The Standardized Streamflow Index (SSI) has frequently been used to quantify drought by comparing periods of streamflow against a river's historical values. This study expands upon previous SSI methodologies by creating a more flexible, regionalized version of the metric for Iowa, a Midwestern state located in the central United States. Five drought regions were developed for Iowa that largely correspond to the state's Major Land Resource Areas. Several United States Geological Survey gauges were identified within each drought region and streamflow data were used to calculate daily water yields from 1960 to the present. SSI values calculated for both individual river sites and the entire drought region provide insights into the relative dryness of Iowa watersheds over the past several decades. The SSI methodology can be used to evaluate river dryness with limited data records and water yields of specific streams can be compared to each other and across the overall region. This study demonstrates the potential for developing versions of the SSI that enable real‐time calculations at a daily scale at locations with limited historical streamflow data. The new SSI metric may be a valuable tool for decision‐makers as state and federal agencies continue to identify and manage drought.

Societal risks from flooding are evident at a range of spatial scales and climate change will exacerbate these risks in the future. Assessing flood risks across broad geographical regions is a challenge, and often done using streamflow time-series records or hydrologic models. In this study, we used a national-scale hydrological model to identify, assess, and map 16 different streamflow metrics that could be used to describe flood risks across 34,987 HUC12 subwatersheds within the Mississippi-Atchafalaya River Basin (MARB). A clear spatial difference was observed among two different classes of metrics. Watersheds in the eastern half of the MARB exhibited higher overall flows as characterized by the mean, median, and maximum daily values, whereas western MARB watersheds were associated with flood indicative of high extreme flows such as skewness, standardized streamflow index and top days. Total agricultural and building losses within HUC12 watersheds were related to flood metrics and those focused on higher overall flows were more correlated to expected annual losses (EAL) than extreme value metrics. Results from this study are useful for identifying continental scale patterns of flood risks within the MARB and should be considered a launching point from which to improve the connections between watershed scale risks and the potential use of natural infrastructure practices to reduce these risks.

Streambank erosion is an important source of sediment to river systems but is difficult to quantify at watershed scales. In this study, high‐resolution Light Detection and Ranging (LiDAR) measurements collected from 2009 and 2020 were used to quantify the difference in land surface elevation that occurred along the fourth and fifth‐order streams in Old Mans Creek watershed in southeast Iowa. Study objectives were to quantify the volume of streambank sediment erosion and deposition occurring along the river systems and compare net channel erosion to watershed sediment export. Results indicated that streambank erosion and deposition along the fourth and fifth‐order channels totaled nearly 720,000 m³ and 148,000 m³, respectively, over the 11‐year study period. Five times more streambank erosion occurred than deposition, and the difference between the two totals (net sediment erosion) comprised 77% of the sediment export from the watershed. The contribution of streambank sediment to basin export, along with estimates of mean annual streambank recession derived from the analyses, were consistent with results reported in other studies of streambank erosion. The LiDAR differencing methodology was able to identify areas of both sediment erosion and deposition occurring in the stream channels and quantify the net difference, which is related to watershed‐scale sediment export.

Phosphorus (P) is a widespread waterborne pollutant that impairs many waterbodies. However, it is challenging to measure directly, and much research has been dedicated to developing surrogacy models that can repeatedly predict its concentration. Optimal approaches for modeling strategies are often unclear and depend upon local P dynamics and the availability of financial and technical resources. This study presents a schema for developing P surrogacy models at a statewide scale (16 major rivers in Iowa, USA). Specifically, we examined the relationship between particulate phosphorus (Part P) and orthophosphate (OP) and explored the viability of eight potential surrogates in predicting their concentrations using multiple linear regression and power regression methods. We also investigated similarities between surrogate models for Part P and total suspended solids (TSS). At all sites, OP and Part P were not strongly correlated (mean R = 0.20 ± 0.17). Many instances were observed where samples had high concentrations of one form but not the other. Modeling results demonstrated that turbidity was consistently the best predictor (t-statistics >10) of Part P, and adding other surrogates alongside turbidity did little to improve model performance. No surrogates proved useful in estimating OP. Viable power regression models were created using turbidity to predict Part P (mean R² = 0.69 ± 0.12). These models had a nonlinear form where Part P concentrations leveled off as waters became exceptionally turbid. This contrasted with TSS, which maintained a strong linear relationship across all turbidity levels. Turbidity-based models show promise in quantifying statewide P levels, as they enable high-resolution and real-time Part P estimates.

Flood‐mitigation reservoirs have long been known to impact pollutant transport by retaining or removing incoming sediment and nutrients. However, historical reductions in these systems have rarely been well quantified. In this study, we used water quality data to estimate inputs and outputs of total suspended solids (TSS), two phosphorus (P) forms, and three nitrogen (N) forms in three Iowa reservoirs (Coralville, Red Rock, and Saylorville). We also explored the influence of reservoir residence times on removal rates. Annual residence times were largely consistent across the basins, ranging from roughly 6 to 100 days (mean of 19 days). Between 2001 to 2023, most TSS (~ 80%) entering the reservoirs was retained. This sedimentation corresponded to average volume losses in the reservoirs' normal storage pools of 0.37%–0.85%/year. About 40% of P and 12% of N were likewise reduced—driven mainly by decreases in particulate P and nitrate. Residence time appeared unrelated to removal rates of TSS and particulate nutrient forms, but longer residence times coincided with increased nitrate loss. Reservoir impact on statewide nutrient export was significant, with loads in Iowa's major rivers being reduced by 9.8% (for P) and 4.7% (for N) due to reservoir capture. These findings suggest that reservoir operators may be able to facilitate further nitrate removal by lengthening storage durations without incurring additional sedimentation or generating other nutrient forms.