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Results for “Journal of Hydrometeorology”

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64 records · Page 4Linked to original sources
USGS2024

A new water temperature modeling approach to predict thermal habitat suitability for nonnative cichlids in Florida rivers

As global temperatures increase, the spatiotemporal arrangement of thermal habitats in Florida rivers may shift, creating the potential for greater dispersal and establishment of nonnative tropical freshwater fishes. To understand how water temperature changes may affect the spatial distribution of these nonnative species, more effective water temperature prediction models are necessary. Currently, most models employ either a generalized air–water temperature relationship or require expensive and complicated tools to measure hydrometeorological factors (e.g. groundwater input). Thus, we developed a novel modeling approach that is accurate, accessible, and cost-effective in allowing fisheries managers to project water temperatures in rivers across Central and North Florida. To characterize the potential for nonnative fishes to spread northward, we evaluated two hardy and abundant species currently found primarily in South Florida: Mayan Cichlid ( Mayaheros urophthalmus ) and Oscar ( Astronotus ocellatus ). Our results show an increase in thermally suitable winter days for both species in 10 of 11 rivers studied, consistent with predicted water temperature warming under 16 climate-change scenarios spanning different levels of air temperature warming (+1 °C, +2 °C, +3 °C, +4 °C) and precipitation/groundwater thermal sensitivity (0, 0.33, 0.66, 1). Considering resource limitations, fisheries managers can use our water temperature modeling approach to predict effects of climate change on Mayan Cichlid and Oscar survival, growth, and dispersal and take actions to manage potential northward movement of these species.

Florida↗
USGS2016

Prototypic automated continuous recreational water quality monitoring of nine Chicago beaches

Predictive empirical modeling is used in many locations worldwide as a rapid, alternative recreational water quality management tool to eliminate delayed notifications associated with traditional fecal indicator bacteria (FIB) culturing (referred to as the persistence model, PM) and to prevent errors in releasing swimming advisories. The goal of this study was to develop a fully automated water quality management system for multiple beaches using predictive empirical models (EM) and state-of-the-art technology. Many recent EMs rely on samples or data collected manually, which adds to analysis time and increases the burden to the beach manager. In this study, data from water quality buoys and weather stations were transmitted through cellular telemetry to a web hosting service. An executable program simultaneously retrieved and aggregated data for regression equations and calculated EM results each morning at 9:30 AM; results were transferred through RSS feed to a website, mapped to each beach, and received by the lifeguards to be posted at the beach. Models were initially developed for five beaches, but by the third year, 21 beaches were managed using refined and validated modeling systems. The adjusted R 2 of the regressions relating Escherichia coli to hydrometeorological variables for the EMs were greater than those for the PMs, and ranged from 0.220 to 0.390 (2011) and 0.103 to 0.381 (2012). Validation results in 2013 revealed reduced predictive capabilities; however, three of the originally modeled beaches showed improvement in 2013 compared to 2012. The EMs generally showed higher accuracy and specificity than those of the PMs, and sensitivity was low for both approaches. In 2012 EM accuracy was 70–97%; specificity, 71–100%; and sensitivity, 0–64% and in 2013 accuracy was 68–97%; specificity, 73–100%; and sensitivity 0–36%. Factors that may have affected model capabilities include instrument malfunction, non-point source inputs, and sparse calibration data. The modeling system developed is the most extensive, fully-automated system for recreational water quality developed to date. Key insights for refining and improving large-scale empirical models for beach management have been developed through this multi-year effort.

Illinois↗
USGS2025

Mobile radar provides insights into hydrologic responses in burn areas

Background Wildfires often occur in mountainous terrain, regions that pose substantial challenges to operational meteorological and hydrologic observing networks. Aims A mobile, post-fire hydrometeorological observatory comprising remote-sensing and in situ instrumentation was developed and deployed in a burnt area to provide unique insights into rainfall-induced post-fire hazards. Methods Mobile radar-based rainfall estimates were produced throughout the burn area at 75-m resolution and compared with rain gauge accumulations and basin response variables. Key results The mobile radar was capable of resolving details in intra-basin rain fields as well as detecting storms approaching the burn area with accuracy equivalent to rain gauges. Runoff responses were complex and dependent on spatiotemporal patterns and magnitude of rainfall intensity over the burn area. Conclusions The complement of the mobile radar with the near-field, non-contact instruments measuring the hydrologic response provided valuable information in regions that are difficult to access and are not routinely monitored by conventional observing networks. Implications Post-fire observatories equipped with mobile radars deployed on burn areas provide real-time data, early alerting capabilities and visualizations to potentially guide impact-based decision support for local authorities.

