Matej Durcik

Quantifying the role of climate and landscape characteristics on hydrologic partitioning and vegetation response

[1] There is no consensus on how changes in both temperature and precipitation will affect regional vegetation. We investigated controls on hydrologic partitioning at the catchment scale across many different ecoregions, and compared the resulting estimates of catchment wetting and vaporization (evapotranspiration) to remotely sensed indices of vegetation greenness. The fraction of catchment wetting vaporized by plants, known as the Horton index, is strongly related to the ratio of available energy to available water at the Earth’s surface, the aridity index. Here we show that the Horton index is also a function of catchment mean slope and elevation, and is thus related to landscape characteristics that control how much and how long water is retained in a catchment. We compared the power of the components of the water and energy balance, as well as landscape characteristics, to predict Normalized Difference Vegetation Index (NDVI), a surrogate for vegetation productivity, at 312 Model Parameter Estimation Experiment (MOPEX) catchments across the United States. Statistical analysis revealed that the Horton index provides more precision in predicting maximum annual NDVI for all catchments than mean annual precipitation, potential evapotranspiration, or their ratio, the aridity index. Models of vegetation productivity should emphasize plant-available water, rather than just precipitation, by incorporating the interaction of climate and landscape. Major findings related to the Horton index are: (1) it is a catchment signature that is relatively constant from year-to-year; (2) it is related to specific landscape characteristics; (3) it can be used to create catchment typologies; and (4) it is related to overall catchment greenness.

New data sets to estimate terrestrial water storage change

The total amount of water stored in a river basin affects streamflow at various timescales and defines the river basins response to atmospheric forcing. For example, spring runoff in mountainous midlatitude catchments depends on winter snowpack, and groundwater storage sustains flow during dry periods. An accurate estimation of terrestrial water storage (TWS) is thus paramount for improved water management. Direct determination of TWS is difficult due to insufficient in situ data on space-time variability of hydrologic stores (snow, soil moisture, groundwater) and fluxes (precipitation, evapotranspiration). However, alternative methods using new data sets show great potential to improve the estimation of intra-annual and interannual TWS dynamics.

Effects of climate variability on water storage in the Colorado River Basin

Understanding the long-term (interannual‐decadal) variability of water availability in river basins is paramount for water resources management. Here, the authors analyze time series of simulated terrestrial water storage components, observed precipitation, and discharge spanning 74 yr in the Colorado River basin and relate them to climate indices that describe variability of sea surface temperatureand sea level pressure in the �� ��

Evaluation of the Performance of Three Satellite Precipitation Products over Africa

Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT); GeoAguas Consultores (Chile); NASA SERVIR Program [11-SERVIR11-0058]; International Center for Integrated Water Resources Management (ICIWaRM) a Category II UNESCO Center

Accuracy and Performance Assessment of a Window-Based Heuristic Algorithm for Real-Time Routing in Map-Based Mobile Applications

The demand for routing algorithms that produce optimal solutions in real time is continually growing. Real-time routing algorithms are needed in many existing and emerging applications and services. An example is map-based mobile applications where real-time routing is required. Conventional optimal routing algorithms often do not provide acceptable real-time responses when applied to large real road network data. As a result, in certain real-time applications, especially those with limited computing resources (e.g., mobile devices), heuristic algorithms that can provide good solutions, though not necessarily optimal, in real time are employed. In this chapter, we present two approaches for limiting the search space using a window-based heuristic algorithm to compute shortest routes and analyze their solutions and performances using real road network data. The results of a set of experiments on the two approaches show that the window-based heuristic algorithm produces aceptable response times using real road network data and that window sizes and orientations impact accuracy and performance of the algorithm.

Why Do Large‐Scale Land Surface Models Produce a Low Ratio of Transpiration to Evapotranspiration?

Figure 1. Modeled 2-year averages of (a) net radiation (W/m), (b) sensible heat (W/m2), (c) latent heat (W/m2), (d) soil surface evaporation (mm/day), (e) transpiration (mm/day), (f) T/ET ratio, (g) soil wetness (saturation), (h) gross primary productivity (GPP; g C/m2/day), and (i) Leaf area index (m2/m2) by a control experiment (CTRL). • Problem: Land surface models (LSMs) used in climate models generally produce a weaker ecosystem resilience to climate change as indicated from the T/ET ratio that is lower than field estimates. This may degrade the credibility of the climate projection by these Earth System Models and the modeled ecosystem responses to climate change.