Development and evaluation of a watershed-scale hybrid hydrologic model

A watershed-scale hybrid hydrologic model (Distributed-Clark), which is a lumped conceptual and distributed feature model, was developed to predict spatially distributed short- and long-term rainfall runoff generation and routing using relatively simple methodologies and state-of-the-art spatial data in a GIS environment. In Distributed-Clark, spatially distributed excess rainfall estimated with the SCS curve number method and a GIS-based set of separated unit hydrographs (spatially distributed unit hydrograph) are utilized to calculate a direct runoff flow hydrograph, and time-varied SCS CN values and conditional unit hydrograph approach for different runoff depth-based flow convolution are also used to compute long-term rainfall-runoff flow hydrographs. Spatial data processing and model execution can be performed by Python script tools that were developed in a GIS platform. Model case studies of short- and long-term hydrologic application for four river watersheds to evaluate performance using spatially distributed (Thiessen polygon and NEXRAD radar-based) precipitation data demonstrate relatively good fit against observed streamflow as well as improved fit in comparison with the outputs of spatially averaged rainfall data simulations as follows: (1) application with 24 single storm events using Thiessen polygon distributed rainfall provided overall statistical results in ENS of 0.84 and R2 of 0.86 (improved ENS by 1.8% and R2 by 2.1% relative to averaged data inputs) for direct runoff, (2) simulation of direct runoff flow for the same storm events using NEXRAD data provided ENS of 0.85 and R2 of 0.89 (increase of ENS by 3.0% and R 2 by 6.0%), and (3) 6-year long-term daily NEXRAD data provided total simulated streamflow statistics of ENS 0.71 and R2 0.72 (increased ENS of 42.0% and R2 of 33.3%). These results also indicate that NEXRAD radar-based data are more appropriate for rainfall-runoff flow predictions than rain gauge observations by capturing spatially distributed rainfall amounts and having fewer missing or erroneous records. The Distributed-Clark model presented in this research is, therefore, potentially significant to improved implementation of hydrologic simulation, particularly for spatially distributed rainfall-runoff routing using gridded types of quantitative precipitation estimation (QPE) data in a GIS environment, as a relatively simple (few parameter) hydrologic model

Fuels treatment and wildfire effects on runoff from Sierra Nevada mixed-conifer forests

We applied an eco-hydrologic model (Regional Hydro-Ecologic Simulation System [RHESSys]), constrained with spatially distributed field measurements, to assess the impacts of forest-fuel treatments and wildfire on hydrologic fluxes in two Sierra Nevada firesheds. Strategically placed fuels treatments were implemented during 2011–2012 in the upper American River in the central Sierra Nevada (43 km2) and in the upper Fresno River in the southern Sierra Nevada (24 km2). This study used the measured vegetation changes from mechanical treatments and modelled vegetation change from wildfire to determine impacts on the water balance. The well-constrained headwater model was transferred to larger catchments based on geologic and hydrologic similarities. Fuels treatments covered 18% of the American and 29% of the Lewis catchment. Averaged over the entire catchment, treatments in the wetter central Sierra Nevada resulted in a relatively light vegetation decrease (8%), leading to a 12% runoff increase, averaged over wet and dry years. Wildfire with and without forest treatments reduced vegetation by 38% and 50% and increased runoff by 55% and 67%, respectively. Treatments in the drier southern Sierra Nevada also reduced the spatially averaged vegetation by 8%, but the runoff response was limited to an increase of less than 3% compared with no treatment. Wildfire following treatments reduced vegetation by 40%, increasing runoff by 13%. Changes to catchment-scale water-balance simulations were more sensitive to canopy cover than to leaf area index, indicating that the pattern as well as amount of vegetation treatment is important to hydrologic response

