A new drought index termed the “soil moisture drought index (SODI)” is developed to characterize droughts. The premise of the index is based on how much water is required to attain soil moisture at field capacity. SODI captures variations of precipitation, temperature, and soil moisture over time. Three widely used drought indices, including the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrated palmer drought index (sc-PDSI) are compared with SODI along with local hydrological variables such as streamflow, reservoir storage, and groundwater level for cross-validation. The result indicates that SODI reacts more evidently to relate changes in precipitation and temperature than SPI and SPEI by characterizing soil moisture over time. Results also show that SODI outperforms the existing drought indices in the sense that SODI can detect and quantify the extended severe droughts associated with climate variability and change. SODI will add momentum to build a case toward the use of soil moisture information for drought analysis in a changing environment.

Urbanization, land use change, and agricultural activities continue to affect water quality standards at the urban–rural interface, such as the Boise River System located in Idaho, USA. This project demonstrates how the off-the-shelf unmanned aircraft system (UAS, also known as drone) equipped with other necessary hardware attachments can be used to monitor real-time water quality components, including pH, water temperature, electric conductivity (EC), and dissolved oxygen at open waterbodies. The proposed UAS-based hardware platform for water quality studies (UASWQP) appears a promising tool to advance environmental research activities, especially for impaired waterways (e.g., rivers, lakes, and reservoirs). The preliminary result shows that the proposed UASWQP effectively displays water quality components in real-time to the ThingSpeak Cloud web services, while an adequate water sample was also collected easily for further analysis at laboratory facilities, when needed. It is anticipated that UASWQP will be a useful tool to promote environmental stewardship by contributing to the water research communities in years to come.

A low-cost open-source autonomous unmanned surface vehicle (USV) named “iDroneboat” is developed for real-time monitoring and visualization of water quality. The iDroneboat equipped with Internet of Things (IoT) sensors transmits real-time water quality data, including dissolved oxygen (DO), electronical conductivity (EC), pH, and water temperature (WT) to the cloud for data sharing through Long-term Evolution (LTE) communication protocols. Since material and supplies needed are readily accessible from online marketplaces or local hardware stores, the iDroneboat is easily replicable for local water quality studies and citizen-science activities. The iDroneboat appears to be a promising tool to advance environmental research activities, especially for impaired waterways (e.g., lakes, rivers, and reservoirs). The preliminary result shows that the proposed low-cost platform, iDroneboat, effectively displays water quality components in real-time to the cloud web services (e.g., ThingSpeak), ultimately contributing to citizen science activities and environmental stewardship in water research ecosystems.

Streamflow disaggregation from monthly to daily was performed using a relatively simple, flexible, and adaptive method. Only streamflow acts as a decision variable in this disaggregation process. To disaggregate monthly to daily flow at the target station (TS), monthly counterparts at the source station (SS) were selected on the basis of minimum error criteria, which are calculated with respect to streamflow volume within a three-month time window. Daily streamflow indexes at SS were then calculated to disaggregate monthly to daily streamflow at TS during the disaggregation process. The effectiveness of the proposed method has been demonstrated through its application at both regulated and unregulated waterways located in the northwest states, including Idaho and Wyoming. For both regulated and unregulated monthly streamflow, the proposed method well represents daily streamflow realizations similar to historical flows and preserves both mass balance and a series of statistical characteristics. However, the results also indicate that the quality of disaggregated daily streamflow varies for individual applications depending on the selection of stations, their geographic information, and data availability. The disaggregation model used in this research is transparent, user friendly, less intensive, and less time-consuming so that it can be used at any watershed without difficulty or much effort. Consequently, because development and availability of daily streamflow is important for water resources planning and management, including reservoir operation, water quality study, and environmental/ecological modeling, this research will help bridge the gap among interdisciplinary water research activities, especially for studies of the impacts of hydrologic events possibly driven by extreme weather variability and climate change.