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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Response of Aquatic Invertebrates to Ecological Rehabilitation of Southeastern USA Depressional Wetlands

We assessed aquatic invertebrate response to ecological rehabilitation treatment in 20 depression wetlands on the Savannah River Site, South Carolina, USA. All wetlands had been ditched for 50+ years. Sixteen of the 20 wetlands received rehabilitation treatment, and four wetlands remained untreated as a control group. Treatment included logging of all trees, plugging drainage ditches, and planting wetland trees and grasses. Hydroperiods were consequently extended in most of the treatment wetlands. As part a larger study, we sampled macroinvertebrates and microcrustaceans during the pre-habilitation (1998–2000) and rehabilitation (2001–2003) phases. Our study spanned 2 years of high rainfall (1998 and 2003) and 4 years of low rainfall (1999–2002). Samples were collected bimonthly from any wetlands holding water. Macroinvertebrate assemblages in treatment wetlands in 2003 had changed from previous years (1998–2002) and compared to control wetlands (1998–2003), with abundances of Baetidae, Coenagrionidae, Dytiscidae, Chironomidae, and Chaoboridae driving variation. For microcrustaceans (Copepoda and Branchiopoda, including Cladocera, Anostraca and Laevicaudata), assemblage composition and species richness responded mainly to hydrologic conditions. Rehabilitation efforts in these wetlands induced diverse and abundant invertebrate communities to develop, but some responses appeared opportunistic; several taxa that benefitted were not typical residents of depressional wetlands in the region.

Microcrustaceans↗
DOE OSTI2017

Evaluation of a multi-scale WRF-CAM5 simulation during the 2010 East Asian Summer Monsoon

The Weather Research and Forecasting model with Chemistry (WRF-Chem) with the physics package of the Community Atmosphere Model Version 5 (CAM5) has been applied at multiple scales over Eastern China (EC) and the Yangtze River Delta (YRD) to evaluate how increased horizontal resolution with physics designed for a coarser resolution climate model impacts aerosols and clouds, and the resulting precipitation characteristics and performance during the 2010 East Asian Summer Monsoon (EASM). Despite large underpredictions in surface aerosol concentrations and aerosol optical depth, there is good spatial agreement with surface observations of chemical predictions, and increasing spatial resolution tends to improve performance. Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD. Increasing model horizontal resolution tends to reduce model bias and error for precipitation predictions. The surface and column aerosol loading is maximized between about 32N and 42N in early to mid-May during the 2010 EASM, and then shifts north while decreasing in magnitude during July and August. Changing model resolution moderately changes the spatiotemporal relationships between aerosols, cloud properties, and precipitation during the EASM, thus demonstrating the importance of model grid resolution in simulating EASM circulation and rainfall patterns over EC and the YRD. Results from this work demonstrate the capability and limitations in the aerosol, cloud, and precipitation representation of WRF-CAM5 for regional-scale applications down to relatively fine horizontal resolutions. Further WRF-CAM5 model development and application in this area is needed.

Campbell, Patrick↗
DOE OSTI2017

Threatened southern African soils: A need for appropriate ecotoxicological risk assessment

In southern Africa arable soils are limited due to low rainfall and are threatened by anthropogenic activities like agriculture and mining making it susceptible to degradation. The aim of this study is to review the existing information available with regards to soil contamination and its possible threats towards biodiversity and quality of southern African soils. Some of the issues being addressed in this paper include the focus areas of ecotoxicological research in southern African countries, levels of contaminants in soils, the impacts of climate on soil animals and the representativity of standardised test species. In order to address this, we report on a literature search, which was done to determine the main focus areas of soil ecotoxicological research, highlighting strengths and research needs in comparison to approaches elsewhere in the world. Further, to address if the risk assessment approaches of Europe and the USA are valid for southern African environmental conditions; this in the light of differences in temperature, rainfall and fauna. It is concluded that risk assessment procedures for Europe and the USA were based on non-southern African conditions making it necessary to rethink risk assessment studies; although limited, in southern Africa. We recommend future research that has to be undertaken to address these issues. This research should include investigating species sensitivities in responses to contamination and including insects likes ants and termites in ecological risk assessment studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia

Study region: Norfolk, Virginia, United States Study focus: Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. New hydrologic insights for the region: The hybrid CNN-LSTM model was trained using the physics-based hydrodynamic model simulations obtained from the Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. The reduced computational time from four to six hours using TUFLOW to 3.2 min per event using CNN-LSTM enables rapid flood inundation mapping and early warning applications. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5-m spatial resolution and 15-min temporal resolution and a one-hour-ahead prediction horizon. While challenges remain in terms of transferability to new regions and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.

