There is one active project within this sub-area:

Using multi-objective parameter optimization and snow survey data to improve operational forecasting in the Upper Rio Grande
D. Boyle (DRI/UNR), S. Fassnacht (UA-HWR), J. McConnell (DRI/UNR), T. Bardsley (DRI/UNR), Gorham (DRI/UNR), Hobson (DRI/UNR), Kirick (DRI/UNR)

Physically based, spatially distributed, dynamic models of stream discharge offer the potential to significantly improve stream discharge forecasts and so lead to improved reservoir management. A primary goal of SAHRA is to foster and facilitate the use of state-of-the-art hydrologic models and improve understanding of them in water resource management. However, acceptance of new management approaches by water resource managers and implementation of new technologies is often not easily accomplished. Calibrated, physically based, distributed basin-scale models to evaluate and integrate process level studies and related computer modules are required, together with mechanisms to transfer this understanding and modeling capability directly to the stakeholders. Our research approach is multi-faceted. First, in close collaboration with the USGS and other agencies and with significant leveraging of SAHRA funds, we are developing, implementing, and calibrating a semi-physically based, spatially distributed hydrologic model in twenty-one headwater basins in the Rio Grande. The model output will replace the empirical predictions currently used as input to the Upper Rio Grande Water Operations Model (URGWOM). Second, we have undertaken field snow surveys in the Colorado portion of the Rio Grande and in Sagehen basin (a local test basin in the Sierra Nevada) that are designed to evaluate the performance and accuracy of both the spatially distributed hydrologic model and the SNOTEL data traditionally used to drive such models, with particular emphasis on the distribution of snow and snow water equivalence, evolution of the snow pack, and water balance. Third, with a fully calibrated and operational hydrologic model in place and with a clearly established link with water resource managers in the Rio Grande, it is now possible to efficiently implement and evaluate new computer modules developed from SAHRA-funded process level studies.

Activities and Results

In anticipation of a week-long snow measurement field campaign in spring 2002, we purchased two snowmobiles and other field sampling equipment using funds leveraged from DRI and the State of Nevada. However, because the snowpack in the Rio Grande in 2002 was much lower than normal, the field campaign was scaled back to include: 1) a 3-day, focused field campaign and modeling effort in the South Fork of the Rio Grande, and 2) graduate student Tim Bardsley's ongoing 1-km2 snow distribution measurements around the SNOTEL sites in the area. USGS researcher S. Markstrom and incoming DRI graduate student A. Hobson joined DRI faculty J. McConnell and D. Boyle and DRI graduate students T. Gorham and T. Bardsley in the field. Monthly 1-km2 snow distribution measurements around the Independence Lake SNOTEL site in the Sagehen test basin were also made throughout the year.

MMS/PRMS modeling was implemented and calibrated for headwaters basins in the Rio Grande using SAHRA, USGS, DRI and State of Nevada funds with the objective of improving stream discharge forecast capability at 21 input nodes of the URGWOM operations model. Evaluation of the results and historical comparisons with the NRCS-based forecasts used previously by URGWOM are underway.
High spatial resolution MMS/PRMS modeling in the headwaters of the Rio Grande above the Del Norte gage was undertaken. This included high resolution modeling in the South Fork of the Rio Grande designed in conjunction with DRI's field measurement campaign in April 2002. A second objective of the high spatial resolution modeling is to allow optimization of the MMS/PRMS model using the Del Norte gage and model validation using internal gauged nodes within the headwater basin. Initial results are promising. We have also initiated comparisons between MMS/PRMS modeled snow water equivalence/snow distribution and similar remotely sensed products from colleagues at Arizona. We are currently evaluating various ways of assimilating the remotely sensed products into the model. With the development of a MOCOM module for MMS/PRMS in year 2 and MOGSA more recently, we have begun to look at the use of multi-objective criteria optimization in our hydrologic modeling above the Del Norte gage on the Rio Grande and on the Sagehen test basin.


Results from 2001/2002

• Use of the semi-empirical, distributed MMS/PRMS hydrologic model to produce stream discharge forecasts in the twenty-one headwater basins in the upper Rio Grande showed significant improvement over the purely empirical NRCS based forecasts used previously by water resource managers.
• Field measurements of SWE around SNOTEL sites in the upper Rio Grande headwaters and in the Sagehen test basin showed that the SNOTEL point measurements significantly overestimate SWE at 1-km2 scales. Furthermore, the degree of overestimation changes in time with overestimation increasing sharply as the snow season progresses.
  
• Comparisons of field measured SWE and snow depth with MMS/PRMS modeled estimates showed generally good agreement in the South Fork of the Rio Grande basin. Similar comparisons between time series of MMS/PRMS modeled SWE and estimates derived from remotely sensed products also showed general agreement. More extensive and detailed comparisons and development of methods to incorporate remotely sensed products into the model are underway.
• Validation of MMS/PRMS modeling above the Del Norte stream gage using internal stream gage nodes suggested that the model reasonably predicts flows within most of the basin, although the model consistently underestimates discharge from the South Fork of the Rio Grande. We are investigating the reasons for this underestimation and testing possible solutions.
• Initial tests of multi-objective criteria optimization in the MMS/PRMS modeling suggested that significant improvements in stream discharge modeling in the Rio Grande headwater basins, and hence flow forecasts, are possible.

Plans

We propose to continue our current efforts aimed at improving the implementation, optimization, and validation of the MMS/PRMS and other models in the headwaters of the Rio Grande through coordinated field, modeling and remote sensing studies. We will evaluate different methods for distributing precipitation around the headwaters above the Del Norte gage by trying different parameterizations of the current XYZ method used elsewhere by USGS researchers in conjunction with MMS/PRMS. Continued use of internal gage nodes within the basin for evaluation and comparisons with remotely sensed SWE and SCA are crucial to these efforts.

We will continue and extend field snow surveys and coordination with related Arizona studies of snow distribution and soil moisture. Complete evaluation of current MMS/PRMS modeled flow forecasts and historical comparisons with NRCS-based discharge forecasts at the twenty-one headwaters basin nodes used to drive the URGWOM operations model. Complete current initial evaluation of MOCOM and MOGSA multi-objective criteria optimization of MMS/PRMS in the headwaters basin above the Del Norte gage.