochastic soil moisture model which yields seasonal evolution of soil moisture and relevant hydrological fluxes. These approximations allow seasonal climatic forcings (e.g. rainfall and potential evapotranspiration) to be fully resolved, extending the analysis of soil water partitioning to account explicitly for the seasonal amplitude and the phase difference between the climatic forcings. The results provide accurate descriptions of probabilistic soil moisture dynamics under seasonal climates without requiring extensive numerical simulations. We also find that the transfer of soil moisture between the wet to the dry season is responsible for hysteresis in the hydrological response, showing asymmetrical trajectories in the mean soil moisture and in the transient Budyko's curves during the ‘dry-down‘ versus the ‘rewetting‘ phases of the year. Furthermore, in some dry climates where rainfall and potential evapotranspiration are in-phase, annual evapotranspiration can be shown to increase because of inter-seasonal soil moisture transfer, highlighting the importance of soil water storage in the seasonal context.
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Peterson, T. J., and A. W. Western. "Multiple hydrological attractors under stochastic daily forcing: 1. Can multiple attractors exist?" Water Resources Research 50, no. 4 (April 2014): 2993–3009. http://dx.doi.org/10.1002/2012wr013003.
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Peterson, T. J., A. W. Western, and R. M. Argent. "Multiple hydrological attractors under stochastic daily forcing: 2. Can multiple attractors emerge?" Water Resources Research 50, no. 4 (April 2014): 3010–29. http://dx.doi.org/10.1002/2012wr013004.
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Bertassello, L. E., E. Bertuzzo, G. Botter, J. W. Jawitz, A. F. Aubeneau, J. T. Hoverman, A. Rinaldo, and P. S. C. Rao. "Dynamic spatio-temporal patterns of metapopulation occupancy in patchy habitats." Royal Society Open Science 8, no. 1 (January 13, 2021): 201309. http://dx.doi.org/10.1098/rsos.201309.
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Spatio-temporal dynamics in habitat suitability and connectivity among mosaics of heterogeneous wetlands are critical for biological diversity and species persistence in aquatic patchy landscapes. Despite the recognized importance of stochastic hydroclimatic forcing in driving wetlandscape hydrological dynamics, linking such effects to emergent dynamics of metapopulation poses significant challenges. To fill this gap, we propose here a dynamic stochastic patch occupancy model (SPOM), which links parsimonious hydrological and ecological models to simulate spatio-temporal patterns in species occupancy in wetlandscapes. Our work aims to place ecological studies of patchy habitats into a proper hydrologic and climatic framework to improve the knowledge about metapopulation shifts in response to climate-driven changes in wetlandscapes. We applied the dynamic version of the SPOM (D-SPOM) framework in two wetlandscapes in the US with contrasting landscape and climate properties. Our results illustrate that explicit consideration of the temporal dimension proposed in the D-SPOM is important to interpret local- and landscape-scale patterns of habitat suitability and metapopulation occupancy. Our analyses show that spatio-temporal dynamics of patch suitability and accessibility, driven by the stochasticity in hydroclimatic forcing, influence metapopulation occupancy and the topological metrics of the emergent wetlandscape dispersal network. D-SPOM simulations also reveal that the extinction risk in dynamic wetlandscapes is exacerbated by extended dry periods when suitable habitat decreases, hence limiting successful patch colonization and exacerbating metapopulation extinction risks. The proposed framework is not restricted only to wetland studies but could also be applied to examine metapopulation dynamics in other types of patchy habitats subjected to stochastic external disturbances.
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Pham, Minh Tu, Hilde Vernieuwe, Bernard De Baets, and Niko E. C. Verhoest. "A coupled stochastic rainfall–evapotranspiration model for hydrological impact analysis." Hydrology and Earth System Sciences 22, no. 2 (February 20, 2018): 1263–83. http://dx.doi.org/10.5194/hess-22-1263-2018.
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Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall–evapotranspiration model has great potential for hydrological impact analysis.
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Janatian, Nasime, Kalle Olli, and Peeter Nõges. "Phytoplankton responses to meteorological and hydrological forcing at decadal to seasonal time scales." Hydrobiologia 848, no. 11 (April 30, 2021): 2745–59. http://dx.doi.org/10.1007/s10750-021-04594-x.