Coastal urban flooding↗
DOE OSTI2016

Surface water areas significantly impacted 2014 dengue outbreaks in Guangzhou, China

Dengue transmission in urban areas is strongly influenced by a range of biological and environmental factors, yet the key drivers still need further exploration. To better understand mechanisms of environment–mosquito–urban dengue transmission, we propose an empirical model parameterized and cross-validated from a unique dataset including viral gene sequences, vector dynamics and human dengue cases in Guangzhou, China, together with a 36-year urban environmental change maps investigated by spatiotemporal satellite image fusion. The dengue epidemics in Guangzhou are highly episodic and were not associated with annual rainfall over time. Our results indicate that urban environmental changes, especially variations in surface area covered by water in urban areas, can substantially alter the virus population and dengue transmission. The recent severe dengue outbreaks in Guangzhou may be due to the surge in an artificial lake construction, which could increase infection force between vector (mainly Aedes albopictus) and host when urban water area significantly increased. Impacts of urban environmental change on dengue dynamics may not have been thoroughly investigated in the past studies and more work needs to be done to better understand the consequences of urbanization processes in our changing world. - Highlights: • Urban dengue outbreak is associated with water area in Guangzhou, 1978–2014. • Surface water area can alter population size of dengue virus in urban area. • Urban dengue outbreak is not associated with annual rainfall in Guangzhou. • Spatiotemporal satellite image fusion can investigate urban environmental change. • Urban environmental change could induce virus, vector, and dengue epidemic change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The G2 erosion model: An algorithm for month-time step assessments

Highlights: • G2 is a soil erosion model for developing monthly erosion maps at regional scale. • G2 enhances the spatio-temporal variability of cover management and erosivity factors. • The V-factor models the impact of vegetation cover, land use and imperviousness layer. • G2 uses data from ESDAC, LUCAS, COPERNICUS, CORINE, SENTINEL and EU-DEM databases. • G2 has been applied in Strymonas Catchment, Crete, Cyprus and Albania catchments. A detailed description of the G2 erosion model is presented, in order to support potential users. G2 is a complete, quantitative algorithm for mapping soil loss and sediment yield rates on month-time intervals. G2 has been designed to run in a GIS environment, taking input from geodatabases available by European or other international institutions. G2 adopts fundamental equations from the Revised Universal Soil Loss Equation (RUSLE) and the Erosion Potential Method (EPM), especially for rainfall erosivity, soil erodibility, and sediment delivery ratio. However, it has developed its own equations and matrices for the vegetation cover and management factor and the effect of landscape alterations on erosion. Provision of month-time step assessments is expected to improve understanding of erosion processes, especially in relation to land uses and climate change. In parallel, G2 has full potential to decision-making support with standardised maps on a regular basis. Geospatial layers of rainfall erosivity, soil erodibility, and terrain influence, recently developed by the Joint Research Centre (JRC) on a European or global scale, will further facilitate applications of G2.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km 2 . The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Unraveling the 2021 Central Tennessee flood event using a hierarchical multi-model inundation modeling framework

Flood prediction systems need hierarchical atmospheric, hydrologic, and hydraulic models to predict rainfall, runoff, streamflow, and floodplain inundation. The accuracy of such systems depends on the error propagation through the modeling chain, sensitivity to input data, and choice of models. In this study, we used multiple precipitation forcings (hindcast and forecast) to drive hydrologic and hydrodynamic models to analyze the impacts of various drivers on the estimates of flood inundation depth and extent. We implement this framework to unravel the August 2021 extreme flooding event that occurred in Central Tennessee, USA. We used two radar-based quantitative precipitation estimates (STAGE4 and MRMS) as well as quantitative precipitation forecasts (QPF) from the National Weather Service Weather Prediction Center (WPC) to drive a series of models in the hierarchical framework, including the Variable Infiltration Capacity (VIC) land surface model, the Routing Application for Parallel Computation of Discharge (RAPID) river routing model, and the AutoRoute and TRITON inundation models. An evaluation with observed high-water marks demonstrates that the framework can reasonably simulate flood inundation. Despite the complex error propagation mechanism of the modeling chain, we show that inundation estimates are most sensitive to rainfall estimates. Most notably, QPF significantly underestimates flood magnitudes and inundations leading to unanticipated severe flooding for all stakeholders involved in the event. Finally, we discuss the implications of the hydrodynamic modeling framework for real-time flood forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City