Improving hydrologic modeling of runoff processes using data-driven models

2021 Spring.Includes bibliographical references.Accurate rainfall–runoff simulation is essential for responding to natural disasters, such as floods and droughts, and for proper water resources management in a wide variety of fields, including hydrology, agriculture, and environmental studies. A hydrologic model aims to analyze the nonlinear and complex relationship between rainfall and runoff based on empirical equations and multiple parameters. To obtain reliable results of runoff simulations, it is necessary to consider three tasks, namely, reasonably diagnosing the modeling performance, managing the uncertainties in the modeling outcome, and simulating runoff considering various conditions. Recently, with the advancement of computing systems, technology, resources, and information, data-driven models are widely used in various fields such as language translation, image classification, and time-series analysis. In addition, as spatial and temporal resolutions of observations are improved, the applicability of data-driven models, which require massive amounts of datasets, is rapidly increasing. In hydrology, rainfall–runoff simulation requires various datasets including meteorological, topographical, and soil properties with multiple time steps from sub-hourly to monthly. This research investigates whether data-driven approaches can be effectively applied for runoff analysis. In particular, this research aims to explore if data-driven models can 1) reasonably evaluate hydrologic models, 2) improve the modeling performance, and 3) predict hourly runoff using distributed forcing datasets. The details of these three research aspects are as follows: First, this research developed a hydrologic assessment tool using a hybrid framework, which combines two data-driven models, to evaluate the performance of a hydrologic model for runoff simulation. The National Water Model, which is a fully distributed hydrologic model, was used as the physical-based model. The developed assessment tool aims to provide easy-to-understand performance ratings for the simulated hydrograph components, namely, the rising and recession limbs, as well as for the entire hydrograph, against observed runoff data. In this research, four performance ratings were used. This is the first research that tries to apply data-driven models for evaluating the performance of the National Water Model and the results are expected to reasonably diagnose the model's ability for runoff simulations based on a short-term time step. Second, correction of errors inherent in the predicted runoff is essential for efficient water management. Hydrologic models include various parameters that cannot be measured directly, but they can be adjusted to improve the predictive performance. However, even a calibrated model still has obvious errors in predicting runoff. In this research, a data-driven model was applied to correct errors in the predicted runoff from the National Water Model and improve its predictive performance. The proposed method uses historic errors in runoff to predict new errors as a post-processor. This research shows that data-driven models, which can build algorithms based on the relationships between datasets, have strong potential for correcting errors and improving the predictive performance of hydrologic models. Finally, to simulate rainfall-runoff accurately, it is essential to consider various factors such as precipitation, soil property, and runoff coming from upstream regions. With improvements in observation systems and resources, various types of forcing datasets, including remote-sensing based data and data-assimilation system products, are available for hydrologic analysis. In this research, various data-driven models with distributed forcing datasets were applied to perform hourly runoff predictions. The forcing datasets included different hydrologic factors such as soil moisture, precipitation, land surface temperature, and base flow, which were obtained from a data assimilation system. The predicted results were evaluated in terms of seasonal and event-based performances and compared with those of the National Water Model. The results demonstrated that data-driven models for hourly runoff forecasting are effective and useful for short-term runoff prediction and developing flood warning system during wet season

A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Deglacial δ18O and hydrologic variability in the tropical Pacific and Indian Oceans