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AbstractOne of the challenges for predicting global change effects on aquatic ecosystems is the vague understanding of the mechanisms of multiple controlling factors affecting phytoplankton dynamics at different time scales. Here we distinguish between hydrometeorological forcing of phytoplankton dynamics at time scales from days to decades based on a 54-year monthly phytoplankton time series from a large shallow Lake Võrtsjärv (58°16′N, 26°02′E) in Estonia, combined with daily data on forcing factors—thermal-, wind-, light- and water-level regimes. By using variance partitioning with linear mixed effect modelling (LME), we found a continuum from the large dominant K-selected filamentous cyanobacteria with strongest decadal scale variation (8–30%) to r-selected phytoflagellates with large stochastic variability (80–96%). External forcing revealed strong seasonal variation (up to 80%), while specifically water level and wind speed had a robust decadal variation (8% and 20%, respectively). The effect of external variables was proportionally manifested in the time scales of phytoplankton variation. Temperature, with a clear seasonal variation, had no impact on the dominant cold tolerant filamentous cyanobacteria in Lake Võrtsjärv. We found the LME as a reliable method for resolving the temporal cross-scale problem. It yielded quantitative results that matched our intuitive understanding of the dynamics of different variables.
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Sordo-Ward, Alvaro, Ivan Gabriel-Martín, Paola Bianucci, Giuseppe Mascaro, Enrique R. Vivoni, and Luis Garrote. "Stochastic Hybrid Event Based and Continuous Approach to Derive Flood Frequency Curve." Water 13, no. 14 (July 13, 2021): 1931. http://dx.doi.org/10.3390/w13141931.
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This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.
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Portoghese, I., E. Bruno, and M. Vurro. "From regional climate simulations to the hydrological information needed for basin scale impact studies." Advances in Geosciences 26 (June 30, 2010): 25–31. http://dx.doi.org/10.5194/adgeo-26-25-2010.
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Abstract. The accuracy of local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes because the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows, and groundwater recharge. In this study, the output of a regional climate model (RCM) is downscaled using a stochastic modelling of the point rainfall process able to adequately reproduce the daily rainfall intermittency which is one of the crucial aspects for the hydrological processes characterizing Mediterranean environments. The historical time-series from a dense rain-gauge network were used for the analysis of the RCM bias in terms of dry and wet daily period and then to investigate the predicted alteration in the local rainfall regime. A Poisson Rectangular Pulse (PRP) model (Rodriguez-Iturbe et al., 1987) was finally adopted for the stochastic generation of local daily rainfall as a continuous-time point process with forcing parameters resulting from the bias correction of the RCM scenario.
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Gelfan, A., V. A. Semenov, E. Gusev, Y. Motovilov, O. Nasonova, I. Krylenko, and E. Kovalev. "Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability." Hydrology and Earth System Sciences 19, no. 6 (June 15, 2015): 2737–54. http://dx.doi.org/10.5194/hess-19-2737-2015.
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Abstract. An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties). Importantly, the role of internal atmospheric variability is most visible over spatio-temporal scales of water management in large river basins. Internal atmospheric variability is represented by large ensemble simulations (45 members) with the ECHAM5 atmospheric general circulation model. Ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979–2012) and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of bias-corrected ECHAM5 outputs and ensemble averaged ECHAM5 output are used as a distributed input for the ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and Northern Dvina rivers. A number of runoff statistics including the mean and the standard deviation of annual, monthly and daily runoff, as well as annual runoff trend, are assessed. Uncertainties of runoff statistics caused by internal atmospheric variability are estimated. It is found that uncertainty of the mean and the standard deviation of runoff has a significant seasonal dependence on the maximum during the periods of spring–summer snowmelt and summer–autumn rainfall floods. Noticeable nonlinearity of the hydrological models' results in the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvina River basin. It is shown that the averaging over ensemble members effectively filters the stochastic term related to internal atmospheric variability. Simulated discharge trends are close to normally distributed around the ensemble mean value, which fits well to empirical estimates and, for the Lena River, indicates that a considerable portion of the observed trend can be externally driven.
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Toth, E. "Catchment classification based on characterisation of streamflow and precipitation time-series." Hydrology and Earth System Sciences Discussions 9, no. 9 (September 26, 2012): 10805–28. http://dx.doi.org/10.5194/hessd-9-10805-2012.
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Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process. The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, providing an overall reasonable grouping of catchments on the basis of their hydrological response. In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesize the variability and correlation properties of the precipitation time-series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally classifies the ungauged catchments, through a leave-one-out cross-validation, to one of the above identified hydrologic response classes. The approach delivers quite satisfactory results for ungauged catchments, since the comparison of the two cluster sets shows an acceptable overlap. Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time-series may be a promising way for better representing the hydrologic and climatic character of the study catchments.
