Statistical downscaling model for late-winter rainfall over Southwest China

A statistical downscaling model is built for the late-winter rainfall over Southwest China(SWC).A partial-correlation method is used for selecting factors.The results show that the selected factors for late-winter rainfall in SWC are sea level pressure in Western Europe(SNAO)and sea surface temperature in Western Pacific(WPT).SNAO is related to the southern pole of North Atlantic Oscillation(NAO)and excites Southern Eurasian teleconnection,which influences the development of the southern branch trough and the water vapor transport to SWC.WPT indicates the variability of ENSO in the tropical Western Pacific.WPT excites Pacific-East Asia teleconnection and an anticyclone(cyclone)is formed in the southern part of China and suppresses(enhances)rainfall over SWC.A regression statistical downscaling model using SNAO and WPT shows good performance in fitting the variability of late-winter rainfall in the whole SWC region and every observation station,and the model also shows strong robustness in the independent validation.The statistical model can be used for downscaling output from seasonal forecast numerical models and improve the SWC late winter rainfall prediction in the future.     

Temporal stability of soil water content as affected by climate and soil hydraulic properties: a simulation study

Temporal stability of soil water content (TS SWC) is an often‐observed phenomenon, which characterization finds multiple applications. Climate and variability in soil properties are usually mentioned as factors of TS SWC, but their effects are far from clear. The objective of this work was to use SWC modeling to evaluate the effects of climate and soil hydraulic properties on the TS of soil water at different measurement schedules. We selected four representative climates found in USA and simulated the multiyear SWC dynamics for sandy loam, loam, and silty clay loam soils, all having the lognormal spatial distribution of the saturated hydraulic conductivity. The CLIMGEN and the HYDRUS6 codes were used to generate weather patterns and to simulate SWC, respectively. Four different methods were applied to select the representative location (RL). The low probability of having the same variability of mean relative differences of soil water under different climates was found in most of the cases. The probability that the variance of mean relative differences depended on sampling frequency was generally higher than 91% for the three soils. The interannual difference in mean relative differences variation from short and intensive summer campaigns was highly probable for all climates and soils. The RLs changed as climate and measurement scheduling changed, and they were less pronounced for coarse‐textured soils. The RL selection methods based solely on bias provided more consistency as compared with other methods. The TS appears to be the result of the interplay between climate, soil properties, and survey protocols. One implication of this factor interaction effect on TS SWC is that a simulation study can be useful to decide on the feasibility of including a search for TS‐based RLs for a specific site. Copyright © 2013 John Wiley & Sons, Ltd.     

Stratification response of soil water content during rainfall events under different rainfall patterns

Many researchers have studied the influence of rainfall patterns on soil water movement processes using rainfall simulation experiments. However, less attention has been paid to the influence under natural condition. In this paper, rainfall, soil water content (SWC), and soil temperature at 10‐, 20‐, 30‐, 40‐, and 50‐cm depths were simultaneously monitored at 1‐min intervals to measure the variation in SWC (SWCv) in response to rainfall under different rainfall patterns. First, we classified rainfall events into four patterns. During the study period, the main pattern was the advanced rainfall pattern (38% of all rainfall events), whereas the delayed, central, and uniform rainfall patterns had similar frequencies of about 20%. During natural rainfall, rainwater rapidly passed through the top soil layers (10–40 cm) and was accumulated in the bottom layer (50 cm). When a high rainfall pulse occurred, the water storage balance was disturbed, resulting in the drainage of initial soil water from the top layers into the deeper layers. Therefore, the critical function of the top layers and the bottom layers was infiltration and storage, respectively. The source of water stored in the bottom layer was not only rainfall but also the initial soil water in the upper soil layers. Changes in soil temperature at each soil depth were comonitored with SWCv to determine the movement characteristics of soil water under different rainfall patterns. Under the delayed rainfall pattern, preferential flows preferred to occur. Under the other rainfall patterns, matrix flow was the main form of soil water movement. Rainfall amount was a better indicator than rainfall intensity for SWCv in the bottom layer under the delayed rainfall pattern. These results provide insights into the responses of SWCv under different rainfall patterns in northern China.     

