scholarly journals The GEFS-Based Daily Reference Evapotranspiration (ETo) Forecast and Its Implication for Water Management in the Southeastern United States

2014 ◽  
Vol 15 (3) ◽  
pp. 1152-1165 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez
Keyword(s):  
Water Management ◽  
United States ◽  
Reference Evapotranspiration ◽  
Southeastern United States ◽  
Demand Forecasting ◽  
Skill Score ◽  
Irrigation Scheduling ◽  
Forecast Skill ◽  
Ensemble Forecasts ◽  
Relative Operating Characteristic

Abstract NOAA’s second-generation retrospective forecast (reforecast) dataset was created using the currently operational Global Ensemble Forecast System (GEFS). It has the potential to accurately forecast daily reference evapotranspiration ETo and can be useful for water management. This study was conducted to evaluate daily ETo forecasts using the GEFS reforecasts in the southeastern United States (SEUS) and to incorporate the ETo forecasts into irrigation scheduling to explore the usefulness of the forecasts for water management. ETo was estimated using the Penman–Monteith equation, and ensemble forecasts were downscaled and bias corrected using a forecast analog approach. The overall forecast skill was evaluated using the linear error in probability space skill score, and the forecast in five categories (terciles and 10th and 90th percentiles) was evaluated using the Brier skill score, relative operating characteristic, and reliability diagrams. Irrigation scheduling was evaluated by water deficit WD forecasts, which were determined based on the agricultural reference index for drought (ARID) model driven by the GEFS-based ETo forecasts. All forecast skill was generally positive up to lead day 7 throughout the year, with higher skill in cooler months compared to warmer months. The GEFS reforecast improved ETo forecast skill for all lead days over the SEUS compared to the first-generation reforecast. The WD forecasts driven by the ETo forecasts showed higher accuracy and less uncertainty than the forecasts driven by climatology, indicating their usefulness for irrigation scheduling, hydrological forecasting, and water demand forecasting in the SEUS.

scholarly journals Stakeholders Reach Consensus in Troubled Waters

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Kathleen Rugel
Keyword(s):  
Water Management ◽  
United States ◽  
Environmental Protection ◽  
Southeastern United States ◽  
Management Strategies ◽  
Management Plan ◽  
Freshwater Biodiversity ◽  
Transboundary Waters ◽  
Water Users ◽  
Basin Water

Surface water and groundwater catchments rarely align with the boundaries of cities, states, or nations. More often, water runs through, over, and under man-made sociopolitical divisions, making the governance of transboundary waters a formidable task. Although much of the public conversation regarding the availability and management of shared waters may appear to be dire (e.g., reports of “water wars”), there are transboundary basin water management strategies across the globe which offer hope. These include the efforts of the Apalachicola-Chattahoochee-Flint Stakeholders (ACFS) in the southeastern United States, which may serve as a useful template for future conversations around the water sharing table. The Apalachicola-Chattahoochee-Flint Basin (ACF Basin) is a vital economic engine in the southeastern United States. The waters of the ACF are shared between three states—Alabama, Florida, and Georgia—and harbor some of the richest freshwater biodiversity in North America, including sturgeon, rock bass, madtom, sculpin, bass, darters, and the highest densities of freshwater mussels in the world. Many of these are species of concern or threatened or endangered species; therefore, water management strategies in multiple portions of the ACF must comply with habitat protection plans under the U.S. Environmental Protection Act of 1970 (https://www.enr.gov.nt.ca/en/environmental-protection-act). The ACFS was organized in 2009 in the hopes of overcoming a decades-long stalemate between Alabama, Florida, and Georgia, regarding the use of shared waters in the ACF Basin. Despite years of litigious relationships among these three states, the ACFS managed to bring a diverse and previously contentious set of water users to the table and build consensus on a shared water management plan for the entire ACF Basin. While the ACFS holds no regulatory power, they made more progress in breaking through existing distrust and deadlock than any previous efforts in this basin to date. In the end, they developed cooperation, respect, and a sustainable and adaptive water management plan which included input and buy-in from all identified water sectors in the ACF Basin. It is, therefore, a valuable exercise to examine the ACFS model and contemplate whether it contains exportable methodologies for other catchments challenged with managing transboundary waters.

