scholarly journals Hydrological model assessment for flood early warning in a tropical high mountain basin

2016 ◽  
Author(s):  
María Carolina Rogelis ◽  
Micha Werner ◽  
Nelson Obregón ◽  
Nigel Wright
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Model Performance ◽  
Distributed Model ◽  
High Mountain ◽  
Model Assessment ◽  
Lumped Model ◽  
Water Storage Capacity ◽  
Soil Moisture Accounting ◽  
The Impact

Abstract. A distributed model (TETIS), a semi-distributed model (TOPMODEL) and a lumped model (HEC HMS soil moisture accounting) were used to simulate the discharge response of a tropical high mountain basin characterized by soils with high water storage capacity and high conductivity. The models were calibrated with the Shuffle Complex Evolution algorithm, using the Kling and Gupta efficiency as objective function. Performance analysis and diagnostics were carried out using the signatures of the flow duration curve and through analysis of the model fluxes in order to identify the most appropriate model for the study area for flood early warning. The impact of varying grid sizes was assessed in the TETIS model and the TOPMODEL in order to chose a model with balanced model performance and computational efficiency. The sensitivity of the models to variation in the precipitation input was analysed by forcing the models with a rainfall ensemble obtained from Gaussian simulation. The resulting discharge ensembles of each model were compared in order to identify differences among models structures. The results show that TOPMODEL is the most realistic model of the three tested, albeit showing the largest discharge ensemble spread. The main differences among models occur between HEC HMS soil moisture accounting and TETIS, and HEC HMS soil moisture accounting and TOPMODEL, with HEC HMS soil moisture accounting producing ensembles in a range lower than the other two models. The ensembles of TETIS and TOPMODEL are more similar.

scholarly journals Case Study: Comparative Analysis of Hydrologic Simulations with Areal-Averaging of Moving Rainfall

Hydrology ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 12 ◽  
Author(s):  
Zheng N. Fang ◽  
Michael J. Shultz ◽  
Kevin J. Wienhold ◽  
Jiaqi Zhang ◽  
Shang Gao
Keyword(s):  
Comparative Analysis ◽  
Peak Discharge ◽  
Distributed Model ◽  
Lumped Model ◽  
Modeling Approaches ◽  
Flood Peaks ◽  
The Difference ◽  
Hydrologic Responses ◽  
The Impact

The goal of this investigation is to compare the hydrologic simulations caused by the areal-averaging of dynamic moving rainfall. Two types of synthetic rainfall are developed: spatially varied rainfall (SVR) is the typical input to a distributed model while temporally varied rainfall (TVR) emulates SVR but is spread uniformly over the entire watershed as in the case of a lumped model. This study demonstrates a direct comparison of peak discharge and peak timing generated by synthetic moving storms over idealized rectangular basins and a real watershed. It is found that the difference between the hydrologic responses from SVR and TVR reflects the impact from the areal-averaging of rainfall; the areal-averaging of rainfall for the movement from upstream to downstream over a lumped model can result in underestimated and delayed peak values in comparison to those from a distributed model; the flood peaks from SVR and TVR are found similar when the storm moves from downstream to upstream. The findings of the study suggest that extra cautions are needed for practitioners when evaluating simulated results from distributed and lumped modeling approaches even using the same rainfall information.

scholarly journals Development of a Unified Land Model for Prediction of Surface Hydrology and Land–Atmosphere Interactions

2011 ◽  
Vol 12 (6) ◽  
pp. 1299-1320 ◽  
Author(s):  
Ben Livneh ◽  
Pedro J. Restrepo ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Climate Models ◽  
A Priori ◽  
Model Performance ◽  
Surface Flux ◽  
Surface Fluxes ◽  
Frozen Soil ◽  
Surface Model ◽  
Soil Moisture Accounting

