Assessing the Effects of Climate Change in a Semiarid Basin Utilizing a Fully Distributed Hydrologic Model: A Case Study of Beaver Creek, Arizona.

abstract: The North American Monsoon (NAM) is characterized by high inter- and intra-seasonal variability, and potential climate change effects have been forecasted to increase this variability. The potential effects of climate change to the hydrology of the southwestern U.S. is of interest as they could have consequences to water resources, floods, and land management. I applied a distributed watershed model, the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS), to the Beaver Creek basin in Arizona. This sub-basin of the Verde River is representative of the regional topography, land cover, and soils distribution. As such, it can serve to illustrate the utility of distributed models for change assessment studies. Model calibration was performed utilizing radar-based NEXRAD data, and comparisons were done to two additional sources of precipitation data: ground-based stations and the North American Land Data Assimilation System (NLDAS). Comparisons focus on the spatiotemporal distributions of precipitation and stream discharge. Utilizing the calibrated model, I applied scenarios from the HadCM3 General Circulation Model (GCM) which was dynamically downscaled by the Weather Research and Forecast (WRF) model, to refine the representation of Arizona's regional climate. Two time periods were examined, a historical 1990-2000 and a future 2031-2040, to evaluate the hydrologic consequence in the form of differences and similarities between the decadal averages for temperature, precipitation, stream discharge and evapotranspiration. Results indicate an increase in mean air temperature over the basin by 1.2 ºC. The average decadal precipitation amounts increased between the two time periods by 2.4 times that of the historical period and had an increase in variability that was 3 times the historical period. For the future period, modeled streamflow discharge in the summer increased by a factor of 3. There was no significant change in the average evapotranspiration (ET). Overall trends of increase precipitation and variability for future climate scenarios have a more significant effect on the hydrologic response than temperature increases in the system during NAM in this study basin. The results from this study suggest that water management in the Beaver Creek will need to adapt to higher summer streamflow amounts.Dissertation/ThesisM.S. Civil and Environmental Engineering 201

Evaluation of a distributed streamflow forecast model at multiple watershed scales

The 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. With critical hydrologic observation networks in decline worldwide, future expansion of existing networks into current ungauged locations seem unlikely. Spatially distributed models can help improve hydrologic predictions in ungauged basins because of their ability to model hydrologic processes at small scales, thus providing estimates at multiple subbasin locations. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) is used to explore the accuracy of a distributed hydrologic model to simulate discharge at interior points representing various watershed scales. Basin sizes range from 20 – 2500 km2, with subbasins nested in three National Weather Service (NWS) forecast basins in the upper Midwest. The model is calibrated and validated using USGS observed discharge data at the basin outlets, and subbasin discharge is then evaluated. Two different precipitation products, NLDAS-2 with a nominal 12.5 km resolution and Stage IV with an approximate 4 km resolution, were tested to characterize the role of input uncertainty and resolution on the discharge simulations at the various scales. In general, across study basins, model performance decreased as basin size decreased, where correlation coefficients for NLDAS-2 and Stage IV simulations were 0.65 and 0.04, respectively. Once basin area was less than 250 km2 or 30% of the total watershed area, model performance became unreliable. Nash-Sutcliffe efficiency (NSE) scores were highest using the NLDAS-2 product, where basin outlets ranged from 0.50 to 0.75 during calibration and subbasins less than 250 km2 ranged from 0.11 to 0.40. Subbasins located further away from the watershed outlet had an increased chance of poorer model performance, especially for the Stage IV product (correlation = 0.35). The lower resolution NLDAS-2 data tended to improve discharge simulations during the verification period based on NSE and Percent bias (Pbias) scores compared to the higher resolution Stage IV. However, simulated discharge using Stage IV performed better for low flow periods leading to better Mean Absolute Error (MAE) scores, but the relative influence of errors versus spatial scale was difficult to characterize

A Parameter Estimation Scheme for Multiscale Kalman Smoother (MKS) Algorithm Used in Precipitation Data Fusion

