Search Results

You are looking at 1 - 7 of 7 items for

  • Author or Editor: Anne Steinemann x
  • All content x
Clear All Modify Search
Anne Steinemann
Full access
Anne C. Steinemann

Abstract

Drought hazards, and the ability to mitigate them with advance warning, offer potentially valuable applications of climate forecast products. Yet the value is often untapped, owing to the gap between climate science and societal decisions. This study bridged that gap; it determined forecast needs among water managers, translated forecasts to meet those needs, and shaped drought decision making to take advantage of forecasts. NOAA Climate Prediction Center (CPC) seasonal precipitation outlooks were converted into a forecast precipitation index (FPI) tailored for water managers in the southeastern United States. The FPI expresses forecasts as a departure from the climatological normal and is consistent with other drought indicators. Evaluations of CPC seasonal forecasts issued during 1995–2000 demonstrated positive skill for drought seasons in the Southeast. In addition, using evaluation criteria of water managers, 88% of forecasts for drought seasons would have appropriately prompted drought responses. Encouraged by these evaluations, and the understandability of the FPI, state water managers started using the forecasts in 2001 for deciding whether to pay farmers to suspend irrigation. Economic benefits of this forecast information were estimated at $100–$350 million in a state-declared drought year (2001, 2002) and $5–$30 million in the other years (2003, 2004). This study provides four main contributions: 1) an investigation of the needs and potential benefits of seasonal forecast information for water management, 2) a method for translating the CPC forecasts into a format needed by water managers, 3) the integration of forecast information into agency decision making, and 4) the economic valuation of that forecast information.

Full access
Shraddhanand Shukla, Anne Steinemann, Sam F. Iacobellis, and Daniel R. Cayan

Abstract

Annual precipitation in California is more variable than in any other state and is highly influenced by precipitation in winter months. A primary question among stakeholders is whether low precipitation in certain months is a harbinger of annual drought in California. Historical precipitation data from 1895 to 2013 are investigated to identify leading monthly indicators of annual drought in each of the seven climate divisions (CDs) as well as statewide. For this study, drought conditions are defined as monthly/annual (October–September) precipitation below the 20th/30th percentile, and a leading indicator is defined as a monthly drought preceding or during an annual drought that has the strongest association (i.e., joint probability of occurrence) with a statewide annual drought. Monthly precipitation variability and contributions to annual precipitation, along with joint probabilities of drought among the winter months, are first analyzed. Then the probabilities of annual drought and the variability in leading indicators are analyzed according to different climate phases and CDs. This study identified December within a water year as being the leading indicator that is most frequently associated with annual drought statewide (56%) and in most of the CDs (the highest was CD2 at 65%). Associated with its leading-indicator status, December drought was most frequently associated with drought in other winter months (joint probability > 30%). Results from this study can help stakeholders to understand and assess the likelihood of annual drought events given monthly precipitation preceding or early in the water year.

Full access
Eric A. Rosenberg, Andrew W. Wood, and Anne C. Steinemann

Abstract

A hydrometric network design approach is developed for enhancing statistical seasonal streamflow forecasts. The approach employs gridded, model-simulated water balance variables as predictors in equations generated via principal components regression in order to identify locations for additional observations that most improve forecast skill. The approach is applied toward the expansion of the Natural Resources Conservation Service (NRCS) Snowpack Telemetry (SNOTEL) network in 24 western U.S. basins using two forecasting scenarios: one that assumes the currently standard predictors of snow water equivalent and water year-to-date precipitation and one that considers soil moisture as an additional predictor variable. Resulting improvements are spatially and temporally analyzed, attributed to dominant predictor contributions, and evaluated in the context of operational NRCS forecasts, ensemble-based National Weather Service (NWS) forecasts, and historical as-issued NRCS/NWS coordinated forecasts. Findings indicate that, except for basins with sparse existing networks, substantial improvements in forecast skill are only possible through the addition of soil moisture variables. Furthermore, locations identified as optimal for soil moisture sensor installation are primarily found in regions of low to mid elevation, in contrast to the higher elevations where SNOTEL stations are traditionally situated. The study corroborates prior research while demonstrating that soil moisture data can explicitly improve operational water supply forecasts (particularly during the accumulation season), that statistical forecasts are comparable in skill to ensemble-based forecasts, and that simulated hydrologic data can be combined with observations to improve statistical forecasts. The approach can be generalized to other settings and applications involving the use of point observations for statistical prediction models.