Colorado↗
USGS2020

Linking mesoscale meteorology with extreme landscape response: Effects of narrow cold frontal rainbands (NCFR)

Landscapes evolve in response to prolonged and/or intense precipitation resulting from atmospheric processes at various spatial and temporal scales. Whereas synoptic (large‐scale) features (e.g., atmospheric rivers and hurricanes) govern regional‐scale hydrologic hazards such as widespread flooding, mesoscale features such as thunderstorms or squall lines are more likely to trigger localized geomorphic hazards such as landslides. Thus, to better understand relations between hydrometeorological drivers and landscape response, a knowledge of mesoscale meteorology and its impacts is needed. Here we investigate the extreme geomorphic response associated with one type of mesoscale meteorological feature, the narrow cold frontal rainband (NCFR). Resulting from low‐level convergence and shallow convection along a cold front, NCFRs are narrow bands of high‐intensity rainfall that occur in midlatitude areas of the world. Our study examines an NCFR impacting the Sierra Nevada foothills (California, USA) that initiated over 500 landslides, mobilized ~360,000 metric tons of sediment to the fluvial system (as much as 16 times the local annual sediment yield), and severely damaged local infrastructure and regional water transport facilities. Coupling geomorphological field investigations with meteorological analyses, we demonstrate that precipitation associated with the NCFR was both intense (maximum 15 min intensity of 70 mm/hr) and localized, resulting in a highly concentrated band of shallow landsliding. This meteorological phenomenon likely plays an important role in landscape evolution and hazard initiation. Other types of mesoscale meteorological features also occur globally and offer new avenues for understanding the effects of storms on landscapes.

California↗
USGS2020

The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses

Hydrometeorologic monitoring networks are ubiquitous in contemporary earth-system science. Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from synthetic data and 800 random observation locations (sites) as an analog to a groundwater-level network. Using generalized additive models for potentiometric surface prediction, simulations show that a subsample of support vectors from the 800 sites will out perform random samples of sample size equaling the support vector count. Support vector percentages from simulation quantify the recurrence that SVMs assign each site as a support vector, and these percentages in turn measure site importance. An example application of support vector percentages identifies important monitoring sites needed to regionalize the 0.1 annual exceedance probability peak streamflow. The results indicate that 152 of 283 streamgages with support vector percentages equalling 100 percent have not operated since about 2000 and generally have much smaller drainage areas than the greater streamgage network in Texas. The drainage area disparity is an indication of historical imbalance in peak streamflow data acquisition from various stream sizes in Texas.

Texas↗
USGS2021

Hydrometeorology and hydrology of flooding in Cape Fear River basin during Hurricane Florence in 2018

Hurricanes are the major flood generating mechanism dominating the upper tail of the peak discharge distribution over the Cape Fear River Basin (CFRB). In 2018, Hurricane Florence swamped CFRB as the ninth-most-destructive hurricane ever hit the United States and set new records of peak discharges over the main river channel and three out of five of its major tributaries. In this study, we examined the hydrometeorology and hydrology of this flood via combined observation and numerical experiment analyses. Our results suggest that the slow-motion in combination to the “L-shaped” path was the most distinctive feature of the hurricane that incurred catastrophic and widespread rainfall and flooding over CFRB. The total rainfall from the storm played a controlling role in the magnitude and spatial distribution of the flood peaks at basin scale. Above that, the spatial heterogeneities of rainfall distribution and hydrologic characteristics was responsible for the distinctive flood responses within the basin. The bi-peak shape of the flood hydrograph for the Deep River was due to the combined effects of rainfall distribution, land cover, and topographic gradient. The exceptional unit peak discharge over the Black River basin was associated with its drainage network structure, topographic gradient and rainfall distribution. The floodplain downstream of the Cape Fear River temporarily stored flood water and attenuated both the riverine floods from upstream and the compound flood over the coastal area. Furthermore, numerical analyses found that re-infiltration accounted for 76% of the total infiltration on average. Re-infiltration was superior to local infiltration over CFRB during Hurricane Florence.

