Search Results (20)

This study aimed to conduct a drought trend analysis using the standardized precipitation evapotranspiration index (SPEI) in two mountainous and cold-climate regions in Iran and the United States (US). The Mann–Kendall test was employed to assess the trend in the Upper Colorado River Basin (UCRB) in the US and Lorestan province. The results reveal a predominantly decreasing trend in drought occurrences across Lorestan, especially in southern and southwestern areas with lower elevations. In contrast, the UCRB showed a positive trend, indicating a wet period. The western parts of the UCRB were predominantly affected by droughts. Among the stations, the Khorram Abad station exhibited the most statistically significant trend at the 99% confidence level (Z > 2.57). A temporal trend analysis of droughts revealed more positive and negative abrupt changes in the UCRB than in Lorestan. This indicates a higher degree of small-scale variability in the UCRB compared to Lorestan. This study indicates that factors such as elevation, land use changes, and proximity to water sources may contribute to the observed variations in drought trends. Additionally, the findings highlight that rising temperatures have a significantly greater impact on drought severity than reductions in precipitation. This study provides a temperature-responsive method for drought assessments, supporting the development of adaptive strategies that address snowmelt variability, seasonal water availability, and shifting drought patterns in cold regions. Full article

Streamflow prediction is vital for effective water resource management, enabling a better understanding of hydrological variability and its response to environmental factors. This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. Using 30 years of monthly streamflow data from 20 monitoring stations, the STGNN predicted streamflow over a 36-month horizon and was evaluated against traditional models, including random forest regression (RFR), LSTM, gated recurrent units (GRU), and seasonal auto-regressive integrated moving average (SARIMA). The STGNN outperformed these models across multiple metrics, achieving an R² of 0.78, an RMSE of 0.81 mm/month, and a KGE of 0.79 at critical locations like Lees Ferry. A sequential analysis of input–output configurations identified the (36, 36) setup as optimal for balancing historical context and forecasting accuracy. Additionally, the STGNN showed strong generalizability when applied to other locations within the UCRB. These results underscore the importance of integrating spatial dependencies and temporal dynamics in hydrological forecasting, offering a scalable and adaptable framework to improve predictive accuracy and support adaptive water resource management in river basins. Full article

Humans have been monitoring light from the solar system to tell the time and plan activities since Time Immemorial. This is an analysis regarding why Native Americans living in the upper Colorado River Basin chose to monitor light from the western sky using a light marker that is approximately 4.02 miles long and 2.07 miles wide, or approximately 12.7 square miles. The light catching is accomplished in a massive geoscape by carefully calibrated and engineered stone markers. The scale of this light marker and its functional topographic components makes it one of the biggest and most elaborate in North America. As such, it is a World-Balancing geosite. This analysis is based on 522 ethnographic interviews, with 316 that were conducted during the Canyonlands National Park (Canyonlands NP) ethnographic study and 206 that were conducted during two BLM ethnographic studies. The findings are situated among tribally approved ethnographic findings from more than a dozen other studies conducted by the authors. Full article

Recent above-normal temperatures, which exacerbated the impacts of precipitation deficits, are recognized as the primary driver of droughts in the Upper Colorado River Basin (UCRB), USA. This research aims to enhance drought prediction models by addressing structural changes in non-stationary temperature time series and minimizing drought misclassification through the ES-CBS-SVR model, which integrates ESSVR and CBS-SVR. The research investigates whether this coupling improves prediction accuracy. Furthermore, the model’s performance will be tested in a region distinct from those originally used to evaluate its generalizability and effectiveness in forecasting drought conditions. We used a change point detection technique to divide the non-stationary time series into stationary subsets. To minimize the chances of drought mis-categorization, category-based scoring was used in ES-CBS-SVR. In this study, we tested and compared the ES-CBS-SVR and SVR models in the Upper Colorado River Basin (UCRB) using data from the Global Land Data Assimilation System (GLDAS), where the periods 1950–2004 and 2005–2014 were used for training and testing, respectively. The results indicated that ES-CBS-SVR outperformed SVR consistently across of the drought indices used in this study in a higher portion of the UCRB. This is mainly attributed to variable hyperparameters (regularization constant and tube size) used in ES-CBS-SVR to deal with structural changes in the data. Overall, our analysis demonstrated that the ES-CBS-SVR can predict drought more accurately than traditional SVR in a warming climate. Full article

This study estimates the relative contribution of different factors to the wide variation in agricultural economic water productivity (EWP) across Colorado River Basin counties. It updates EWP measures for Basin counties using more detailed, localized data for the Colorado River mainstem. Using the Schwarz Bayesian Information Criterion for variable selection, regression analysis and productivity accounting methods identified factors contributing to EWP differences. The EWP was USD 1033 (USD 2023)/acre foot (af) for Lower Basin Counties on the U.S.–Mexico Border, USD 729 (USD 2023)/af for other Lower Basin Counties, and USD 168 (USD 2023)/af for Upper Basin Counties. Adoption rates for improved irrigation technologies showed little inter-county variation and so did not have a statistically significant impact on EWP. Counties with the lowest EWP consumed 25% of the Basin’s agricultural water (>2.3 million af) to generate 3% of the Basin’s crop revenue. Low populations/remoteness and more irrigated acreage per farm were negatively associated with EWP. Warmer winter temperatures and greater July humidity were positively associated with EWP. When controlling for other factors, being on the Border increased a county’s EWP by USD 570 (2023 USD)/af. Border Counties have greater access to labor from Mexico, enabling greater production of high-value, labor-intensive specialty crops. Full article

