Research > Water System Dynamics and Resilience Program


To develop an internationally recognized program that enhances our understanding of the complex system dynamics that connect water with energy, natural resources, agriculture, ecosystem integrity, communities, and economic sustainability in the arid region.

Water-Salt-Agriculture System Dynamic Modeling

System dynamics modeling is designed to enable decision making. It starts with a system diagram for the complex non-linear system under investigation. The dynamic behavior of interacting systems may be simulated by mathematically representing the flows from key stocks (reservoirs) in the system diagram and interactions between stocks.

This project aims to better represent the complex interactions for the water-salt-agriculture linked system and enable decision making for enhancing the overall sustainability of irrigated agriculture in the arid region. This modeling will directly aid growers to assess the varying impacts of salt in irrigation water on soil and crops.

Complex linkages that describe the interactions between irrigation water, soils, and crops. A key stock here is water that runs off, seeps in the soil, leaches out of the root-zone, or is consumed by the crops. The other key stock is the salt that is carried by the water and maybe deposited on the soil. Salt deposition changes the water flows and water flows affect the salt deposition. Together, the amount of salt and water applied influences the ability of crops to gainfully use water.

Remote Sensing to Assess Field-Scale Spatial Heterogeneity

Fields are not uniform. The heterogeneity in soil and salt-buildup has an impact on the overall yield and sustainability of farms. However, these heterogeneities are hard to describe for targeted interventions, numerical modeling, and ‘what-if’ type analysis. Aerial remote sensing (using drones) may provide a solution to get around this dilemma. We are developing new methods that use remotely sensed data products (e.g., surface temperatures) in combination with water-salt-agriculture system dynamic modeling to illustrate the spatial heterogeneity.

a) Simplified algorithm to convert observed surface temperature to evapotranspiration (key for predicting crop growth and stress), b) surface temperature observed from aerial remote sensing over a pecan orchard; the aircraft was flying 400ft over the orchard. c) surface temperature converted to evapotranspiration in mm/day.

Using Hydroinformatics for Enabling Actionable Decision Making

Perhaps the most important goals for developing models and collecting long-term environmental data is to enable decision making and educate the stakeholder on the complex dynamics. Often that involves providing lay stakeholders access to mathematical models designed by experts for ‘what-if’ type analysis. This project aims to make models and data available to stakeholders directly for interaction.

Schematic of a web-based environmental decision support system (EDSS) for northern-Virginia that enables stakeholder interactions with complex natural system modeling. The system is based on a framework that uses distributed computing for faster executions.

Representation of daily fluvial loads of total nitrogen in the Potomac River near Washington DC generated by a web-based OCCVIZ engine. The red blocks are days with high loading problematic for the Chesapeake Bay. OCCVIZ is developed and maintained by the research group that allows long-term water resources data management, curation, and visualization.

Deciphering TMDL Modeling

Establishing a total maximum daily load (TMDL) is the primary regulatory mechanism in the United States to restore the designated use for the impaired waterbodies. TMDL may be thought of as a pollution diet that may be allowed to enter the waterbody that will allow the waterbody to maintain its designated use. Quantifying and allocating the load requires mathematical modeling of the waterbody and often the tributary watershed system. There are over 70K reports submitted to the U.S. EPA describing such modeling efforts in the past four decades. We are working on understanding the linkages between various factors that influence modeling for TMDL to improve the TMDL development process.


Showing the observed linkages between TMDL models and impairment (salinity). This is from a web-based tool that allows users to see what models have been used for developing TMDLs for selected impairment. The tool was developed by using natural language processing (NLP) and machine learning techniques that helped to identify relationships form over 70,000 documents submitted to U.S. EPA on TMDL modeling.

Grower Tools

A set of tools for ornamental nursery growers.

  1. Irrigation Volume Calculator may be used to determine how much water grower is applying at each irrigation cycle.
  2. Leaching Fraction based on salinity tool can determine how long the irrigation system needs to run for leaching salts.
  3. Pond Refill/Runoff volume tool will help determine how much irrigation or rainfall is returning to the reservoir.
  4. Slow Sand Filter sizing tool will help determine how large of a system is required to treat water.
  5. Pathogen Disease Risk Model tool can help to determine likely sources of disease and methods of spread in nursery operation.
  6. Pond Volume Calculator tool can help determine the volume of water in a reservoir.
  7. Chlorine Contact Time tool will help determine the adequate contact time for your chlorine-based disinfectants.
  8. Dilution Dosage calculator will help compute the correct dilution of chlorine for a stock tank and other applications.
  9. Coefficient of Uniformity tool can help determine if irrigation is being applied uniformly.
  10. Interception Efficiency tool can help determine how much of the water applied makes it into the top of your container vs falling between containers when using overhead irrigation.


  • Quinn, N., S. Kumar, R. LaPlante, F. Cubas (2019).  Tool for Searching USEPA’s TMDL Reports Repository to Analyze TMDL Modeling State of the Practice. Journal of Hydrologic Engineering24(9).
  • Quinn, N., S. Kumar, and S. Imen (2019). Use of Remote Sensing and GIS in Watershed Analysis and Developing TMDLs. Journal of Hydrologic Engineering, 24(4).
  • Borah D., G. Padmanabhan, and S. Kumar (2019). Total Maximum Daily Load Analysis and Modeling: Assessment and Advancement. Journal of Hydrologic Engineering, 24(11).
  • Fathi, A., S. M. Haeri, M. Mazari, A. Hosseini, S. Kumar, & C. Zhu, (2019). Estimation of rocking capacity of soil-structure systems using a hybrid inverse solver. SN Applied Sciences, 1(7), 703.
  • Ganjegunte, G., J. Clark, M. Parajulee, J. Enciso, S. Kumar (2018). Salinity Management in Pima Cotton Fields Using Sulfur Burner. Agrostems, Geosciences & Environment. 1(1).
  • Kumar, S., G. Moglen, A. N. Godrej, H. Post, and T. J. Grizzard (2018). Trends in water yield under climate change and urbanization in the U.S. Mid-Atlantic region. Journal of Water Resources Planning and Management, 144 (8) pp: 05018009.
  • Kumar, S., T. J. Grizzard, and A. N. Godrej, (2016). Pre-development conditions to assess the impact of growth in an urbanizing watershed in northern Virginia. Journal of Hydrology, 540, 1066–1077.
  • Kumar, S., A. N. Godrej, and T. J. Grizzard (2015). A web-based environmental decision support system for legacy models. Journal of Hydroinformatics, 7, 874-890.
  • Kumar, S., A. N. Godrej, and T. J. Grizzard (2013). Watershed size effects on the applicability of regression-based methods for fluvial loads estimation. Water Resources Research, 49, 7698–7710.
  • Book: Total Maximum Daily Load Analysis and Modeling: Assessment of the Practice