Search NASA⌕ Search

Engineering topics

Shahryar Khalique Ahmad

Publications and source records attributed to Shahryar Khalique Ahmad.

7 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2025

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale of post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of the impacts of fire on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify the impacts of wildfire on runoff generation. We consider the disturbances in the vegetation and soil as the two main factors contributing to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We consider the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration for two case studies. These case studies demonstrate the general applicability of hydrophobicity formulation to serve as a guideline for exploring the range of hydrologic responses post-fire.

Fire Hydrology↗
NASA NTRS2023

Operational Forecasting Inundation Extents using REOF analysis (FIER) over Lower Mekong and its Economic Impact on Agriculture

In the Lower Mekong River Basin floodplains, rice cultivation is highly crucial for regional and global food security. However, prolonged flooding can pose damage to rice cultivation and other socio-economic aspects. Yet, there is no rapid operational inundation forecasting system that can help decision-makers proactively mitigate flood damages. Here, we integrated the so-called Forecasting Inundation Extents using Rotated empirical orthogonal function analysis (FIER) framework with an altimetry-based operational Mekong River level forecasting system and built an operational web application, FIER-Mekong, (https://fier-mekong.streamlit.app/) that generates daily skillful forecasted inundation extents (>70% of critical success index) and depths in about 3 and 30 s, respectively, with up to 18-day lead times. One of its applications, predicting flood-induced rice economic losses, is also presented. Had FIER-Mekong being adopted, we estimated that the rice damages, up to 87 and 53 million US dollars during the 2020 and 2021 harvest time, respectively, could have been avoided.

Chi-Hung Chang↗
NASA NTRS2023

Understanding Volume Estimation Uncertainty of Lakes and Wetlands Using Satellites and Citizen Science

We studied variations in the volume of water stored in small lakes and wetlands using satellite remote sensing and lake water height data contributed by citizen scientists. A total of 94 water bodies across the globe were studied using satellite data in the optical and microwave wavelengths from Landsat 8, Sentinel-1, and Sentinel-2. The uncertainty in volume estimation as a function of geography and geophysical factors, such as cloud cover, precipitation, and water surface temperature, was studied. The key finding that emerged from this global study is that uncertainty is highest in regions with a distinct precipitation season, such as in the monsoon dominated South Asia or the Pacific Northwestern region of the USA. This uncertainty is further compounded when small lakes and wetlands are seasonal with alternating land use as a water body and agricultural land, such as the wetlands of Northeastern Bangladesh. On an average, 45% of studied lakes could be estimated of their volume change with a statistical significant uncertainty that is less than the expected volume in South Asia. In North America, this statistically significant uncertainty in volume estimation was found to be around 50% in lakes eastward of the 108th meridian with lowest uncertainty found in lakes along the East coast of the USA. The article provides a baseline for understanding the current state of the art in estimating volumetric change of lakes and wetlands using citizen science in anticipation of the recently launched Surface Water and Ocean Topography Mission.

Shahzaib Khan↗
NASA NTRS2023

Interconnected Hydrologic Extreme Drivers and Impacts Depicted By Remote Sensing Data Assimilation

In a changing climate, the likelihood of hydrologic extremes has been increasing as climate change can impact both means and extremes4 of hydrologic cycle processes, potentially resulting in an increased frequency of floods in some regions and decreases in others. In a warming world, the physical processes that affect hydrologic response, such as rain-on snow runoff events, are also changing, such that the seasonality of streamflow has been shifting. The geography of rain-on-snow runoff events is predicted to move from low to high elevations. In addition to floods, there is also potential for an increase in dry extremes in a warming world with increased drought frequency and occurrences in many parts of the world. The increased frequency of drought and heatwave events is expected to have consequences such as escalating crop failures in future projection scenarios1. Thus, the consensus of literature shows that climate change is increasing the magnitude and frequency of extreme hydrologic events, and the human influence in many of these events is substantial.

Timothy M. Lahmers↗
NASA NTRS2021

Exploring Sentinel-1 and Sentinel-2 diversity for Flood inundation mapping using deep learning

Identification of flood water extent from satellite images has historically relied on either synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to cloud free conditions, whereas SAR imagery is plagued by noise-like speckle. Prior studies that use combinations of MS and SAR data to overcome individual limitations of these sensors have not fully examined sensitivity of flood mapping performance to different combinations of SAR and MS derived spectral indices or band transformations in color space. This study explores the use of diverse bands of Sentinel 2 (S2) through well-established water indices and Sentinel 1 (S1) derived SAR imagery along with their combinations to assess their capability for generating accurate flood inundation maps. The robustness in performance of S-1 and S-2 band combinations was evaluated using 446 hand labeled flood inundation images spanning across 11 flood events from Sen1Floods11 dataset which are highly diverse in terms of land cover as well as location. A modified K-fold cross validation approach is used to evaluate the performance of 32 combinations of S1 and S2 bands using a fully connected deep convolutional neural network known as U-Net. Our results indicated that usage of elevation information has improved the capability of S1 imagery to produce more accurate flood inundation maps. Compared to a median F1 score of 0.62 when using only S1 bands, the combined use of S1 and elevation information led to an improved median F1 score of 0.73. Water extraction indices based on S2 bands have a statistically significant superior performance in comparison to S1. Among all the band combinations, HSV (Hue, Saturation, Value) transformation of S2 bands provides a median F1 score of 0.9, outperforming the commonly used water spectral indices owing to HSV’s transformation’s superior contrast distinguishing abilities. Additionally, U-Net algorithm was able to learn the relationship between raw S2 based water extraction indices and their corresponding raw S2 bands, but not of HSV owing to relatively complex computation involved in the latter. Results of the paper establishes important benchmarks for the extension of S1 and S2 data-based flood inundation mapping efforts over large spatial extents.

Goutam Konapala↗
NASA NTRSDate not supplied

An Observation-Driven Framework for Modeling Post-Fire Hydrologic Response: Evaluation for Two Central California Case Studies

In a warming climate, wildfires are becoming increasingly common, especially in semi-arid environments. Wildfires can disrupt forest ecosystems and induce changes to the land surface. Collectively, these impacts can alter the hydrologic response of a catchment following a fire, resulting in increased potential for surface runoff, reduced evapotranspiration, and, ultimately, a higher risk for flash flooding and mass wasting. The timescale for post-fire recovery of hydrological processes to return to pre-fire conditions is not well established due to the lack of ground measurements. Accurate characterization of fire impacts on hydrologic response is also challenging to simulate, given the complex interplay of various processes. Here, we present a generalized framework to quantify wildfire impacts on runoff generation. We consider the vegetation and soil disturbances as the primary contributors to post-fire floods. Using an ensemble modeling structure to account for parameter uncertainty and consider model sensitivity, remotely sensed leaf area index (LAI) is assimilated into a land surface model (LSM) to simulate vegetation disturbance, and the maximum land surface saturation LSM parameter is decreased to parameterize the soil disturbance following observed fires. We evaluated the impacts of fire-induced changes to LAI and soil saturation on hydrologic states like runoff and evapotranspiration. Analysis of two events in central California shows that 1) the LAI assimilation has a greater impact on water balance; and 2) the soil scheme captures a range of outcomes, with substantial impacts on surface runoff for some ensemble members, which could affect flood potential.

Timothy M Lahmers↗