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Key Points Modeled ecosystem response to climate follows the “geo‐ecological law of distribution,” highlights the importance of ecohdyrologic refugia Woody Plant Encroachment is predicted as a three‐phase phenomenon: early establishment, rapid expansion, and woody plant equilibrium Regime shifts from grassland to shrubland are marked by vegetation cover thresholds 

Abstract. Dryland regions are characterised by water scarcity and are facingmajor challenges under climate change. One difficulty is anticipating howrainfall will be partitioned into evaporative losses, groundwater, soilmoisture, and runoff (the water balance) in the future, which has importantimplications for water resources and dryland ecosystems. However, in orderto effectively estimate the water balance, hydrological models in drylandsneed to capture the key processes at the appropriate spatio-temporal scales.These include spatially restricted and temporally brief rainfall, highevaporation rates, transmission losses, and focused groundwater recharge.Lack of available input and evaluation data and the high computational costsof explicit representation of ephemeral surface–groundwater interactionsrestrict the usefulness of most hydrological models in these environments.Therefore, here we have developed a parsimonious distributed hydrologicalmodel for DRYland Partitioning (DRYP). The DRYP model incorporates the keyprocesses of water partitioning in dryland regions with limited datarequirements, and we tested it in the data-rich Walnut Gulch ExperimentalWatershed against measurements of streamflow, soil moisture, andevapotranspiration. Overall, DRYP showed skill in quantifying the maincomponents of the dryland water balance including monthly observations ofstreamflow (Nash–Sutcliffe efficiency, NSE, ∼ 0.7),evapotranspiration (NSE > 0.6), and soil moisture (NSE ∼ 0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment andthat < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost throughephemeral channels as transmission losses. However, only ∼ 35 % of the total transmission losses percolate to the groundwater aquiferas focused groundwater recharge, whereas the rest is lost to the atmosphereas riparian evapotranspiration. Overall, DRYP is a modular, versatile, andparsimonious Python-based model which can be used to anticipate and plan forclimatic and anthropogenic changes to water fluxes and storage in drylandregions. 

Abstract. Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab. 

Abstract. Assessments of water balance changes, watershed response, and landscapeevolution to climate change require representation of spatially andtemporally varying rainfall fields over a drainage basin, as well as theflexibility to simply modify key driving climate variables (evaporativedemand, overall wetness, storminess). An empirical–stochastic approach to theproblem of rainstorm simulation enables statistical realism and the creationof multiple ensembles that allow for statistical characterization and/or timeseries of the driving rainfall over a fine grid for any climate scenario.Here, we provide details on the STOchastic Rainfall Model (STORM), which usesthis approach to simulate drainage basin rainfall. STORM simulates individualstorms based on Monte Carlo selection from probability density functions(PDFs) of storm area, storm duration, storm intensity at the core, and stormcenter location. The model accounts for seasonality, orography, and theprobability of storm intensity for a given storm duration. STORM alsogenerates time series of potential evapotranspiration (PET), which arerequired for most physically based applications. We explain how the modelworks and demonstrate its ability to simulate observed historical rainfallcharacteristics for a small watershed in southeast Arizona. We explain the datarequirements for STORM and its flexibility for simulating rainfall forvarious classes of climate change. Finally, we discuss several potentialapplications of STORM.

 

Facets formed along the footwalls of active normal‐fault blocks display a variety of longitudinal profile forms, with variations in gradient, shape, degree of soil cover, and presence or absence of a slope break at the fault trace. We show that a two‐dimensional, process‐oriented cellular automaton model of facet profile evolution can account for the observed morphologic diversity. The model uses two dimensionless parameters to represent fault slip, progressive rock weathering, and downslope colluvial‐soil transport driven by gravity and stochastic disturbance events. The parameters represent rock weathering and soil disturbance rates, respectively, scaled by fault slip rate; both can be derived from field‐estimated rate coefficients. In the model's transport‐limited regime, slope gradient depends on the ratio of disturbance to slip rate, with a maximum that represents the angle of repose for colluvium. In this regime, facet evolution is consistent with nonlinear diffusion models of soil‐mantled hillslope evolution. Under the weathering‐limited regime, bedrock becomes partly exposed but microtopography helps trap some colluvium even when facet gradient exceeds the threshold angle. Whereas the model predicts a continuous gradient from footwall to colluvial wedge under transport‐limited behavior, fully weathering‐limited facets tend to develop a slope break between footwall and basal colluvium as a result of reduced transport efficiency on the rocky footwall slope. To the extent that the model provides a reasonable analogy for natural facets, its behavior suggests that facet profile morphology can provide useful constraints on relative potential rates of rock weathering, soil disturbance, and fault slip.