A new approach is presented in this paper to effectively obtain parameter estimations for the Multiscale Kalman Smoother (MKS) algorithm. This new approach has demonstrated promising potentials in deriving better data products based on data of different spatial scales and precisions. Our new approach employs a multi-objective (MO) parameter estimation scheme (called MO scheme hereafter), rather than using the conventional maximum likelihood scheme (called ML scheme) to estimate the MKS parameters. Unlike the ML scheme, the MO scheme is not simply built on strict statistical assumptions related to prediction errors and observation errors, rather, it directly associates the fused data of multiple scales with multiple objective functions in searching best parameter estimations for MKS through optimization. In the MO scheme, objective functions are defined to facilitate consistency among the fused data at multiscales and the input data at their original scales in terms of spatial patterns and magnitudes. The new approach is evaluated through a Monte Carlo experiment and a series of comparison analyses using synthetic precipitation data. Our results show that the MKS fused precipitation performs better using the MO scheme than that using the ML scheme. Particularly, improvements are significant compared to that using the ML scheme for the fused precipitation associated with fine spatial resolutions. This is mainly due to having more criteria and constraints involved in the MO scheme than those included in the ML scheme. The weakness of the original ML scheme that blindly puts more weights onto the data associated with finer resolutions is overcome in our new approach

SENSITIVITY OF A SHENANDOAH WATERSHED MODEL TO DISTRIBUTED RAINFALL DATA

Precipitation is a primary input for hydrologic modeling. In this study, the use of spatially varying gridded precipitation data obtained from the North American Land Data Assimilation System (NLDAS) is investigated for possible improvements to stream flow predictions within the Shenandoah River watershed over the use of gage based precipitation data. The sensitivity of hydrologic responses to gridded and gage precipitation data is analyzed using the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS). Precipitation is the only input which varies between models. Model performance of each precipitation input is assessed by comparing predicted and measured stream flow during the 1995 to 1996 water year and calculating a number of goodness of fit measures. Results indicate that the use of spatially varying gridded precipitation data can improve stream flow predictions within the Shenandoah River watershed. Future research directions include the calibration and validation of the HEC-HMS model using the gridded precipitation data

Response of hydrologic calibration to replacing gauge-based with NEXRAD-based precipitation data in the USEPA Chesapeake Bay Watershed model

This study investigated the response of hydrologic calibration to replacing gauge-based with radar-based precipitation data in the USEPA Chesapeake Bay Program (CBP) Watershed (CBW) model over the Potomac River Basin. Specific objectives were to (1) compare gauge-based and NEXRAD radar-based (Multisensor Precipitation Estimator, MPE) data at the (a) point-pixel and (b) spatially aggregated level; (2) evaluate the model's calibration accuracy using the different precipitation data sets; and (3) examine the response of model hydrology. Hourly gauge-point and MPE-pixel data were compared at 80 locations. The CBP's interpolated and aggregated precipitation data at the model unit (county) level were compared with MPE data aggregated to the same 114 county-based spatial segments. The model calibration followed the CBP's automated approach, using observed streamflow at 37 gauge stations. Model performance was evaluated using calibration and hydrologic statistics, and GIS-aided spatial information. Calibrated parameters and model hydrologic fluxes were compared. The average annual gauge-point and MPE-pixel values (excluding hours when either was missing) agreed well. Differences in average annual values between the spatially aggregated data sets were, however, significant in parts of the study area. When parameter constraints were relaxed to allow calibration to adjust to the smaller volume of precipitation, the model using MPE outperformed the model calibrated to CBP precipitation data at 65% of the 37calibration sites. The model response was controlled largely by the seasonal difference in precipitation inputs: (1) calibration process could not compensate for large differences in seasonal flow bias caused by the seasonal volume of precipitation; (2) seasonal flow bias affected the lower zone nominal soil moisture storage parameter (LZSN), mainly affecting interflow and groundwater flow. The surface flow component was generally the same for the different precipitation inputs. The two precipitation data types can be used interchangeably to simulate surface-flow dominated processes, but care must be taken in simulations where subsurface pathways and residence times are important. MPE is a strong alternative to gauge-based precipitation data because of its spatiotemporal coverage and rare missing records. Using MPE in hydrologic modeling is appealing because of the improved calibration accuracy of the CBW model demonstrated in this study

