Posted on June 2018
Check out the poster here by Sahoo, S., Russo, T.A., Edwards, P., Bucklew, V. and Liu, Z. on Quantification of nutrient-stress using a handheld spectrophotometer and machine learning models presented at RFG 2018 in Vancouver.
Key points:
Croptix has developed an innovative, low cost, and high-performing spectrophotometer.
Smartphone-based platform offers application in agriculture for crop-health monitoring, including disease detection, water and nutrient stress assessment.
The combined spectrometer technology and machine learning models can be used as an important diagnostic tool for making nutrient management decisions.
This is a rapid and non-destructive way of determining stresses, and would help growers to monitor nutrient status early enough to mitigate it without reducing yields.
Posted on April 20, 2018
Check out talk slides here on Estimation of future groundwater stress in a changing climate using machine learning methods, presented at Big Data and the Earth Sciences: Grand Challenges, UCSD, San Diego, CA, USA.
Abstract:
Warming temperatures, changes in precipitation patterns, and subsequent increasing agricultural water demands are creating stress on groundwater resources in the United States. Many agricultural regions have experienced groundwater decline in the past three decades, which has consequently affected food production and ecosystem health. This study investigates how projected temporal variability in precipitation, temperature, stream discharge and irrigation demand will influence groundwater availability in the United States. Large raw datasets include time series point measurements of groundwater level available at nearly 900,000 wells from the US Geological Survey, and gridded processed climate data and simulated irrigation demand at 5 arcminute spatial resolution. Overall, the groundwater model integrates an artificial neural network (ANN) model with input data preprocessing using single spectrum analysis, mutual information, and genetic algorithms. The model is calibrated using 33 years of climate, streamflow and ocean temperature observations, and simulated crop water demand. Model runs using projected environmental parameters and irrigation demand are used to simulate changes in future groundwater storage. Climate data from two GCMs each running RCP 4.5 and 8.5 are used to generate scenario input parameters; the climate data is used directly in the ANN model, and also in a land surface hydrology model to predict streamflow, and in a crop model to predict irrigation demand.
The models are run in a high performance parallel computing environment to obtain estimates of future groundwater level change for thousands of wells. Based on this combined climate-agriculture-groundwater model, changes in future groundwater storage for several major agricultural regions are projected up to 2049. These results will be useful for identifying the locations of future groundwater stress, which will have implications for sustainable agricultural production, and will help inform management decisions in a rapidly changing and resource-constrained world.
Posted on March 30, 2018
This past Wednesday, Debashish and I have participated in a science outreach event to inspire young minds to get involved in science. We demonstrated some cool projects on sensors and the kids enjoyed it thoroughly. The event was held at Mount Nittany Middle School.
Posted on May 15, 2017
Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003–2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown.
Posted on April 1, 2017
Process-based groundwater models are useful to understand complex aquifer systems and make predictions about their response to hydrological changes. A conceptual model for evaluating responses to environmental changes is presented, considering the hydrogeologic framework, flow processes, aquifer hydraulic properties, boundary conditions, and sources and sinks of the groundwater system. Based on this conceptual model, a quasi-three-dimensional transient groundwater flow model was designed using MODFLOW to simulate the groundwater system of Mahanadi River delta, eastern India. The model was constructed in the context of an upper unconfined aquifer and lower confined aquifer, separated by an aquitard. Hydraulic heads of 13 shallow wells and 11 deep wells were used to calibrate transient groundwater conditions during 1997–2006, followed by validation (2007–2011). The aquifer and aquitard hydraulic properties were obtained by pumping tests and were calibrated along with the rainfall recharge. The statistical and graphical performance indicators suggested a reasonably good simulation of groundwater flow over the study area. Sensitivity analysis revealed that groundwater level is most sensitive to the hydraulic conductivities of both the aquifers, followed by vertical hydraulic conductivity of the confining layer. The calibrated model was then employed to explore groundwater-flow dynamics in response to changes in pumping and recharge conditions. The simulation results indicate that pumping has a substantial effect on the confined aquifer flow regime as compared to the unconfined aquifer. The results and insights from this study have important implications for other regional groundwater modeling studies, especially in multi-layered aquifer systems.
Posted on Dec 9, 2016
Check out the Penn State news here. My research poster on “Estimation of Groundwater Storage Changes in the Ogallala Aquifer, United States” awarded first place in Penn State’s eighth annual Postdoctoral Research Poster Exhibition. This study focused on the evaluation of changes in groundwater storage and showed that groundwater is depleting in major aquifers of the United States, leading to a serious threat to the sustainability of vital groundwater resources.