Hydrology, Hydraulics, and Water Management Technical Report

U.S. Bureau of Reclamation (USBR) | December 15th, 2014

This Technical Report describes pertinent hydrologic and hydraulic condition and water management operations for Shasta Lake, the Sacramento River, the Delta, and the CVP

This Technical Report describes pertinent hydrologic and hydraulic condition and water management operations for Shasta Lake, the Sacramento River, the Delta, and the CVP/State Water Project (SWP) service areas, under existing conditions, the No-Action Alternative, and the Action Alternatives for the SLWRI.

Hydrometeorological characteristics of rain-on-snow events associated with atmospheric rivers

American Geophysical Union (AGU) | March 29th, 2016

Atmospheric rivers (ARs) are narrow, elongated, synoptic corridors of enhanced water vapor transport that play an important role in regional weather/hydrology. Rain-on-s

Atmospheric rivers (ARs) are narrow, elongated, synoptic corridors of enhanced water vapor transport that play an important role in regional weather/hydrology. Rain-on-snow (ROS) events during ARs present enhanced flood risks due to the combined effects of rainfall and snowmelt. Focusing onCalifornia’s Sierra Nevada, the study identifies ROS occurrences and their connection with ARs during the 1998–2014 winters. AR conditions, which occur during 17% of all precipitation events, are associated with 50% of ROS events (25 of 50). Composite analysis shows that compared to ARs without ROS, ARs with ROS areon average warmer by ~2 K, with snow water equivalent loss of ~0.7 cm/d (providing 20% of the combined water available for runoff) and ~50% larger streamflow/precipitation ratios. Atmospheric Infrared Sounderretrievals reveal distinct offshore characteristics of the two types of ARs. The results highlight the potential value of observing these events for snow, rain, and flood prediction.

Identifying Major Hydrologic Change Drivers in a Highly Managed Transboundary Endorheic Basin: Integrating Hydro-ecological Models and Time-Series Data Mining Techniques

American Geophysical Union (AGU) | August 23rd, 2022

The fragile balance of endorheic lakes in highly managed semi-arid basins with transboundary water issues has been altered by the intertwined effects of global warming an

The fragile balance of endorheic lakes in highly managed semi-arid basins with transboundary water issues has been altered by the intertwined effects of global warming and long-term water mismanagement to support agricultural and industrial demand. The alarming rate of global endorheic lakes’ depletion in recent decades necessitates formulating mitigation strategies for ecosystem restoration. However, detecting and quantifying the relative contribution of causal factors (climate variability and anthropogenic stressors) is challenging. This study developed a diagnostic multivariate framework to identify major hydrologic drivers of lake depletion in a highly managed endorheic basin with a complex water distribution system. The framework integrates the Soil and Water Assessment Tool (SWAT) simulations with time-series decomposition and clustering methods to identify the major drivers of change. This diagnostic framework was applied to the Salton Sea Transboundary Basin (SSTB), the host of the world's most impaired inland lake. The results showed signs of depletion across the SSTB since late 1998 with no significant changes in climate conditions. The time-series data mining of the SSTB water balance components indicated that decreases in lake tributary inflows (-16.4 Mm3 yr-2) in response to decline in Colorado River inflows, associated with state water transfer agreements, are causing the Salton Sea to shrink, not changes in the irrigation operation as commonly believed. The developed multivariate detection and attribution framework is useful for identifying major drivers of change in coupled natural-human systems.

Impacts of Predicted Sea-Level Rise and Extreme Storm Events on the Transportation Infrastructure in the San Francisco Bay Region

California Energy Commission (CEC) | July 31st, 2012

Literature concerning the potential effect of climate change (sea‐level rise inundation and 100‐year storm events) on the San Francisco Bay region’s transportation

Literature concerning the potential effect of climate change (sea‐level rise inundation and 100‐year storm events) on the San Francisco Bay region’s transportation infrastructure is reviewed. Currently available geographical information system data is employed, and a review of how those datasets have been used in previous studies is reported. The second part of this paper presents methods. They include a higher‐resolution digital elevation model for the Bay Area; a new approach using a digital surface model is introduced to improve the surface elevations of features and better calculate the risk of over‐topping by sea level shifts and storm surges. A metric to assess change in the transportation infrastructure is introduced that calculates accessibility of first responders to the population at large. Sea level rise is incremented to the expected 1.4 meters in tandem with a 100‐year flood to analyze the extent to which transportation assets are at risk of inundation. The increased travel time from first responder locations to all neighborhoods in the region is measured for each iteration of the model. Local accessibility analysis for the entire San Francisco Bay region is performed to provide a synoptic view. Two localities are chosen to view in detail the impact on first‐responder accessibility caused by sea level rise and a 100‐year storm event. Next, the regional vulnerability of the transportation network to these events is assessed. This is accomplished by creating nodes that are the intersections of the major regional highways that surround the Bay. The loss of accessibility is measured by calculating the changes in travel time between these major nodes through iterations of our inundation model. Finally, the accessibility impacts to the hinterland from the major highway intersections for each peak water level iteration is determined, calculating the first and last 20 minutes of an origin‐destination journey.

