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At least 19 recordsLinked to original sources
USGS2024

A spatiotemporal optimization engine for prescribed burning in the Southeast US

Many ecosystems in the Southeast US are dependent upon frequent low-intensity surface fires to sustain native biodiversity, ecosystem services, and endangered species populations. Today, landscape-scale prescribed fire is required to manage these systems for conservation objectives and to mitigate wildland fire risk. Successful application of prescribed fire in this region requires careful planning and assessment of the risks and tradeoffs involved when deciding whether or not to conduct a burn. Many of these risks are closely tied to ambient environmental conditions and are reflected in sets of ‘prescription’ parameters that define safe and effective operating conditions to meet objectives or regulatory requirements. To facilitate effective decision making and acknowledge growing uncertainties related to climate change effects on wildland fire operations, we developed a spatiotemporal optimization engine to identify near-term optimal burning opportunities for prescribed fire implementation. By mining historical 3-day numerical weather forecasts and observation-based weather data for 2015–2021, we have developed a Bayesian hierarchical model for forecast verification that provides calibrated daily weather forecasts and joint uncertainty estimates on meteorological variables of interest, with the latter serving as a measure of risk associated with prescribed fire activities. Burn allocation decisions are then optimized by considering this risk jointly with the utility of burning a particular habitat parcel. The initial iteration of the optimization engine is demonstrated through a case study of short-term meteorological conditions for the Eglin Air Force Base, located in Florida, USA. Results indicate agreement between the optimization engine and the observed past decision-making, with the largest divergences likely arising primarily from differences between utility functions presumed important and used to develop the optimization engine versus the true utility functions driving management behavior in practice.

Florida↗
USGS2013

Emergency assessment of post-fire debris-flow hazards for the 2013 Mountain fire, southern California

Wildfire dramatically alters the hydrologic response of a watershed such that even modest rainstorms can produce dangerous flash floods and debris flows. We use empirical models to predict the probability and magnitude of debris flow occurrence in response to a 10-year rainstorm for the 2013 Mountain fire near Palm Springs, California. Overall, the models predict a relatively high probability (60–100 percent) of debris flow for six of the drainage basins in the burn area in response to a 10-year recurrence interval design storm. Volumetric predictions suggest that debris flows that occur may entrain a significant volume of material, with 8 of the 14 basins identified as having potential debris-flow volumes greater than 100,000 cubic meters. These results suggest there is a high likelihood of significant debris-flow hazard within and downstream of the burn area for nearby populations, infrastructure, and wildlife and water resources. Given these findings, we recommend that residents, emergency managers, and public works departments pay close attention to weather forecasts and National Weather Service–issued Debris Flow and Flash Flood Outlooks, Watches and Warnings and that residents adhere to any evacuation orders.

California↗
USGS2014

Emergency assessment of post-fire debris-flow hazards for the 2013 Springs Fire, Ventura County, California

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can produce dangerous flash floods and debris flows. In this report, empirical models are used to predict the probability and magnitude of debris-flow occurrence in response to a 10-year rainstorm for the 2013 Springs fire in Ventura County, California. Overall, the models predict a relatively high probability (60–80 percent) of debris flow for 9 of the 99 drainage basins in the burn area in response to a 10-year recurrence interval design storm. Predictions of debris-flow volume suggest that debris flows may entrain a significant volume of material, with 28 of the 99 basins identified as having potential debris-flow volumes greater than 10,000 cubic meters. These results of the relative combined hazard analysis suggest there is a moderate likelihood of significant debris-flow hazard within and downstream of the burn area for nearby populations, infrastructure, wildlife, and water resources. Given these findings, we recommend that residents, emergency managers, and public works departments pay close attention to weather forecasts and National Weather Service-issued Debris Flow and Flash Flood Outlooks, Watches, and Warnings, and that residents adhere to any evacuation orders.