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Gelfan, A., V. A. Semenov, E. Gusev, Y. Motovilov, O. Nasonova, I. Krylenko, and E. Kovalev. "Large-basin hydrological response to climate model outputs: uncertainty caused by the internal atmospheric variability." Hydrology and Earth System Sciences Discussions 12, no. 2 (February 24, 2015): 2305–48. http://dx.doi.org/10.5194/hessd-12-2305-2015.
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Abstract. An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to the uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties). Importantly, the role of the internal atmospheric variability is the most visible over the spatial–temporal scales of water management in large river basins. The internal atmospheric variability is represented by large ensemble simulations (45 members) with the ECHAM5 atmospheric general circulation model. The ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979–2012) and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of the bias-corrected ECHAM5-outputs as well as ensemble averaged ECHAM5-output are used as the distributed input for ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and the Northern Dvina rivers. A number of runoff statistics including the mean and the SD of the annual, monthly and daily runoff, as well as the annual runoff trend are assessed. The uncertainties of runoff statistics caused by the internal atmospheric variability are estimated. It is found that the uncertainty of the mean and SD of the runoff has a distinguished seasonal dependence with maximum during the periods of spring-summer snowmelt and summer-autumn rainfall floods. A noticeable non-linearity of the hydrological models' response to the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvine River basin. It is shown that the averaging over ensemble members effectively filters stochastic term related to internal atmospheric variability. The simulated trends are close to normally distributed around ensemble mean value that indicates that a considerable portion of the observed trend can be externally driven.
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Toth, E. "Catchment classification based on characterisation of streamflow and precipitation time series." Hydrology and Earth System Sciences 17, no. 3 (March 15, 2013): 1149–59. http://dx.doi.org/10.5194/hess-17-1149-2013.
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Abstract. The formulation of objective procedures for the delineation of homogeneous groups of catchments is a fundamental issue in both operational and research hydrology. For assessing catchment similarity, a variety of hydrological information may be considered; in this paper, gauged sites are characterised by a set of streamflow signatures that include a representation, albeit simplified, of the properties of fine time-scale flow series and in particular of the dynamic components of the data, in order to keep into account the sequential order and the stochastic nature of the streamflow process. The streamflow signatures are provided in input to a clustering algorithm based on unsupervised SOM neural networks, obtaining groups of catchments with a clear hydrological distinctiveness, as highlighted by the identification of the main patterns of the input variables in the different classes and the interpretation of their interrelations. In addition, even if no geographical, morphological nor climatological information is provided in input to the SOM network, the clusters exhibit an overall consistency as far as location, altitude and precipitation regime are concerned. In order to assign ungauged sites to such groups, the catchments are represented through a parsimonious set of morphometric and pluviometric variables, including also indexes that attempt to synthesise the variability and correlation properties of the precipitation time series, thus providing information on the type of weather forcing that is specific to each basin. Following a principal components analysis, needed for synthesizing and better understanding the morpho-pluviometric catchment properties, a discriminant analysis finally assigns the ungauged catchments, through a leave-one-out cross validation, to one of the above identified hydrologic response classes. The approach delivers a quite satisfactory identification of the membership of ungauged catchments to the streamflow-based classes, since the comparison of the two cluster sets shows a misclassification rate of around 20%. Overall results indicate that the inclusion of information on the properties of the fine time-scale streamflow and rainfall time series may be a promising way for better representing the hydrologic and climatic character of the study catchments.
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Quenum, Gandomè Mayeul Leger Davy, Joël Arnault, Nana Ama Browne Klutse, Zhenyu Zhang, Harald Kunstmann, and Philip G. Oguntunde. "Potential of the Coupled WRF/WRF-Hydro Modeling System for Flood Forecasting in the Ouémé River (West Africa)." Water 14, no. 8 (April 8, 2022): 1192. http://dx.doi.org/10.3390/w14081192.