scholarly journals Evaluation of an Irrigation Scheduling Model for Drip-irrigated Potato in Southeastern United States

HortScience ◽  
2002 ◽  
Vol 37 (1) ◽  
pp. 104-107 ◽  
Author(s):  
Eric Simonne ◽  
Nadia Ouakrim ◽  
Arnold Caylor
Keyword(s):  
United States ◽  
Soil Water ◽  
Sandy Loam ◽  
Southeastern United States ◽  
Total Yield ◽  
Weather Conditions ◽  
Potato Production ◽  
Irrigation Scheduling ◽  
Marketable Yield ◽  
Planting Dates

Potato (Solanum tuberosum L.) is often produced as a nonirrigated crop in the southeastern United States. This practice makes tuber yields dependent on rainfall pattern and amount. An irrigation scheduling method based on a water balance and daily class A pan evaporation (Ep) was evaluated during 1996 and 1998 on a Hartsells fine sandy loam soil for `Red LaSoda' potatoes. Planting dates were 9 and 7 Apr. in 1996 and 1998, respectively, and standard production practices were followed each year. The model tested was (13 DAH + 191) * 0.5 ASW = D DAH-1 + [Ep * (0.12 + 0.023 DAH - 0.00019 DAH2) - RDAH - IDAH], where DAH was days after hilling, ASW was available soil water (0.13 mm/mm), D was soil water deficit (mm), R was rainfall (mm), and I was irrigation (mm). Controlled levels of water application ranging between 0% and 200% of the model rate were created with drip tapes. Four and seven irrigations were scheduled in 1996 and 1998, respectively. For both years, no interaction between irrigation regime and nitrogen rate was observed. Irrigation rate significantly influenced total yield and marketable yield (R2 > 0.88, P < 0.01). Highest total yields occurred at 99% and 86% of the model rate in 1996 and 1998, respectively. These results show that supplementing rainfall with irrigation and controlling the amount of water applied by adjusting irrigation to actual weather conditions increased potato marketable yield. Over the 2-year period of the study, an average additional profit of $563/ha/year was calculated from costs and returns due to irrigation, suggesting that drip-irrigation may be economical for potato production.

Generating families of synthetic forecasts of different skills from an existing forecast product

2021 ◽  
Author(s):  
Charles Rougé
Keyword(s):  
Water Management ◽  
Absolute Error ◽  
Skill Score ◽  
Statistical Properties ◽  
Forecast Skill ◽  
Systematic Exploration ◽  
Probabilistic Forecasts ◽  
Hydropower Production ◽  
Skill Improvement ◽  
Inform Decision Making

&lt;p&gt;An emerging literature evaluates the water management benefits of hydroclimatic forecasts with timescales of a few days to several months ahead. These studies rely on existing forecast products, which they compare to one another and to baseline scenarios, such as perfect forecast or usual climate or streamflow conditions. Results compare the different products and baselines, both in terms of forecast skill and in terms of value for water management. Yet, the means to systematically explore the link between forecast skill and value (e.g., in terms of water supply reliability or hydropower production) are hampered by the lack of techniques to generate synthetic forecasts that 1) are realistic in that they present similar statistical properties to existing products, and 2) foster productive two-ways conversations between the analysts and academics who propose new products and those who use them to inform decision-making, so they can determine where to focus further product development efforts.&lt;/p&gt;&lt;p&gt;This work proposes a methodology for generating forecasts from an existing product and existing hydroclimatic records (rainfall, temperature, streamflow&amp;#8230;). It perfects and extends a recent synthetic forecast generation technique that deterministically generates a forecast for a point in the future with the desired bias and accuracy, using a linear combination of the quantity to predict with a predictor. It perfects it by proposing a methodology to generate a family of forecasts with desired skill and bias, for several of the most common skill measures, including mean absolute error and (root) mean square error. Generated synthetic forecasts are therefore based on existing products and retain their statistical properties while presenting improved skill. The skill improvement can apply to the whole forecast or only to targeted conditions, e.g., drought or flood conditions, or forecasts during and for a certain period of the year. This opens the doors to systematic exploration of the benefits of marginal forecast improvements. The technique is also extended to ensemble (or probabilistic) forecasts, to allow for generating synthetic ensembles with targeted improvements to the continuous ranked probability skill score (CRPSS).&lt;/p&gt;

scholarly journals Dynamic-Model-Based Seasonal Prediction of Meteorological Drought over the Contiguous United States

