Sacramento Soil Moisture Accounting Model Research Articles

Improving the shuffled complex evolution scheme for optimization of complex nonlinear hydrological systems: Application to the calibration of the Sacramento soil‐moisture accounting model

An innovative algorithm, shuffled complexes with principal components analysis (SP‐UCI), is developed to overcome a critical deficiency of the shuffled complex evolution scheme: population degeneration. Population degeneration means that, during the evolutionary search process, the population of search particles may degenerate into a subspace of the full parameter space, thereby missing the capacity of fully exploring the parameter space. Being confined in a subspace may even lead the particle population to converge to nonstationary points, which is a fatal malfunction. To overcome this problem, SP‐UCI employs the principal components analysis to detect the occurrence of population degeneration and remedy the adverse effects. The ensemble of calibrations of the Sacramento soil moisture accounting model with the SP‐UCI method over the Leaf River basin, Mississippi, retrieves the optimal parameter values with the lowest recorded root‐mean‐squared error of simulated daily runoff against the observation. Moreover, the result also provides consistent (narrow ranges) model parameter distribution, which results in a better understanding of the model's behavior, given the watershed's hydrologic features.

Algorithms of automatic calibration of multi-parameter models used in operational systems of flash flood forecasting

In the second article of the serie, new tools of fast and effective automatic calibration of multi-parameter hydrological models are presented. In particular, it is shown that the basic method, the Stepwise Line Search (SLS), two its modifications (SLS-F and SLS-2L) and their combination (SLS-2LF) can be used in automated systems of operational flash flood forecasting where computational efficiency is critical due to a great number of catchments that should be calibrated. Results of implementation of the SLS-based calibration procedures for parametrization of the Sacramento Soil Moisture Accounting Model, which demonstrate their high effectiveness and efficiency, are shown.

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

Summary 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.

Operational snow modeling: Addressing the challenges of an energy balance model for National Weather Service forecasts

Summary Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow–Atmosphere–Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of mid-winter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to be primary factors affecting the implementation of an energy balance model in operational streamflow prediction.

Characterization of watershed model behavior across a hydroclimatic gradient

A fundamental tradeoff exists in watershed modeling between a model's flexibility for representing watersheds with different characteristics versus its potential for overparameterization. This study uses global sensitivity analysis to investigate how a commonly used intermediate‐complexity model, the Sacramento Soil Moisture Accounting Model (SAC‐SMA), represents a wide range of watersheds with diverse physical and hydroclimatic characteristics. The analysis aims to establish a detailed understanding of model behavior across watersheds and time periods with the ultimate objective to guide model calibration and evaluation studies. Sobol's sensitivity analysis is used to evaluate the SAC‐SMA in 12 Model Parameter Estimation Experiment (MOPEX) watersheds in the US. The watersheds span a wide hydroclimatic gradient from arid to humid systems. Four evaluation metrics reflecting base flows, midrange flows, peak flows, and long‐term water balance were used to comprehensively characterize trends in sensitivity and model behavior. Results show significant variation in parameter sensitivities that are correlated with the hydroclimatic characteristics of the watersheds and time periods analyzed. The sensitivity patterns are consistent with the expected dominant processes and demonstrate the need for moderate model complexity to represent different hydroclimatic regimes. The analysis reveals that the primary model controls for some aspects of the simulated hydrograph are different from those typically assumed for the SAC‐SMA. Results also show that between 6 and 10 parameters are regularly identifiable from daily hydrologic data, which is about twice the range that is often assumed (i.e., 3 to 5). Synthesized results provide comprehensive SAC‐SMA calibration guidance, demonstrate the flexibility of the model for representing multiple hydroclimatic regimes, and highlight the great difficulty in generalizing model behavior across watersheds.

