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Anthony R. Buda, Seann M. Reed, Gordon J. Folmar, Casey D. Kennedy, David J. Millar, Peter J. A. Kleinman, Douglas A. Miller, and Patrick J. Drohan

Abstract

Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for predicting rainfall–runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003–17) simulation of quickflow in the 420-km2 Mahantango Creek watershed and in WE-38, a 7.3-km2 headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24–72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017–October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE″ = 0.81) with observations. At the headwater scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE″ = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (>90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture.

Significance Statement

Daily runoff forecasts can alert farmers to rainfall–runoff events that have the potential to wash off recently applied fertilizers and manures. To gauge whether daily runoff forecasts are accurate and reliable, we used runoff monitoring data from a large agricultural watershed and one of its headwater tributaries to evaluate the quality of short-term runoff forecasts (1–3 days ahead) that were generated by a National Weather Service watershed model. Results showed that the accuracy and reliability of daily runoff forecasts generally improved in both watersheds as lead times increased from 1 to 3 days. Study findings highlight the potential for National Weather Service models to provide useful short-term runoff forecasts that can inform operational decision-making in agriculture.

Open access
Ethan D. Gutmann, Joseph. J. Hamman, Martyn P. Clark, Trude Eidhammer, Andrew W. Wood, and Jeffrey R. Arnold

Abstract

Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically post-process output from weather and climate models. The En_semble G_eneralized A_nalog R_egression D_ownscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations, for use in parametric or non-parametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Open access
William A. Turner, Greg Husak, Chris Funk, Dar A. Roberts, and Charles Jones

Abstract

A simple—yet powerful—indicator for monitoring agricultural drought is the Water Requirement Satisfaction Index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for “average conditions,” little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions’ growing seasons, from 1981-2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2-23% in east, west, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. Put in comparison to the Extended WRSI, which creates a single forecast scenario using average inputs that are not co-varying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events, as well as the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook, in a way that is smoothed over in the Extended WRSI. We find the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast, and presents a less-biased, and more accurate, scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid.

Open access
Rajesh R. Shrestha, Yonas B. Dibike, and Barrie R. Bonsal

Abstract

Anthropogenic climate change–induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041–70 and 2071–2100 future periods against the1981–2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.

Significance Statement

The purpose of this study is to evaluate potential changes in seasonal streamflow predictability in relation to snowpack change under future climate. This is highly relevant because snowpack storage provides a means of predicting available freshet water supply, as well as peak flow events in cold regions. We use a machine learning model as an emulator of a hydrologic model in a testbed ensemble prediction system. Our results provide insights on hydroclimatic controls and interactions that affect future streamflow predictability across two river basins in western Canada. We conclude that besides snowpack, predictability depends on a number of other factors (basin/subbasin characteristics, streamflow variables, and future periods), and the loss of snowpack alone is not a sufficient condition for the reduction in streamflow predictability.

Open access
Denis Macharia, Katie Fankhauser, John S. Selker, Jason C. Neff, and Evan A. Thomas

Abstract

Increasingly, satellite-derived rainfall data are used for climate research and action in Africa. In this study, we use 6 years of rain gauge data from 596 stations operated by the Trans-African Hydrometeorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products—CHIRPS, TAMSAT, and GSMaP_wGauge—and one satellite-only rainfall product, GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, southern Africa, and West Africa at daily, pentadal, and monthly time scales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5% (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7%). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29%; wet bias of 26.3 mm), TAMSAT in southern Africa (13%; dry bias of 10.4 mm), and GSMaP in West Africa (23%; wet bias of 19.6 mm). Considerable biases in seasonal rainfall are observed in all subregions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1-km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple time scales relative to CHIRPS and TAMSAT.

Open access
Hanyu Deng, Gong Zhang, Changwei Liu, Renhao Wu, Jianqiao Chen, Zhen Zhang, Murong Qi, Xu Xiang, and Bo Han

Abstract

This paper assesses the water vapor flux performance of three reanalysis datasets (ERA5, JRA55, NCEP-2) on the South China Sea. The radiosonde data were from the South China Sea Scientific Expedition organized by Sun Yat-sen University in the 2019 summer (SCSEX2019). The comparison shows that all reanalyses underestimate the temperature and specific humidity under 500 hPa. As for the wind profile, the most significant difference appeared at 1800 UTC when there was no conventional radiosonde observation around the experiment area. As for the water vapor flux, ERA5 seems to give the best zonal flux but the worst meridional one. A deeper analysis shows that the bias in the wind mainly caused the difference in water vapor flux from ERA5. As for JRA55 and NCEP-2, the humidity and wind field bias coincidentally canceled each other, inducing a much smaller bias, especially in meridional water vapor flux. Therefore, to get a more realistic water vapor flux, a correction in the wind profile was most needed for ERA5. In contrast, the simultaneous improvement on both wind and humidity fields might produce a better water vapor flux for JRA55 and NCEP-2.

Significance Statement

This paper mainly aims to assess three atmospheric reanalyses from the viewpoint of the water vapor flux over the South China Sea during the monsoon period. The observation data contain more than 120 radiosonde profiles. Our work has given an objective comparison among the reanalyses and observations. We also tried to explain the bias in the water vapor flux over the ocean from the reanalyses. The results of our work might help understand the monsoon precipitation given by atmospheric reanalyses or regional climate models and enlighten the development of atmospheric assimilation products.

Open access
Xiaolu Li, Eli Melaas, Carlos M. Carrillo, Toby Ault, Andrew D. Richardson, Peter Lawrence, Mark A. Friedl, Bijan Seyednasrollah, David M. Lawrence, and Adam M. Young

Abstract

Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.

Open access
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Liao-Fan Lin, Ming Hu, David D. Turner, and Siwei He

Abstract

Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer.

Significance Statement

Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions.

Open access
Free access
Matthew B. Switanek and Thomas M. Hamill

Abstract

The water resources of the western United States have enormous agricultural and municipal demands. At the same time, droughts like the one enveloping the West in the summer of 2021 have disrupted supply of this strained and precious resource. Historically, seasonal forecasts of cool-season (November–March) precipitation from dynamical models such as North American Multi-Model Ensemble (NMME) and the Seasonal Forecasting System 5 (SEAS5) from the European Centre for Medium-Range Weather Forecasts have lacked sufficient skill to aid in Western stakeholders’ and water managers’ decision-making. Here, we propose a new empirical–statistical framework to improve cool-season precipitation forecasts across the contiguous United States (CONUS). This newly developed framework is called the Statistical Climate Ensemble Forecast (SCEF) model. The SCEF framework applies a principal component regression model to predictors and predictands that have undergone dimensionality reduction, where the predictors are large-scale meteorological variables that have been prefiltered in space. The forecasts of the SCEF model captures 12.0% of the total CONUS-wide standardized observed variance over the period 1982/83–2019/20, whereas NMME captures 7.2%. Over the more recent period 2000/01–2019/20, the SCEF, NMME, and SEAS5 models respectively capture 11.8%, 4.0%, and 4.1% of the total CONUS-wide standardized observed variance. An important finding is that much of the improved skill in the SCEF, with respect to models such as NMME and SEAS5, can be attributed to better forecasts across most of the western United States.

Open access