145 resultados para water balance modelling


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The purpose of this study was to test the hypothesis that soil water content would vary spatially with distance from a tree row and that the effect would differ according to tree species. A field study was conducted on a kaolinitic Oxisol in the sub-humid highlands of western Kenya to compare soil water distribution and dynamics in a maize monoculture with that under maize (Zea mays L.) intercropped with a 3-year-old tree row of Grevillea robusta A. Cunn. Ex R. Br. (grevillea) and hedgerow of Senna spectabilis DC. (senna). Soil water content was measured at weekly intervals during one cropping season using a neutron probe. Measurements were made from 20 cm to a depth of 225 cm at distances of 75, 150, 300 and 525 cm from the tree rows. The amount of water stored was greater under the sole maize crop than the agroforestry systems, especially the grevillea-maize system. Stored soil water in the grevillea-maize system increased with increasing distance from the tree row but in the senna-maize system, it decreased between 75 and 300 cm from the hedgerow. Soil water content increased least and more slowly early in the season in the grevillea-maize system, and drying was also evident as the frequency of rain declined. Soil water content at the end of the cropping season was similar to that at the start of the season in the grevillea-maize system, but about 50 and 80 mm greater in the senna-maize and sole maize systems, respectively. The seasonal water balance showed there was 140 mm, of drainage from the sole maize system. A similar amount was lost from the agroforestry systems (about 160 mm in the grevillea-maize system and 145 mm in the senna-maize system) through drainage or tree uptake. The possible benefits of reduced soil evaporation and crop transpiration close to a tree row were not evident in the grevillea-maize system, but appeared to greatly compensate for water uptake losses in the senna-maize system. Grevillea, managed as a tree row, reduced stored soil water to a greater extent than senna, managed as a hedgerow.

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The paper discusses the observed and projected warming in the Caucasus region and its implications for glacier melt, water availability and potential hazards. A strong positive trend in summer air temperatures of 0.05 degrees C year(-1) is observed in the high-altitude areas (above 2000 m) providing for a strong glacier melt. A widespread glacier retreat has also been reported between 1985 and 2000, with an average rate of 8 m year(-1). A warming of 5-7 degrees C is projected for the Sum mer months in the 2071-2100 period under the A2 emission group of scenarios, Suggesting that enhanced glacier melt and a changing water balance can be expected.

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Reclaimed water provides an important contribution to the water balance in water-scarce Jordan, but the quality of this water presents both benefits and challenges. Careful management of reclaimed water is required to maximize the nutrient benefits while minimizing the salinity risks. This work uses a multi-disciplinary research approach to show that soil response to irrigation with reclaimed water is a function of the management strategies adopted on the farm by the water user. The adoption of management methods to maintain soil productivity can be seen to be a result of farmers’ awareness to potentially plant-toxic ions in the irrigation water (70% of Jordan Valley farmers identified salinization as a hazard from irrigation with reclaimed water). However, the work also suggests that farmers’ management capacity is affected by the institutional management of water. About a third (35%) of farmers in the Jordan Valley claimed that their ability to manage salinization was limited by water shortages. Organizational interviews revealed that institutional awareness of soil management challenges was quite high (34% of interviewees described salinization as a risk from water reuse), but strategies to address this challenge at the institutional level require greater development.

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The performance of three urban land surface models, run in offline mode, with their default external parameters, is evaluated for two distinctly different sites in Helsinki: Torni and Kumpula. The former is a dense city centre site with 22% vegetation, while the latter is a suburban site with over 50% vegetation. At both locations the models are compared against sensible and latent heat fluxes measured using the eddy covariance technique, along with snow depth observations. The cold climate experienced by the city causes strong seasonal variations that include snow cover and stable atmospheric conditions. Most of the time the three models are able to account for the differences between the study areas as well as the seasonal and diurnal variability of the energy balance components. However, the performances are not systematic across the modelled components, season and surface type. The net all-wave radiation is well simulated, with the greatest uncertainties related to snowmelt timing, when the fraction of snow cover has a key role, particularly in determining the surface albedo. For the turbulent fluxes, more variation between the models is seen which can partly be explained by the different methods in their calculation and partly by surface parameter values. For the sensible heat flux, simulation of wintertime values was the main problem, which also leads to issues in predicting near-surface stabilities particularly at the dense city centre site. All models have the most difficulties in simulating latent heat flux. This study particularly emphasizes that improvements are needed in the parameterization of anthropogenic heat flux and thermal parameters in winter, snow cover in spring and evapotranspiration in order to improve the surface energy balance modelling in cold climate cities.