Component analysis of errors in satellite-based precipitation estimates

Satellite-based precipitation estimates have great potential for a wide range of critical applications, but their error characteristics need to be examined and understood. In this study, six (6) high-resolution, satellite-based precipitation data sets are evaluated over the contiguous United States against a gauge-based product. An error decomposition scheme is devised to separate the errors into three independent components, hit bias, missed precipitation, and false precipitation, to better track the error sources associated with the satellite retrieval processes. Our analysis reveals the following. (1) The three components for each product are all substantial, with large spatial and temporal variations. (2) The amplitude of individual components sometimes is larger than that of the total errors. In such cases, the smaller total errors are resulting from the three components canceling one another. (3) All the products detected strong precipitation (\u3e40 mm/d) well, but with various biases. They tend to overestimate in summer and underestimate in winter, by as much as 50% in either season, and they all miss a significant amount of light precipitation (\u3c10 mm/d), up to 40%. (4) Hit bias and missed precipitation are the two leading error sources. In summer, positive hit bias, up to 50%, dominates the total errors for most products. (5) In winter, missed precipitation over mountainous regions and the northeast, presumably snowfall, poses a common challenge to all the data sets. On the basis of the findings, we recommend that future efforts focus on reducing hit bias, adding snowfall retrievals, and improving methods for combining gauge and satellite data. Strategies for future studies to establish better links between the errors in the end products and the upstream data sources are also proposed

High-resolution QPF Uncertainty And Its Implications For Flood Prediction: A Case Study For The Eastern Iowa Flood Of 2016

This study addresses the uncertainty of High-Resolution Rapid Refresh (HRRR) quantitative precipitation forecasts (QPFs), which were recently appended to the operational hydrologic forecasting framework. In this study, we examine the uncertainty features of HRRR QPFs for an Iowa flooding event that occurred in September 2016. Our evaluation of HRRR QPFs is based on the conventional approach of QPF verification and the analysis of mean areal precipitation (MAP) with respect to forecast lead time. The QPF verification results show that the precipitation forecast skill of HRRR significantly drops during short lead times and then gradually decreases for further lead times. The MAP analysis also demonstrates that the QPF error sharply increases during short lead times and starts decreasing slightly beyond 4-h lead time. We found that the variability of QPF error measured in terms of MAP decreases as basin scale and lead time become larger and longer, respectively. The effects of QPF uncertainty on hydrologic prediction are quantified through the hillslope-link model (HLM) simulations using hydrologic performance metrics (e.g., Kling-Gupta efficiency). The simulation results agree to some degree with those from the MAP analysis, finding that the performance achieved from the QPF forcing decreases during 1-3-h lead times and starts increasing with 4-6-h lead times. The best performance acquired at the 1-h lead time does not seem acceptable because of the large overestimation of the flood peak, along with an erroneous early peak that is not observed in streamflow observations. This study provides further evidence that HRRR contains a well-known weakness at short lead times, and the QPF uncertainty (e.g., bias) described as a function of forecast lead times should be corrected before its use in hydrologic prediction