Abstract A unified land model (ULM) is described that combines the surface flux parameterizations in the Noah land surface model (used in most of NOAA’s coupled weather and climate models) with the Sacramento Soil Moisture Accounting model (Sac; used for hydrologic prediction within the National Weather Service). The motivation was to develop a model that has a history of strong hydrologic performance while having the ability to be run in the coupled land–atmosphere environment. ULM takes the vegetation, snow model, frozen soil, and evapotranspiration schemes from Noah and merges them with the soil moisture accounting scheme from Sac. ULM surface fluxes, soil moisture, and streamflow simulations were evaluated through comparisons with observations from the Ameriflux (surface flux), Illinois Climate Network (soil moisture), and Model Parameter Estimation Experiment (MOPEX; streamflow) datasets. Initially, a priori parameters from Sac and Noah were used, which resulted in ULM surface flux simulations that were comparable to those produced by Noah (Sac does not predict surface energy fluxes). ULM with the a priori parameters had streamflow simulation skill that was generally similar to Sac’s, although it was slightly better (worse) for wetter (more arid) basins. ULM model performance using a set of parameters identified via a Monte Carlo search procedure lead to substantial improvements relative to the a priori parameters. A scheme for transfer of parameters from streamflow simulations to nearby flux and soil moisture measurement points was also evaluated; this approach did not yield conclusive improvements relative to the a priori parameters.

scholarly journals Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

2014 ◽  
Vol 11 (9) ◽  
pp. 10635-10681 ◽  
Author(s):  
C. Alvarez-Garreton ◽  
D. Ryu ◽  
A. W. Western ◽  
C.-H. Su ◽  
W. T. Crow ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Distributed Model ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Lumped Model ◽  
Model Soil ◽  
Main Driver ◽  
Semi Arid

Abstract. Assimilation of remotely sensed soil moisture data (SM–DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM–DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM–DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash–Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM–DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM–DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM–DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM–DA does not address systematic errors in the model.

scholarly journals When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation on 3620 flood events

2013 ◽  
Vol 10 (10) ◽  
pp. 12485-12536 ◽  
Author(s):  
F. Lobligeois ◽  
V. Andréassian ◽  
C. Perrin ◽  
P. Tabary ◽  
C. Loumagne
Keyword(s):  
Spatial Variability ◽  
Hydrological Model ◽  
Distributed Model ◽  
Large Set ◽  
Runoff Generation ◽  
Lumped Model ◽  
Climate Conditions ◽  
Key Factor ◽  
The Impact ◽  
Streamflow Simulation

Abstract. Precipitation is the key factor controlling the high-frequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in the hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasted and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole French territory at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semi-distributed mode using three unit sizes of sub-catchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of 181 French catchments representing a variety of size and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semi-distributed model in western France where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. In contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.

scholarly journals Hydrological appraisal of operational weather radar rainfall estimates in the context of different modelling structures

2013 ◽  
Vol 10 (8) ◽  
pp. 10495-10534
Author(s):  
D. Zhu ◽  
Y. Xuan ◽  
I. Cluckie
Keyword(s):  
Hydrological Model ◽  
Scale Effects ◽  
Model Performance ◽  
Extreme Rainfall ◽  
Distributed Model ◽  
Catchment Scale ◽  
Radar Rainfall ◽  
Rainfall Events ◽  
The Impact ◽  
Rainfall Estimates

Abstract. Radar rainfall estimates have become increasingly available for hydrological modellers over recent years, especially for flood forecasting and warning over poorly gauged catchments. However, the impact of using radar rainfall as compared with conventional raingauge inputs, with respect to various hydrological model structures, remains unclear and yet to be addressed. In the study presented by this paper, we analysed the flow simulations of the Upper Medway catchment of Southeast England using the UK NIMROD radar rainfall estimates using three hydrological models based upon three very different structures, e.g. a physically based distributed MIKE SHE model, a lumped conceptual model PDM and an event-based unit hydrograph model PRTF. We focused on the sensitivity of simulations in relation to the storm types and various rainfall intensities. The uncertainty in radar-rainfall estimates, scale effects and extreme rainfall were examined in order to quantify the performance of the radar. We found that radar rainfall estimates were lower than raingauge measurements in high rainfall rates; the resolutions of radar rainfall data had insignificant impact at this catchment scale in the case of evenly distributed rainfall events but was obvious otherwise for high-intensity, localised rainfall events with great spatial heterogeneity. As to hydrological model performance, the distributed model had consistent reliable and good performance on peak simulation with all the rainfall types tested in this study.

scholarly journals Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

2015 ◽  
Vol 19 (4) ◽  
pp. 1659-1676 ◽  
Author(s):  
C. Alvarez-Garreton ◽  
D. Ryu ◽  
A. W. Western ◽  
C.-H. Su ◽  
W. T. Crow ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Distributed Model ◽  
Ensemble Prediction ◽  
Generation Process ◽  
Streamflow Prediction ◽  
Flood Prediction ◽  
Lumped Model ◽  
Model Soil ◽  
Semi Arid

Abstract. Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large (> 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively; the false alarm ratio was reduced by 9% in both cases; the peak volume error was reduced by 58 and 1%, respectively; the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively); and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving some characteristics of the streamflow ensemble prediction; however, the updated prediction is still poor since SM-DA does not address the systematic errors found in the model prior to assimilation.