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The restoration of wetlands as bird habitats often involves the maintenance of a fluctuating water regime by careful, localised ditch water management using pumps and sluices. However, there is evidence in the literature to Suggest that alternate flood/drainage cycles can accelerate nutrient cycling and transport within the soil and, therefore, pose a threat to water quality through the process of eutrophication. This study focused on the dynamics and losses of soil P in a recently re-wetted, eutrophic fen peat developed on alluvium ill South west England. During the 2-year Study (2001 and 2002), soil water tensiometry revealed that the field water table (fluctuating annually between +20 and 60 cm relative to ground level) was extensively influenced across most of the 8.4 ha field site by the management of the adjacent ditch water levels. This conservation-led, prescribed water balance was facilitated by the high hydraulic conductivity (1.1 x 10(-s) ms(-1)) of the lower (70-140 cm), degraded layer of peat. However, only during a 7-day period of water table drawdown by intermittent pump drainage, approximately 45 g ha(-1) of dissolved reactive P (DRP) entered the pumped ditch from the field via this degraded layer. Summer rainfall events >35 mm d(-1) also coincided with significant peaks ill ditch water P concentration (up to 200 mu g L-1 DRP). Even larger peaks (Up to 700 mu g L-1 DRP) Occurred With the annual onset of autumn reflooding. These episodic P loss events pose a serious potential threat to biological water quality. (C) 2009 Elsevier B.V. All rights reserved.

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Intensification of crop production in the mid-hills of Nepal has led to concerns that nitrogen loss by leaching may increase. This study estimated the amount of N leached during two years from rainfed terraces (bari-land) at three locations in Nepal. Maize or upland rice grown in the monsoon season was given either no nutrient inputs or inputs via either nitrogen fertilizer or farmyard manure. Nitrate concentration in soil solution was measured regularly with porous ceramic cup samplers and drainage estimated from a simple soil water balance. Estimated losses of nitrogen by leaching ranged from 0 to 63.5 kg N ha(-1) depending on location and the form of nitrogen applied. Losses from plots receiving no nutrient inputs were generally small (range: 0-35 kg N ha(-1)) and losses from plots where nitrogen was applied as manure (range: 2-41 kg N ha(-1)) were typically half those from plots with nitrogen applied as fertilizer. Losses during the post-monsoon crops of finger millet were small (typically <5% of total loss) although losses from the one site with blackgram were larger (about 13%). The highest concentrations of nitrate in solution were measured early in the season as the monsoon rains began and immediately following fertilizer applications. Leaching losses are likely to be minimised if manure is applied as a basal nutrient dressing followed by fertilizer nitrogen later in the season.

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Soil moisture content, theta, of a bare and vegetated UK gravelly sandy loam soil (in situ and repacked in small lysimeters) was measured using various dielectric instruments (single-sensor ThetaProbes, multi-sensor Profile Probes, and Aquaflex Sensors), at depths ranging between 0.03 and I m, during the summers of 2001 (in situ soil) and 2002 (mini-lysimeters). Half-hourly values of evaporation, E, were calculated from diurnal changes in total soil profile water content, using the soil water balance equation. For the bare soil field, Profile Probes and ML2x ThetaProbes indicated a diurnal course of theta that did not concur with typical soil physical observations: surface layer soil moisture content increased from early morning until about midday, after which theta declined, generally until the early evening. The unexpected course of theta was positively correlated to soil temperature, T-s, also at deeper depths. Aquaflex and ML1 ThetaProbe (older models) outputs, however, reflected common observations: 0 increased slightly during the night (capillary rise) and decreased from the morning until late afternoon (as a result of evaporation). For the vegetated plot, the spurious diurnal theta fluctuations were less obvious, because canopy shading resulted in lower amplitudes of T-s. The unrealistic theta profiles measured for the bare and vegetated field sites caused diurnal estimates of E to attain downward daytime and upward night-time values. In the mini-lysimeters, at medium to high moisture contents, theta values measured by (ML2x) ThetaProbes followed a relatively realistic course, and predictions of E from diurnal changes in vertically integrated theta generally compared well with lysimeter estimates of E. However, time courses of theta and E became comparable to those observed for the field plots when the soil in the lysimeters reached relatively low values of theta. Attempts to correct measured theta for fluctuations in T, revealed that no generally applicable formula could be derived. (c) 2005 Elsevier B.V. All rights reserved.