Investigation of Factors Controlling the Regional-Scale Distribution of Dried Soil Layers Under Forestland on the Loess Plateau,China

The effects of drought on plants have been extensively documented in water-limited systems. However, its effects on soil are seldom considered because of the lack of comparative data on profile soil water content (SWC). A dried soil layer (DSL) within the soil profile is a typical indication of soil drought caused by climate change and/or ill-advised human practices. The regional spatial variability, dominant factors, and predictive models of DSL under forestland were explored in the present study. SWC at 0–600 cm of 125 pre-selected sites across the entire Loess Plateau was measured, and then two evaluation indices of DSL (the thickness of DSL, DSLT; SWC within the DSL, DSL–SWC) were calculated. The corresponding soil, topography, plant, and meteorology factors (a total of 28 variables) for each site were also measured. Most of the forestlands across the Plateau had DSL formation within the soil profile (102 of 125 study sites). The DSL levels were considered to be serious, with DSLT generally exceeding 300 cm with a mean DSL–SWC of only 7.9% (field capacity (FC) = 18.1%). DSLT and DSL–SWC indicated a moderate and strong spatial dependence with ranges of 69 and 513 km, respectively. Thicker DSLs were mainly distributed in the center of the Plateau, whereas thinner DSLs were observed in the southern and southeastern parts. In contrast, DSL–SWC distributions demonstrated an obvious decreasing trend from the southeast to the northwest. Dominant factors affecting DSLT under forestlands were FC, bulk density, slope gradient, slope aspect, and capillary water content; while dominant factors for DSL–SWC were FC, aridity, sand content, altitude, vegetation coverage, and evaporation. Moreover, predictive models developed by multiple regressions were relatively accurate when predicting DSLs, especially DSL–SWC. Understanding these associations with DSLs formation in forestland is helpful for efficient water resource management, silviculture, and eco-environment restoration on the Loess Plateau and in other water-limited regions around the world.     

Temporally stable patterns but seasonal dependent controls of soil water content: Evidence from wavelet analyses

Scale‐ and location‐dependent relationships between soil water content (SWC) and individual environmental factors have been widely explored. SWC is controlled by multiple factors concurrently; however, the multivariate relationship is rarely explored at different scales and locations. Multivariate controls of SWC at different scales and locations in two seasons within a hummocky landscape of North America were identified using bivariate wavelet coherency and multiple wavelet coherence. Results showed that depth to CaCO₃ layer, which was correlated with elevation over all locations at scales of 36–144 m and cos(aspect), provided the best individual factor for explaining SWC variations in spring (May 2) and summer (August 23), respectively. Although spatial patterns of SWC were temporally stable, different topographic indices affected spatial distribution of SWC in different seasons (elevation in spring and aspect in summer) due to different dominating hydrological processes. These varying hydrological processes also resulted in the distinct role of soil organic carbon (SOC) content in different seasons: a positive correlation in spring and a negative correlation in summer. Multiple wavelet coherence identified a combination of depth to CaCO₃ layer and SOC in spring and a combination of cos(aspect) and SOC in summer that controlled SWC at different scales and locations, respectively. This indicated a combined effect of soil and topographic properties on SWC distribution and a clear need for these two factors in developing scale‐dependent prediction of SWC in the hummocky landscape of North America.     

A stochastic model describing the impact of daily rainfall depth distribution on the soil water balance

Simplified, vertically-averaged soil moisture models have been widely used to describe and study eco-hydrological processes in water-limited ecosystems. The principal aim of these models is to understand how the main physical and biological processes linking soil, vegetation, and climate impact on the statistical properties of soil moisture. A key component of these models is the stochastic nature of daily rainfall, which is mathematically described as a compound Poisson process with daily rainfall amounts drawn from an exponential distribution. Since measurements show that the exponential distribution is often not the best candidate to fit daily rainfall, we compare the soil moisture probability density functions obtained from a soil water balance model with daily rainfall depths assumed to be distributed as exponential, mixed-exponential, and gamma. This model with different daily rainfall distributions is applied to a catchment in New South Wales, Australia, in order to show that the estimation of the seasonal statistics of soil moisture might be improved when using the distribution that better fits daily rainfall data. This study also shows that the choice of the daily rainfall distributions might considerably affect the estimation of vegetation water-stress, leakage and runoff occurrence, and the whole water balance.     

