During California's dry season, the natural recession of streamflow often coincides with declines in dissolved oxygen (DO) concentrations, impacting sensitive aquatic species such as salmon. Thus, understanding the relationship between DO and streamflow is important to evaluate risks and set water quality standards. We explored the relationship between minimum DO and riffle crest thalweg (RCT) depth, a proxy for streamflow. We analyzed daily data collected between May to October in 26 pools within streams in a northern California coastal watershed over a six-year period. We first developed statistical models to characterize the local relationship between DO and RCT depth at each pool and then developed a regional model using all observations to predict when DO falls below a critical threshold for salmonids (6.5 mg/L). Observations indicated a mean (range) daily minimum DO value of 7.33 (0.01-10.95) mg/L with a mean RCT of 9.07 (0.10 to 34.63) cm across all sites and pools. On average, the first date that minimum DO fell below the critical threshold was July 8th (May 13th to August 31st). The proportion of DO levels below the 6.5 mg/L threshold increased sharply once RCT fell below about 7 cm, and more than half of all DO measurements were below this threshold when RCT depth was between 3 and 6 cm. Our local model highlighted a significant, non-linear and positive relationship between DO and RCT depth at all sites (mean R2 = of 0.72). The regional model correctly predicted DO values above and below the threshold 78 % of the time, with a mean predicted first date below the threshold of August 5th at a mean RCT of 4.25 cm. In addition to RCT depth, we found that the day of the year, geology, and stream temperature were all important factors for explaining DO variability across watersheds and overtime. Our results can aid managers to predict the risk of DO falling below critical biological thresholds and maintain streamflow to support salmon and other sensitive aquatic species.

AbstractReductions in streamflow caused by groundwater pumping, known as “streamflow depletion,” link the hydrologic process of stream‐aquifer interactions to human modifications of the water cycle. Isolating the impacts of groundwater pumping on streamflow is challenging because other climate and human activities concurrently impact streamflow, making it difficult to separate individual drivers of hydrologic change. In addition, there can be lags between when pumping occurs and when streamflow is affected. However, accurate quantification of streamflow depletion is critical to integrated groundwater and surface water management decision making. Here, we highlight research priorities to help advance fundamental hydrologic science and better serve the decision‐making process. Key priorities include (a) linking streamflow depletion to decision‐relevant outcomes such as ecosystem function and water users to align with partner needs; (b) enhancing partner trust and applicability of streamflow depletion methods through benchmarking and coupled model development; and (c) improving links between streamflow depletion quantification and decision‐making processes. Catalyzing research efforts around the common goal of enhancing our streamflow depletion decision‐support capabilities will require disciplinary advances within the water science community and a commitment to transdisciplinary collaboration with diverse water‐connected disciplines, professions, governments, organizations, and communities.

Stream drying is happening globally, with important ecological and social consequences. Most examples of stream drying come from systems influenced by dam operations or those with highly exploited aquifers. Stream drying is also thought to be driven by anthropogenic climate change; however, examples are surprisingly limited. We explored flow trends from the five recognized Mediterranean-climate regions of the world with a focus on unregulated (non-dammed or non-diverted) streams with long-term gauge records. We found consistent evidence of decreasing discharge trends, increasing zero-flow days, and steeper downward discharge trends in smaller basins. Beyond directional trends, many systems have recently undergone shifts in flow state, including some streams that have transitioned from perennial to intermittent flow states. Our analyses provide evidence of stream drying consistent with climate change but also highlight knowledge gaps and challenges in empirically and statistically documenting flow regime shifts. We discuss the myriad consequences of losing flow and propose strategies for improving detection of and adapting to flow change.

Streamflow regimes that maintain vital functions and processes of aquatic ecosystems are critical to sustaining ecosystem health. In rivers with altered flow regimes, restoring components of the natural flow regime is predicted to conserve freshwater biodiversity by supporting ecological functions and geomorphological processes to which native communities are adapted. However, the effectiveness of environmental flow restoration is poorly understood because of inadequate monitoring and uncertainty in ecological responses to managed changes in specific, quantifiable aspects of the annual streamflow regime. Here, we used time series models to analyze 25 years of fish assemblage data collected before and after environmental flow implementation in a dammed river in California, USA. We examined the response of the fish community to changes in individual components of the flow regime known to support ecosystem functions. We found that as functional flow components shifted toward their predicted natural range, the quasi-extinction risk (likelihood of population declines of >80%) decreased for the native fish assemblage. Following environmental flow implementation, observed changes toward natural ranges of dry season duration, fall pulse flow magnitude, and wet season timing each reduced quasi-extinction risk by at least 40% for the native assemblage. However, functional flow components that shifted away from their predicted natural range, including lower spring recession flows and higher dry season baseflow, resulted in greater quasi-extinction risk for native species. In contrast, non-native species decreased in abundance when flow components shifted toward predicted natural ranges and increased when components shifted away from their natural range. Although most functional flow components remained outside of their natural range following environmental flow implementation, our results indicate that even moderate shifts toward a natural flow regime can benefit native and suppress non-native fish species. Overall, this study provides the most compelling evidence to date of the effectiveness of functional environmental flows in supporting native fish recovery in a highly regulated river.