California↗
USGS2018

Potential for western US seasonal snowpack prediction

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Proceedings of the National Academy of Sciences of↗
USGS2013

Emergency assessment of post-fire debris-flow hazards for the 2013 Powerhouse fire, southern California

Wildfire dramatically alters the hydrologic response of a watershed such that even modest rainstorms can produce dangerous flash floods and debris flows. Existing empirical models were used to predict the probability and magnitude of debris-flow occurrence in response to a 10-year recurrence interval rainstorm for the 2013 Powerhouse fire near Lancaster, California. Overall, the models predict a relatively low probability for debris-flow occurrence in response to the design storm. However, volumetric predictions suggest that debris flows that occur may entrain a significant volume of material, with 44 of the 73 basins identified as having potential debris-flow volumes between 10,000 and 100,000 cubic meters. These results suggest that even though the likelihood of debris flow is relatively low, the consequences of post-fire debris-flow initiation within the burn area may be significant for downstream populations, infrastructure, and wildlife and water resources. Given these findings, we recommend that residents, emergency managers, and public works departments pay close attention to weather forecasts and National-Weather-Service-issued Debris Flow and Flash Flood Outlooks, Watches, and Warnings and that residents adhere to any evacuation orders.

California↗
USGS2013

Emergency assessment of post-fire debris-flow hazards for the 2013 Rim Fire, Stanislaus National Forest and Yosemite National Park, California

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can produce dangerous flash floods and debris flows. In this report, empirical models are used to predict the probability and magnitude of debris-flow occurrence in response to a 10-year rainstorm for the 2013 Rim fire in Yosemite National Park and the Stanislaus National Forest, California. Overall, the models predict a relatively high probability (60–80 percent) of debris flow for 28 of the 1,238 drainage basins in the burn area in response to a 10-year recurrence interval design storm. Predictions of debris-flow volume suggest that debris flows may entrain a significant volume of material, with 901 of the 1,238 basins identified as having potential debris-flow volumes greater than 10,000 cubic meters. These results of the relative combined hazard analysis suggest there is a moderate likelihood of significant debris-flow hazard within and downstream of the burn area for nearby populations, infrastructure, wildlife, and water resources. Given these findings, we recommend that residents, emergency managers, and public works departments pay close attention to weather forecasts and National-Weather-Service-issued Debris Flow and Flash Flood Outlooks, Watches and Warnings and that residents adhere to any evacuation orders.

California↗
USGS2013

Large scale snow water status monitoring: Comparison of different snow water products in the upper Colorado basins

We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS ( r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL ( r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE ( r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values ( r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

Colorado, Utah, Wyoming↗
USGS2020

Partly cloudy with a chance of lava flows: Forecasting volcanic eruptions in the 21st century

A primary goal of volcanology is forecasting hazardous eruptive activity. Despite much progress over the last century, however, volcanoes still erupt with no detected precursors, lives and livelihoods are lost to eruptive activity, and forecasting the onsets of eruptions remains fraught with uncertainty. Long‐term forecasts are generally derived from the geological and historical records, from which recurrence intervals and styles of activity can be inferred, while shorter‐term forecasts are derived from patterns in monitoring data. Information from geology and monitoring data can be evaluated and combined using statistical analysis, expert elicitation, and conceptual and or mathematical models. Integrative frameworks, such as event trees, combine this diversity of information to produce probabilistic forecasts that can inform the style and scale of the societal response to a potential future eruption. Several developments show promise to revolutionize the utility and accuracy of these forecasts. These include growth in the quantity and quality of multidisciplinary monitoring data, coupled with increases in computing power; machine learning algorithms, which will allow far better utilization of this growing volume of data; and new physiochemical volcano models and data assimilation algorithms, which take advantage of a wide range of monitoring data and realistic physics to better predict the evolution of a given physical state. Although eruption forecasts may never be as generally reliable as weather forecasts, and great caution must be exercised when attempting to predict highly complex volcanic behavior, these and other innovations—particularly when combined in integrative, fully probabilistic forecasting frameworks—should help volcanologists to better issue warnings of volcanic activity on societally relevant time frames.