Groundwater recharge estimation using improved soil moisture balance methodology for a tropical climate with distinct dry seasons

Abstract

Groundwater recharge is estimated using an improved daily soil moisture balance based on a single soil water store for a climate classified as tropical with distinct dry seasons; an upland area in northwest Sri Lanka is used as an example. When the water availability is limited and the soil is under stress, the actual evapotranspiration is less than the potential value; the stress factor is estimated in terms of the readily and total available water, soil properties and effective root depth. Runoff is estimated using coefficients which depend on rainfall intensity and soil moisture deficits. A new component, near surface storage, is used to represent continuing evapotranspiration on days following heavy rainfall even though the soil moisture deficit is high. Recharge is estimated for permanent grass and a commonly cultivated vegetable crop. The plausibility of the model outputs is examined using independent information and data, including well water level fluctuations. Uncertainties and variations in parameter values are explored using sensitivity analyses.     

A distributed monthly hydrological model for integrating spatial variations of basin topography and rainfall

Hydrological models at a monthly time‐scale are important tools for hydrological analysis, such as in impact assessment of climate change and regional water resources planning. Traditionally, monthly models adopt a conceptual, lumped‐parameter approach and cannot account for spatial variations of basin characteristics and climatic inputs. A large requirement for data often severely limits the utility of physically based, distributed‐parameter models. Based on the variable‐source‐area concept, we considered basin topography and rainfall to be two major factors whose spatial variations play a dominant role in runoff generation and developed a monthly model that is able to account for their influences in the spatial and temporal dynamics of water balance. As a hybrid of the Xinanjiang model and TOPMODEL, the new model is constructed by innovatively making use of the highly acclaimed simulation techniques in the two existing models. A major contribution of this model development study is to adopt the technique of implicit representation of soil moisture characteristics in the Xinanjiang model and use the TOPMODEL concept to integrate terrain variations into runoff simulation. Specifically, the TOPMODEL topographic index ln(a/tanβ) is converted into an index of relative difficulty in runoff generation (IRDG) and then the cumulative frequency distribution of IRDG is used to substitute the parabolic curve, which represents the spatial variation of soil storage capacity in the Xinanjiang model. Digital elevation model data play a key role in the modelling procedures on a geographical information system platform, including basin segmentation, estimation of rainfall for each sub‐basin and computation of terrain characteristics. Other monthly data for model calibration and validation are rainfall, pan evaporation and runoff. The new model has only three parameters to be estimated, i.e. watershed‐average field capacity WM, pan coefficient η and runoff generation coefficient α. Sensitivity analysis demonstrates that runoff is least sensitive to WM and, therefore, it can be determined by a prior estimation based on the climate and soil properties of the study basin. The other two parameters can be determined using optimization methods. Model testing was carried out in a number of nested sub‐basins of two watersheds (Yuanjiang River and Dongjiang River) in the humid region in central and southern China. Simulation results show that the model is capable of describing spatial and temporal variations of water balance components, including soil moisture content, evapotranspiration and runoff, over the watershed. With a minimal requirement for input data and parameterization, this terrain‐based distributed model is a valuable contribution to the ever‐advancing technology of hydrological modelling. Copyright © 2006 John Wiley & Sons, Ltd.     

The role of rainfall temporal and spatial averaging in seasonal simulations of the terrestrial water balance

The partitioning of rainfall into surface runoff and infiltration influences many other aspects of the hydrologic cycle including evapotranspiration, deep drainage and soil moisture. This partitioning is an instantaneous non-linear process that is strongly dependent on rainfall rate, soil moisture and soil hydraulic properties. Though all rainfall datasets involve some degree of spatial or temporal averaging, it is not understood how this averaging affects simulated partitioning and the land surface water balance across a wide range of soil and climate types. We used a one-dimensional physics-based model of the near-surface unsaturated zone to compare the effects of different rainfall discretization (5-min point-scale; hourly point-scale; hourly 0.125° gridded) on the simulated partitioning of rainfall for many locations across the United States. Coarser temporal resolution rainfall data underpredicted seasonal surface runoff for all soil types except those with very high infiltration capacities (i.e., sand, loamy sand). Soils with intermediate infiltration capacities (i.e., loam, sandy loam) were the most affected, with less than half of the expected surface runoff produced in most soil types when the gridded rainfall dataset was used as input. The impact of averaging on the water balance was less extreme but non-negligible, with the hourly point-scale predictions exhibiting median evapotranspiration, drainage and soil moisture values within 10% of those predicted using the higher resolution 5-min rainfall. Water balance impacts were greater using the gridded hourly dataset, with average underpredictions of ET up to 27% in fine-grained soils. The results suggest that “hyperresolution” modelling at continental to global scales may produce inaccurate predictions if there is not parallel effort to produce higher resolution precipitation inputs or sub-grid precipitation parameterizations.     

Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment

Estimation of runoff is a prerequisite for many applications involving conservation and management of water resources. This study is undertaken in the Upper Damodar Valley Catchment (UDVC) having a drainage area of 17513.08 km² for prediction of monthly runoff. Thirty one microwatersheds and 15 sub-watersheds were selected from a total of 716 microwatersheds in the catchment area for this study. The feasibility of using different soil attributes (particle size distribution, organic matter content and apparent density), topographic attributes (primary, secondary and compound), geomorphologic attributes (basin, relief and network indices) and vegetation attribute as Normalized Difference Vegetation Index (NDVI), on prediction of monthly runoff were explored in this study. Principal Component Analysis (PCA) was applied to minimize the data redundancy of the input variables. Ten significant input variables namely; watershed length (km), elongation ratio, bifurcation ratio, area ratio, coarse sand (%), fine sand (%), elevation (m), slope (°), profile curvature (rad/m) and NDVI were selected. The selected input variables were added in hierarchy with monthly rainfall (mm) as inputs for prediction of monthly runoff (mm) using Bootstrap based artificial neural networks (BANN). The performance of the models was tested using Spearman’s correlation coefficient (r), coefficient of efficiency (COE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Best performance was observed for model with monthly rainfall, slope, coarse sand, bifurcation ratio and Normalized Difference Vegetation Index (NDVI) as inputs (r = 0.925 and COE = 0.839). Increase in number of input variables did not necessarily yield better performances of the BANN models. Selection of relevant inputs and their combinations were found to be key elements in determining the performance of BANN models. Annual runoff map was generated for all the microwatersheds utilizing the weights of the best performing BANN model. This study reveals that the specific combinations of soil, topography, geomorphology and vegetation inputs can be utilized for better prediction of monthly runoff.     

A hillslope hydrology approach for catchment‐scale slope stability analysis

Regional analysis of slope stability is often constrained by availability of data. Model requirements for input data cannot be met at the desired spatial resolution because data are either site‐speci?c or non‐existent. Faced with these dif?culties it has often been the practice to assume that certain parameters are uniform throughout the area of interest. An alternative approach proposed here allows a more detailed discrimination of slope stability conditions. Based on the principles of hillslope hydrology, hydrologic information can be generated at suf?cient resolution to allow higher resolution slope stability analysis. Measurements from an instrumented network in a small area have been used to establish index‐based models for topographic and climate‐related controls of piezometric response. The ability to relate groundwater levels to rainfall and topographic parameters provides a means of up‐scaling to larger catchments and ultimately the opportunity to generate a catchment‐wide prediction of the distribution, magnitude and frequency of rainstorm‐generated groundwater levels. The example provided in this study uses the topography index of TOPMODEL in GIS to predict the spatial patterns of groundwater elevation for seasonal soil moisture conditions and given rainfall inputs. This allows modelling of catchment‐wide response of soil water to rainstorms with different return periods (representing different magnitudes), and is an essential prerequisite for a probabilistic regional slope stability analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Spatio-temporal variability of surface soil water content and its influencing factors in a desert area,China

Abstract

Knowledge of the variability of soil water content (SWC) in space and time plays a key role in hydrological and climatic modelling. However, limited attention has been given to arid regions. The focus of this study was to investigate the spatio-temporal variability of surface soil (0–6 cm) water content and to identify its controlling factors in a region of the Gobi Desert (40 km²). The standard deviation of SWC decreased logarithmically as mean water content decreased, and the coefficient of variation of SWC exhibited a convex upward pattern. The spatial variability of SWC also increased with the size of the investigated area. The spatial dependence of SWC changed over time, with stronger patterns of spatial organization in drier and wetter conditions of soil wetness and stochastic patterns in moderate soil water conditions. The dominant factors regulating the variability of SWC changed from combinations of soil and topographical properties (bulk density, clay content and relative elevation) in wet conditions to combinations of soil and vegetation properties (bulk density, clay content and shrub coverage) in dry conditions. This study has important implications for the assessment of soil quality and the sustainability of land management in arid regions.     