2012 ◽  
Vol 13 (2) ◽  
pp. 463-482 ◽  
Author(s):  
Jin-Ho Yoon ◽  
Kingtse Mo ◽  
Eric F. Wood
Keyword(s):  
United States ◽  
Initial Conditions ◽  
Meteorological Drought ◽  
Seasonal Prediction ◽  
Distribution Functions ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Ensemble Forecasts ◽  
Simple Method ◽  
Princeton University

Abstract A simple method was developed to forecast 3- and 6-month standardized precipitation indices (SPIs) for the prediction of meteorological drought over the contiguous United States based on precipitation seasonal forecasts from the NCEP Climate Forecast System (CFS). Before predicting SPI, the precipitation (P) forecasts from the coarse-resolution CFS global model were bias corrected and downscaled to a regional grid of 50 km. The downscaled CFS P forecasts, out to 9 months, were appended to the P analyses to form an extended P dataset. The SPIs were calculated from this new time series. Five downscaling methods were tested: 1) bilinear interpolation; 2) a bias correction and spatial downscaling (BCSD) method based on the probability distribution functions; 3) a conditional probability estimation approach using the mean P ensemble forecasts developed by J. Schaake, 4) a Bayesian approach that bias corrects and downscales P using all ensemble forecast members, as developed by the Princeton University group; and 5) multimethod ensemble as the equally weighted mean of the BCSD, Schaake, and Bayesian forecasts. For initial conditions from April to May, statistical downscaling methods were compared with dynamic downscaling based on the NCEP regional spectral model and forecasts from a high-resolution CFS T382 model. The skill is regionally and seasonally dependent. Overall, the 6-month SPI is skillful out to 3–4 months. For the first 3-month lead times, forecast skill comes from the P analyses prior to the forecast time. After 3 months, the multimethod ensemble has small advantages, but forecast skill may be too low to be useful in practice.

scholarly journals Determination of crop-coefficients and estimation of evapotranspiration of rapeseed using lysimeter and different reference evapotranspiration models

2021 ◽  
Vol 22 (2) ◽  
pp. 172-178
Author(s):  
ABHIJIT SARMA ◽  
KRISHNA BHARADWAJ
Keyword(s):  
Water Management ◽  
Irrigation Water ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Actual Evapotranspiration ◽  
Accurate Estimation ◽  
Efficient Management ◽  
Best Estimate ◽  
Crop Coefficients

Accurate estimation of evapotranspiration of rapeseed is essentially required for irrigation scheduling and water management. The present study was undertaken during 2015-16 and 2017-18 in ICR Farm, Assam Agricultural University, Jorhat to determine the crop coefficients (Kc) and estimate evapotranspiration of rapeseed using lysimeter and eight reference evapotranspiration models viz. Penman-Monteith, Advection-Aridity (Bruitsaert-Strickler), Granger-Gray, Makkink, Blaney-Criddle, Turc (1961), Hargreaves-Somani and Priestly-Tailor models. During 2015-16, the crop coefficients were developed by these models. Actual evapotranspiration was determined by three weighing type lysimeters. During 2017-18, evapotranspiration was estimated by multiplying reference evapotranspiration with Kc derived by different models and compared with actual evapotranspiration estimated by lysimeter during similar growing periods. All the models except Turc (1961) showed less than 10% deviation between actual and estimated ET. The estimated evapotranspiration using Penman-Monteith and Priestly-Tailor reference evapotranspiration recorded the lowest MAE and RMSE. The study revealed that estimated evapotranspiration using Penman-Monteith reference evapotranspiration gave the best estimate of evapotranspiration of rapeseed followed by Priestly-Tailor. The crop coefficients for initial, mid and end stages were 0.83, 1.20 and 0.65, respectively for Penman-Monteith and 0.70, 1.05 and 0.55, respectively for Priestly-Tailor.These results can be used for efficient management of irrigation water for rapeseed.

scholarly journals Forecasting Reference Evapotranspiration Using Retrospective Forecast Analogs in the Southeastern United States