Restricted access
Shraddhanand Shukla, Anne C. Steinemann, and Dennis P. Lettenmaier

Abstract

A drought monitoring system (DMS) can help to detect and characterize drought conditions and reduce adverse drought impacts. The authors evaluate how a DMS for Washington State, based on a land surface model (LSM), would perform. The LSM represents current soil moisture (SM), snow water equivalent (SWE), and runoff over the state. The DMS incorporates the standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture percentile (SMP) taken from the LSM. Four historical drought events (1976–77, 1987–89, 2000–01, and 2004–05) are constructed using DMS indicators of SPI/SRI-3, SPI/SRI-6, SPI/SRI-12, SPI/SRI-24, SPI/SRI-36, and SMP, with monthly updates, in each of the state’s 62 Water Resource Inventory Areas (WRIAs). The authors also compare drought triggers based on DMS indicators with the evolution of drought conditions and management decisions during the four droughts. The results show that the DMS would have detected the onset and recovery of drought conditions, in many cases, up to four months before state declarations.

Full access
Anne Steinemann, Sam F. Iacobellis, and Daniel R. Cayan

Abstract

Drought indicators can help to detect, assess, and reduce impacts of drought. However, existing indicators often have deficiencies that limit their effectiveness, such as statistical inconsistency, noncomparability, arbitrary metrics, and lack of historic context. Further, indicators selected for drought plans may be only marginally useful, and relatively little prior work has investigated ways to design operationally practical indicators. This study devises a generalizable approach, based on feedback from users, to develop and evaluate indicators for decision-making. This approach employs a percentile-based framework that offers clarity, consistency, and comparability among different indicators, drought levels, time periods, and spatial scales. In addition, it characterizes the evolution of droughts and quantifies their severity, duration, and frequency. User preferences are incorporated into the framework’s parameters, which include percentile thresholds for drought onset and recovery, severity levels, anomalies, and consecutive time periods for triggering. To illustrate the approach and decision-making implications, the framework is applied to California Climate Division 2 and is used with decision-makers, water managers, and other participants in the National Integrated Drought Information System (NIDIS) California Pilot. Stakeholders report that the framework provides an easily understood and beneficial way to assess and communicate drought conditions, validly compare multiple indicators across different locations and time scales, quantify risks relative to historic droughts, and determine indicators that would be valuable for decision-making.

Full access
Eric J. Alfaro, David W. Pierce, Anne C. Steinemann, and Alexander Gershunov

Abstract

The electrical load from irrigation pumps is an important part of the overall electricity demand in many agricultural areas of the U.S. west. The date the pumps turn on and the total electrical load they present over the summer varies from year to year, partly because of climate fluctuations. Predicting this variability would be useful to electricity producers that supply the region. This work presents a contingency analysis and linear regression scheme for forecasting summertime irrigation pump loads in southeastern Idaho. The basis of the predictability is the persistence of spring soil moisture conditions into summer, and the effect it has on summer temperatures. There is a strong contemporaneous relationship between soil moisture and temperature in the summer and total summer pump electrical loads so that a reasonable prediction of summer pump electrical loads based on spring soil moisture conditions can be obtained in the region. If one assumes that decision makers will take appropriate actions based on the forecast output, the net economic benefit of forecast information is approximately $2.5 million per year, making this prediction problem an important seasonal summer forecasting issue with significant economic implications.

Full access