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Since the beginning of the 2000s, most of the West-African countries, particularly Benin, have experienced an increased frequency of extreme flood events. In this study, we focus on the case of the Ouémé river basin in Benin. To investigate flood events in this basin for early warning, the coupled atmosphere–hydrology model system WRF-Hydro is used, and analyzed for the period 2008–2010. Such a coupled model allows exploration of the contribution of atmospheric components into the flood event, and its ability to simulate and predict accurate streamflow. The potential of WRF-Hydro to correctly simulate streamflow in the Ouémé river basin is assessed by forcing the model with operational analysis datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF). Atmospheric and land surface processes are resolved at a spatial resolution of 5 km. The additional surface and subsurface water flow routing are computed at a resolution of 500 m. Key parameters of the hydrological module of WRF-Hydro are calibrated offline and tested online with the coupled WRF-Hydro. The uncertainty of atmospheric modeling on coupled results is assessed with the stochastic kinetic energy backscatter scheme (SKEBS). WRF-Hydro is able to simulate the discharge in the Ouémé river in offline and fully coupled modes with a Kling–Gupta efficiency (KGE) around 0.70 and 0.76, respectively. In the fully coupled mode, the model captures the flood event that occurred in 2010. A stochastic perturbation ensemble of ten members for three rain seasons shows that the coupled model performance in terms of KGE ranges from 0.14 to 0.79. Additionally, an assessment of the soil moisture has been developed. This ability to realistically reproduce observed discharge in the Ouémé river basin demonstrates the potential of the coupled WRF-Hydro modeling system for future flood forecasting applications.
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Do, Hong Xuan, Fang Zhao, Seth Westra, Michael Leonard, Lukas Gudmundsson, Julien Eric Stanislas Boulange, Jinfeng Chang, et al. "Historical and future changes in global flood magnitude – evidence from a model–observation investigation." Hydrology and Earth System Sciences 24, no. 3 (April 1, 2020): 1543–64. http://dx.doi.org/10.5194/hess-24-1543-2020.
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Abstract. To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models (GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change.
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Gabriel-Martin, Sordo-Ward, Garrote, and García. "Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach." Water 11, no. 9 (September 11, 2019): 1896. http://dx.doi.org/10.3390/w11091896.
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This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.
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Arnold, S., S. Attinger, K. Frank, and A. Hildebrandt. "Parameterization and uncertainty in coupled ecohydrological models." Hydrology and Earth System Sciences Discussions 6, no. 3 (June 9, 2009): 4155–207. http://dx.doi.org/10.5194/hessd-6-4155-2009.
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Abstract. In this paper we develop and apply a conceptual ecohydrological model to investigate the effects of model structure and parameter uncertainty on the prediction of vegetation structure and hydrological dynamics. The model is applied for a typical water limited riparian ecosystem along an ephemeral river: the middle section of the Kuiseb River in Namibia. We modelled this system by coupling an ecological model with a conceptual hydrological model. The hydrological model is storage based with stochastical forcing from the flood. The ecosystem is modelled with a population model, and represents three dominating riparian plant populations. In appreciation of uncertainty about population dynamics, we applied three model versions with increasing complexity. Population parameters were found by Latin Hypercube sampling of the parameter space and with the constraint that three species should coexist as observed. Two of the three models were able to reproduce the observed coexistence. However, both models relied on different coexistence mechanisms, and reacted differently to change of long term memory in the flood forcing. The coexistence requirement strongly constrained the parameter space for both successful models. Only very few parameter sets (0.5% of 150 000 samples) allowed for coexistence in a representative number of repeated simulations (at least 10 out of 100) and the success of the coexistence mechanism was controlled by the combination of population parameters. The average values of hydrologic variables like transpiration and depth to ground water were similar for both models, suggesting that they were mainly controlled by the applied hydrological model. The fluctuations of depth to groundwater and transpiration, however, differed significantly, suggesting that they were controlled by the applied ecological model and coexistence mechanisms. Our study emphasizes that uncertainty about ecosystem structure and intra-specific interactions influence the prediction of the hydrosystem.
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Arnold, S., S. Attinger, K. Frank, and A. Hildebrandt. "Uncertainty in parameterisation and model structure affect simulation results in coupled ecohydrological models." Hydrology and Earth System Sciences 13, no. 10 (October 6, 2009): 1789–807. http://dx.doi.org/10.5194/hess-13-1789-2009.
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Abstract. In this paper we develop and apply a conceptual ecohydrological model to investigate the effects of model structure and parameter uncertainty on the simulation of vegetation structure and hydrological dynamics. The model is applied for a typical water limited riparian ecosystem along an ephemeral river: the middle section of the Kuiseb River in Namibia. We modelled this system by coupling an ecological model with a conceptual hydrological model. The hydrological model is storage based with stochastical forcing from the flood. The ecosystem is modelled with a population model, and represents three dominating riparian plant populations. In appreciation of uncertainty about population dynamics, we applied three model versions with increasing complexity. Population parameters were found by Latin hypercube sampling of the parameter space and with the constraint that three species should coexist as observed. Two of the three models were able to reproduce the observed coexistence. However, both models relied on different coexistence mechanisms, and reacted differently to change of long term memory in the flood forcing. The coexistence requirement strongly constrained the parameter space for both successful models. Only very few parameter sets (0.5% of 150 000 samples) allowed for coexistence in a representative number of repeated simulations (at least 10 out of 100) and the success of the coexistence mechanism was controlled by the combination of population parameters. The ensemble statistics of average values of hydrologic variables like transpiration and depth to ground water were similar for both models, suggesting that they were mainly controlled by the applied hydrological model. The ensemble statistics of the fluctuations of depth to groundwater and transpiration, however, differed significantly, suggesting that they were controlled by the applied ecological model and coexistence mechanisms. Our study emphasizes that uncertainty about ecosystem structure and intra-specific interactions influence the prediction of the hydrosystem.