Despite rises in drought frequency and human water demands, streamflow regime shifts from perennial to non-perennial have not been evaluated in many arid/semi-arid regions. To document shifts, we created a methodology that classifies streams as naturally perennial or non-perennial. Our classification used historical, minimally disturbed-quality USGS streamflow gages (1950-2015) across California. The number of consecutive zero flow days (>= 5 days) was used to classify 61% (96/158) and 39% (62/158) of gages as perennial and non-perennial, respectively. We developed a random forest model to predict flow regime class based on climate and watershed characteristics. To identify regime shifts, we compared the observed class of contemporary (1980-2023) minimally disturbed and disturbed gages with their modeled, natural class. For most minimally disturbed gages, the observed and natural predicted classes were the same, but 13% (7/52) of gages had a modeled perennial regime with an observed non-perennial class, indicating a drying trend. Among disturbed gages, 22% (64/290) shifted from perennial to non-perennial and 7% (21/290) from non-perennial to perennial. Trends in the minimum 7-day moving average and number of zero-flow days provided further evidence of drying at minimally disturbed streams, but no pattern at disturbed gages. Our results indicate that few minimally disturbed perennial streams have become non-perennial to date, but many streams have experienced drying from climate. Streams impacted by human activities had greater drying rates, but regulation has caused some non-perennial streams to become perennial. By quantifying expected natural streamflow regimes, this work can help monitor, manage, and conserve stream ecosystems. Our approach classified 158 historical (1950-2015) minimally disturbed gages as perennial (61%) and non-perennial (39%) in California Among 52 active minimally disturbed gages, 13% transitioned to non-perennial (1980-2022), indicating a drying trend in response to climate For 290 disturbed gages, 29% showed shifts in both directions, highlighting distinct anthropogenic effects on streamflow

ABSTRACT Baseflow is an important water resource because it supplies streamflow between precipitation events and during dry seasons. We report the first continental assessment of baseflow in Africa by analyzing the baseflow index, baseflow seasonality, and monthly baseflow trends for three periods (1950–2018, 1950–1980, and 1981–2018). To explain changes in baseflow, we analyze precipitation trends. Results highlight that the baseflow season in west-central Africa occurs during August–November. In southern and northern Africa, the baseflow season is similar (January–May and January–April, respectively). Trend analysis detected monthly baseflow decreases in west-central Africa over the entire record and from 1950 to 1980, with increases after 1980. A clear pattern was absent in southern Africa for the whole record, but increases were detected earlier and decreases from 1981 to 2018. In northern Africa, decreasing baseflow trends were pronounced over the whole record, with no clear shift present. Precipitation trends were only consistent with baseflow changes in west-central Africa.

Baseflow, the groundwater contribution to streamflow, sustains surface water between precipitation events and is an important indicator of groundwater availability. Although many site-specific studies have been completed, there are few studies of long-term Canada-wide baseflow trends. In this work, we detected monthly baseflow trends across Canada and related them to changes to climatic predictors (precipitation, temperature, and ante-cedent wetness) using streamflow data from 1275 hydrometer stations from 1989 to 2019. Lyne and Hollick's one-parameter digital filter is used to obtain a baseflow time series and monotonic trends are identified using the Mann-Kendall Trend Test. Historical baseflow is related to climate parameters by means of Generalised Additive Models for Location, Scale and Shape (GAMLSS) statistical analysis. Results based on trend analysis detected no significant trends for most stations (85.7% of all stations and months). However, notable increasing trends were observed across most of southern Canada during the winter and spring months (October-April). Conversely, negative trends were detected from June to September in Alberta and British Columbia and in southern Northwest Territories. Model selection identified antecedent wetness most often as a climate predictor over the same period as trend analysis. Our results highlight that warmer temperatures and increased snow cover across much of Canada have contributed to increased baseflow likely from shifts in snow melt timing and volumes. During warmer months (June-August), results indicate that increases in temperature were related to decreased baseflow, likely through increased evapotranspiration. Many trends in baseflow were not related to any climate predictors. Furthermore, non-reference basins were twice as likely to have no climate predictor, indicating that anthropogenic activities may be driving changes in baseflow. The results of this work can inform water resources management to identify the direction of change in groundwater availability across Canada and regions where mitigation may be necessary.

