HydroDS: Data Services in Support of Physically Based, Distributed Hydrological Models

Physically based distributed hydrologic models require geospatial and time-series data that take considerable time and effort in processing them into model inputs. Tools that automate and speed up input processing facilitate the application of these models. In this study, we developed a set of web-based data services called HydroDS to provide hydrologic data processing ‘software as a service.’ HydroDS provides functions for processing watershed, terrain, canopy, climate, and soil data. The services are accessed through a Python client library that facilitates developing simple but effective data processing workflows with Python. Evaluations of HydroDS by setting up the Utah Energy Balance and TOPNET models for multiple headwater watersheds in the Colorado River basin show that HydroDS reduces the input preparation time compared to manual processing. It also removes the requirements for software installation and maintenance by the user, and the Python workflows enhance reproducibility of hydrologic data processing and tracking of provenance

Physically Based Modeling of the Impacts of Climate Change on Streamflow Regime

Understanding the implications of climate change on streamflow regime is complex as changes in climate vary over space and time. However, a better understanding of the impact of climate change is required for identifying how stream ecosystems vulnerable to these changes, and ultimately to guide the development of robust strategies for reducing risk in the face of changing climatic conditions. Here I used physically based hydrologic modeling to improve understanding of how climate change may impact streamflow regimes and advance some of the cyberinfrastructure and GIS methodologies that support physically based hydrologic modeling by: (1) using a physically based model to examine the potential effects of climate change on ecologically relevant aspects of streamflow regime, (2) developing data services in support of input data preparation for physically based distributed hydrologic models, and (3) enhancing terrain analysis algorithms to support rapid watershed delineation over large area. TOPNET, a physically based hydrologic model was applied over eight watersheds across the U.S to assess the sensitivity and changes of the streamflow regime due to climate change. Distributed hydrologic models require diverse geospatial and time series inputs, the acquisition and preparation of which are labor intensive and difficult to reproduce. I developed web services to automate the input data preparation steps for a physically based distributed hydrological model to enable water scientist to spend less time processing input data. This input includes terrain analysis and watershed delineation over a large area. However, limitations of current terrain analysis tools are (1) some support only a limited set of specific raster and vector data formats, and (2) all that we know of require data to be in a projected coordinate system. I enhanced terrain analysis algorithms to extend their generality and support rapid, web-based watershed delineation services. Climate change studies help to improve the scientific foundation for conducting climate change impacts assessments, thus building the capacity of the water management community to understand and respond to climate change. Web-based data services and enhancements to terrain analysis algorithms to support rapid watershed delineation will impact a diverse community of researchers involved terrain analysis, hydrologic and environmental modeling

Application of TOPNET model for quantifying stream flow regime variables over different watersheds in US

This paper illustrates the application of TOPNET, a distributed physically based hydrologic model in different watersheds across US to determine streamflow regime variables which are a significant influencing factor for both in and out –of stream environments. The models are being established so as to be able to predict and examined changes in flow regimes due to climate change. This paper discusses the model parameterization and calibration parts of the work. A 30 m DEM obtained from National elevation dataset was used to delineate streams and sub watersheds. TauDEM (Tarboton 2002) software was used for delineating stream networks and obtaining slope and catchment area. For this application, the TOPNET model used daily precipitation and temperature data from Daymet, which has fine spatial (1km x1km) and temporal resolution (daily). Initial model parameters for each subbasin were estimated from SSURGO soil and National Land Cover (NLCD) data. USGS streamflow data was used as observed for model calibration. The calibration used a multiplier for each parameter which was estimated using a controlled elitist multi objective genetic algorithm. Automation of all input data preparation workflows and calibration of the model make implementation of TOPNET over those watersheds efficient. Visual comparison of time series plots and statistical measures namely, Nash-Sutcliffe efficiency (NS), percent bias (PBIAS) and root mean square error (RMSE) were used to evaluate the model performance. For most of the watersheds, the model performed relatively well and gave a good representation of the flow hydrographs of the watersheds. Stream flow regime variables derived from calibrated flow were nicely comparable to those from observed flow. The promising simulation results obtained in this study reveal the usefulness of the TOPNET model for estimating streamflow regime variables

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies

Enhancements to TauDEM to support Rapid Watershed Delineation Services

Watersheds are widely recognized as the basic functional unit for water resources management studies and are important for a variety of problems in hydrology, ecology, and geomorphology. Nevertheless, delineating a watershed spread across a large region is still cumbersome due to the processing burden of working with large Digital Elevation Model. Terrain Analysis Using Digital Elevation Models (TauDEM) software supports the delineation of watersheds and stream networks from within desktop Geographic Information Systems. A rich set of watershed and stream network attributes are computed. However limitations of the TauDEM desktop tools are (1) it supports only one type of raster (tiff format) data (2) requires installation of software for parallel processing, and (3) data have to be in projected coordinate system. This paper presents enhancements to TauDEM that have been developed to extend its generality and support web based watershed delineation services. The enhancements of TauDEM include (1) reading and writing raster data with the open-source geospatial data abstraction library (GDAL) not limited to the tiff data format and (2) support for both geographic and projected coordinates. To support web services for rapid watershed delineation a procedure has been developed for sub setting the domain based on sub-catchments, with preprocessed data prepared for each catchment stored. This allows the watershed delineation to function locally, while extending to the full extent of watersheds using preprocessed information. Additional capabilities of this program includes computation of average watershed properties and geomorphic and channel network variables such as drainage density, shape factor, relief ratio and stream ordering. The updated version of TauDEM increases the practical applicability of it in terms of raster data type, size and coordinate system. The watershed delineation web service functionality is useful for web based software as service deployments that alleviate the need for users to install and work with desktop GIS software

Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them

Leveraging Google Earth Engine for Drought Assessment Using Global Soil Moisture Data

Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions' satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them

Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume