Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river–reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily Nash–Sutcliffe Efficiency (NSE) of 0.52 and a percent bias of −1.1%. Using decision tables to simulate management in land, river, and reservoir models was shown to have several advantages over current approaches, including: (1) mature technology with considerable literature and applications; (2) ability to accurately represent complex, real world decision-making; (3) code that is efficient, modular, and easy to maintain; and (4) tables that are easy to maintain, support, and modify.

The Soil and Water Assessment Tool (SWAT) model is commonly used to predict the impacts of agricultural practices on water quality and quantity. Although widely applied, the data framework used to drive SWAT in the United States (U.S.) is fragmented and inconsistent, varying by user and model interface. This research describes the development of the National Agroecosystems Model (NAM), which provides a unified field to national scale modeling computational framework for research and decision support by using the latest SWAT platform, dubbed SWAT+. NAM has sufficient detail to capture field‐level processes and management actions and spans the full extent of the contiguous U.S. NAM contains 7 million computational units, 4 million of which represent specific cultivated fields. It contains 3 million individually identifiable stream segments and more than 5,000 reservoirs. NAM is intended to serve as a reasonable base framework to be refined for specific applications. This work describes the individual data sources, assumptions, and the processing steps for their inclusion. NAM is constructed with 2,121 individual SWAT+ models which can be executed in a parallel hierarchical structure to dramatically improve runtime. This framework was tested in a case study of the Little River Watershed, Tifton, GA. NAM is developed using only publicly available data sources such that subsets of it can be shared to support research with other government agencies, universities, and others in the public domain.

In recent years, large-scale watershed modeling has been implemented broadly in the field of water resources planning and management. Complex hydrological, sediment, and nutrient processes can be simulated by sophisticated watershed simulation models for important issues such as water resources allocation, sediment transport, and pollution control. Among commonly adopted models, the Soil and Water Assessment Tool (SWAT) has been demonstrated to provide superior performance with a large amount of referencing databases. However, it is cumbersome to perform tedious initialization steps such as preparing inputs and developing a model with each changing targeted study area. In this study, the Hydrologic and Water Quality System (HAWQS) is introduced to serve as a national-scale Decision Support System (DSS) to conduct challenging watershed modeling tasks. HAWQS is a web-based DSS developed and maintained by Texas A & M University, and supported by the U.S. Environmental Protection Agency. Three different spatial resolutions of Hydrologic Unit Code (HUC8, HUC10, and HUC12) and three temporal scales (time steps in daily/monthly/annual) are available as alternatives for general users. In addition, users can specify preferred values of model parameters instead of using the pre-defined sets. With the aid of HAWQS, users can generate a preliminarily calibrated SWAT project within a few minutes by only providing the ending HUC number of the targeted watershed and the simulation period. In the case study, HAWQS was implemented on the Illinois River Basin, USA, with graphical demonstrations and associated analytical results. Scientists and/or decision-makers can take advantage of the HAWQS framework while conducting relevant topics or policies in the future.

For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users; (ii) compiled and integrated directly with SWAT+ source code as a single executable; (iii) supported by the SWAT developer group; and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.