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Manzoni, Stefano, Annalisa Molini, and Amilcare Porporato. "Stochastic modelling of phytoremediation." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467, no. 2135 (June 22, 2011): 3188–205. http://dx.doi.org/10.1098/rspa.2011.0209.
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Leaching of heavy metals and other contaminants from soils poses a significant environmental threat as it affects the quality of downstream water bodies. Quantifying these losses is particularly important when employing phytoremediation approaches to reduce soil contamination, as contaminant escaping the system through leaching cannot be taken up by vegetation. Despite its undoubted importance, the role of such hydrologic forcing has seldom been fully considered in models describing the long-term contaminant mass balance during phytoremediation. The partitioning of contaminants between leaching and vegetation uptake is controlled by a number of biophysical processes as well as rainfall variability. Here, we develop a novel stochastic framework that provides analytical expressions to quantify the partitioning of contaminants between leaching and plant uptake and the probability of phytoremediation duration as a function of rainfall statistics and soil and vegetation characteristics. Simple expressions for the mean phytoremediation duration and effectiveness (defined as the fraction of contaminant that is recovered in plant biomass) are derived. The proposed framework can be employed to estimate under which conditions phytoremediation is more efficient, as well as to design phytoremediation projects that maximize contaminant recovery and minimize the duration of the remediation process.
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Park, Jeryang, Gianluca Botter, James W. Jawitz, and P. Suresh C. Rao. "Stochastic modeling of hydrologic variability of geographically isolated wetlands: Effects of hydro-climatic forcing and wetland bathymetry." Advances in Water Resources 69 (July 2014): 38–48. http://dx.doi.org/10.1016/j.advwatres.2014.03.007.
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Bennett, James C., Quan J. Wang, David E. Robertson, Andrew Schepen, Ming Li, and Kelvin Michael. "Assessment of an ensemble seasonal streamflow forecasting system for Australia." Hydrology and Earth System Sciences 21, no. 12 (November 30, 2017): 6007–30. http://dx.doi.org/10.5194/hess-21-6007-2017.
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Abstract. Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.
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Koutsoyiannis, D., A. Efstratiadis, and K. P. Georgakakos. "Uncertainty Assessment of Future Hydroclimatic Predictions: A Comparison of Probabilistic and Scenario-Based Approaches." Journal of Hydrometeorology 8, no. 3 (June 1, 2007): 261–81. http://dx.doi.org/10.1175/jhm576.1.
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Abstract During the last decade, numerous studies have been carried out to predict future climate based on climatic models run on the global scale and fed by plausible scenarios about anthropogenic forcing to climate. Based on climatic model output, hydrologic models attempt then to predict future hydrologic regimes at regional scales. Much less systematic work has been done to estimate climatic uncertainty and to assess the climatic and hydrologic model outputs within an uncertainty perspective. In this study, a stochastic framework for future climatic uncertainty is proposed, based on the following lines: 1) climate is not constant but rather varying in time and expressed by the long-term (e.g., 30 yr) time average of a natural process, defined on a finescale; 2) the evolution of climate is represented as a stochastic process; 3) the distributional parameters of a process, marginal and dependence, are estimated from an available sample by statistical methods; 4) the climatic uncertainty is the result of at least two factors, the climatic variability and the uncertainty of parameter estimation; 5) a climatic process exhibits a scaling behavior, also known as long-range dependence or the Hurst phenomenon; and 6) because of this dependence, the uncertainty limits of the future are affected by the available observations of the past. The last two lines differ from classical statistical considerations and produce uncertainty limits that eventually are much wider than those of classical statistics. A combination of analytical and Monte Carlo methods is developed to determine uncertainty limits for the nontrivial scaling case. The framework developed is applied with temperature, rainfall, and runoff data from a catchment in Greece, for which data exist for about a century. The uncertainty limits are then superimposed onto deterministic projections up to 2050, obtained for several scenarios and climatic models combined with a hydrologic model. These projections indicate a significant increase of temperature in the future, beyond uncertainty bands, and no significant change of rainfall and runoff as they lie well within uncertainty limits.
