Teklu K. Tesfa

Extraction of hydrological proximity measures from DEMs using parallel processing

Land surface topography is one of the most important terrain properties which impact hydrological, geomorphological, and ecological processes active on a landscape. In our previous efforts to develop a soil depth model based upon topographic and land cover variables, we derived a set of hydrological proximity measures (HPMs) from a Digital Elevation Model (DEM) as potential explanatory variables for soil depth. These HPMs are variations of the distance up to ridge points (cells with no incoming flow) and variations of the distance down to stream points (cells with a contributing area greater than a threshold), following the flow path. The HPMs were computed using the D-infinity flow model that apportions flow between adjacent neighbors based on the direction of steepest downward slope on the eight triangular facets constructed in a 3 x 3 grid cell window using the center cell and each pair of adjacent neighboring grid cells in turn. The D-infinity model typically results in multiple flow paths between 2 points on the topography, with the result that distances may be computed as the minimum, maximum or average of the individual flow paths. In addition, each of the HPMs, are calculated vertically, horizontally, and along the land surface. Previously, these HPMs were calculated using recursive serial algorithms which suffered from stack overflow problems when used to process large datasets, limiting the size of DEMs that could be analyzed. To overcome this limitation, we developed a message passing interface (MPI) parallel approach designed to both increase the size and speed with which these HPMs are computed. The parallel HPM algorithms spatially partition the input grid into stripes which are each assigned to separate processes for computation. Each of those processes then uses a queue data structure to order the processing of cells so that each cell is visited only once and the cross-process communications that are a standard part of MPI are handled in an efficient manner. This parallel approach allows efficient analysis of much larger DEMs than were possible using the serial recursive algorithms. The HPMs given here may also have other, more general modeling applicability in hydrology, geomorphology and ecology, and so are described here from a general perspective. In this paper, we present the definitions of the HPMs, the serial and parallel algorithms used in their computation and their potential applications.

21st century United States emissions mitigation could increase water stress more than the climate change it is mitigating

Significance Devising sustainable climate change mitigation policies with attention to potential synergies and constraints within the climate–energy–water nexus is the subject of ongoing integrated modeling efforts. This study employs a regional integrated assessment model and a regional Earth system model at high spatial and temporal resolutions in the Unites States to compare the implications of two of the representative concentration pathways under consistent socioeconomics. The results clearly show, for the first time to our knowledge, that climate change mitigation policies, if not designed with careful attention to water resources, could increase the magnitude, spatial coverage, and frequency of water deficits. The results challenge the general perception that mitigation that aims at reducing warming also would alleviate water deficits in the future. There is evidence that warming leads to greater evapotranspiration and surface drying, thus contributing to increasing intensity and duration of drought and implying that mitigation would reduce water stresses. However, understanding the overall impact of climate change mitigation on water resources requires accounting for the second part of the equation, i.e., the impact of mitigation-induced changes in water demands from human activities. By using integrated, high-resolution models of human and natural system processes to understand potential synergies and/or constraints within the climate–energy–water nexus, we show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, the reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emissions mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation.

Hydrologic controls on equilibrium soil depths

This paper deals with modelling the mutual feedbacks between runoff production and geomorphological processes and attributes that lead to patterns of equilibrium soil depth. Our primary goal is an attempt to describe spatial patterns of soil depth resulting from long-term interactions between hydrologic forcings and soil production, erosion and sediment transport processes under the framework of landscape dynamic equilibrium. Another goal is to set the premises for exploiting the role of soil depths in shaping the hydrologic response of a catchment. The relevance of the study stems from the massive improvement in hydrologic predictions for ungauged basins that would be achieved by using directly soil depths derived from geomorphic features remotely measured and objectively manipulated. Hydrological processes are here described by explicitly accounting for local soil depths and detailed catchment topography. Geomorphological processes are described by means of well-studied geomorphic transport laws. The modelling approach is applied to the semi-arid Dry Creek Experimental Watershed, located near Boise, Idaho, USA. Modelled soil depths are compared with field data obtained from an extensive survey of the catchment. Our results show the ability of the model to describe properly the mean soil depth and the broad features of the distribution of measured data. However, local comparisons show significant scatter whose origins are discussed.

Scalability of grid‐ and subbasin‐based land surface modeling approaches for hydrologic simulations

This paper investigates the relative merits of grid-and subbasin-based land surface modeling approaches for hydrologic simulations, with a focus on their scalability (i.e., ability to perform consistently across spatial resolutions) in simulating runoff generation. Simulations are produced by the grid- and subbasin-based Community Land Model at 0.125°, 0.25°, 0.5°, and 1° spatial resolutions over the U.S. Pacific Northwest. Using the 0.125° simulation as the “reference” solution, statistical metrics are calculated by comparing simulations at 0.25°, 0.5°, and 1° resolutions with the 0.125° simulation for each approach. Statistical significance test results suggest significant scalability advantage for the subbasin-based approach compared to the grid-based approach. Basin level annual average relative errors of surface runoff at 0.25°, 0.5°, and 1° resolutions compared to the 0.125° simulation are 3%, 4%, and 6% for the subbasin-based configuration and 4%, 7%, and 11% for the grid-based configuration, respectively. The scalability advantages are more pronounced during winter/spring and over mountainous regions. The source of runoff scalability is found to be related to the scalability of major meteorological and land surface parameters of runoff generation. More specifically, the subbasin-based approach is more consistent across spatial scales than the grid-based approach in snowfall/rainfall partitioning because of scalability related to air temperature and surface elevation. Scalability of a topographic parameter used in runoff parameterization also contributes to improved scalability of the rain-driven saturated surface runoff component, particularly during winter. Hence, this study demonstrates the importance of spatial structure for multiscale modeling of hydrological processes.