North Carolina↗
USGS2015

Characterizing Congo Basin rainfall and climate using Tropical Rainfall Measuring Mission (TRMM) satellite data and limited rain gauge ground observations

Quantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r 2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.

Journal of Applied Meteorology and Climatology↗
USGS2023

Developing a stochastic hydrological model for informing lake water level drawdown management

Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates.

Massachusetts↗
USGS1977

Application of a hydrometeorological model to the south-central Sierra Nevada of California

A hydrometeorological streamflow-prediction model (HM model) developed for the North Cascades of Washington has been tested in the south-central Sierra Nevada of California. Twenty-four drainages ranging in mean altitude from 770 to 3,160 metres, including several of the major ones such as those of the Kern, Kings, and Merced Rivers, were examined. Eight U.S. National Oceanic and Atmospheric Administration precipitation stations were evaluated. Of these, three proved to be of significant value for nearly all the drainages used. Results are given for predictions on February 1, March 1, and April 1 of monthly runoff of five major drainages for the April-September season. Also demonstrated is the April 1 prediction of a daily hydrograph for the April-September season for 2 diverse years. The altitude distribution of storage and runoff, both observed and predicted, is determined by using several drainages with different area-altitude profiles. Results of this calculation for two drainages show that, on the average, approximately 50 percent of the April-July runoff originates above 2,800 m. The influence of subsequent precipitation on prediction accuracy is determined by relating prediction error and actual precipitation occurring after the prediction day. Results for three basins show that about 75 percent of the error of a January-September prediction on January 1 is due to precipitation occurring during the prediction season. Comparisons of prediction accuracy are made for five major drainages: the Kern River near Kernville; the Kings River below North Fork, near Trimmer (inflow to Pine Flat Dam); the Kings River at Piedra; the Merced River at Pohono Bridge, near Yosemite; and the Merced River below Merced Falls Dam, near Snelling. The accuracy of the HM model appears to be about 24 percent higher than existing operational methods in predicting the April-July runoff on April 1.

California↗
USGS2025

Forecasting water levels using the ConvLSTM algorithm in the Everglades, USA

Forecasting water levels in complex ecosystems like wetlands can support effective water resource management, ecological conservation, and understanding surface and groundwater hydrology. Predictive models can be used to simulate the complex interactions among natural processes, hydrometeorological factors, and human activities. The Greater Everglades in the USA is a well-known example of an ecosystem where complexity has motivated adoption of machine learning algorithms in water level prediction studies. This paper aims to contribute to extending existing machine learning algorithms by integrating spatiotemporal data with deep-learning algorithms in the forecasting process. In this study, a deep-learning model is developed to predict water levels on a regional scale, covering a large area of approximately 9,138 square kilometers in the Everglades ecosystem. This model has the architecture of Convolutional Long Short-Term Memory which can deal with spatiotemporal data by capturing both spatial and temporal dependencies in the training data. The forecasting capabilities of this model (referred to as the global model) are assessed by comparing the global model to two Artificial Neural Networks developed at two different gaging stations, referred to here as local models. One local model is developed at a gaging station directly influenced by nearby water control structures, whereas the other is developed at a gaging station located farther away from these structures. By leveraging data from the Everglades Depth Estimation Network spanning from January 2002 to May 2023, the global and local models were trained to forecast water levels with a two-day lead time. Our findings suggest that both the global and local models perform with approximately the same level of accuracy, with Mean Absolute Relative Error values ranging from 0.38% to 1.4% at the selected stations. The developed global model has demonstrated strong potential as a standalone forecasting tool for the entire study area in the Everglades and could eliminate the need for developing multiple local models. This finding also highlights how machine learning can capture complex spatial and temporal relationships to generate accurate water level predictions on a regional scale.

Florida↗