DRYP 1.0: A parsimonious hydrological model of DRYland Partitioning of the water balance

Dryland regions are characterized by water scarcity and are facing major challenges under climate change. One difficulty is anticipating how rainfall will be partitioned into evaporative losses, groundwater, soil moisture and runoff (the water balance) in the future, which has important implications for water resources and dryland ecosystems. However, in order to effectively estimate the water balance, hydrological models in drylands need to capture the key processes at the appropriate spatiotemporal scales including spatially restricted and temporally brief rainfall, high evaporation rates, transmission losses and focused groundwater recharge. Lack of available data and the high computational costs of explicit representation of ephemeral surface-groundwater interactions restrict the usefulness of most hydrological models in these environments. Therefore, here we have developed a parsimonious hydrological model (DRYP) that incorporates the key processes of water partitioning in dryland regions, and we tested it in the data-rich Walnut Gulch Experimental Watershed against measurements of streamflow, soil moisture and evapotranspiration. Overall, DRYP showed skill in quantifying the main components of the dryland water balance including monthly observations of streamflow (Nash efficiency (NSE) ~0.7), evapotranspiration (NSE > 0.6) and soil moisture (NSE ~0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment, and that < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost through ephemeral channels as transmission losses. However, only ~35 % of the total transmission losses percolate to the groundwater aquifer as focused groundwater recharge, whereas the rest is lost to the atmosphere as riparian evapotranspiration. Overall, DRYP is a modular, versatile and parsimonious Python-based model which can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions

Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds

An important step in flood control planning is identification of flood source areas (FSAs). This study presents a methodology for identifying FSAs. Unit flood response (UFR) approach has been proposed to quantify FSAs at subwatershed and/or cell scale. In this study, a distributed ModClark model linked with Muskingum flow routing was used for hydrological simulations. Furthermore, a fuzzy hybrid clustering method was adopted to identify hydrological homogenous regions (HHRs) resulting in clusters involving the most effective variables in runoff generation as selected through factor analysis (FA). The selected variables along with 50-year rainfall were entered into an artificial neural network (ANN) model optimized via genetic algorithm (GA) to predict flood index (FI) at cell scale. The case studies were two semi-arid watersheds, Tangrah in northeastern Iran and Walnut Gulch Experimental Watershed in Arizona. The results revealed that the predicted values of FI via ANN-GA were slightly different from those derived via UFR in terms of mean squared error (MSE), mean absolute error (MAE), and relative error (RE). Also, the prioritized FSAs via ANN-GA were almost similar to those of UFR. The proposed methodology may be applicable in prioritization of HHRs with respect to flood generation in ungauged semi-arid watersheds.

Enhancing the Structure of the WRF-Hydro Hydrologic Model for Semiarid Environments

In August 2016, the National Weather Service Office of Water Prediction (NWS/OWP) of the National Oceanic and Atmospheric Administration (NOAA) implemented the operational National Water Model (NWM) to simulate and forecast streamflow, soil moisture, and other model states throughout the contiguous United States. Based on the architecture of the WRF-Hydro hydrologic model, the NWM does not currently resolve channel infiltration, an important component of the water balance of the semiarid western United States. Here, we demonstrate the benefit of implementing a conceptual channel infiltration function (from the KINEROS2 semidistributed hydrologic model) into the WRF-Hydro model architecture, configured as NWM v1.1. After calibration, the updated WRF-Hydro model exhibits reduced streamflow errors for the Walnut Gulch Experimental Watershed (WGEW) and the Babocomari River in southeast Arizona. Model calibration was performed using NLDAS-2 atmospheric forcing, available from the NOAA National Centers for Environmental Prediction (NCEP), paired with precipitation forcing from NLDAS-2, NCEP Stage IV, or local gauge precipitation. Including channel infiltration within WRF-Hydro results in a physically realistic hydrologic response in the WGEW, when the model is forced with high-resolution, gauge-based precipitation in lieu of a national product. The value of accounting for channel loss is also demonstrated in the Babocomari basin, where the drainage area is greater and the cumulative effect of channel infiltration is more important. Accounting for channel infiltration loss thus improves the streamflow behavior simulated by the calibrated model and reduces evapotranspiration bias when gauge precipitation is used as forcing. However, calibration also results in increased high soil moisture bias, which is likely due to underlying limitations of the NWM structure and calibration methodology.

A fuzzy hybrid clustering method for identifying hydrologic homogeneous regions

Identification of hydrologic homogeneous regions (HHR) facilitates prioritization of watershed management measures. In this study, a new methodology involving a combination of self-organizing features maps (SOFM) method and fuzzy C-means algorithm (FCM), designated as SOMFCM, is presented to identify HHRs. The case study region is Walnut Gulch Experimental Watershed (WGEW) located in Arizona. The input data consisted of a number of factors that influence runoff generation processes, including ten surface features as well as various rainfall values corresponding to 25, 50, and 100 years return periods. Factor analysis (FA) was applied for the selection of effective surface features along with rainfall value, used in the clustering algorithm. Validation procedure indicated that the best clustering scenario was achieved through merging three layers including TPI (topographic position index), CN (curve number), and P50 (50-year rainfall). The optimum number of clusters turned out to be six while the fuzzification parameter became 1.6. The presented methodology may be proposed as a simple approach for identifying HHRs.

Rainfall simulation experiments in the southwestern USA using the Walnut Gulch Rainfall Simulator

This dataset contains hydrological, erosion, vegetation, ground cover, and other supplementary information from 272 rainfall simulation experiments conducted on 23 semiarid rangeland locations in Arizona and Nevada between 2002 and 2013. On 30 % of the plots, simulations were conducted up to five times during the decade of study. The rainfall was generated using the Walnut Gulch Rainfall Simulator on 2 m by 6 m plots. Simulation sites included brush and grassland areas with various degrees of disturbance by grazing, wildfire, or brush removal. This dataset advances our understanding of basic hydrological and biological processes that drive soil erosion on arid rangelands. It can be used to estimate runoff, infiltration, and erosion rates at a variety of ecological sites in the Southwestern USA. The inclusion of wildfire and brush treatment locations combined with long-term observations makes it important for studying vegetation recovery, ecological transitions, and the effect of management. It is also a valuable resource for erosion model parameterization and validation. The dataset is available from the National Agricultural Library at https://data.nal.usda.gov/search/type/dataset (DOI: https://doi.org/10.15482/USDA.ADC/1358583).