In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common uncertainty quantification techniques and the effective integration of relevant, multi-modal features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Strontium isotope investigation of ungulate movement patterns on the Pleistocene Paleo-Agulhas Plain of the Greater Cape Floristic Region, South Africa

Middle Stone Age sites located within the Greater Cape Floristic Region on the South African southern coast have material culture with early evidence for key modern human behaviors such as projectile weaponry, large animal hunting, and symbolic behavior. In order to interpret how and why these changes evolved, it is necessary to understand their ecological context as it has direct relevance to foraging behavior. During periods of lowered sea level, a largely flat and vast expanse of land existed south of the modern coastline, but it is now submerged by higher sea levels. This exposed area, the Paleo-Agulhas Plain, likely created an ecological context unlike anything in the region today, as evidenced by fossil assemblages dominated by migratory ungulates. One hypothesis is that the Paleo-Agulhas Plain supported a migration ecosystem of large grazers driven by summer rainfall, producing palatable forage during summer in the east, and winter rainfall, producing palatable forage during winter in the west. Furthermore, ungulates may have been moving from the coastal plain in the south to the interior north of the Cape Fold Mountains, as observed for elephants in historic times.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An isotopic test of the seasonal migration hypothesis for large grazing ungulates inhabiting the Palaeo-Agulhas Plain

The Greater Cape Floristic Region of South Africa was critical to the evolution of early modern humans ( Homo sapiens ) during the Pleistocene. The now submerged continental shelf formed its own ecosystem, the Palaeo-Agulhas Plain (PAP), where early humans lived and foraged. Grazing animals living on the plain might have migrated east and west tracking seasonally varying rains—a hypothesis tested here by examining δ 13 C and δ 18 O of serially-drilled teeth from seven ungulate genera: Alcelaphus , Connochaetes , Antidorcus , Redunca , Damaliscus , and Hippotragus . Modern observations and paleoecological reconstructions indicate that summer rainfall areas to the east have more C 4 grasses while the winter rainfall areas to the west have more C 3 grasses, and that summer and winter rains differ in δ 18 O. Thus, we analyze δ 13 C and δ 18 O preserved in herbivore teeth from the site of PP30 (a hyena den dating to ~151 ka) to infer diet and water source throughout tooth formation. On a generic level, we report none of the samples exhibit δ 13 C or δ 18 O values that differ significantly from a taxon that likely foraged locally through the year (Southern reedbuck, Redunca arundium ). Overall, results indicate that the PAP could support herbivore populations year-round without substantial migration, providing an ecosystem ideal for human inhabitants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates is largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields (> 250 m). Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000-2020 across a 12 state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015-2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forests model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008-2014. We also evaluated the full 2000-2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R 2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R 2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000-2020, each standard deviation increase in rainfall delayed planting by ~ 2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Susceptibility of new soil organic carbon to mineralization during dry-wet cycling in soils from contrasting ends of a precipitation gradient