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Sugimoto, Takayuki, András Bárdossy, Geoffrey G. S. Pegram, and Johannes Cullmann. "Investigation of hydrological time series using copulas for detecting catchment characteristics and anthropogenic impacts." Hydrology and Earth System Sciences 20, no. 7 (July 11, 2016): 2705–20. http://dx.doi.org/10.5194/hess-20-2705-2016.
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Abstract. Global climate change can have impacts on characteristics of rainfall–runoff events and subsequently on the hydrological regime. Meanwhile, the catchment itself changes due to anthropogenic influences. However, it is not easy to prove the link between the hydrology and the forcings. In this context, it might be meaningful to detect the temporal changes of catchments independent from climate change by investigating existing long-term discharge records. For this purpose, a new stochastic system based on copulas for time series analysis is introduced in this study.A statistical tool like copula has the advantage to scrutinize the dependence structure of the data and, thus, can be used to attribute the catchment behavior by focusing on the following aspects of the statistics defined in the copula domain: (1) copula asymmetry, which can capture the nonsymmetric property of discharge data, differs from one catchment to another due to the intrinsic nature of both runoff and catchment; and (2) copula distances can assist in identifying catchment change by revealing the variability and interdependency of dependence structures.These measures were calculated for 100 years of daily discharges for the Rhine River and these analyses detected epochs of change in the flow sequences. In a follow-up study, we compared the results of copula asymmetry and copula distance applied to two flow models: (i) antecedent precipitation index (API) and (ii) simulated discharge time series generated by a hydrological model. The results of copula-based analysis of hydrological time series seem to support the assumption that the Neckar catchment had started to change around 1976 and stayed unusual until 1990.
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Hwang, S., and W. D. Graham. "Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation." Hydrology and Earth System Sciences 17, no. 11 (November 13, 2013): 4481–502. http://dx.doi.org/10.5194/hess-17-4481-2013.
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Abstract. There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.
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Slater, Andrew G., and Martyn P. Clark. "Snow Data Assimilation via an Ensemble Kalman Filter." Journal of Hydrometeorology 7, no. 3 (June 1, 2006): 478–93. http://dx.doi.org/10.1175/jhm505.1.
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Abstract A snow data assimilation study was undertaken in which real data were used to update a conceptual model, SNOW-17. The aim of this study is to improve the model’s estimate of snow water equivalent (SWE) by merging the uncertainties associated with meteorological forcing data and SWE observations within the model. This is done with a view to aiding the estimation of snowpack initial conditions for the ultimate objective of streamflow forecasting via a distributed hydrologic model. To provide a test of this methodology, the authors performed experiments at 53 stations in Colorado. In each case the situation of an unobserved location is mimicked, using the data at any given station only for validation; essentially, these are withholding experiments. Both ensembles of model forcing data and assimilated data were derived via interpolation and stochastic modeling of data from surrounding sources. Through a process of cross validation the error for the ensemble of model forcing data and assimilated observations is explicitly estimated. An ensemble square root Kalman filter is applied to perform assimilation on a 5-day cycle. Improvements in the resulting SWE are most evident during the early accumulation season and late melt period. However, the large temporal correlation inherent in a snowpack results in a less than optimal assimilation and the increased skill is marginal. Once this temporal persistence is removed from both model and assimilated observations during the update cycle, a result is produced that is, within the limits of available information, consistently superior to either the model or interpolated observations.
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Hwang, S., and W. D. Graham. "Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation." Hydrology and Earth System Sciences Discussions 10, no. 2 (February 20, 2013): 2141–81. http://dx.doi.org/10.5194/hessd-10-2141-2013.
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Abstract. There are a number of statistical techniques that downscale coarse climate information from global circulation models (GCM). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (bias-correction and stochastic analog method, BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve the spatial autocorrelation structure of observed daily precipitation sequences. This approach was designed to reproduce observed spatial and temporal variability as well as mean climatology. We used the BCSA method to downscale 4 GCM precipitation predictions from 1961 to 1999 over the state of Florida and compared the skill of the method to the results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, bias-correction and constructed analog (BCCA) method, and a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC). Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean climatology of daily precipitation while the BCSD, SDBC and BCSA methods accurately reproduced it, (2) the BCSD and BCCA methods underestimated temporal variability because of the interpolation and regression schemes used for downscaling and thus, did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in precipitation resulting in under-prediction of spatial variance and over-prediction of spatial correlation, whereas the new stochastic technique (BCSA) accurately reproduces observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be accurately predicted. For low-relief, rainfall-dominated watersheds where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.