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth and Planetary Science Letters 387 (2014): 240–251, doi:10.1016/j.epsl.2013.11.032.Evidence from geologic archives suggests that there were large changes in the tropical hydrologic cycle associated with the two prominent northern hemisphere deglacial cooling events, Heinrich Stadial 1 (HS1; ∼19 to 15 kyr BP; kyr BP = 1000 yr before present) and the Younger Dryas (∼12.9 to 11.7 kyr BP). These hydrologic shifts have been alternatively attributed to high and low latitude origin. Here, we present a new record of hydrologic variability based on planktic foraminifera-derived δ18O of seawater (δ18Osw) estimates from a sediment core from the tropical Eastern Indian Ocean, and using 12 additional δ18Osw records, construct a single record of the dominant mode of tropical Eastern Equatorial Pacific and Indo-Pacific Warm Pool (IPWP) hydrologic variability. We show that deglacial hydrologic shifts parallel variations in the reconstructed interhemispheric temperature gradient, suggesting a strong response to variations in the Atlantic Meridional Overturning Circulation and the attendant heat redistribution. A transient model simulation of the last deglaciation suggests that hydrologic changes, including a southward shift in the Intertropical Convergence Zone (ITCZ) which likely occurred during these northern hemisphere cold events, coupled with oceanic advection and mixing, resulted in increased salinity in the Indonesian region of the IPWP and the eastern tropical Pacific, which is recorded by the δ18Osw proxy. Based on our observations and modeling results we suggest the interhemispheric temperature gradient directly controls the tropical hydrologic cycle on these time scales, which in turn mediates poleward atmospheric heat transport.ThisworkwasfundedbytheNationalScienceFoundation;theOceanandClimateChangeInstituteandtheAcademicProgramsOfficeatWoodsHoleOceano-graphicInstitution;BMBF(PABESIA);andDFG(He3412/15-1

From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models

Modeling the effect of spatial variability of precipitation and basin characteristics on streamflow requires the use of distributed or semi-distributed hydrologic models. This paper addresses a DMIP 2 study that focuses on the advantages of using a semi-distributed modeling structure. We first present a revised semi-distributed structure of the NWS SACramento Soil Moisture Accounting (SAC-SMA) model that separates the routing of fast and slow response runoff components, and thus explicitly accounts for the differences between the two components. We then test four different calibration strategies that take advantage of the strengths of existing optimization algorithms (SCE-UA) and schemes (MACS). These strategies include: (1) lumped parameters and basin averaged precipitation, (2) semi-lumped parameters and distributed precipitation forcing, (3) semi-distributed parameters and distributed precipitation forcing and (4) lumped parameters and basin averaged precipitation, modified using a priori parameters of the SAC-SMA model. Finally, we explore the value of using discharge observations at interior points in model calibration by assessing gains/losses in hydrograph simulations at the basin outlet. Our investigation focuses on two key DMIP 2 science questions. Specifically, we investigate (a) the ability of the semi-distributed model structure to improve stream flow simulations at the basin outlet and (b) to provide reasonably good simulations at interior points.The semi-distributed model is calibrated for the Illinois River Basin at Siloam Springs, Arkansas using streamflow observations at the basin outlet only. The results indicate that lumped to distributed calibration strategies (1 and 4) both improve simulation at the outlet and provide meaningful streamflow predictions at interior points. In addition, the results of the complementary study, which uses interior points during the model calibration, suggest that model performance at the outlet can be further improved by using a semi-distributed structure calibrated at both interior points and the outlet, even when only a few years of historical record are available. © 2009 Elsevier B.V

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget

Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society

A Conceptual Framework for Integration Development of GSFLOW Model: Concerns and Issues Identified and Addressed for Model Development Efficiency

In Coupled Groundwater and Surface-Water Flow (GSFLOW) model, the three-dimensional finite-difference groundwater model (MODFLOW) plays a critical role of groundwater flow simulation, together with which the Precipitation-Runoff Modeling System (PRMS) simulates the surface hydrologic processes. While the model development of each individual PRMS and MODFLOW model requires tremendous time and efforts, further integration development of these two models exerts additional concerns and issues due to different simulation realm, data communication, and computation algorithms. To address these concerns and issues in GSFLOW, the present paper proposes a conceptual framework from perspectives of: Model Conceptualization, Data Linkages and Transference, Model Calibration, and Sensitivity Analysis. As a demonstration, a MODFLOW groundwater flow system was developed and coupled with the PRMS model in the Lehman Creek watershed, eastern Nevada, resulting in a smooth and efficient integration as the hydrogeologic features were well captured and represented. The proposed conceptual integration framework with techniques and concerns identified substantially improves GSFLOW model development efficiency and help better model result interpretations. This may also find applications in other integrated hydrologic modelings