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Climate change science is increasingly concerned with methods for managing and integrating sources of uncertainty from emission storylines, climate model projections, and ecosystem model parameterizations. In tropical ecosystems, regional climate projections and modeled ecosystem responses vary greatly, leading to a significant source of uncertainty in global biogeochemical accounting and possible future climate feedbacks. Here, we combine an ensemble of IPCC-AR4 climate change projections for the Amazon Basin (eight general circulation models) with alternative ecosystem parameter sets for the dynamic global vegetation model, LPJmL. We evaluate LPJmL simulations of carbon stocks and fluxes against flux tower and aboveground biomass datasets for individual sites and the entire basin. Variability in LPJmL model sensitivity to future climate change is primarily related to light and water limitations through biochemical and water-balance-related parameters. Temperature-dependent parameters related to plant respiration and photosynthesis appear to be less important than vegetation dynamics (and their parameters) for determining the magnitude of ecosystem response to climate change. Variance partitioning approaches reveal that relationships between uncertainty from ecosystem dynamics and climate projections are dependent on geographic location and the targeted ecosystem process. Parameter uncertainty from the LPJmL model does not affect the trajectory of ecosystem response for a given climate change scenario and the primary source of uncertainty for Amazon 'dieback' results from the uncertainty among climate projections. Our approach for describing uncertainty is applicable for informing and prioritizing policy options related to mitigation and adaptation where long-term investments are required.

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Canopy interception of incident precipitation is a critical component of the forest water balance during each of the four seasons. Models have been developed to predict precipitation interception from standard meteorological variables because of acknowledged difficulty in extrapolating direct measurements of interception loss from forest to forest. No known study has compared and validated canopy interception models for a leafless deciduous forest stand in the eastern United States. Interception measurements from an experimental plot in a leafless deciduous forest in northeastern Maryland (39°42'N, 75°5'W) for 11 rainstorms in winter and early spring 2004/05 were compared to predictions from three models. The Mulder model maintains a moist canopy between storms. The Gash model requires few input variables and is formulated for a sparse canopy. The WiMo model optimizes the canopy storage capacity for the maximum wind speed during each storm. All models showed marked underestimates and overestimates for individual storms when the measured ratio of interception to gross precipitation was far more or less, respectively, than the specified fraction of canopy cover. The models predicted the percentage of total gross precipitation (PG) intercepted to within the probable standard error (8.1%) of the measured value: the Mulder model overestimated the measured value by 0.1% of PG; the WiMo model underestimated by 0.6% of PG; and the Gash model underestimated by 1.1% of PG. The WiMo model’s advantage over the Gash model indicates that the canopy storage capacity increases logarithmically with the maximum wind speed. This study has demonstrated that dormant-season precipitation interception in a leafless deciduous forest may be satisfactorily predicted by existing canopy interception models.

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We present an intercomparison and verification analysis of 20 GCMs (Global Circulation Models) included in the 4th IPCC assessment report regarding their representation of the hydrological cycle on the Danube river basin for 1961–2000 and for the 2161–2200 SRESA1B scenario runs. The basin-scale properties of the hydrological cycle are computed by spatially integrating the precipitation, evaporation, and runoff fields using the Voronoi-Thiessen tessellation formalism. The span of the model- simulated mean annual water balances is of the same order of magnitude of the observed Danube discharge of the Delta; the true value is within the range simulated by the models. Some land components seem to have deficiencies since there are cases of violation of water conservation when annual means are considered. The overall performance and the degree of agreement of the GCMs are comparable to those of the RCMs (Regional Climate Models) analyzed in a previous work, in spite of the much higher resolution and common nesting of the RCMs. The reanalyses are shown to feature several inconsistencies and cannot be used as a verification benchmark for the hydrological cycle in the Danubian region. In the scenario runs, for basically all models the water balance decreases, whereas its interannual variability increases. Changes in the strength of the hydrological cycle are not consistent among models: it is confirmed that capturing the impact of climate change on the hydrological cycle is not an easy task over land areas. Moreover, in several cases we find that qualitatively different behaviors emerge among the models: the ensemble mean does not represent any sort of average model, and often it falls between the models’ clusters.

