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As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement: Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets. [ABSTRACT FROM AUTHOR]

Anticipated future temperature changes in the mountainous U.S. Pacific Northwest will cause reduced spring snow pack, earlier melt, earlier spring peak flow and lower summer flow in transient rain-snow and snowmelt dominant river basins. In the context of managed flood control, these systematic changes are likely to disrupt the balance between flood control and reservoir refill in existing reservoir systems. To adapt to these hydrologic changes, refill timing and evacuation requirements for flood control need to be modified. This work poses a significant systems engineering problem, especially for large, multiobjective water systems. An existing optimization/ simulation procedure is refined for rebalancing flood control and refill objectives for the Columbia River Basin for anticipated global warming. To calibrate the optimization model for the 20th century flow, the objective function is tuned to reproduce the current reliability of reservoir refill, while providing comparable levels of flood control to those produced by current flood control practices. After the optimization model is calibrated using the 20th century flow the same objective function is used to develop flood control curves for a global warming scenario which assumes an approximately 2[degrees]C increase in air temperature. Robust decreases in system storage deficits are simulated for the climate change scenario when optimized flood rule curves replace the current flood control curves, without increasing monthly flood risks. DOI: 10.1061/(ASCE)0733-9496(2009)135:6(440) CE Database subject headings: Climate changes: Floods: Reservoir operation: Columbia River: Optimization: Simulation: Hydrologic models; Global warming.

We describe an approach to seasonal streamflow forecasting for the Snake River Basin in the Pacific Northwest that uses the variable infiltration capacity (VIC) macroscale hydrology model in conjunction with adjustments of the model's initial snow state using moderate resolution imaging spectroradiometer (MODIS) imagery for Winters 2000-2004. We evaluated seasonal streamflow forecasts made on March 1, April 1, and May 1 through the end of July, as well as short lead forecasts for two-week durations beginning on April 15 and May 15 retrospectively for 2000-2004 (2004 forecasts are archived forecasts made in real time). In general, reductions in mean absolute error were more prevalent and larger for the two-week forecasts than for the longer seasonal forecasts, and the forecast skill improvements resulting from updating were greatest for forecasts made earliest in the season. Inclusion of MODIS data resulted in forecast error reduction (or no change in forecasts) in 59% of the seasonal forecasts (54% of the two-week forecasts), however when only the largest adjustments were considered, the fraction of improvement was much higher. We also evaluated the effect of MODIS adjustment on reservoir storage volume forecasts using a monthly time step reservoir simulation model. For two reservoirs (Dworshak and American Falls) where the reservoir model performed well in retrospective simulations, storage forecast errors were reduced for most forecasts at Dworshak, but were mostly unchanged or degraded at American Falls. The differences are attributed to reservoir operating characteristics, which are more constrained at American Falls than at Dworshak. DOI: 10.1061/(ASCE)0733-9496(2006)132:2(97) CE Database subject headings: Streamftow; Reservoirs; River basins; Washington; Seasonal variations.

Recent advances in long-lead climate forecasting have made it possible to produce useful streamflow forecasts for the Columbia River basin roughly six months earlier than current forecasts that rely on snowpack measurements. The resulting increase in forecast lead time facilitates considerable improvements in system operating performance, especially in years of expected above average flows. In the current reservoir operating system, the so called 'critical' and 'assured refill' rule curves that restrict releases for hydropower generation in the period from August to December are based on the critical (most severe low flow) and third lowest flow sequences of record, respectively. These role curves provide appropriate protection of energy capacity and reservoir refill in extreme low flow conditions, but are restrictive in normal and high flow years until midwinter when operational streamflow forecasts based on observed snowpack become available, and the climatological constraints are relaxed to account for expected summer streamflows. The use of long lead time streamflow forecasts allows current operating constraints to be relaxed in years when there is a high likelihood of ample streamflow. In these years, more spot market energy sales can be made in the late summer and fall/early winter because of increased available water for releases, and spill from reservoirs in wet years is also reduced. Reservoir model simulations using alternative reservoir rule curves based upon retrospective long-lead streamflow forecasts from water years 1931 to 1987 show that the proposed alternative operating system based on climate forecasts can increase nonfirm energy production from the major Columbia River hydropower dams by as much as 5.5 million MW/h/year, resulting in an average increase in annual revenue of approximately $153 million per year in comparison with the status quo. Other uses of the Columbia River are largely unaffected by the proposed changes in the operating system. In particular, firm energy, the reliability of storage reservoir refill, and the frequency of meeting of streamflow targets for salmon protection would be essentially unchanged in comparison with the status quo. The increased hydropower revenue is therefore directly attributed to use of long-lead forecast information and does not represent a trade-off among other management objectives. DOI: 10.1061/(ASCE)0733-9496(2002)128:2(91) CE Database keywords: Economic factors; Streamflow; Columbia River; Hydroelectric power; Forecasting.

A simple method has been devised to incorporate the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) climate signals into the well-known extended streamflow prediction forecasting approach. Forecasts of ENSO are currently available up to a year or more in advance, which facilitates forecasting of the streamflow response to this climate signal at interannual forecast lead times. The biomodal phase of the PDO can be identified in real time using a combination of assumed persistence of the existing phase and the tracking of extreme events to identify transitions. The technique makes use of a gridded meteorological data set to drive a macroscale hydrology model at 1 degree spatial resolution over the Columbia River Basin above The Dalles. A streamflow forecast ensemble is created by resampling from the historical meteorological data according to six predefined PDO/ENSO categories. Given a forecast of the ENSO climate signal for the coming water year and the existing phase of the PDO, these meteorological ensembles are then used to drive the hydrology model based on the initial soil and snow conditions as of the forecast date. To evaluate the technique, a retrospective forecast of the historic record was prepared (1989-1998), using October-September as the forecast period, as well as an ensemble forecast for water years 1999 and 2000 that were prepared on June 1, 1998 and May 10, 1999, respectively. The results demonstrate the increase in lead time and forecast specificity over climatology that can be achieved by using PDO and ENSO climate information to condition the forecast ensembles.