2012 ◽  
Vol 13 (6) ◽  
pp. 1874-1892 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez
Keyword(s):  
United States ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Reference Evapotranspiration ◽  
Southeastern United States ◽  
Department Of Energy ◽  
Maximum Temperature ◽  
Accurate Estimation ◽  
Hydrologic Simulation ◽  
Mean Values

Abstract Accurate estimation of reference evapotranspiration (ET0) is needed for determining agricultural water demand and reservoir losses and driving hydrologic simulation models. This study was conducted to explore the application of the National Centers for Environmental Prediction’s (NCEP’s) Global Forecast System (GFS) retrospective forecast (reforecast) dataset combined with the NCEP–U.S. Department of Energy (DOE) Reanalysis 2 dataset (R2) to forecast ET0 in the southeastern United States using a forecast analog approach. Seven approaches of estimating ET0 using the Penman–Monteith (PM) and Thornthwaite equations were evaluated by substitution of climatological mean values of variables or by bias correcting variables including solar radiation, maximum temperature, and minimum temperature using the R2 dataset. The skill of both terciles and extremes (10th and 90th percentiles) were evaluated. Overall, for the ET0 forecast approaches that combined R2 solar radiation with temperature, relative humidity, and wind speed from GFS, the reforecasts produced higher skill than methods that estimated parameters using GFS the reforecasts data only. The primary increase in skill was due to the use of relative humidity from the GFS reforecasts and long-term climatological mean values of solar radiation from the R2 dataset, indicating its importance in forecasting ET0 in the region. While the five categorical forecasts were skillful, the skill of upper and lower tercile forecasts was greater than that of lower and upper extreme forecasts and middle tercile forecasts. Most of the forecasts were skillful in the first 5 lead days.

scholarly journals The Influence of Assimilated Upstream, Preconvective Dropsonde Observations on Ensemble Forecasts of Convection Initiation during the Mesoscale Predictability Experiment

2017 ◽  
Vol 145 (12) ◽  
pp. 4747-4770 ◽  
Author(s):  
Alexandra M. Keclik ◽  
Clark Evans ◽  
Paul J. Roebber ◽  
Glen S. Romine
Keyword(s):  
Short Range ◽  
Wrf Model ◽  
Skill Score ◽  
Forecast Skill ◽  
Weather Research And Forecasting ◽  
Moist Convection ◽  
Ensemble Forecasts ◽  
High Bias ◽  
Deep Moist Convection ◽  
Convection Initiation

This study tests the hypothesis that assimilating mid- to upper-tropospheric, meso- α- to synoptic-scale observations collected in upstream, preconvective environments is insufficient to improve short-range ensemble convection initiation (CI) forecast skill over the set of cases considered by the 2013 Mesoscale Predictability Experiment (MPEX) because of a limited influence upon the lower-tropospheric phenomena that modulate CI occurrence, timing, and location. The ensemble Kalman filter implementation within the Data Assimilation Research Testbed as coupled to the Advanced Research Weather Research and Forecasting (WRF) Model is used to initialize two nearly identical 30-member ensembles of short-range forecasts for each case: one initial condition set that incorporates MPEX dropsonde observations and one that excludes these observations. All forecasts for a given mission begin at 1500 UTC and are integrated for 15 h on a convection-permitting grid encompassing much of the conterminous United States. Forecast verification is conducted probabilistically using fractions skill score and deterministically using a 2 × 2 contingency table approach at multiple neighborhood sizes and spatiotemporal event-matching thresholds to assess forecast skill and support hypothesis testing. The probabilistic verification represents the first of its kind for numerical CI forecasts. Forecasts without MPEX observations have high fractions skill score and probabilities of detection on the meso- α scale but exhibit a considerable high bias for forecast CI event count. Assimilating MPEX observations has a negligible impact upon forecast skill for the cases considered, independent of verification metric, as the MPEX observations result in only subtle differences primarily manifest in the position and intensity of atmospheric features responsible for focusing and/or triggering deep, moist convection.