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We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover, composition and 5 height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, 10 and are compared to scores based on the temporal or spatial mean value of the observations and a “random” model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), and the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global 15 vegetation models (DGVMs). SDBM reproduces observed CO2 seasonal cycles, but its simulation of independent measurements of net primary production (NPP) is too high. The two DGVMs show little difference for most benchmarks (including the interannual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified 20 several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change 25 impacts and feedbacks.

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The primary role of land surface models embedded in climate models is to partition surface available energy into upwards, radiative, sensible and latent heat fluxes. Partitioning of evapotranspiration, ET, is of fundamental importance: as a major component of the total surface latent heat flux, ET affects the simulated surface water balance, and related energy balance, and consequently the feedbacks with the atmosphere. In this context it is also crucial to credibly represent the CO2 exchange between ecosystems and their environment. In this study, JULES, the land surface model used in UK weather and climate models, has been evaluated for temperate Europe. Compared to eddy covariance flux measurements, the CO2 uptake by the ecosystem is underestimated and the ET overestimated. In addition, the contribution to ET from soil and intercepted water evaporation far outweighs the contribution of plant transpiration. To alleviate these biases, adaptations have been implemented in JULES, based on key literature references. These adaptations have improved the simulation of the spatio-temporal variability of the fluxes and the accuracy of the simulated GPP and ET, including its partitioning. This resulted in a shift of the seasonal soil moisture cycle. These adaptations are expected to increase the fidelity of climate simulations over Europe. Finally, the extreme summer of 2003 was used as evaluation benchmark for the use of the model in climate change studies. The improved model captures the impact of the 2003 drought on the carbon assimilation and the water use efficiency of the plants. It, however, underestimates the 2003 GPP anomalies. The simulations showed that a reduction of evaporation from the interception and soil reservoirs, albeit not of transpiration, largely explained the good correlation between the carbon and the water fluxes anomalies that was observed during 2003. This demonstrates the importance of being able to discriminate the response of individual component of the ET flux to environmental forcing.

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This paper demonstrates the impracticality of a comprehensive mathematical definition of the term `drought' which formalises the general qualitative definition that drought is `a deficit of water relative to normal conditions'. Starting from the local water balance, it is shown that a universal description of drought requires reference to water supply, demand and management. The influence of human intervention through water management is shown to be intrinsic to the definition of drought in the universal sense and can only be eliminated in the case of purely meteorological drought. The state of `drought' is shown to be predicated on the existence of climatological norms for a multitude of process specific terms. In general these norms are either difficult to obtain or even non-existent in the non-stationary context of climate change. Such climatological considerations, in conjunction with the difficulty of quantifying human influence, lead to the conclusion that we cannot reasonably expect the existence of any workable generalised objective definition of drought.

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This paper introduces and evaluates DryMOD, a dynamic water balance model of the key hydrological process in drylands that is based on free, public-domain datasets. The rainfall model of DryMOD makes optimal use of spatially disaggregated Tropical Rainfall Measuring Mission (TRMM) datasets to simulate hourly rainfall intensities at a spatial resolution of 1-km. Regional-scale applications of the model in seasonal catchments in Tunisia and Senegal characterize runoff and soil moisture distribution and dynamics in response to varying rainfall data inputs and soil properties. The results highlight the need for hourly-based rainfall simulation and for correcting TRMM 3B42 rainfall intensities for the fractional cover of rainfall (FCR). Without FCR correction and disaggregation to 1 km, TRMM 3B42 based rainfall intensities are too low to generate surface runoff and to induce substantial changes to soil moisture storage. The outcomes from the sensitivity analysis show that topsoil porosity is the most important soil property for simulation of runoff and soil moisture. Thus, we demonstrate the benefit of hydrological investigations at a scale, for which reliable information on soil profile characteristics exists and which is sufficiently fine to account for the heterogeneities of these. Where such information is available, application of DryMOD can assist in the spatial and temporal planning of water harvesting according to runoff-generating areas and the runoff ratio, as well as in the optimization of agricultural activities based on realistic representation of soil moisture conditions.

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We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.