Evaluating enhanced hydrological representations in Noah LSM over transition zones : an ensemble-based approach to model diagnostics

textThis work introduces diagnostic methods for land surface model (LSM) evaluation that enable developers to identify structural shortcomings in model parameterizations by evaluating model 'signatures' (characteristic temporal and spatial patterns of behavior) in feature, cost-function, and parameter spaces. The ensemble-based methods allow researchers to draw conclusions about hypotheses and model realism that are independent of parameter choice. I compare the performance and physical realism of three versions of Noah LSM (a benchmark standard version [STD], a dynamic-vegetation enhanced version [DV], and a groundwater-enabled one [GW]) in simulating high-frequency near-surface states and land-to-atmosphere fluxes in-situ and over a catchment at high-resolution in the U.S. Southern Great Plains, a transition zone between humid and arid climates. Only at more humid sites do the more conceptually realistic, hydrologically enhanced LSMs (DV and GW) ameliorate biases in the estimation of root-zone moisture change and evaporative fraction. Although the improved simulations support the hypothesis that groundwater and vegetation processes shape fluxes in transition zones, further assessment of the timing and partitioning of the energy and water cycles indicates improvements to the movement of water within the soil column are needed. Distributed STD and GW underestimate the contribution of baseflow and simulate too-flashy streamflow. This work challenges common practices and assumptions in LSM development and offers researchers more stringent model evaluation methods. I show that, because of equifinality, ad-hoc evaluation using single parameter sets provides insufficient information for choosing among competing parameterizations, for addressing hypotheses under uncertainty, or for guiding model development. Posterior distributions of physically meaningful parameters differ between models and sites, and relationships between parameters themselves change. 'Plug and play' of modules and partial calibration likely introduce error and should be re-examined. Even though LSMs are 'physically based,' model parameters are effective and scale-, site- and model-dependent. Parameters are not functions of soil or vegetation type alone: they likely depend in part on climate and cannot be assumed to be transferable between sites with similar physical characteristics. By helping bridge the gap between the model identification and model development, this research contributes to the continued improvement of our understanding and modeling of environmental processes.Geological Science

Regional Aspects of the North American Land Surface-Atmosphere Interactions and Their Contributions to the Variability and Predictability of the Regional Hydrologic Cycle

In this study, we investigate the pathways responsible for soil moisture-precipitation interactions and the mechanisms for soil moisture memory at regional scales through analysis of NCEP's North American Regional Reanalysis dataset, which is derived from a system using the mesoscale Eta model coupled with Noah land surface model. The consideration of the relative availability of water and energy leads to the relative strengths of land-atmosphere interaction and soil moisture memory, which are related to the predictability of the regional hydrologic cycle. The seasonal and geographical variations in estimated interaction and memory may establish the relative predictability among the North American basins. The potential for seasonal predictability of the regional hydrologic cycle is conditioned by the foreknowledge of the land surface soil state, which contributes significantly to summer precipitation: (i) The precipitation variability and predictability by strong land-atmosphere interactions are most important in the monsoon regions of Mexico; (ii) Although strong in interactions, the poor soil moisture memory in the Colorado basin and the western part of the Mississippi basin lowers the predictability; (iii) The Columbia basin and the eastern part of the Mississippi basin also stand out as low predictability basins, in that they have good soil moisture memory, but weak strength in interactions, limiting their predictabilities. Our analysis has revealed a highly physically and statistically consistent picture, providing solid support to studies of predictability based on model simulations

Research on Dimensionless Unit Hydrograph and Time of Concentration for Maryland Watersheds

SHA/UM/5-04Observed data from 100 rainfall-runoff events on 54 watersheds in Maryland and Delaware were used to investigate two watershed characteristics: the dimensionless unit hydrograph (DUH) and time of concentration (Tc). Streamflow hydrograph data were obtained from US Geological Survey gaging stations. Event hyetographs were generated from US Weather Service NEXRAD Radar Stage III data (DPR) using a method developed in this study. The gamma-function form of the Natural Resources Conservation Service unit hydrograph was assumed. For each event, an optimization method was used to determine the time to peak and gamma parameter (related to the Peak Rate Factor, PRF) that give the best-fit direct runoff hydrograph when convolved with the rainfall excess hyetograph. Tc was estimated by differentiation of the unit hydrograph. Efforts to predict PRF and Tc using watershed properties, and to update an existing regression equation, were inconclusive. Future investigations will focus on improving the event baseflow separation and determination of rainfall excess