scholarly journals Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models

Forecasting ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 526-548
Author(s):  
Haksu Lee ◽  
Haojing Shen ◽  
Dong-Jun Seo
Keyword(s):  
Mean Squared Error ◽  
Mean Field ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Catchment Scale ◽  
Soil Moisture Accounting ◽  
Distributed Hydrologic Model ◽  
Outlet Flow ◽  
Accounting Model ◽  
Field Bias

When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan.

scholarly journals Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model

2017 ◽  
Vol 21 (2) ◽  
pp. 879-896 ◽  
Author(s):  
Tirthankar Roy ◽  
Hoshin V. Gupta ◽  
Aleix Serrat-Capdevila ◽  
Juan B. Valdes
Keyword(s):  
Model Performance ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Hydrologic Models ◽  
Catchment Scale ◽  
Model Driven ◽  
Alternative Approach ◽  
Precipitation Estimates ◽  
Daily Water ◽  
Rainfall Runoff Model

Abstract. Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

scholarly journals Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1340
Author(s):  
Woodson ◽  
Adams ◽  
Dymond
Keyword(s):  
Hydrological Modeling ◽  
Mean Squared Error ◽  
Uniform Estimate ◽  
Mean Field ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Estimation Methods ◽  
Urban Watersheds ◽  
Precipitation Estimation

Quantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling and prediction, particularly in small, urban watersheds, which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies that have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The research distributed hydrologic model (RDHM) was used to model a basin in Roanoke, Virginia, USA, forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a six-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean squared error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB-corrected radar data consistently and significantly overpredicted discharge, but had the highest accuracy in predicting the timing of peak flows.

scholarly journals Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa

2010 ◽  
Vol 7 (4) ◽  
pp. 4785-4816 ◽  
Author(s):  
S. I. Khan ◽  
P. Adhikari ◽  
Y. Hong ◽  
H. Vergara ◽  
T. Grout ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Water Cycle ◽  
Lake Victoria ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
East African ◽  
Catchment Scale ◽  
Distributed Hydrologic Model

Abstract. Floods and droughts are common, recurring natural hazards in East African nations. Studies of hydro-climatology at daily, seasonal, and annual time scale is an important in understanding and ultimately minimizing the impacts of such hazards. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia Basin. The spatiotemporal variability of the water cycle components were quantified using a physically-based, distributed hydrologic model, with in-situ and multi-satellite remote sensing datasets. Moreover, the hydrologic capability of remote sensing data such as TRMM-3B42V6 was tested in terms of reconstruction of the water cycle components. The spatial distribution and time series of modeling results for precipitation (P), evapotranspiration (ET), and change in storage (dS/dt) showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The hydrologic model captured the spatial variability of the soil moisture storage. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. Relatively high flows were experienced near the basin outlet from previous rainfall, with a new flood peak responding to the rainfall in the upper part of the basin. The monthly peak runoff was observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. The model was found to be useful in poorly gauged catchments using satellite forcing data and showed the potential to be used not only for the investigation of the catchment scale water balance but also for addressing issues pertaining to sustainability of the resources within the catchment.

scholarly journals Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

2005 ◽  
Vol 6 (4) ◽  
pp. 497-517 ◽  
Author(s):  
Koray K. Yilmaz ◽  
Terri S. Hogue ◽  
Kuo-lin Hsu ◽  
Soroosh Sorooshian ◽  
Hoshin V. Gupta ◽  
...  
Keyword(s):  
Model Performance ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Soil Moisture Accounting ◽  
Time Period ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
System Maps ◽  
Accounting Model ◽  
Rain Gauge Network

Abstract This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

Operationalizing Continental-Domain Hydrologic Models: What can we learn?