The persistence of soil organic carbon (SOC) is influenced by soil physicochemical properties, organic matter quality, and climatic conditions that govern its vulnerability to microbial activity. We compared the susceptibility of newly formed SOC to mineralization in two soils (Andosols) that developed under contrasting precipitation regimes. Soil from the high rainfall region (‘high rain ’) had higher SOC and lower iron concentrations than soils exposed to less rainfall (‘low rain ’). We amended soils with 13 C-labeled carbohydrates and measured the amount of substrate-derived SO 13 C mineralized when exposed to dry-wet cycling following months-long incubations. We hypothesized that susceptibility would differ due to iron content and mineralogy, initial SOC, substrate solubility (cellulose versus glucose amendment), and microbial substrate use efficiency (SUE). We found that SO 13 C was less susceptible to dry-wet cycling when more 13 C was assimilated into microbial biomass and co-localized with mineral surfaces than when co-localized with existing organo-mineral surfaces, according to microscale NanoSIMS imaging. Considerably less SO 13 C was susceptible to mineralization in the ferrihydrite-rich (low SOC) low rain soil than the leached (high SOC) high rain soil when C was added as either glucose (7.3-fold less C mineralized) or cellulose (15.2-fold less). Additionally, the SUE of glucose was comparable to cellulose in low rain soil where SO 13 C was less water soluble and coprecipitated with ferrihydrite, and used half as efficiently as cellulose in high rain soil. Our results show that the susceptibility of newly formed SOC to mineralization is modified by the effects of bioavailability on microbial metabolism and the availability of mineral surfaces for forming new organo-mineral complexes.

21 NanoSIMS↗
DOE OSTI2022

Active populations and growth of soil microorganisms are framed by mean annual precipitation in three California annual grasslands

Climate influences soil microbial composition and function, but the relative importance of a site's historic climate versus its more immediate environmental conditions is unclear. Using quantitative stable isotope probing (qSIP), we characterized actively growing soil microbial communities and soil properties in three California annual grasslands that span a rainfall gradient and have developed on similar parent material. The soils were assayed in the wet winter season, when environmental conditions are most similar across sites. Since growing populations might be expected to be most responsive to contemporary environmental conditions, we hypothesized that the structure of growing microbial communities would be more similar across the gradient than that of total communities (i.e., including non-growing populations). In addition, we hypothesized that population growth rates would be slowest in the driest site, reflecting a legacy effect of low soil moisture on microbial growth. Soils along the rainfall gradient differed in pH, texture, and cation exchange capacity, but not in total C, C:N or dominant minerals. The radiocarbon (14C) age of soil C (reflecting turnover time) increased with mean annual precipitation but soil respiration was uniformly modern, reflecting microbial reliance on recent C inputs across the sites. The structure of both total and growing microbial communities differed across sites. Across major microbial phyla, including the Actinobacteria, Acidobacteria, Bacteroidetes, Gemmatimonadetes and Proteobacteria, bacterial growth rates were consistently lower in the site with the lowest mean annual precipitation. Taxa that were growing at the dry site alone grew more slowly than taxa that grew at multiple sites. These results reflect the influence of climate history and point to the role of environmental filtering at the driest site in shaping its slower growing microbial community, possibly reflecting adaptation to repeated exposure to water stress. Lastly, across taxa, the growth rate of a taxon at one site was correlated with its growth rate in the other sites. Furthermore, this growth rate coherence is likely a consequence of genetically determined physiological traits and is consistent with the idea that evolutionary history constrains growth rate.

Environmental filtering↗
DOE OSTI2014

Sources and transport of nitrogen in arid urban watersheds

Urban watersheds are often sources of nitrogen (N) to downstream systems, contributing to poor water quality. However, it is unknown which components (e.g., land cover and stormwater infrastructure type) of urban watersheds contribute to N export and which may be sites of retention. In this study we investigated which watershed characteristics control N sourcing, biogeochemical processing of nitrate (NO3–) during storms, and the amount of rainfall N that is retained within urban watersheds. We used triple isotopes of NO3– (δ15N, δ18O, and Δ17O) to identify sources and transformations of NO3– during storms from 10 nested arid urban watersheds that varied in stormwater infrastructure type and drainage area. Stormwater infrastructure and land cover—retention basins, pipes, and grass cover—dictated the sourcing of NO3– in runoff. Urban watersheds can be strong sinks or sources of N to stormwater depending on the proportion of rainfall that leaves the watershed as runoff, but we found no evidence that denitrification occurred during storms. Our results suggest that watershed characteristics control the sources and transport of inorganic N in urban stormwater but that retention of inorganic N at the timescale of individual runoff events is controlled by hydrologic, rather than biogeochemical, mechanisms.

arid-urban watersheds↗
DOE OSTI2018

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. We report our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