scholarly journals Measuring Non-Market Values for Hydropower Production and Water Storage on the Colorado River: A White Paper Investigation

2017 ◽  
Author(s):  
Thomas Stephen Lowry ◽  
Janie M. Chermak ◽  
David S. Brookshire ◽  
Calvin Shaneyfelt ◽  
Peter H. Kobos
Keyword(s):  
Colorado River ◽  
Water Storage ◽  
White Paper ◽  
Market Values ◽  
Hydropower Production

scholarly journals Effects of Climate Variability on Water Storage in the Colorado River Basin

2009 ◽  
Vol 10 (5) ◽  
pp. 1257-1270 ◽  
Author(s):  
Ruud Hurkmans ◽  
Peter A. Troch ◽  
Remko Uijlenhoet ◽  
Paul Torfs ◽  
Matej Durcik
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Colorado River ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
Water Storage ◽  
Low Frequency ◽  
Colorado River Basin ◽  
Climate Indices ◽  
Deep Soil

Abstract Understanding the long-term (interannual–decadal) variability of water availability in river basins is paramount for water resources management. Here, the authors analyze time series of simulated terrestrial water storage components, observed precipitation, and discharge spanning 74 yr in the Colorado River basin and relate them to climate indices that describe variability of sea surface temperature and sea level pressure in the tropical and extratropical Pacific. El Niño–Southern Oscillation (ENSO) indices in winter [January–March (JFM)] are related to winter precipitation as well as to soil moisture and discharge in the lower Colorado River basin. The low-frequency mode of the Pacific decadal oscillation (PDO) appears to be strongly correlated with deep soil moisture. During the negative PDO phase, saturated storage anomalies tend to be negative and the “amplitudes” (mean absolute anomalies) of shallow soil moisture, snow, and discharge are slightly lower compared to periods of positive PDO phases. Predicting interannual variability, therefore, strongly depends on the capability of predicting PDO regime shifts. If indeed a shift to a cool PDO phase occurred in the mid-1990s, as data suggest, the current dry conditions in the Colorado River basin may persist.

scholarly journals Evaluating the hydrological consistency of evaporation products using satellite-based gravity and rainfall data

2017 ◽  
Vol 21 (1) ◽  
pp. 323-343 ◽  
Author(s):  
Oliver López ◽  
Rasmus Houborg ◽  
Matthew Francis McCabe
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Colorado River ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Rainfall Data ◽  
Colorado River Basin ◽  
Degree Correlation ◽  
Degree Correlations ◽  
Niger River

Abstract. Advances in space-based observations have provided the capacity to develop regional- to global-scale estimates of evaporation, offering insights into this key component of the hydrological cycle. However, the evaluation of large-scale evaporation retrievals is not a straightforward task. While a number of studies have intercompared a range of these evaporation products by examining the variance amongst them, or by comparison of pixel-scale retrievals against ground-based observations, there is a need to explore more appropriate techniques to comprehensively evaluate remote-sensing-based estimates. One possible approach is to establish the level of product agreement between related hydrological components: for instance, how well do evaporation patterns and response match with precipitation or water storage changes? To assess the suitability of this consistency-based approach for evaluating evaporation products, we focused our investigation on four globally distributed basins in arid and semi-arid environments, comprising the Colorado River basin, Niger River basin, Aral Sea basin, and Lake Eyre basin. In an effort to assess retrieval quality, three satellite-based global evaporation products based on different methodologies and input data, including CSIRO-PML, the MODIS Global Evapotranspiration product (MOD16), and Global Land Evaporation: the Amsterdam Methodology (GLEAM), were evaluated against rainfall data from the Global Precipitation Climatology Project (GPCP) along with Gravity Recovery and Climate Experiment (GRACE) water storage anomalies. To ensure a fair comparison, we evaluated consistency using a degree correlation approach after transforming both evaporation and precipitation data into spherical harmonics. Overall we found no persistent hydrological consistency in these dryland environments. Indeed, the degree correlation showed oscillating values between periods of low and high water storage changes, with a phase difference of about 2–3 months. Interestingly, after imposing a simple lag in GRACE data to account for delayed surface runoff or baseflow components, an improved match in terms of degree correlation was observed in the Niger River basin. Significant improvements to the degree correlations (from  ∼  0 to about 0.6) were also found in the Colorado River basin for both the CSIRO-PML and GLEAM products, while MOD16 showed only half of that improvement. In other basins, the variability in the temporal pattern of degree correlations remained considerable and hindered any clear differentiation between the evaporation products. Even so, it was found that a constant lag of 2 months provided a better fit compared to other alternatives, including a zero lag. From a product assessment perspective, no significant or persistent advantage could be discerned across any of the three evaporation products in terms of a sustained hydrological consistency with precipitation and water storage anomaly data. As a result, our analysis has implications in terms of the confidence that can be placed in independent retrievals of the hydrological cycle, raises questions on inter-product quality, and highlights the need for additional techniques to evaluate large-scale products.