Hawaii↗
USGS2025

Supporting dryland restoration success with applied ecological forecasting of seeding outcomes

Introduction Ecological restoration is increasingly used to sustain biodiversity and ecosystem services. In drylands of the western United States (US), post-disturbance restoration often involves seeding treatments to promote the recovery of native plant communities. Spatial and temporal variability in environmental conditions influences plant establishment and contributes to low restoration success in certain locations and years. Objectives Here, we discuss how forecasts for plant establishment can be developed and delivered to help land managers anticipate the impacts of near-term (months to years) environmental conditions on restoration. Methods We developed an ecological forecast system that predicts the outcome of restoration seeding by integrating weather forecasts, an ecosystem water balance model, and plant establishment models. Results In this article, we focus on a conceptual approach to developing, delivering, and applying ecological forecasts for restoration. We illustrate the potential of this approach by adapting existing ecological models to build an initial version of a decision support tool that delivers a species-specific ecological forecast for big sagebrush ( Artemisia tridentata ) establishment. Integrating ecological forecasts into plans for restoration seeding presents opportunities to anticipate and account for environmental variability. Conclusions Finally, we discuss how connecting research, forecast delivery, and management can maximize the impact of ecological forecasts on restoration success.

Idaho, Nevada↗
USGS2024

Near-term ecological forecasting for climate change action

A substantial increase in predictive capacity is needed to anticipate and mitigate the widespread change in ecosystems and their services in the face of climate and biodiversity crises. In this era of accelerating change, we cannot rely on historical patterns or focus primarily on long-term projections that extend decades into the future. In this Perspective, we discuss the potential of near-term (daily to decadal) iterative ecological forecasting to improve decision-making on actionable time frames. We summarize the current status of ecological forecasting and focus on how to scale up, build on lessons from weather forecasting, and take advantage of recent technological advances. We also highlight the need to focus on equity, workforce development, and broad cross-disciplinary and non-academic partnerships.

