Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist, even after the cessation of meteorological droughts, due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorologicaland agriculture-related drought indicators using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2, and Palmer Z-index to assess intraseasonal droughts, and between SPI-6, SPEI-6, and PDSI for full-season evaluations. SPI was seen to correlate well with selected agriculture-related drought indicators, but did not explain all the variability noted in them. The correlation between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for in order to devise better drought-preparedness planning.
Predicting streamflow in intermittent rivers and ephemeral streams (IRES), particularly those in climate hotspots such as the headwaters of the Colorado River in Texas, is a necessity for all planning and management endeavors associated with these ubiquitous and valuable surface water resources. In this study, the performance of three deep learning algorithms, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Self-Attention LSTM models, were evaluated and compared against a baseline Extreme Learning Machine (ELM) model for monthly streamflow prediction in the headwaters of the Texas Colorado River. The predictive performance of the models was assessed over the entire range of flow as well as for capturing the extreme hydrologic events (no-flow events and extreme floods) using a suite of model evaluation metrics. According to the results, the deep learning algorithms, especially the LSTM-based models, outperformed the ELM with respect to all evaluation metrics and offered overall higher accuracy and better stability (more robustness against overfitting). Unlike its deep learning counterparts, the simpler ELM model struggled to capture important components of the IRES flow time-series and failed to offer accurate estimates of the hydrologic extremes. The LSTM model (K.G.E. > 0.7, R2 > 0.75, and r > 0.85), with better evaluation metrics than the ELM and CNN algorithm, and competitive performance to the SA–LSTM model, was identified as an appropriate, effective, and parsimonious streamflow prediction tool for the headwaters of the Colorado River in Texas.
19Meteorological drought indicators are commonly used for agricultural drought contingency planning in 20Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by 21 meteorological droughts, the timing and duration of agricultural droughts need not coincide with the 22 onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can 23 persist even after the cessation of meteorological droughts due to delayed hydrologic processes. 24Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An 25 evaluation framework was developed to compare meteorological and agricultural droughts using a suite 26 of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand 27 the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-28 2, SPEI-2 and Palmer Z-index to assess intra-seasonal droughts and between SPI-6, SPEI-6 and PDSI for full-29 season evaluations. SPI was seen to correlate well with selected agricultural drought indicators but did 30 not explain all the variability noted in agricultural droughts. The relationships between meteorological 31 and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited 32to predict non-agricultural drought states more so than agricultural drought states. Differences between 33 agricultural and meteorological droughts must be accounted for better drought-preparedness planning. 34 blue water) causing hydrologic droughts. The relationships between meteorological, agricultural and 1 hydrological droughts are not always straightforward. The onset and cessation of agricultural and 2 hydrological droughts do not typically coincide with meteorological droughts as the former are affected 3 by other factors (e.g., soil and watershed characteristics) that control the rate of water movement and 4 storage in soil, surface water, and groundwater compartments [6]. 5Understanding the relationship between meteorological and agricultural droughts is important for proper 6 drought contingency planning in rural areas of Ethiopia. As most of the agriculture is rainfed, a strong 7 correlation between meteorological and agricultural drought is to be expected. However, meteorological 8 and agricultural droughts need not be coincident nor the relationships between these two types of 9 drought be perfect or even strong. The soil moisture at any time can be affected by precipitation in 10 previous months or seasons and is also affected by other factors including but not limited to soil type and 11 atmospheric temperature. In Ethiopia, while many farmers grow crops during the Meher growing season 12 that coincides with the longer Kerimt (June -October) rainy season, the shorter Belg (February -May) 13 rains often provides the soil moisture necessary for tillage and planting activities and also improve 14 pastures for livestock [7]. Therefore, lagged re...
Flooding in urban streams can occur suddenly and cause major environmental and infrastructure destruction. Due to the high amounts of impervious surfaces in urban watersheds, runoff from precipitation events can cause a rapid increase in stream water levels, leading to flooding. With increasing urbanization, it is critical to understand how urban stream channels will respond to precipitation events to prevent catastrophic flooding. This study uses the Prophet time series machine learning algorithm to forecast hourly changes in water level in an urban stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine learning was highly accurate in predicting changes in water level for five locations along the stream with R2 values greater than 0.9. Yet, it can be challenging to understand how these water level prediction values will translate to water volume in the stream channel. Therefore, this study collected terrestrial Light Detection and Ranging (LiDAR) data for Hunnicutt Creek to model these areas in 3D to illustrate how the predicted changes in water levels correspond to changes in water levels in the stream channel. The predicted water levels were also used to calculate upstream flood volumes to provide further context for how small changes in the water level correspond to changes in the stream channel. Overall, the methodology determined that the areas of Hunnicutt Creek with more urban impacts experience larger rises in stream levels and greater volumes of upstream water during storm events. Together, this innovative methodology combining machine learning, terrestrial LiDAR, 3D modeling, and volume calculations provides new techniques to understand flood-prone areas in urban stream environments.
Abstract. Intermittent Rivers and Ephemeral Streams (IRES) comprise 60 % of all streams in the US and about 50 % of the streams worldwide. Furthermore, climate-driven changes are expected to force a shift towards intermittency in currently perennial streams. Most modeling studies have treated intermittent streamflows as a continuum. However, it is better to envision flow data of IRES as a “mixture-type”, comprised of both flow and no-flow regimes. It is therefore hypothesized that data-driven models with both classification and regression cells can improve the streamflow forecasting abilities in these streams. Deep and wide Artificial Neural Networks (ANNs) comprising of classification and regression cells were developed here by stacking them in series and parallel configurations. These deep and wide network architectures were compared against the commonly used single hidden layer ANNs (shallow), as a baseline, for modeling IRES flow series under the continuum assumption. New metrics focused on no-flow persistence and transitions between flow and no-flow states were formulated using contingency tables and Markov chain analysis. Nine IRES across the state of Texas, US, were used as a wide range of testbeds with different hydro-climatic characteristics. Model overfitting and the curse-of-dimensionality were reduced using extreme learning machines (ELM), and balancing training data using the synthetic minority oversampling technique (SMOTE), greedy learning and Least Absolute Shrinkage and Selection Operator (LASSO). The addition of classifier cells greatly improved the ability to distinguish between no-flow and flow states, in turn, improving the ability to capture no-flow persistence (dryness) and transitions to and from flow states (dryness initiation and cessation). The wide network topology provided better results when the focus was on capturing low flows and the deep topology did well in capturing extreme flows (zero and > 75th percentile).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.