Methods to estimate irrigated reference crop evapotranspiration – a review

2012 ◽  
Vol 66 (3) ◽  
pp. 525-535 ◽  
Author(s):  
R. Kumar ◽  
M. K. Jat ◽  
V. Shankar
Keyword(s):  
Water Management ◽  
Management Practices ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Climatic Conditions ◽  
Water Requirement ◽  
Essential Information ◽  
Climatic Regions ◽  
Daily Water

Efficient water management of crops requires accurate irrigation scheduling which, in turn, requires the accurate measurement of crop water requirement. Irrigation is applied to replenish depleted moisture for optimum plant growth. Reference evapotranspiration plays an important role for the determination of water requirements for crops and irrigation scheduling. Various models/approaches varying from empirical to physically base distributed are available for the estimation of reference evapotranspiration. Mathematical models are useful tools to estimate the evapotranspiration and water requirement of crops, which is essential information required to design or choose best water management practices. In this paper the most commonly used models/approaches, which are suitable for the estimation of daily water requirement for agricultural crops grown in different agro-climatic regions, are reviewed. Further, an effort has been made to compare the accuracy of various widely used methods under different climatic conditions.

scholarly journals Evaluating convection-permitting ensemble forecasts of precipitation over Southeast Asia 

2021 ◽  
Author(s):  
Samantha Ferrett ◽  
Thomas Frame ◽  
John Methven ◽  
Christopher Holloway ◽  
Stuart Webster ◽  
...  
Keyword(s):  
Southeast Asia ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Skill Score ◽  
The Philippines ◽  
Forecast Skill ◽  
Ensemble Forecasts ◽  
Weather Events ◽  
The Tropics

&lt;p&gt;Forecasting extreme rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realised. This study describes and evaluates recently developed Met Office Unified Model CP ensemble forecasts of varying resolutions over three domains in Southeast Asia, covering Malaysia, Indonesia and the Philippines.&lt;/p&gt;&lt;p&gt;Fractions Skill Score is used to assess the spatial scale-dependence of skill in forecasts of precipitation during October 2018 - March 2019. CP forecasts are skilful for 3-hour precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts but all ensembles have low spread relative to forecast skill. Skill decreases with lead time and is highly dependent on the diurnal cycle over Malaysia and Indonesia. Skill is largest during daytime when precipitation is over land and is constrained by orography, but is lower at night when precipitation is over the ocean. Comparisons of CP ensembles using 2.2, 4.5 and 8.8 km grid spacing and an 8.8km ensemble with parameterised convection are made to examine the role of resolution and convection parameterisation on forecast skill for the three domains.&lt;/p&gt;

scholarly journals Verification of Quantitative Precipitation Reforecasts over the Southeastern United States

2014 ◽  
Vol 29 (5) ◽  
pp. 1199-1207 ◽  
Author(s):  
Martin A. Baxter ◽  
Gary M. Lackmann ◽  
Kelly M. Mahoney ◽  
Thomas E. Workoff ◽  
Thomas M. Hamill
Keyword(s):  
United States ◽  
Lead Time ◽  
Southeastern United States ◽  
Skill Score ◽  
Forecast System ◽  
Time Period ◽  
Score Analysis ◽  
Forecast Lead Time ◽  
Ensemble Mean ◽  
Deterministic Control

Abstract NOAA’s second-generation reforecasts are approximately consistent with the operational version of the 2012 NOAA Global Ensemble Forecast System (GEFS). The reforecasts allow verification to be performed across a multidecadal time period using a static model, in contrast to verifications performed using an ever-evolving operational modeling system. This contribution examines three commonly used verification metrics for reforecasts of precipitation over the southeastern United States: equitable threat score, bias, and ranked probability skill score. Analysis of the verification metrics highlights the variation in the ability of the GEFS to predict precipitation across amount, season, forecast lead time, and location. Beyond day 5.5, there is little useful skill in quantitative precipitation forecasts (QPFs) or probabilistic QPFs. For lighter precipitation thresholds [e.g., 5 and 10 mm (24 h)−1], use of the ensemble mean adds about 10% to the forecast skill over the deterministic control. QPFs have increased in accuracy from 1985 to 2013, likely due to improvements in observations. Results of this investigation are a first step toward using the reforecast database to distinguish weather regimes that the GEFS typically predicts well from those regimes that the GEFS typically predicts poorly.

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