Use of soil moisture observations to improve parameter consistency in watershed calibration

Calibration is a critical component in the implementation of operational models for river forecasting. It has traditionally relied on minimizing the errors between simulated and observed basin outlet hydrographs. However, considering numerous sources of uncertainty and the complexity of recently-developed models, this approach often fails to reduce parameter uncertainties. One of the possibilities to reduce parameter uncertainty would be use of additional independent data in the model evaluation. Unfortunately, such data are limited and their quality is usually not well defined. This study investigates the potential use of soil moisture measurements in the model calibration process. While these data are not commonly available, there is potential for considerable expansion of soil moisture measurements in the near future. Comprehensive soil moisture measurements from the Oklahoma Mesonet are used in the analysis. The Sacramento Soil Moisture Accounting model with a new heat transfer component (SAC-HT) is applied to more than 20 watersheds of sizes ranging from 200 to 4000 km 2 to answer the question: can the use of soil moisture data improve calibration reliability without an unacceptable reduction in the accuracy of the simulated outlet hydrograph. Three cases of simulated soil moisture and hydrographs are analysed: (1) the control run with the use of a priori parameters; (2) automatic calibration based on outlet hydrograph goodness-of-fit only; and (3) automatic calibration based on outlet hydrographs and basin average soil moisture computed at two depths. Results show deficiencies in model calibration using only outlet hydrograph goodness-of-fit as a measure. The automatic calibration in this case improves runoff simulation results on average by 45% compared to the use of a priori parameters. Soil moisture dynamics and trends are also reproduced reasonably well; however, large soil moisture biases can be seen. These biases in the top soil layer are 36% higher than in the control run. Addition of soil moisture measurements into the calibration process reduces soil moisture biases at the both soil layers by 40% without considerable reduction in runoff accuracy (5%) and improves internal consistency of calibration. The use of soil moisture measurements provides more benefit for ‘dry’ watersheds when there is no strong direct interconnection between runoff and soil moisture.

Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Abstract. This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.

A new Bayesian recursive technique for parameter estimation

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall‐runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC‐SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC‐SMA is a very well known model that estimates runoff. It has a 13‐dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the “parent” bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.

Multiobjective particle swarm optimization for parameter estimation in hydrology

Modeling of complex hydrologic processes has resulted in models that themselves exhibit a high degree of complexity and that require the determination of various parameters through calibration. In the current application we introduce a relatively new global optimization tool, called particle swarm optimization (PSO), that has already been applied in various other fields and has been reported to show effective and efficient performance. The PSO approach initially dealt with a single‐objective function but has been extended to deal with multiobjectives in a form called multiobjective particle swarm optimization (MOPSO). The algorithm is modified to account for multiobjective problems by introducing the Pareto rank concept. The new MOPSO algorithm is tested on three case studies. Two test functions are used as the first case study to generate the true Pareto fronts. The approach is further tested for parameter estimation of a well‐known conceptual rainfall‐runoff model, the Sacramento soil moisture accounting model having 13 parameters, for which the results are very encouraging. We also tested the MOPSO algorithm to calibrate a three‐parameter support vector machine model for soil moisture prediction.

Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model

Hydrological models generally contain parameters that cannot be measured directly, but can only be meaningfully inferred by calibration against a historical record of input–output data. While considerable progress has been made in the development and application of automatic procedures for model calibration, such methods have received criticism for their lack of rigor in treating uncertainty in the parameter estimates. In this paper, we apply the recently developed Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) to stochastic calibration of the parameters in the Sacramento Soil Moisture Accounting model (SAC-SMA) model using historical data from the Leaf River in Mississippi. The SCEM-UA algorithm is a Markov Chain Monte Carlo sampler that provides an estimate of the most likely parameter set and underlying posterior distribution within a single optimization run. In particular, we explore the relationship between the length and variability of the streamflow data and the Bayesian uncertainty associated with the SAC-SMA model parameters and compare SCEM-UA derived parameter values with those obtained using deterministic SCE-UA calibrations. Most significantly, for the Leaf River catchments under study our results demonstrate that most of the 13 SAC-SMA parameters are well identified by calibration to daily streamflow data suggesting that this data contains more information than has previously been reported in the literature.