Vulnerability of water resources,vegetation productivity and soil erosion to climate change in Mediterranean watersheds

Climate change is expected to increase temperatures and lower rainfall in Mediterranean regions; however, there is a great degree of uncertainty as to the amount of change. This limits the prediction capacity of models to quantify impacts on water resources, vegetation productivity and erosion. This work circumvents this problem by analysing the sensitivity of these variables to varying degrees of temperature change (increased by up to 6·4 °C), rainfall (reduced by up to 40%) and atmospheric CO₂ concentrations (increased by up to 100%). The SWAT watershed model was applied to 18 large watersheds in two contrasting regions of Portugal, one humid and one semi‐arid; incremental changes to climate variables were simulated using a stochastic weather generator. The main results indicate that water runoff, particularly subsurface runoff, is highly sensitive to these climate change trends (down by 80%). The biomass growth of most species showed a declining trend (wheat down by 40%), due to the negative impacts of increasing temperatures, dampened by higher CO₂ concentrations. Mediterranean species, however, showed a positive response to milder degrees of climate change. Changes to erosion depended on the interactions between the decline in surface runoff (driving erosion rates downward) and biomass growth (driving erosion rates upward). For the milder rainfall changes, soil erosion showed a significant increasing trend in wheat fields (up to 150% in the humid watersheds), well above the recovery capacity of the soil. Overall, the results indicate a shift of the humid watersheds to acquire semi‐arid characteristics, such as more irregular river flows and increasingly marginal conditions for agricultural production. Copyright © 2007 John Wiley & Sons, Ltd.     

Wavelet–copula‐based mutual information for rainfall forecasting applications

Under a climate change, the physical factors that influence the rainfall regime are diverse and difficult to predict. The selection of skilful inputs for rainfall forecasting models is, therefore, more challenging. This paper combines wavelet transform and Frank copula function in a mutual information‐based input variable selection (IVS) for non‐linear rainfall forecasting models. The marginal probability density functions (PDFs) of a set of potential rainfall predictors and the rainfall series (predictand) were computed using a wavelet density estimator. The Frank copula function was applied to compute the joint PDF of the predictors and the predictand from their marginal PDFs. The relationship between the rainfall series and the potential predictors was assessed based on the mutual information computed from their marginal and joint PDFs. Finally, the minimum redundancy maximum relevance was used as an IVS stopping criterion to determine the number of skilful input variables. The proposed approach was applied to four stations of the Nigerien Sahel with rainfall series spanning the period 1950–2016 by considering 24 climate indices as potential predictors. Adaptive neuro‐fuzzy inference system, artificial neural networks, and random forest‐based forecast models were used to assess the skill of the proposed IVS method. The three forecasting models yielded satisfactory results, exhibiting a coefficient of determination between 0.52 and 0.69 and a mean absolute percentage error varying from 13.6% to 21%. The adaptive neuro‐fuzzy inference system performed better than the other models at all the stations. A comparison made with KDE‐based mutual information showed the advantage of the proposed wavelet–copula approach.     

Spatial patterns of soil water balance on intensively cultivated hillslopes in a semi‐arid environment: the impact of rock fragments and soil thickness

During past decades, a diverse system of subsistence agriculture in south‐east Spain (annual rainfall of less than 300 mm) has been overturned in favour of large‐scale plantations of almond trees without consideration for topography and related spatial patterns in soil hydrological properties. The objective of this paper is to investigate the spatial pattern in soil physical properties induced by this cultivation system, and to highlight its impact on the water balance. Soil properties were recorded along hillslopes with shallow soils developed on slates and greywackes in the upper part of the Guadelentin drainage basin (Murcia region). Frequent tillage of these almond plantations covering entire hillslopes has resulted in denudation by tillage erosion on the topographic convexities, as well as transport of rock fragments and fine earth along the slopes. These processes have created a systematic spatial pattern of soil thickness and rock fragment content: shallow and stony soils on the topographic convexities and deep soils with a rock fragment mulch in the concavities at the foot of the slopes. At the same time, a negative relationship between rock fragment content and fine earth bulk density was observed. The impact of this spatial pattern in soil properties on the water balance was evaluated using the PATTERN one‐dimensional hydrological and plant growth model. The model simulates the water balance of soil profiles covering the observed variation in soil thickness, stoniness and bulk density. The model results indicate that the highest rates of infiltration, evaporation and drainage, as well as the lowest rates of overland flow are restricted to shallow soils on the hilltops. In contrast, the deeper soils in the valley bottoms produce a more stable moisture regime than shallower soils, which tend to saturate and dry out quickly. These model results are in agreement with the spatial patterns of almond productivity: an asymptotic increase with soil thickness. Copyright © 2000 John Wiley & Sons, Ltd.     