2020 ◽  
Author(s):  
William Farmer ◽  
Jessica Driscoll
Keyword(s):  
Large Scale ◽  
The United States ◽  
Hydrologic Model ◽  
Water Model ◽  
Cloud Infrastructure ◽  
Hydrologic Models ◽  
Management Scenarios ◽  
Catchment Scale ◽  
Spatial And Temporal Scales ◽  
Technological Advances

<p>The explosion in the number of hydrologic models and technological advances in cloud infrastructure have combined to create new opportunities in operationalization of hydrologic science and models over the past decade. Colloquially, operationalization has been used to refer to deployments of previously existing model codes, themselves realizations of existing hydrologic science and conceptualizations, run in an unsupervised manner (e.g., automatically) driven by climate input variables that are contemporary (now-casting) or projected (forecasting). With advances in computational infrastructure and power, it has become possible to read, run, and visualize output from automated, operational models across continental domains. In the United States, recent endeavors include U.S. Geological Survey’s integrated water availability assessments, an operational configuration of the Precipitation Runoff Modeling System in the National Hydrologic Model Infrastructure; the National Water Model, an operational configuration of WRF-Hydro for flood forecasting; and several more nascent efforts. While these efforts show significant technological advances in the communication of results of hydrologic models, we ask how they have contributed to advances towards expanding knowledge of the hydrologic sciences more generally. Operational configurations of continental-domain models build upon advances of catchment-scale hydrology generally focused on addressing a single management scenario. The extent to which these model configurations have the fidelity to address a wider range of management scenarios and the translation across spatial and temporal scales is not straightforward. In addition, continental-domain operational deployments allow for the visualization of large-scale hydrologic events (e.g., droughts and floods), but perpetuate problems with communication of accuracy and uncertainty at management-relevant scales. Here we explore how these technological advances can be leveraged to advance the hydrologic science that underlies our models.</p>

scholarly journals Effects of Resolution of Satellite-Based Rainfall Estimates on Hydrologic Modeling Skill at Different Scales

2014 ◽  
Vol 15 (2) ◽  
pp. 593-613 ◽  
Author(s):  
Humberto Vergara ◽  
Yang Hong ◽  
Jonathan J. Gourley ◽  
Emmanouil N. Anagnostou ◽  
Viviana Maggioni ◽  
...  
Keyword(s):  
Hydrologic Modeling ◽  
Hydrologic Model ◽  
Multiscale Approach ◽  
Hydrologic Models ◽  
Distributed Hydrologic Model ◽  
Modeling And Forecasting ◽  
Satellite Rainfall ◽  
The Impact ◽  
Source Of Error ◽  
Rainfall Estimates

Abstract Uncertainty due to resolution of current satellite-based rainfall products is believed to be an important source of error in applications of hydrologic modeling and forecasting systems. A method to account for the input’s resolution and to accurately evaluate the hydrologic utility of satellite rainfall estimates is devised and analyzed herein. A radar-based Multisensor Precipitation Estimator (MPE) rainfall product (4 km, 1 h) was utilized to assess the impact of resolution of precipitation products on the estimation of rainfall and subsequent simulation of streamflow on a cascade of basins ranging from approximately 500 to 5000 km2. MPE data were resampled to match the Tropical Rainfall Measuring Mission’s (TRMM) 3B42RT satellite rainfall product resolution (25 km, 3 h) and compared with its native resolution data to estimate errors in rainfall fields. It was found that resolution degradation considerably modifies the spatial structure of rainfall fields. Additionally, a sensitivity analysis was designed to effectively isolate the error on hydrologic simulations due to rainfall resolution using a distributed hydrologic model. These analyses revealed that resolution degradation introduces a significant amount of error in rainfall fields, which propagated to the streamflow simulations as magnified bias and dampened aggregated error (RMSEs). Furthermore, the scale dependency of errors due to resolution degradation was found to intensify with increasing streamflow magnitudes. The hydrologic model was calibrated with satellite- and original-resolution MPE using a multiscale approach. The resulting simulations had virtually the same skill, suggesting that the effects of rainfall resolution can be accounted for during calibration of hydrologic models, which was further demonstrated with 3B42RT.

scholarly journals Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment

2012 ◽  
Vol 16 (7) ◽  
pp. 2233-2251 ◽  
Author(s):  
H. Lee ◽  
D.-J. Seo ◽  
Y. Liu ◽  
V. Koren ◽  
P. McKee ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Fast Response ◽  
Hydrologic Model ◽  
The State ◽  
State Variables ◽  
Soil Moisture Accounting ◽  
Spatial Correlation Structure ◽  
The Us ◽  
Outlet Flow ◽  
Highly Correlated

Abstract. State updating of distributed rainfall-runoff models via streamflow assimilation is subject to overfitting because large dimensionality of the state space of the model may render the assimilation problem seriously under-determined. To examine the issue in the context of operational hydrologic forecasting, we carried out a set of real-world experiments in which streamflow data is assimilated into the gridded Sacramento Soil Moisture Accounting (SAC-SMA) and kinematic-wave routing models of the US National Weather Service (NWS) Research Distributed Hydrologic Model (RDHM) via variational data assimilation (DA). The nine study basins include four in Oklahoma and five in Texas. To assess the sensitivity of the performance of DA to the dimensionality of the control vector, we used nine different spatiotemporal adjustment scales, with which the state variables are adjusted in a lumped, semi-distributed, or distributed fashion and biases in precipitation and PE are adjusted at hourly or 6-hourly scale, or at the scale of the fast response of the basin. For each adjustment scale, three different assimilation scenarios were carried out in which streamflow observations are assumed to be available at basin interior points only, at the basin outlet only, or at all locations. The results for the nine basins show that the optimum spatiotemporal adjustment scale varies from basin to basin and between streamflow analysis and prediction for all three streamflow assimilation scenarios. The most preferred adjustment scale for seven out of the nine basins is found to be distributed and hourly. It was found that basins with highly correlated flows between interior and outlet locations tend to be less sensitive to the adjustment scale and could benefit more from streamflow assimilation. In comparison with outlet flow assimilation, interior flow assimilation produced streamflow predictions whose spatial correlation structure is more consistent with that of observed flow for all adjustment scales. We also describe diagnosing the complexity of the assimilation problem using spatial correlation of streamflow and discuss the effect of timing errors in hydrograph simulation on the performance of the DA procedure.

scholarly journals Evaluation of a Distributed Streamflow Forecast Model at Multiple Watershed Scales

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1279
Author(s):  
Tyler Madsen ◽  
Kristie Franz ◽  
Terri Hogue
Keyword(s):  
Local Population ◽  
Model Performance ◽  
Forecast Model ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Discharge Data ◽  
Distributed Hydrologic Model ◽  
Verification Period ◽  
Silver Spring

Demand for reliable estimates of streamflow has increased as society becomes more susceptible to climatic extremes such as droughts and flooding, especially at small scales where local population centers and infrastructure can be affected by rapidly occurring events. In the current study, the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) (NOAA/NWS, Silver Spring, MD, USA) was used to explore the accuracy of a distributed hydrologic model to simulate discharge at watershed scales ranging from 20 to 2500 km2. The model was calibrated and validated using observed discharge data at the basin outlets, and discharge at uncalibrated subbasin locations was evaluated. Two precipitation products with nominal spatial resolutions of 12.5 km and 4 km were tested to characterize the role of input resolution on the discharge simulations. In general, model performance decreased as basin size decreased. When sub-basin area was less than 250 km2 or 20–40% of the total watershed area, model performance dropped below the defined acceptable levels. Simulations forced with the lower resolution precipitation product had better model evaluation statistics; for example, the Nash–Sutcliffe efficiency (NSE) scores ranged from 0.50 to 0.67 for the verification period for basin outlets, compared to scores that ranged from 0.33 to 0.52 for the higher spatial resolution forcing.

scholarly journals In lieu of the paired catchment approach: Hydrologic model change detection at the catchment scale

2010 ◽  
Vol 46 (11) ◽  
Author(s):  
Nicolas Zégre ◽  
Arne E. Skaugset ◽  
Nicholas A. Som ◽  
Jeffrey J. McDonnell ◽  
Lisa M. Ganio
Keyword(s):  
Change Detection ◽  
Hydrologic Model ◽  
Catchment Scale ◽  
Paired Catchment
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