Value of Reservoir Storage for Resilient Power Plant Cooling and Basin-Wide Water Availability

2012 ◽  
Author(s):  
Ashlynn S. Stillwell ◽  
Michael E. Webber
Keyword(s):  
Power Plant ◽  
River Basin ◽  
Water Availability ◽  
Power Plants ◽  
Colorado River ◽  
Water Storage ◽  
River Basins ◽  
Reservoir Storage ◽  
Thermoelectric Power Plants ◽  
Power Plant Cooling

Since many thermoelectric power plants use water for cooling, the power sector is vulnerable to droughts, heat waves, and other water constraints. At the same time, large water demands for power generation can strain water availability for other users in a river basin. Opportunities exist for power plants to decrease freshwater demands, increasing both drought resiliency of power plants and water availability for other users in the basin. One particular method of decreasing freshwater demands for power plants is by incorporating reservoir storage into cooling operations. Using reservoir storage allows water to be recirculated and reused for power plant cooling, thereby decreasing water withdrawal requirements. Water storage also has the added benefit of making water available during times of shortage. While storage is known to be beneficial, no tools exist to explicitly quantify the basin-wide water availability impacts and increased power generation resiliency possible via constructing water storage at thermoelectric power plants without existing reservoirs. Here we present the results of modeling efforts regarding the value (both in terms of resiliency and water availability) of reservoir storage for power plant cooling and basin-wide water availability in the Brazos and Colorado River basins, using a customized river basin based-model along with existing Texas Water Availability Models. Results vary between river basins and different water availability models, with construction of new reservoirs generally increasing basin-wide water availability in the Brazos River basin and generally decreasing basin-wide water availability in the Colorado River basin. We conclude that the value of reservoir storage for power plant resiliency and basin-wide water availability is highly site-specific.

Predicting water discharge on alpine catchments with downscaled seasonal forecasts

2020 ◽  
Author(s):  
Mattia Callegari ◽  
Valentina Cavedon ◽  
Alice Crespi ◽  
Felix Greifeneder ◽  
Marcello Petitta ◽  
...  
Keyword(s):  
Water Availability ◽  
Bias Correction ◽  
Snow Water Equivalent ◽  
Water Discharge ◽  
Water Storage ◽  
Seasonal Forecast ◽  
Reanalysis Data ◽  
Snow Water ◽  
Hydropower Production ◽  
Alpine Catchments

<p>The prediction of seasonal water availability is a key element for an effective water storage management and hydropower production optimization. Here we propose a machine learning model for monthly water discharge prediction, which is based on statistical relationships between time series of a target, i.e. monthly water discharge, and predictors. The considered predictors can be divided into two classes: the initial catchment state variables and the seasonal forecast variables. Snow plays a crucial role as water storage component in alpine catchments. Thus, snow water equivalent is the predictor employed to describe the initial state of the catchment. To ensure the scalability of the method, snow water equivalent is represented here by ERA-5 climate reanalysis data (0.25° x 0.25° resolution). Depending on the prediction season, seasonal forecast of temperature can drive snowmelt or evapotranspiration, while precipitation provides a natural contribution to the total water availability. To describe these prediction variables, we employed a downscaled and bias-correction version of the ECMWF’s seasonal forecasting system (SEAS5) for temperature and precipitation. More specifically, the seasonal forecast fields were bilinearly downscaled from the original 1° x 1° resolution to the target ERA-5 grid and statistically corrected for bias in respect with ERA-5 data by means of a quantile mapping procedure. ERA-5 reanalysis data were used as reference for the bias-correction in order to allow the approach to be easily applied over different areas.</p><p>We tested the proposed method over an alpine catchment in Ulten Valley, South Tyrol, Italy, which is managed by three artificial reservoirs for hydropower production. For this catchment, a time series from 1992 to 2017 of measured daily water discharge is available. The water discharge prediction performances of the proposed method are compared with the ones obtained by considering the water discharge monthly climatology.</p>

scholarly journals Addressing the Water–Energy Nexus by Coupling the Hydrological Model with a New Energy LISENGY Model: A Case Study in the Iberian Peninsula

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 762 ◽  
Author(s):  
Marko Adamovic ◽  
Emiliano Gelati ◽  
Berny Bisselink ◽  
Ad Roo
Keyword(s):  
Iberian Peninsula ◽  
Hydrological Model ◽  
Spatial Dynamics ◽  
Water Storage ◽  
Energy Demands ◽  
Water Energy Nexus ◽  
New Energy ◽  
Hydropower Production ◽  
Lakes And Reservoirs ◽  
Linked Model

As water is required for producing hydropower, and subsequently the water balance is changed for downstream areas, the linking of hydrological and energy models is needed to properly address the interactions among them. In this study, volume–depth-based water storage estimation models were proposed for individual lakes and reservoirs in the Iberian Peninsula using the 30-year Global Water Surface dataset and reservoir morphometry methodology which enables to evaluate reservoirs where data were not available before. The models were subsequently implemented within the new hydropower model called LISENGY that provides the first comprehensive assessment of the temporal and spatial dynamics of water storage, water depth and hydropower production in the Iberian Peninsula. The LISENGY model was coupled with the distributed LISFLOOD hydrological model. The seasonal and interannual changes in energy production were assessed for 168 studied reservoirs with diverse morphometries, which is unique. Conical, concave and convex regression reservoir relationships were distinguished, and optimized turbine discharge and power production were computed. A 10-year water–energy linked system for the 2007–2016 period has been established for the Iberian Peninsula which was not available before. The results showed that it is possible to connect those two models and that the timing and magnitude of simulated storage were well reproduced. The study represents the first step towards integrated pan-European water–energy modeling. Future climate scenarios and energy demands are to be fed into the linked model system to evaluate expected future hydropower generation and possible water scarcity issues.

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