Baseflow is the portion of streamflow that comes from groundwater and subsurface sources. Although baseflow is essential for sustaining streams during low flow and drought periods, we have little information about how and why it has changed over large regions of the continental United States. The objective of this study was to evaluate how changes in the climate system have affected observed monthly baseflow records at 3,283 USGS gauges over the last 30 years (1989-2019). We developed a statistical modeling framework to determine the relationship between monthly baseflow and monthly climate predictors (i.e., precipitation, temperature, and antecedent wetness). Overall, we found that baseflow trends and the factors influencing them vary by region and month. In the US Northeast, increases were detected earlier in the year (February and March) and in the summer (May and June), and were likely due to increasing precipitation, warmer temperature, and subsequent changes in snowmelt. Increasing baseflow in the US Pacific Northwest and Midwest were associated with increases in precipitation and antecedent wetness throughout the year. Decreasing trends were located in the US Southeast and Southwest. Baseflow trends in the US Southeast were only detected in March, possibly as a result of decreased precipitation during the spring. On the other hand, decreases in baseflow in the Central Southwestern United States occurred throughout the year. These trends were associated with a lack of precipitation and increases in temperature. Finally, we examined the relationship between monthly baseflow trends and changes in total water storage using monthly Gravity Recovery and Climate Experiment mascon products from the Jet Propulsion Laboratory. In this study, trends in total water storage were strongly associated with baseflow trends across the United States. The spatial and temporal variability in baseflow response to climate reported here can aid water managers in adapting to future climate change. (C) 2022 American Society of Civil Engineers.

Baseflow, or the groundwater component of streamflow, is an important source of water for several applications, from increasing demands on freshwater resources to ecosystem health. Despite its relevance, our understanding of the processes driving baseflow and its interannual variability is limited. In this study, we focus on 458 U.S. Geological Survey streamflow gauges that have at least 50 years of daily data. We use a statistical modeling framework to select a set of predictors that represent the role of climate (i.e., precipitation, temperature and antecedent wetness) and land use (harvested acres of corn and soybeans). The models are able to describe well the variability in monthly baseflow across the region, with an average correlation coefficient between the observational records and the median of the fitted distribution of 0.70 among all months. Our results indicate that precipitation and antecedent wetness are the strongest predictors, where the latter was selected the most often. Temperature is an important predictor during the spring when snow-related processes are the most relevant. Agriculture was frequently selected in the Cornbelt region during the growing season (from March to July). The results of this study can inform future watershed management that sustains low flows and improves water quality.

There is an ongoing need to increase our understanding of the sources and timing of stream nitrate loads across agricultural watersheds in Iowa as water quality improvement strategies are implemented. The goal of this study was to model the relationship between nitrate load and the two components of streamflow (i.e., baseflow and stormflow) to quantify in-stream nitrate patterns and develop a new method for estimating loads on days when monitoring data are not available. We analyzed eight watersheds in Iowa that had long-term water quality data where grab samples have been collected from 1987 to 2019. Four regression models were developed that related daily nitrate load to daily baseflow, stormflow, and streamflow discharge. The first model considered baseflow as a predictor, the second model used stormflow, the third model included both baseflow and stormflow as two different covariates, and the final model used total streamflow (unseparated). For all eight watersheds, the baseflowstormflow models had the highest correlation coefficients, which indicates that both components are necessary and together improve nitrate load estimates. While baseflow models estimated lower nitrate loads better, stormflow models captured the variability associated with larger loads. In addition, streamflow models tended to overestimate large nitrate loads. This simple modeling framework can be used to calculate daily, monthly and annual nitrate loads. Delineating nitrate loads between stormflow and baseflow can help identify differences in nitrate sources for nutrient reduction and remediation.

Baseflow is an essential water resource because it is the groundwater discharged to streams and represents long‐term storage. Understanding its future changes is a major concern for water supply and ecosystem health. This study examines the impacts of climate and agriculture on monthly baseflow in the U.S. Midwest through the end of the 21st century. We use a statistical approach to evaluate three scenarios. The first scenario is based on downscaled and bias corrected global climate model (GCM) outputs and the representative concentration pathway (RCP) 8.5, and agriculture is held constant (and equal to the mean from 2013 to 2019). In the next two scenarios, climate is held constant (2010–2019) to isolate the impact of agriculture on baseflow. In terms of agricultural changes, we consider scenarios representative of either increases or decreases with respect to the production of corn and soybeans. Changes in the climate system point to increases in baseflow that are likely a result of increased precipitation and antecedent wetness. Seasonally, warmer temperature in the winter and spring (i.e., February to July) is expected to cause increasing trends in baseflow. Changes in land use showed that agriculture would either mitigate the impact of climate change or possibly amplify it. Expanding corn and soybean areas would increase baseflow in the Corn Belt region. On the other hand, converting land back to perennial vegetation would decrease baseflow throughout the entire year. Despite its simplicity, this study can provide basic information to understand where to expect adverse effects on baseflow and thus improve land management practices in those areas.