Nature Climate Change↗
USGS2024

Special topic—Eruption plumes and clouds

Introduction Explosive eruptions create plumes of volcanic ash and gas that can rise more than 30,000 feet (9.1 kilometers [km]) above sea level within minutes of eruption onset. The resulting clouds disperse under prevailing winds and may cause hazardous conditions hundreds to thousands of kilometers from the volcano, including in international airspace. Rapid detection and characterization of explosive activity is vital to mitigate the wide-ranging effects of volcanic ash. Ashfall thicknesses as small as a millimeter or so on the ground can affect infrastructure, agriculture, and air quality, requiring extensive clean-up procedures (Schuster, 1981; Warrick and others, 1981, U.S. Geological Survey, 2022). Volcanic clouds also pose substantial threats to aircraft. Since 1953, 88 encounters between airplanes and ash clouds have been documented worldwide (International Civil Aviation Organization, 2015, appendix F), resulting in aircraft damage and, in 9 cases, engine failure (Guffanti and others, 2010). In 1982, two large passenger planes suffered complete engine failure owing to eruptions in Indonesia (Global Volcanism Program, 1982) and a similar incident occurred over Alaska in 1989 (Casadevall, 1994). In all three cases, they were able to restart some engine capability and land safely once they emerged from the ash clouds, although with substantial damage (Guffanti and others, 2010). The clear threat to aviation has led to establishment of nine Volcanic Ash Advisory Centers (VAAC) around the world to monitor and rapidly disseminate information about volcanic eruptions to the aviation community. U.S. Geological Survey (USGS) volcano observatories issue the Volcano Observatory Notice for Aviation that informs of preeruptive unrest or eruptive activity. When ash-producing eruptions do occur, volcano observatories work closely with their regional VAAC to ensure consistency and accuracy in eruption onset time, cloud altitude, ash production, and duration as reported in Volcanic Ash Advisories. Explosive volcanism in the United States and Commonwealth of the Northern Mariana Islands prompts 50–100 such advisories in any given year (table J1). This collaborative effort is greatly aided by USGS detection and monitoring of eruption clouds to ensure a timely and coordinated response. To support these efforts to provide guidance on ash transport and fallout, the USGS developed the Ash3d volcanic ash dispersion model ( https://vsc-ash.wr.usgs.gov/ash3d-gui ) (Schwaiger and others, 2012). Automated simulations are run daily by the USGS for volcanoes that are in elevated states of unrest, and in response mode when eruptions occur. During eruptions, the model output is provided to local National Weather Service Weather Forecast Offices to guide them in the issuance of their information products (such as special weather statements, ashfall advisories, or ashfall warnings), as well as to State and local governments and the public. Characterization of the eruption source is needed to estimate the parameters used to initialize the Ash3d model, and by the Anchorage and Washington VAACs to initialize other dispersion models that inform forecasts for the airborne volcanic cloud. The source parameters that can be provided by observation during an eruption include eruption start time, eruption cloud height over time, and eruption duration. Other, nonobservable source parameters, such as mass eruption rate and grain-size distribution, are based on empirical correlations and study of historical deposits. The goal is to provide a time series of cloud heights, mass eruption rates, and particle-size distributions that accurately reflects current conditions. When feasible, the USGS also provides guidance on the nature of ongoing eruptions and forecasts future activity using petrologic monitoring of collected tephra samples. The aims of providing accurate observable parameters are achieved through analysis of (1) near-real-time meteorological satellite data, (2) ground-based cameras (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–G, 11 p., https://doi.org/10.3133/sir20245062g.">chapter G , this volume; Orr and others, 2024), (3) weather radar, (4) volcanic lightning detection, and (5) ground-based ash sensors and sampling. Explosive eruptions can be detected by a variety of geophysical monitoring, including infrasound (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–C, 11 p., https://doi.org/10.3133/sir20245062c.">chapter C , this volume; Lyons and others, 2024) and seismicity (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–B, 9 p., https://doi.org/10.3133/sir20245062b.">chapter B , this volume; Thelen and others, 2024). However, those methods cannot quantify the altitude, ash content, and dispersal dynamics of resulting volcanic clouds. Ideally, all available sources of monitoring data are synthesized to develop a coherent understanding of eruptive activity. The guidance summarized here provides a framework for characterizing volcanic clouds in the atmosphere and tracking the evolution of explosive eruption dynamics.

Scientific Investigations Report↗
USGS2026

How to accelerate advances in ecological forecasting

Ecological forecasting offers critical insights for managing natural resources and safeguarding public well-being. Despite growing demand for these forecasts, progress is hindered by fragmented systems, redundant workflows, and limited interoperability. Drawing lessons from weather forecasting and recent successes like the NEON Ecological Forecasting Challenge, shared cyberinfrastructure is important for advancing ecological prediction. By adopting common standards, open-source tools, and scalable architectures, and fostering transdisciplinary collaboration, the ecological forecasting community can overcome technical and institutional barriers. Such investments could accelerate scientific understanding, improve forecast reliability, and empower decisionmakers to anticipate environmental change and respond effectively.

Eos, American Geophysical Union↗
USGS2025

Forecast, monitor, adapt: A multi-agency strategy to protect people from postfire debris flows

In 2020, a wildfire burned across Glenwood Canyon in Colorado, USA. A history of postfire debris flows in the region and a hazard assessment for the burn area indicated that potentially life-threatening debris flows could be triggered by rainfall within months of a wildfire. As a result, four government agencies evaluated strategies to help mitigate hazards, including the loss of human life, that may be associated with debris-flow events. After the fire, 26 large debris flows occurred in the summer of 2021 and three sediment-laden flows occurred in the summer of 2023, but there were no major injuries or fatalities reported. We found that integrating hazard assessment/ forecasting, monitoring, and adaptation scenarios was a successful strategy for reducing postfire debris-flow risks to human life (including injuries and fatalities). Weather forecasts and estimates of debris-flow triggering rainfall thresholds, likelihood, and volume were used to anticipate the timing, location, and magnitude of debris-flow events. Rainfall monitoring and detailed recordkeeping of storms that triggered debris flows were used to validate and update debris-flow warning thresholds that varied with time following the wildfire. Although the governmental agencies working in this burn area had distinct and differing agency mandates, they were able to integrate information to reduce the risk of debris-flow events to human life.