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Grouillet, Benjamin, Denis Ruelland, Pradeebane Vaittinada Ayar, and Mathieu Vrac. "Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean." Hydrology and Earth System Sciences 20, no. 3 (March 8, 2016): 1031–47. http://dx.doi.org/10.5194/hess-20-1031-2016.
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Abstract. This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20-year period (1986&ndsh;2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled data sets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function–transform approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods.
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Grouillet, B., D. Ruelland, P. V. Ayar, and M. Vrac. "Sensitivity analysis of runoff modeling to statistical downscaling models in the western Mediterranean." Hydrology and Earth System Sciences Discussions 12, no. 10 (October 1, 2015): 10067–108. http://dx.doi.org/10.5194/hessd-12-10067-2015.
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Abstract. This paper analyzes the sensitivity of a hydrological model to different methods to statistically downscale climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20 year period (1986–2005) to capture different climatic conditions in the basins. Streamflow was simulated using the GR4j conceptual model. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically downscaled datasets to reproduce various aspects of the hydrograph. Three different statistical downscaling models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the "cumulative distribution function – transform" approach (CDFt). We used the models to downscale precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two GCMs (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various downscaled data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution downscaled climate values leads to a major improvement of runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical downscaling models to be used in climate change impact studies to minimize the range of uncertainty associated with such downscaling methods.
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Steinman, Byron A., Michael F. Rosenmeier, and Mark B. Abbott. "The isotopic and hydrologic response of small, closed-basin lakes to climate forcing from predictive models: Simulations of stochastic and mean state precipitation variations." Limnology and Oceanography 55, no. 6 (October 17, 2010): 2246–61. http://dx.doi.org/10.4319/lo.2010.55.6.2246.
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Lewis, Sophie C. "Assessing the Stationarity of Australian Precipitation Extremes in Forced and Unforced CMIP5 Simulations." Journal of Climate 31, no. 1 (December 11, 2017): 131–45. http://dx.doi.org/10.1175/jcli-d-17-0393.1.
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Abstract Knowledge of the range of precipitation variability and extremes is restricted in regions such as Australia, where instrumental records are short and paleoclimatic records are limited in spatial and temporal extent and resolution. In such comparatively data-poor regions, there is limited context for understanding the statistical unusualness of recently observed extreme events, such as heavy rain and drought, and the influence of stochastic and anthropogenic forcings on their magnitude. This study attempts to further understandings of the range of forced and unforced variability using CMIP5 climate models. Focusing on extremes in the magnitude of monthly, seasonal, and annual precipitation, the distribution of instrumental-period observed precipitation in various Australian regions is compared to simulated precipitation in historical experiments as well as various long experiment (preindustrial control and Last Millennium) and anthropogenically forced simulations of the twenty-first century (RCP2.6 and RCP8.5). There is no systematic increase in the magnitude of simulated extremes corresponding to the length of model simulations, although many realizations reveal higher magnitude extremes compared to those observed, suggesting that the duration of the instrumental record may not capture the potential severity of stochastically driven extremes. A coherent increase in both wet and dry extremes is simulated throughout Australian regions in high greenhouse gas emissions scenarios, demonstrating a forced hydrological response.
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Maggioni, Viviana, Humberto J. Vergara, Emmanouil N. Anagnostou, Jonathan J. Gourley, Yang Hong, and Dimitrios Stampoulis. "Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations." Journal of Hydrometeorology 14, no. 4 (August 1, 2013): 1194–211. http://dx.doi.org/10.1175/jhm-d-12-074.1.
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Abstract This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.
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Bellier, Joseph, Michael Scheuerer, and Thomas M. Hamill. "Precipitation Downscaling with Gibbs Sampling: An Improved Method for Producing Realistic, Weather-Dependent, and Anisotropic Fields." Journal of Hydrometeorology 21, no. 11 (November 2020): 2487–505. http://dx.doi.org/10.1175/jhm-d-20-0069.1.