Improved Bias Correction Techniques for Hydrological Simulations of Climate Change

American Meteorological Society (AMS) | December 13th, 2015

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction met

Global climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM’s mean climate change signal, with differences of up to 28C and 30% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models’ simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season’s values at once.

Improved Imaging of the Large-Scale Structure of a Groundwater System with Airborne Electromagnetic Data

American Geophysical Union (AGU) | March 17th, 2022

A groundwater model is a representation of a groundwater system used to both understand the properties and processes of the subsurface, and to manage the groundwater reso

A groundwater model is a representation of a groundwater system used to both understand the properties and processes of the subsurface, and to manage the groundwater resource. An important step in developing a groundwater model is describing the large-scale structure, the "plumbing system" that controls the groundwater flow. Given the size of many groundwater models, obtaining an accurate description of the large-scale structure can be a challenging task. The traditional approach has involved the drilling of wells. While wells can provide accurate information at the location of the wells, the spatial sampling of the subsurface that can be accomplished through the drilling of wells is limited. An airborne electromagnetic (AEM) method provides an alternative by using a geophysical measurement to map out the electrical resistivity to depths of hundreds of meters. Working with AEM data acquired in the Kaweah Subbasin in the Central Valley of California, we were able to image the large-scale structure - the bedrock surface and a thick clay layer. The key improvement was made by incorporating prior knowledge into the inversion of the data. Once we had obtained the large-scale structure, we found that this could be used to improve the imaging of the smaller-scale structure.

Improving Groundwater Model Calibration with Repeat Microgravity Measurements

National Groundwater Association (NGWA) | December 23rd, 2021

Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation—change in storage measured using

Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation—change in storage measured using repeat microgravity—can also be used for parameter estimation by simulating the expected change in gravity from a groundwater model and including the observation misfit in the objective function. The method is demonstrated using new software linked to MODFLOW input and output files and field data from the vicinity of the All American Canal in southeast California, USA. Over a 10-year period following lining of the previously highly permeable canal with concrete, gravity decreased by over 100 μGal (equivalent to about 2.5 m of free-standing water) at some locations as seepage decreased and the remnant groundwater mound dissipated into the aquifer or was removed by groundwater pumping. Simulated gravity from a MODFLOW model closely matched observations, and repeat microgravity data proved useful for constraining both hydraulic conductivity and specific yield estimates. Specific yield estimated using the infinite-horizontal slab approximation agreed well with model-derived values, and the departure from the linear, flat-water-table approximation was small, less than 2%, despite relatively large and dynamic water-table slope. First-order second-moment parameter uncertainty analysis shows reduction in uncertainty for all hydraulic conductivity and specific yield parameter estimates with the addition of repeat microgravity data, as compared to drawdown data alone.

Incorporating anthropogenic influences into fire probability models: Effects of human activity and climate change on fire activity in California

PLOS | April 28th, 2016

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the fa

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state’s fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change.

Increased drought and extreme events over continental United States under high emissions scenario

Nature Portfolio (Springer Nature) | December 6th, 2023

Increased future occurrences of the exceptional 2018–2019 Central European drought under global warming

Nature Portfolio (Springer Nature) | August 6th, 2020

Since the spring 2018, a large part of Europe has been in the midst of a record-setting drought. Using long-term observations, we demonstrate that the occurrence of the

Since the spring 2018, a large part of Europe has been in the midst of a record-setting drought. Using long-term observations, we demonstrate that the occurrence of the 2018–2019 (consecutive) summer drought is unprecedented in the last 250 years, and its combined impact on the growing season vegetation activities is stronger compared to the 2003 European drought. Using a suite of climate model simulation outputs, we underpin the role of anthropogenic warming on exacerbating the future risk of such a consecutive drought event. Under the highest Representative Concentration Pathway, (RCP 8.5), we notice a seven-fold increase in the occurrence of the consecutive droughts, with additional 40 ( ±5 ) million ha of cultivated areas being affected by such droughts, during the second half of the twenty-first century. The occurrence is significantly reduced under low and medium scenarios (RCP 2.6 and RCP 4.5), suggesting that an effective mitigation strategy could aid in reducing the risk of future consecutive droughts.