Soil moisture is a key variable in hydrology because it affects the production of runoff at the ground surface, transpiration rates from crops and other vegetation, and recharge to shallow aquifers. Unfortunately, direct measurement of soil moisture is difficult. In-situ methods observe soil moisture only at the sampling locations, and extrapolating these measurements to the spatial scales of most practical applications is unreliable. In contrast, satellite remote sensing methods typically observe soil moisture near the ground surface and at coarse spatial resolutions. Dr. Niemann’s research group is studying the physical processes that control soil moisture, statistical properties of soil moisture patterns, impacts of soil moisture on regional hydrology, methods to estimate soil moisture from remote sensing, and methods to interpolate and downscale soil moisture observations. Understanding the spatial patterns and temporal dynamics of soil moisture will benefit water resources planning, flood forecasting, land management, and other applications.
Hydrologic models are used in dam safety evaluations to determine the flow rates that dams must safely convey. The design flows are typically determined by first applying precipitation from the probable maximum precipitation or an annual exceedance probability design storm. The precipitation is then converted to runoff and streamflow using hydrologic models. The predictions from such models involve significant uncertainty, particularly in mountainous basins, due to difficulties in estimating realistic design storms, the active streamflow production mechanisms, and the flood-wave propagation rates. Dr. Niemann’s research group is studying the runoff production mechanisms that are active in Colorado’s mountainous basins for extreme events, developing methods estimate model parameters and their associated uncertainties, and analyzing the impacts of nonlinear behavior on peak streamflow rates. These results are expected to inform updates to Colorado’s dam safety modeling guidelines.
Landslides and debris flows represent a significant natural hazard in many mountain basins. For example, a September 2013 storm in the Colorado Front Range caused more than 1100 debris flows. Slope stability is determined by many factors including the soil’s friction angle and cohesion, the cohesion from roots, and the soil suction stress (which depends on the soil moisture). Dr. Niemann’s research group is examining the impacts of soil moisture variability on the spatial patterns of debris flow initiation and developing tools that downscale remotely sensed soil moisture to provide real-time tracking of slope stability and identification of the locations most at risk of failure.
Basin morphology plays a key role in both the production of runoff and the transfer of storm flows out of watersheds. Basic characteristics of basin geometry are commonly used to forecast storm flow hydrographs for hydrologic design, but detailed consideration of the geometric structure of the basin is usually neglected. Dr. Niemann’s research group is examining the role that channel network structure plays in hydrologic response. This research topic includes identification of different classifications of channel networks, understanding the origins of those classifications, and using the characteristics of those network classifications to better predict hydrologic response. This research will help improve models for simulating basin hydrology in both natural and urban basins.
Snowpack is an important source for water supply and floods in many parts of the world. In the western U.S., for example, runoff from snowmelt provides a large majority of annual water supplies. Rain on snowpack can also produce major floods. Similarly, the presence of frozen ground can impede infiltration and enhance flood production. Both snowpack and frozen ground are often heterogeneous within watersheds due to spatial variations in the topography, ground cover, and soil properties. Dr. Niemann’s research group is developing improved methods to model the spatial and temporal patterns of snowpack and frozen ground in watersheds and their response to wildfires. These tools are expected to improve the accuracy of water supply and flood forecasting methods.
Many water agencies face decisions that involve the potential for economic losses, environmental impacts, and even loss of life if made incorrectly. These decisions are often based on the results of numerical models that simulate watershed hydrology, channel flow, and/or sediment transport. However, it is well-known that the predictions from these models have significant uncertainty. Dr. Niemann's research group is developing practical tools to quantify and ultimately reduce the uncertainty associated with hydrologic, hydraulic, and geomorphic models. These tools aim to identify and characterize uncertainties due to the assumed mathematical structure of the model as well as the uncertainty due to parameter estimation and model inputs. Ultimately, these tools could result in more reliable predictions and more efficient designs of water infrastructure.
River basin topography commonly appears similar when viewed at different magnifications. Without an indication of scale, it is difficult to determine whether a photograph of a river basin displays one hundred square kilometers or ten thousand square kilometers. This property is known as scaling invariance and is closely related to fractals and chaos. Many hydrologic variables exhibit this tendency including precipitation rates, soil moisture patterns, and hydrologic conductivity. In fact, a wide variety of natural objects have fractal geometry. Scaling invariance is a useful property for hydrologists because it statistically relates the properties of small features to those of large features. Thus, it can help characterize, simulate, and interpolate hydrologic variables. Dr. Niemann’s research group is examining the scaling invariant properties of topography and soil moisture. They are quantitatively characterizing the scaling invariance, understanding its dynamic origin (and mechansisms that lead to deviations from scaling), and exploring the interconnections between the scaling of topography and soil moisture.
Numerous regions with irrigated agricultural have water tables that are near the ground surface. Shallow water tables can reduce crop productivity not only due to waterlogging but also due to the high salinity that sometimes occurs in groundwater. In addition, evapotranspiration from uncropped areas, which can be supplied by shallow groundwater, may represent a significant non-beneficial consumptive use of water. Dr. Niemann’s research group is studying how evapotranspiration from uncropped areas with shallow water tables depends on the water table depth and other factors such as the vegetation cover and soil salinity. This research couples evapotranspiration estimates from satellite remote-sensing with measurements of water table depth, soil moisture, soil salinity, and vegetation properties. Ultimately, this research is expected to improve our understanding of the water balance in agri-ecological systems and numerical models that are used to evaluate potential solutions to waterlogging and salinization problems.
One of the more exciting advances in geomorphological research is the development of sophisticated computer models that simulate the evolution of river basins over long periods of time. These models successfully reproduce many empirical features of river basin topography including dendritic river networks, concave profiles of stream courses, and convex profiles of hillslopes. These models can also be used to understand the long-term impacts of climate and land-use changes, as well as the movement of sediment and long-lived pollutants in basins. Dr. Niemann’s research group is developing better representations of hydrologic processes in these models and studying the role these processes play in landscape evolution. They are also investigating how the historical persistence of hydrologic processes impacts the current hydrologic behavior of basins.