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Mean Square Error of 2.25 and R-squared ($R^2$) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin. Full article

The record-setting winter of 2022–2023 came as an answer to both figurative and literal prayers for political leaders, policy makers, and water managers reliant on snowpacks in the Upper Colorado River Basin, a vital source of water for tens of millions of people across the Western United States. But this “drought-busting” winter was not well-predicted, in part because while interannual patterns of tropical ocean temperatures have a well-known relationship to precipitation patterns across much of the American West, the Upper Colorado is part of a liminal region where these connections tend to be comparatively weak. Using historical sea surface temperature and snowpack records, and leveraging a long-term cross-basin relationship to extend the timeline for evaluation, this analysis demonstrates that the 2022–2023 winter did not present in accordance with other high-snowpack winters in this region, and that the associative pattern of surface temperatures in the tropical Pacific, and snow water equivalent in the regions that stored and supplied most of the water to the Colorado River during the 2022–2023 winter, was not substantially different from a historically incoherent arrangement of long-term correlation. These findings suggest that stochastic variability plays an outsized role in influencing water availability in this region, even in extreme years, reinforcing the importance of other trends to inform water policy and management. Full article

The Colorado River provides water to 40 million people in the U.S. Southwest, with river basin spanning 250,000 square miles (647,497 km²). Quantitative water rights assigned to U.S. states, Mexico, and tribes in the Colorado Basin exceed annual streamflows. Climate change is expected to limit streamflows further. To balance water demands with supplies, unprecedented water-use cutbacks have been proposed, primarily for agriculture, which consumes more than 60% of the Basin’s water. This study develops county-level, Basin-wide measures of agricultural economic water productivity, water footprints, and irrigation cash rent premiums, to inform conservation programs and compensation schemes. These measures identify areas where conservation costs in terms of foregone crop production or farm income are high or low. Crop sales averaged USD 814 per acre foot (AF) (USD 0.66/m³) of water consumed in the Lower Basin and 131 USD/AF (USD 0.11/m³) in the Upper Basin. Crop sales minus crop-specific input costs averaged 485 USD/AF (USD 0.39/m³) in the Lower Basin and 93 USD/AF (USD 0.08 per m³) in the Upper Basin. The blue water footprint (BWF) was 1.2 AF/USD 1K (1480 m³/USD1K) of water per thousand dollars of crop sales in the Lower Basin and 7.6 AF/USD 1K (9374 m³/USD1K) in the Upper Basin. Counties with higher water consumption per acre have a lower BWF. Full article

The Colorado River is a principal source of water for 40 million people and farmlands in seven states in the western US and the Republic of Mexico. The river has been under intense pressure from the effects of climate change and anthropogenic activities associated with population growth leading to elevated total dissolved solid (TDS) and total suspended solid (TSS) concentrations. Elevated TDS- and TSS-related issues in the basin have a direct negative impact on the water usage and the ecological health of aquatic organisms. This study, therefore, analyzed the spatiotemporal variability in the TDS and TSS concentrations along the river. Results from our analysis show that TDS concentration was significantly higher in the Upper Colorado River Basin while the Lower Colorado River Basin shows a generally high level of TSSs. We found that the activities in these two basins are distinctive and may be a factor in these variations. Results from the Kruskal–Wallis significance test show there are statistically significant differences in TDSs and TSSs from month to month, season to season, and year to year. These significant variations are largely due to seasonal rises in consumptive use, agriculture practices, snowmelts runoffs, and evaporate rates exacerbated by increased temperature in the summer months. The findings from this study will aid in understanding the river’s water quality, detecting the sources and hotspots of pollutions to the river, and guiding legislative actions. The knowledge obtained forms a strong basis for management and conservation efforts and consequently helps to reduce the economic damage caused by these water quality parameters including the over USD 300 million associated with TDS damages. Full article