Using SSURGO data to improve Sacramento Model a priori parameter estimates

As it transitions to smaller scale, distributed hydrologic modeling approaches, the National Weather Service (NWS) is improving methods of estimating parameters for the Sacramento Soil Moisture Accounting model (SAC-SMA). This is the major hydrologic model used for flood forecasting at most of the 13 river forecasting centers throughout the United States. A physically based approach based on the nationally available State Soil Geographic Database (STATSGO) has been developed (Koren, V.I., Smith, M., Wang, D., Zhang, Z., 2000. Use of soil property data in the derivation of conceptual rainfall–runoff model parameters. Proceedings of the 15th Conference on Hydrology, AMS, Long Beach, CA, pp. 103–106; Koren, V., Smith, M., Duan, Q., 2003. Use of a priori parameter estimates in the derivation of spatially consistent parameter sets of rainfall–runoff models. In: Duan, Q., Sorooshian, S., Gupta, H., Rosseau, H., Turcotte, H. (Eds.), Calibration of Watershed Models, Water Science and Applications 6, AGU, pp. 239–254), leading to objective, spatially consistent parameter estimates. This paper shows that a better representation of basin physical properties and potential improvements in hydrologic simulation performance can be obtained by basing parameter estimates on a finer-scale database of soils data, the Soil Survey Geographic Database (SSURGO), combined with high-resolution land use/land cover data. Results also suggest that an intermediate level of improvement may be obtained by combining detailed land cover data with STATSGO to refine current parameter estimates. This latter finding is significant because the SSURGO data are not yet available for the entire country.

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

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.

Analytical results for operational flash flood guidance

The theoretical basis of developing operational flash flood guidance systems is studied using analytical methods. The Sacramento soil moisture accounting model is used operationally in the United States to produce flash flood guidance estimates of a given duration from threshold runoff estimates. Analytical results to (a) shed light on the properties of this model's short-term surface runoff predictions under substantial rainfall forcing and (b) to facilitate flash flood computations in real time. The results derived pertain to the computation of pervious-area surface runoff from the upper zone tension and free water elements of the model, and to surface runoff from both the permanently impervious and the variable additional impervious area of the model that provides spatially distributed characteristics in the production of surface runoff. The time to the production of surface runoff is also computed on the basis of the model parameters and initial water element conditions and is related to the flash flood guidance duration. Analytical solutions for time to saturation and surface runoff are also derived for the case of saturation-excess models, and these results are compared to those derived for the Sacramento model. Various characteristics of the flash flood guidance to threshold runoff relationship are discussed and considerations for real-time application are offered. Uncertainty analysis of the threshold runoff to flash flood guidance transformation is also performed.

Application of temporal streamflow descriptors in hydrologic model parameter estimation

This paper presents a parameter estimation approach based on hydrograph descriptors that capture dominant streamflow characteristics at three timescales (monthly, yearly, and record extent). The scheme, entitled hydrograph descriptors multitemporal sensitivity analyses (HYDMUS), yields an ensemble of model simulations generated from a reduced parameter space, based on a set of streamflow descriptors that emphasize the timescale dynamics of streamflow record. In this procedure the posterior distributions of model parameters derived at coarser timescales are used to sample model parameters for the next finer timescale. The procedure was used to estimate the parameters of the Sacramento soil moisture accounting model (SAC‐SMA) for the Leaf River, Mississippi. The results indicated that in addition to a significant reduction in the range of parameter uncertainty, HYDMUS improved parameter identifiability for all 13 of the model parameters. The performance of the procedure was compared to four previous calibration studies on the same watershed. Although our application of HYDMUS did not explicitly consider the error at each simulation time step during the calibration process, the model performance was, in some important respects, found to be better than in previous deterministic studies.

신뢰성 분석을 통한 기존 댐 재개발의 적정규모 결정의 관한 연구

본 연구에서는 기존댐 재개발에 따른 댐 재개발 최적 규모 선택을 위한 방법론을 제시하고자 한다. 최적규모의 분석을 위한 과정은 다음과 같다. 강우량은 비동질성 Markov 모형으로 모의 발생하여 비교적 실제 구조에 가깝도록 설계된 토양함수 모형(Sacramento Soil Moisture Accounting Model, SAC-SMA)과 HEC-1의 Kinematic wave 하도추적 모형을 결합하여 만든 미 기상국의 NWS-PC 모형을 이용하여 유입량으로 변환하였다. 유입량은 저수지 모의운영을 통하여 저수지의규모를 결정하였다. 괴산댐을 대상으로 Hashimoto등 (1982)이 제시한 신뢰성 기준을 바탕으로 재개발 적정규모에 대한 신뢰성 분석을 실시하였으며 대략 155EL.M가 최적 수위로 판단되었으며 댐규모를 판단하기 위한 보조수단으로 이용이 가능하리라 사료된다. This paper presents a procedure of evaluation of reservoir capacity for additional water storage for dam rehabilitation. One of the techniques on the extension of rainfall has been developed, and the daily stream flows were simulated by the NWS-PC long-term rainfall-runoff model with the input of the extended daily rainfall which was stochastically generated by the nonhomogeneous markov chain model. We peformed a reliability analysis to Guisan dam about the optimal capacity of dam rehabilitation by using performance criteria that Hashimoto et al. (1982) presented. We estimated that the most suitable water level is approximately 155EL.M. suggested that this method can use supplemental methods to estimate optimum dam scale.