Responses of soil moisture at different topographic positions to rainfall events along a steep subtropical forested hillslope

Understanding the dynamic response of soil moisture to rainfall is crucial for describing hydrological processes at the hillslope scale. However, because of sparse monitoring coupled with the complexity of water movement and steep topography, the findings of rainfall-related soil moisture dynamics have not always been consistent, indicating a need for systematic investigations of soil moisture dynamics and infiltration patterns following rainfall inputs at multiple topographic positions along a hillslope. This study aimed to examine the nature of these responses by characterizing and quantifying the response amplitude, rate and time for 37 large rainfall events at 25 combinations of topographic positions and soil depths along a steep forested hillslope. Our results showed that soil moisture responses under different rainfall patterns could be attributed to one or the other rainfall characteristics, such as rainfall intensity and amount. However, soil moisture dynamics at different hillslope positions after rainfall varied widely due to the controls of soil properties, topography, and non-equilibrium flow. Preferential flow was more evident under dry initial soil conditions than under wet initial soil conditions. Findings of this study reveal that the dynamic response patterns of soil moisture to rainfall do not always follow topographic controls, which can improve our understanding of water cycling related to the infiltration process at the hillslope scale, and support water resources management in subtropical mountain ecosystems.     

Modelling to analyse the impacts of animal treading effects on soil infiltration

Animal treading can change soil physical properties, and thus is an important factor in hydrological modelling. We investigated the impacts of animal treading on infiltration by using a series of rainfall simulation experiments at Whatawhata Research Center, Waikato, New Zealand. The study identified significant variables for estimating soil steady‐state infiltration at a micro‐site (0·5 m²) and fitted the Green and Ampt equation by modifying or including variables for soil and water parameters and animal activities on grazing paddocks. A regression function for estimating steady‐state infiltration rate was created for each of four scenarios: between tracks (inter‐track), track, easy slope with ash soil, and easy slope with clay soil. Significant variables included the number of days after treading, antecedent soil moisture, field capacity, percentage of bare ground, bulk density, and the high degree of soil damage (damage not compacted). Regression models explained more than 71% of the variance in steady‐state infiltration for three scenarios, but only 53% for the easy slope with clay soil. The remodified Green and Ampt equation provided satisfactory estimation of infiltration for all scenarios (accuracy > 80%), and thus enables us to use the modified model for Waikato hill country pastures of different topography, soil physical condition, season and grazing management. Copyright © 2006 John Wiley & Sons, Ltd.     

Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation

We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.     

Use of terrain variables for mapping gully erosion susceptibility in Lebanon

This paper predicts the geographic distribution and size of gullies across central Lebanon using a geographic information system (GIS) and terrain analysis. Eleven primary (elevation; upslope contributing area; aspect; slope; plan, profile and tangential curvature; flow direction; flow width; flow path length; rate of change of specific catchment area along the direction of flow) and three secondary (steady‐state; quasi‐dynamic topographic wetness; sediment transport capacity) topographic variables were generated and used along with digital data collected from other sources (soil, geology) to statistically explain gully erosion field measurements. Three tree‐based regression models were developed using (1) all variables, (2) primary topographic variables only and (3) different pairs of variables. The best regression tree model combined the steady‐state topographic wetness and sediment transport capacity indices and explained 80% of the variability in field gully measurements. This model proved to be simple, quick, realistic and practical, and it can be applied to other areas of the Mediterranean region with similar environmental conditions, thereby providing a tool to help with the implementation of plans for soil conservation and sustainable management. Copyright © 2007 John Wiley & Sons, Ltd.