The Colorado River has flowed across the dextral strike-slip plate boundary between the North American and Pacific plates since the latest Miocene or earliest Pliocene. The Fish Creek-Vallecito Basin (FCVB) lies on the Pacific Plate in southern California, dextrally offset from the point where the modern Colorado river enters the Salton Trough; it contains a record of ancestral Colorado River sedimentation from 5.3–2.5 Ma. The basin stratigraphy exhibits a changing balance between locally derived (L-Suite) and Colorado River (C-Suite) sediments. This paper focuses on the Palm Springs Group (PSG), a thick fluvial and alluvial sequence deposited on the upper delta plain (between 4.2–2.5 Ma) when the Colorado was active in the area, allowing the detailed examination of the processes of sediment mixing from two distinct provenance areas. The PSG consists of three coeval formations: 1) Canebrake Conglomerate, a basin margin that has coarse alluvial fan deposits derived from surrounding igneous basement; 2) Olla Formation, fan-fringe sandstones containing L-Suite, C-Suite, and mixed units; and 3) Arroyo Diablo Formation, mineralogically mature C-Suite sandstones. Stratigraphic analysis demonstrates that the river flowed through a landscape with relief up to 2000 m. Satellite mapping and detailed logging reveal a variable balance between the two suites in the Olla Formation with an apparent upward increase in L-Suite units before abrupt cessation of Colorado sedimentation in the basin. Stable heavy mineral indices differentiate L-Suite (high rutile:zircon index: RZi 40–95) from C-Suite (RZi: 0–20). Both suites have garnet:zircon index (GZi) and apatite:tourmaline index (ATi) mostly above 50, although many L-suite and mixed Olla samples have much lower ATi (20–50), suggesting that the distal floodplain was wet and the local sediment had a longer residence time there, or went through several cycles of erosion and redeposition. Heavy mineral analysis, garnet geochemical analysis, and detrital zircon U-Pb age spectra allow us to quantify the amount of mixing from different sediment sources. These data show that about 30% of the mixed units are derived from the Colorado River and that up to 20% of the L-Suite is also derived from the Colorado River, suggesting that there was mutual cannibalisation of older deposits by fluvial channels in a transitional area at the basin margin. Although this study is local in scope, it provides an insight into the extent and nature of sediment mixing in a two-source system. We conclude that most ‘mixing’ is actually interbedding from separate sources; true mixing is facilitated by low subsidence rates and the rapid migration of fluvial channels. Full article

Streamflow prediction plays a vital role in water resources planning in order to understand the dramatic change of climatic and hydrologic variables over different time scales. In this study, we used machine learning (ML)-based prediction models, including Random Forest Regression (RFR), Long Short-Term Memory (LSTM), Seasonal Auto- Regressive Integrated Moving Average (SARIMA), and Facebook Prophet (PROPHET) to predict 24 months ahead of natural streamflow at the Lees Ferry site located at the bottom part of the Upper Colorado River Basin (UCRB) of the US. Firstly, we used only historic streamflow data to predict 24 months ahead. Secondly, we considered meteorological components such as temperature and precipitation as additional features. We tested the models on a monthly test dataset spanning 6 years, where 24-month predictions were repeated 50 times to ensure the consistency of the results. Moreover, we performed a sensitivity analysis to identify our best-performing model. Later, we analyzed the effects of considering different span window sizes on the quality of predictions made by our best model. Finally, we applied our best-performing model, RFR, on two more rivers in different states in the UCRB to test the model’s generalizability. We evaluated the performance of the predictive models using multiple evaluation measures. The predictions in multivariate time-series models were found to be more accurate, with RMSE less than 0.84 mm per month, R-squared more than 0.8, and MAPE less than 0.25. Therefore, we conclude that the temperature and precipitation of the UCRB increases the accuracy of the predictions. Ultimately, we found that multivariate RFR performs the best among four models and is generalizable to other rivers in the UCRB. Full article

The Colorado River is a critical water resource in the southwestern United States, supplying drinking water for 40 million people in the region and water for irrigation of 2.2 million hectares of land. Extended drought in the Upper Colorado River Basin (UCOL) and the prospect of a warmer climate in the future pose water availability challenges for those charged with managing the river. Limited water availability in the future also may negatively affect aquatic ecosystems and wildlife that depend upon them. Water availability components of special importance in the UCOL include streamflow, salinity in groundwater and surface water, groundwater levels and storage, and the role of snow in the UCOL water cycle. This manuscript provides a review of current “state of the science” for these UCOL water availability components with a focus on identifying gaps in data, modeling, and trends in the basin. Trends provide context for evaluations of current conditions and motivation for further investigation and modeling, models allow for investigation of processes and projections of future water availability, and data support both efforts. Information summarized in this manuscript will be valuable in planning integrated assessments of water availability in the UCOL. Full article

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (R_s) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET. Full article

Otolith microchemistry has emerged as a powerful technique with which to identify the natal origins of fishes, but it relies on differences in underlying geology that may occur over large spatial scales. An examination of how small a spatial scale on which this technique can be implemented, especially in water bodies that share a large proportion of their flow, would be useful for guiding aquatic invasive species control efforts. We examined trace isotopic signatures in northern pike (Esox lucius) otoliths to estimate their provenance between two reservoirs in the Upper Yampa River Basin, Colorado, USA. This is a challenging study area as these reservoirs are only 11-rkm apart on the same river and thus share a high proportion of their inflow. We found that three isotopes (⁸⁶Sr, ¹³⁷Ba, and ⁵⁵Mn) were useful in discriminating between these reservoirs, but their signatures varied annually, and the values overlapped. Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) were different between sites and relatively stable across three years, which made them an ideal marker for determining northern pike provenance. Our study demonstrates the usefulness of otolith microchemistry for natal origin determination within the same river over a relatively small spatial scale when there are geologic differences between sites, especially geologic differences underlying tributaries between sites. Full article

Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production. Full article