Continuous streamflow simulation with the HRCDHM distributed hydrologic model

The objective of the authors’ work in the area of distributed modeling is to determine the manner with which rainfall input and model parameter uncertainty shapes the character of the flow simulation and prediction uncertainty of distributed hydrologic models. Toward this end and as a tool for the investigation, a distributed model, HRCDHM, has been formulated and tested as part of the NOAA Distributed Model Intercomparison Project (DMIP). This paper examines hourly flow simulations from HRCDHM applied with operational data obtained for the DMIP study watersheds. HRCDHM is a catchment-based, distributed input, distributed parameter hydrologic model. The hydrologic processes of infiltration/percolation, evapotranspiration, surface and subsurface flow (includes leakage to deep groundwater) are modeled along the vertical direction on a subcatchment basis in a manner similar to the Sacramento Soil Moisture Accounting model, and kinematic channel routing carries the flow through the network of subcatchments to the watershed outlet, providing capability for spatially distributed flow simulations. Subcatchment physical properties are derived from various digital terrain and land-characteristics databases through GIS processing and they are used to derive spatially distributed model parameter values. The NWS operational WSR-88D hourly radar rainfall estimates (Stage III product with pixel scale of approximately 4 km) constitute the rainfall forcing and a combination of model-derived and observed hourly surface meteorological data are used to produce the potential evapotranspiration forcing. HRCDHM was applied to and was calibrated for five watersheds for the period May 1993 through June 2000. Validation was done with data not used during the calibration period. This application shows that: (a) the HRCDHM, when forced with hourly data, is able to reproduce well the observed hourly streamflow at the outlet of each study watershed; and (b) beyond these outlet locations, HRCDHM is able to reproduce adequately the hourly flows at several interior locations. A companion paper [J. Hydrol. (2004)], in this issue details the use of the model for the characterization of simulation uncertainty within a Monte Carlo framework.

Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system

An important goal of spatially distributed hydrologic modeling is to provide estimates of streamflow (and river levels) at any point along the river system. To encourage collaborative research into appropriate levels of model complexity, the value of spatially distributed data, and methods suitable for model development and calibration, the US National Weather Service Hydrology Laboratory (NWSHL) is promoting the distributed modeling intercomparison project (DMIP). In particular, the project is interested in how spatially distributed estimates of precipitation provided by the next generation radar (NEXRAD) network, high resolution digital elevation models (DEM), soil, land-use and vegetation data can be integrated into an improved system for distributed hydrologic modeling that provides more accurate and informative flood forecasts. The goal of this study is to explore four questions: Can a semi-distributed approach improve the streamflow forecasts at the watershed outlet compared to a lumped approach? What is a suitable calibration strategy for a semi-distributed model structure, and how much improvement can be obtained? What is the minimum level of spatial complexity required, above which the improvement in forecast accuracy is marginal? What spatial details must be included to enable flow prediction at any point along the river network? The study compares lumped, semi-lumped and semi-distributed versions of the SAC-SMA (Sacramento Soil Moisture Accounting) model for the Illinois River basin at Watts (OK). A kinematic wave scheme is used to rout the flow along the river channel to the outlet. A Multi-step Automatic Calibration Scheme (MACS) using the Shuffled Complex Evolution (SCE-UA) optimization algorithm is applied for model calibration. The calibration results reveal that moving from a lumped model structure, driven by spatially averaged NEXRAD data over the entire basin, to a semi-distributed model structure, with forcing data averaged over each sub-basin while having identical parameters for all the sub-basins, improves the simulation results. However, varying the parameters between sub-basins does not further improve the simulation results, either at the outlet or at an interior testing point.

Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations

Currently, the river forecasting system deployed in each of 13 River Forecast Centers of the National Weather Service primarily uses lumped parameter models to generate hydrologic simulations. With the deployment of the weather surveillance radar 1988 Doppler radars, more and more precipitation data with high spatial and temporal resolution have become available for hydrologic modeling. Hydrologists inside and outside the National Weather Service are now investigating how to effectively use these data to enhance river-forecasting capabilities. In this paper, six years of continuously simulated hydrographs from an eight-subbasin model are compared to those from a single-basin ~or lumped! model, both applied to the Blue River basin ~1,232 km 2 ! in Oklahoma. The Sacramento soil moisture accounting model is used to generate runoff in all cases. Synthetic unit hydrographs for each subbasin convey the water to the outlet of the basin without explicit flow routing. Subdividing the basin into eight subbasins captures spatially variable rainfall reflected in the next generation weather radar products and produces improved results without greatly increasing the computational and data requirements. Strategies for calibrating the hydrologic model parameters for multiple subbasins are explored.

POTENTIAL IMPACTS OF CLIMATE CHANGE ON CALIFORNIA HYDROLOGY1

ABSTRACT: Previous reports based on climate change scenarios have suggested that California will be subjected to increased wintertime and decreased summertime streamflow. Due to the uncertainty of projections in future climate, a new range of potential climatological future temperature shifts and precipitation ratios is applied to the Sacramento Soil Moisture Accounting Model and Anderson Snow Model in order to determine hydrologic sensitivities. Two general circulation models (GCMs) were used in this analysis: one that is warm and wet (HadCM2 run 1) and one that is cool and dry (PCM run B06.06), relative to the GCM projections for California that were part of the Third Assessment Report of the Intergovernmental Panel on Climate Change. A set of specified incremental temperature shifts from 1.5°C to 5.0°C and precipitation ratios from 0.70 to 1.30 were also used as input to the snow and soil moisture accounting models, providing for additional scenarios (e.g., warm/dry, cool/wet). Hydrologic calculations were performed for a set of California river basins that extend from the coastal mountains and Sierra Nevada northern region to the southern Sierra Nevada region; these were applied to a water allocation analysis in a companion paper. Results indicate that for all snow‐producing cases, a larger proportion of the streamflow volume will occur earlier in the year. The amount and timing is dependent on the characteristics of each basin, particularly the elevation. Increased temperatures lead to a higher freezing line, therefore less snow accumulation and increased melting below the freezing height. The hydrologic response varies for each scenario, and the resulting solution set provides bounds to the range of possible change in streamflow, snowmelt, snow water equivalent, and the change in the magnitude of annual high flows. An important result that appears for all snowmelt driven runoff basins, is that late winter snow accumulation decreases by 50 percent toward the end of this century.

NWSRFS CALIBRATION PARAMETER SELECTION AND GEOLOGIC REASONING: PACIFIC NORTHWEST CASES1

ABSTRACT: The National Weather Service River Forecast System (NWSRFS) is the new hydrologic prediction model for the National Weather Service (NWS) and provides guidance to meteorologists who issue NWS Flood Warnings to the public. The primary submodel within NWSRFS is the Sacramento Soil Moisture Accounting (SAC‐SMA) model, which predicts surface runoff as a function of meteorological, geological, and soil data calibrated over a watershed. The research presented here focuses on a different approach to NWSRFS calibrations: greater utilization of geologic and soil data, in order to give the model better predictive capability. Geologic understanding can create better insights for the initial estimation and subsequent adjustment of SAC‐SMA parameters. Fifteen calibrated Pacific Northwest drainages reveal a variety of hydrogeologic responses. For example, results for the Mount Rainier drainages show the complex interaction between active glaciers, impermeable volcanic surfaces, and glacial sedimentary valleys. Unweathered volcanic terrains show flashy peak flows, fast flow recessions, and low baseflow. Sedimentary terrains display subdued peak flows, slow flow recessions, and higher baseflow. Operational implementation of these calibrations at the NWS's Northwest River Forecast Center has yielded more accurate predictive results since 1995. NWS hydrologic forecasters nationwide could benefit from using drainage basin geologic characteristics in understanding and improving model calibrations and real time forecasts.