Colorado↗
USGS2020

Fostering real-time climate adaptation: Analyzing past, current, and forecast temperature to understand the dynamic risk to Hawaiian honeycreepers from avian malaria

Various vector control options are increasingly being considered to safeguard forest birds in their natural habitats from avian malaria transmission. However, vector control options require localized deployment that is not logistically, ethically, ecologically, nor economically viable everywhere and all the time. Based on thermal tolerances of the sporogonic stages of avian malaria (Plasmodium relictum) parasite and its vector, the southern house mosquito (Culex quinquefasciatus), we examined the long-term weather trends for three high value, forest bird refuges (Alakai Wilderness Preserve on Kaua’i, Hanawi Natural Area Reserve on Maui, and Hakalau Forest National Wildlife Refuge on Hawai’i Island) to understand the temporal and site-specific differences of temperature-driven suitability for localized avian malaria transmission. On average, Alakai had mean ambient temperatures suitable for both the vector’s immature stage development and parasite sporogonic development most of the time (85.3%), indicating that observed variability in vector abundance or disease transmission may be driven by other factors. At higher elevation sites like Hakalau and Hanawi, current mean ambient temperatures suitable only for vector development prevail (91.7% and 96.6%, respectively), while mean ambient temperatures for both vector and parasite sporogonic development seldom occur (4.4% and 0% respectively). Our results not only show differences in the temperature suitability for transmission across elevation, but also different levels of vulnerability to avian malaria transmission with any additional projected increase in temperature. For instance, under a conservative warming scenario of 1.0 °C, the joint temperature suitability of parasite and vector development increases at higher elevation sites such as Hakalau (+35.8%) and Hanawi (+15.4%). While mean ambient temperatures suitable for both vector and parasite development already occur most of the time at Alakai, the occurrence also increases (+8.4%) as well under this conservative warming scenario. By linking current site-specific weather data to real-time weather forecasts, we developed a real-time avian malaria warning system to assist managers in identifying conditions when vector control is most needed at these three selected study sites. This online tool determines when conditions are likely to be suitable for local development of P. relictum and C. quinquefasciatus at Alakai, Hanawi, and Hakalau. This tool illustrates how managers can incorporate climate and current weather patterns into decision making without having to consider the uncertainties of long-term climatic and ecological projections.

Hawaii↗
USGS2014

Forecasting the dry‐weather flow of Pond Creek, Oklahoma: A progress report

Pond Creek in west‐central Oklahoma drains an area of 319 sq mi above the gaging station near Fort Cobb, Caddo County. Ground water, contained in the Permian Rush Springs sandstone under water‐table conditions, moves toward the creek at an almost unchanging rate. The discharge of ground water into the creek as dry‐weather flow is modified by evapotranspiration and antecedent overland runoff. Multiple correlations have been computed relating the dry‐weather flow to the water level in a well and to a factor indicative of the rate of evapotranspiration. A method for forecasting the factor indicative of evapotranspiration and one for forecasting the water level in the well during dry weather are given; the result is a method for forecasting the dry‐weather flow of the Creek. Forecasts of dry‐weather flow for seven and 21 days compare favorably with observed flows. The technique may be utilized to extend a forecast for several months.

Oklahoma↗
USGS2020

Observations of an extreme atmospheric river storm with a diverse sensor network

Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.

California↗
USGS2008

Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin

In this study, we have described a hydrologic modelling system that uses satellite-based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

Journal of Flood Risk Management↗
USGS1997

Forecasting drought risks for a water supply storage system using bootstrap position analysis

Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

Conference Paper↗