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AbstractDownscaling precipitation fields is a necessary step in a number of applications, especially in hydrological modeling where the meteorological forcings are frequently available at too coarse resolution. In this article, we review the Gibbs sampling disaggregation model (GSDM), a stochastic downscaling technique originally proposed by Gagnon et al. The method is capable of introducing realistic, weather-dependent, and possibly anisotropic fine-scale details, while preserving the mean rain rate over the coarse-scale pixels. The main developments compared to the former version are (i) an adapted Gibbs sampling algorithm that enforces the downscaled fields to have a similar texture to that of the analysis fields, (ii) an extensive test of various meteorological predictors for controlling specific aspects of the texture such as the anisotropy and the spatial variability, and (iii) a review of the regression equations used in the model for defining the conditional distributions. A perfect-model experiment is conducted over a domain in the southeastern United States. The metrics used for verification are based on the concept of gridded, stratified variogram, which is introduced as an effective way of reproducing the abilities of human eyes for detecting differences in the field texture. Results indicate that the best overall performances are obtained with the most sophisticated, predictor-based GSDM variant. The 600-hPa wind is found to be the best year-round predictor for controlling the anisotropy. For the spatial variability, kinematic predictors such as wind shear are found to be best during the convective periods, while instability indices are more informative elsewhere.
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Bowers, Corinne, Katherine A. Serafin, and Jack Baker. "A performance-based approach to quantify atmospheric river flood risk." Natural Hazards and Earth System Sciences 22, no. 4 (April 19, 2022): 1371–93. http://dx.doi.org/10.5194/nhess-22-1371-2022.
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Abstract. Atmospheric rivers (ARs) are a class of meteorologic phenomena that cause significant precipitation and flooding on the US West Coast. This work presents a new Performance-based Atmospheric River Risk Analysis (PARRA) framework that adapts existing concepts from probabilistic risk analysis and performance-based engineering for application in the context of AR-driven fluvial flooding. The PARRA framework is a chain of physically based models that link the atmospheric forcings, hydrologic impacts, and economic consequences of AR-driven fluvial flood risk together at consistent “pinch points”. Organizing around these pinch points makes the framework modular, meaning that models between pinch points can be updated without affecting the rest of the model chain, and it produces a probabilistic result that quantifies the uncertainty in the underlying system states. The PARRA framework can produce results beyond analyses of individual scenario events and can look toward prospective assessment of events or system changes that have not been seen in the historic record. The utility of the PARRA framework is demonstrated through a series of analyses in Sonoma County, CA, USA. Individual component models are fitted and validated against a historic catalog of AR events occurring from 1987 to 2019. Comparing simulated results from these component model implementations against observed historic ARs highlights what we can learn about the drivers of extremeness in different flood events by taking a probabilistic perspective. The component models are then run in sequence to generate a first-of-its-kind AR flood loss exceedance curve for Sonoma County. The prospective capabilities of the PARRA framework are presented through the evaluation of a hypothetical mitigation action. Elevating 200 homes, selected based on their proximity to the Russian River, was sufficient to reduce the average annual loss by half. Although expected benefits were minimal for the smallest events in the stochastic record, the larger, more damaging ARs were expected to see loss reductions of approximately USD 50–75 million per event. These results indicate the potential of the PARRA framework to examine other changes to flood hazard, exposure, and vulnerability at the community level.
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Bertassello, Leonardo E., Antoine F. Aubeneau, Gianluca Botter, James W. Jawitz, and P. S. C. Rao. "Emergent dispersal networks in dynamic wetlandscapes." Scientific Reports 10, no. 1 (September 7, 2020). http://dx.doi.org/10.1038/s41598-020-71739-8.
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Abstract The connectivity among distributed wetlands is critical for aquatic habitat integrity and to maintain metapopulation biodiversity. Here, we investigated the spatiotemporal fluctuations of wetlandscape connectivity driven by stochastic hydroclimatic forcing, conceptualizing wetlands as dynamic habitat nodes in dispersal networks. We hypothesized that spatiotemporal hydrologic variability influences the heterogeneity in wetland attributes (e.g., size and shape distributions) and wetland spatial organization (e.g., gap distances), in turn altering the variance of the dispersal network topology and the patterns of ecological connectivity. We tested our hypotheses by employing a DEM-based, depth-censoring approach to assess the eco-hydrological dynamics in a synthetically generated landscape and three representative wetlandscapes in the United States. Network topology was examined for two end-member connectivity measures: centroid-to-centroid (C2C), and perimeter-to-perimeter (P2P), representing the full range of within-patch habitat preferences. Exponentially tempered Pareto node-degree distributions well described the observed structural connectivity of both types of networks. High wetland clustering and attribute heterogeneity exacerbated the differences between C2C and P2P networks, with Pareto node-degree distributions emerging only for a limited range of P2P configuration. Wetlandscape network topology and dispersal strategies condition species survival and biodiversity.