This study examines the role played by changes in the climate system and land use in the observed monthly baseflow records (1966-2015) for 458 U. S. Geological Survey sites across the U.S. Midwest. We developed parsimonious statistical models in which monthly baseflow is related to any combination of four predictors (precipitation, temperature, antecedent wetness, and agriculture). We found that precipitation and antecedent wetness were the strongest predictors for all months, pointing to the role of water availability and infiltration in driving baseflow. Temperature was an important factor in the winter and spring where snow-melt processes are the most relevant. Agriculture was selected in the Corn Belt region during the growing season (from April to August) indicating that corn and soybean production in the Midwest promote baseflow discharge to streams. Overall, the goodness-of-fit for our models and cross validation strongly support our modeling results for all months. Differences in model selection reported here can aid water managers in decision making for water availability, food security and economic growth.

Characterizing streamflow changes in the agricultural U.S. Midwest is critical for effective planning and management of water resources throughout the region. The objective of this study is to determine if and how baseflow has responded to land alteration and climate changes across the study area during the 50-year study period by exploring hydrologic variations based on long-term stream gage data. This study evaluates monthly contributions to annual baseflow along with possible trends over the 1966-2016 period for 458 U.S. Geological Survey streamflow gages within 12 different Midwestern states. It also examines the influence of climate and land use factors on the observed baseflow trends. Monthly contribution breakdowns demonstrate how the majority of baseflow is discharged into streams during the spring months (March, April, and May) and is overall more substantial throughout the spring (especially in April) and summer (June, July, and August). Baseflow has not remained constant over the study period, and the results of the trend detection from the Mann-Kendall test reveal that baseflows have increased and are the strongest from May to September. This analysis is confirmed by quantile regression, which suggests that for most of the year, the largest changes are detected in the central part of the distribution. Although increasing baseflow trends are widespread throughout the region, decreasing trends are few and limited to Kansas and Nebraska. Further analysis reveals that baseflow changes are being driven by both climate and land use change across the region. Increasing trends in baseflow are linked to increases in precipitation throughout the year and are most prominent during May and June. Changes in agricultural intensity (in terms of harvested corn and soybean acreage) are linked to increasing trends in the central and western Midwest, whereas increasing temperatures may lead to decreasing baseflow trends in spring and summer in northern Wisconsin, Kansas, and Nebraska.

This work presents updated hydrologic projections for the Upper Colorado River Basin (UCRB) using downscaled (approximately 12 km) General Circulation Model (GCM) output from Coupled Model Intercomparison Project - Phase 5 (CMIP5) with a comparison to CMIP3 GCMs. We use the Soil and Water Assessment Tool model to simulate the impacts of end-of-century climate change on the UCRB using 21 CMIP5 and 18 CMIP3 GCMs, collected into one CMIP5 ensemble and one CMIP3 ensemble, respectively. Previous CMIP3 studies have identified a drier climate for the UCRB because of projected increases in temperature and decreases/little change in precipitation. Hydrologic simulations from CMIP5 inputs suggest wetter conditions than simulations based on CMIP3 inputs, yet drier conditions than the historical climate. Both ensembles lead to timing shifts in peak streamflow during the snowmelt season from changes in snowmelt, but the higher CMIP5 projected precipitation leads to, on average, peak streamflows 200-300 m(3) s(-1) larger (25-40% difference) than the CMIP3 projections. This difference is largely generated in the northern UCRB region, where CMIP5 simulations project much more significant increases in streamflow than CMIP3. This increase is largely due to an overall larger rise in precipitation in the CMIP5 ensemble (57% of the total UCRB area) compared to the CMIP3 ensemble (5%). Even with projected increases in precipitation, snowmelt is projected to decrease dramatically throughout the UCRB for both ensembles. The increases in precipitation and decreases in snowmelt leads to significant differences in hydrologic flux components between the CMIP3 and CMIP5 ensembles, such as end-of-century rises in soil water content and evapotranspiration in the CMIP5 ensemble compared to the CMIP3 ensemble. The difference between the dry CMIP3 and the somewhat wetter CMIP5 projections may be critical for water management in the already over-allocated UCRB.