Effects of spatially distributed sectoral water management on the redistribution of water resources in an integrated water model

Realistic representations of sectoral water withdrawals and consumptive demands and their allocation to surface and groundwater sources are important for improving modeling of the integrated water cycle. To inform future model development, we enhance the representation of sectoral water management in a regional Earth system (ES) model with a spatially distributed allocation of sectoral water demands simulated by a regional integrated assessment (IA) model to surface and groundwater systems. The integrated modeling framework (IA-ES) is evaluated by analyzing the simulated regulated flow and sectoral supply deficit in major hydrologic regions of the conterminous U.S, which differ from ES studies looking at water storage variations. Decreases in historical supply deficit are used as metrics to evaluate IA-ES model improvement in representating the complex sectoral human activities for assessing future adaptation and mitigation strategies. We also assess the spatial changes in both regulated flow and unmet demands, for irrigation and non-irrigation sectors, resulting from the individual and combined additions of groundwater and return flow modules. Results show that groundwater use has a pronounced regional and sectoral effect by reducing water supply deficit. The effects of sectoral return flow exhibit a clear east-west contrast in the hydrologic patterns, so the return flow component combined with the IA sectoral demands is a major driver for spatial redistribution of water resources and water deficits in the U.S. Our analysis highlights the need for spatially distributed sectoral representation of water management to capture the regional differences in inter-basin redistribution of water resources and deficits.

A Generalized Additive Soil Depth Model for a Mountainous Semi-Arid Watershed Based Upon Topographic and Land Cover Attributes

Soil depth is an important input parameter in hydrological and ecological modeling. Presently, the soil depth data available in national soil databases (STATSGO, SSURGO) is provided as averages within generalized map units. Spatial uncertainty within these units limits their applicability for spatially distributed modeling. This work reports a statistical model for prediction of soil depth in a semi-arid mountainous watershed that is based upon topographic and other landscape attributes. Soil depth was surveyed by driving a rod into the ground until refusal at geo-referenced locations selected to represent the range of topographic and land cover variations in Dry Creek Experimental Watershed, Boise, Idaho, USA. The soil depth survey consisted of a model calibration set, measured at 819 locations over 8 sub-watersheds, and a model testing set, measured at 130 locations randomly distributed over the remainder of the watershed. Topographic attributes were derived from a Digital Elevation Model. Land cover attributes were derived from Landsat TM remote sensing images and high resolution aerial photographs. A Generalized Additive Model was developed to predict soil depth over the watershed from these attributes. This model explained about 50% of the soil depth spatial variation and is an important improvement towards solving the need in distributed modeling for distributed soil depth input data.

A Global Data Analysis for Representing Sediment and Particulate Organic Carbon Yield in Earth System Models

Although sediment yield (SY) from water erosion is ubiquitous and its environmental consequences are well recognized, its impacts on the global carbon cycle remain largely uncertain. This knowledge gap is partly due to the lack of soil erosion modeling in Earth System Models (ESMs), which are important tools used to understand the global carbon cycle and explore its changes. This study analyzed sediment and particulate organic carbon yield (CY) data from 1,081 and 38 small catchments (0.1–200 km), respectively, in different environments across the globe. Using multiple statistical analysis techniques, we explored environmental factors and hydrological processes important for SY and CY modeling in ESMs. Our results show clear correlations of high SY with traditional agriculture, seismicity and heavy storms, as well as strong correlations between SY and annual peak runoff. These highlight the potential limitation of SY models that represent only interrill and rill erosion because shallow overland flow and rill flow have limited transport capacity due to their hydraulic geometry to produce high SY. Further, our results suggest that SY modeling in ESMs should be implemented at the event scale to produce the catastrophic mass transport during episodic events. Several environmental factors such as seismicity and land management that are often not considered in current catchment-scale SY models can be important in controlling global SY. Our analyses show that SY is likely the primary control on CY in small catchments and a statistically significant empirical relationship is established to calculate SY and CY jointly in ESMs. Plain Language Summary Sediment and organic carbon in the rivers produced by soil erosion are ubiquitous. Although they have important effects on the global carbon cycle, current models have limitations in representing sediment and particulate organic carbon (POC) yield at temporal and spatial scales relevant to Earth System Models (ESMs). By analyzing the sediment yield data from over 1000 small catchments across the globe, we identified environmental factors and hydrological processes important for modeling sediment yield in ESMs. Based on the POC yield data, we indicated that sediment yield is likely the primary control on POC yield. Importantly, we also established a statistical significant empirical relationship relating POC yield to sediment yield that can be used in ESMs.

Integrated Modeling and Decision-Support System for Water Management in the Puget Sound Basin: Snow Caps to White Caps

Final Report for the EPA-sponsored project Snow Caps to White Caps that provides data products and insight for water resource managers to support their predictions and management actions to address future changes in water resources (fresh and marine) in the Puget Sound basin. This report details the efforts of a team of scientists and engineers from Pacific Northwest National Laboratory (PNNL) and the University of Washington (UW) to examine the movement of water in the Snohomish Basin, within the watershed and the estuary, under present and future conditions, using a set of linked numerical models.