Distributed conceptual modelling in a Swedish lowland catchment: a multi-criteria model assessment

2012 ◽  
Vol 44 (2) ◽  
pp. 318-333 ◽  
Author(s):  
Sebastian Wrede ◽  
Jan Seibert ◽  
Stefan Uhlenbrook
Keyword(s):  
Spatial Representation ◽  
Environmental Flows ◽  
Model Performance ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Model Assessment ◽  
Runoff Generation ◽  
Catchment Scale ◽  
Model Concept ◽  
Flow Measurements

Operational management and prediction of water quantity and quality often requires a spatially meaningful simulation of environmental flows and storages at the catchment scale. In this study, the performance of a fully distributed conceptual hydrologic model was evaluated based on the HBV (Hydrologiska Byråns Vattenbalansavdelning) and TACD (Tracer Aided Catchment model – Distributed) model concept in the meso-scale Fyrisån catchment in the Central Swedish lowlands. For a more spatially explicit representation of runoff generation processes of small landscape elements such as wetlands, a new sub-grid parameterization scheme was implemented in the model. In addition, a simple flow distribution and lake retention routine was introduced to better conceptualize the flow routing. During intensive model evaluation and comparison the model underwent conventional split-sample and proxy-basin tests. In this process, shortcomings of the model in the transferability of parameter sets and in the spatial representation of runoff generating processes were found. It was also demonstrated how a detailed comparison with a lumped benchmark model and the additional use of synoptic stream flow measurements allowed further insights into the model performance. It could be concluded that such a thorough model assessment can help to detect shortcomings in the spatial representation of the model and help facilitate model development.

scholarly journals The Impact of Variable Horizon Shade on the Growing Season Energy Budget of a Subalpine Headwater Wetland

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1473
Author(s):  
Dylan M. Hrach ◽  
Richard M. Petrone ◽  
Brandon Van Huizen ◽  
Adam Green ◽  
Myroslava Khomik
Keyword(s):  
Soil Moisture ◽  
Surface Energy ◽  
Energy Budget ◽  
Growing Season ◽  
Shortwave Radiation ◽  
Energy Budgets ◽  
Water Storage Capacity ◽  
Headwater Wetland ◽  
Downstream Flow ◽  
The Impact

Surface energy budgets are important to the ecohydrology of complex terrain, where land surfaces cycle in and out of shadows creating distinct microclimates. Shading in such environments can help regulate downstream flow over the course of a growing season, but our knowledge on how shadows impact the energy budget and consequently ecohydrology in montane ecosystems is very limited. We investigated the influence of horizon shade on the surface energy fluxes of a subalpine headwater wetland in the Canadian Rocky Mountains during the growing season. During the study, surface insolation decreased by 60% (32% due to evolving horizon shade and 28% from seasonality). The influence of shade on the energy budget varied between two distinct periods: (1) Stable Shade, when horizon shade was constant and reduced sunlight by 2 h per day; and (2) Dynamic Shade, when shade increased and reduced sunlight by 0.18 h more each day, equivalent to a 13% reduction in incoming shortwave radiation and 16% in net radiation. Latent heat flux, the dominant energy flux at our site, varied temporally because of changes in incoming radiation, atmospheric demand, soil moisture and shade. Horizon shade controlled soil moisture at our site by prolonging snowmelt and reducing evapotranspiration in the late growing season, resulting in increased water storage capacity compared to other mountain wetlands. With the mounting risk of climate-change-driven severe spring flooding and late season droughts downstream of mountain headwaters, shaded subalpine wetlands provide important ecohydrological and mitigation services that are worthy of further study and mapping. This will help us better understand and protect mountain and prairie water resources.

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