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The Sinai Peninsula in Egypt is highly vulnerable to flash flooding due to its huge variation in relief and erratic rainfall. However, assessing flood risk in the Sinai is a difficult challenge due to the almost complete lack of accurate flood observations and relevant drainage basin characteristics. Therefore, this study evaluates the flood hazard in the Sinai Peninsula through multi-criteria analysis of morphometric characteristics of the drainage patterns, which can be easily inferred from readily available remote sensing data and geographical analysis tools. From a digital elevation model with a spatial resolution of 30 m, 112 catchments are identified, each characterized by twenty morphometric parameters, grouped into three categories: geometry, drainage network and relief. The importance of the morphometric parameters on flooding is assessed with the analytical hierarchy process. Normalized weights for each parameter and category, obtained from pairwise comparison matrices, allow to derive a flood sensitivity index and create a map showing different degrees of flash flood hazard. The results show that basins characterized as highly sensitive to flood- ing generally have high values for all three morphometric parameter categories: geometry, flow network and relief. About 17% of the Sinai appears to be very highly sensitive to flooding, 39% highly sensitive and 34% moderately sensitive. The very highly flood-prone basins are all located in the southern Sinai mountain ranges, while the highly flood-prone basins are mainly found along the outer edges of the southern Sinai mountain ranges and in some sub-basins of wadi El-Aris. Comparison with case studies reported in other publications and the media shows a strong agreement, indicating that the proposed methodology is reliable and accurate. Based on the obtained flash flood sensitivity map, management plans are proposed to reduce the risk of flash flooding and to protect major cities and roads by installing side channels and culverts to collect and drain the excess water from areas most affected prone to flooding., The Sinai Peninsula in Egypt is highly vulnerable to flash flooding due to its huge variation in relief and erratic rainfall. However, assessing flood risk in the Sinai is a difficult challenge due to the almost complete lack of accurate flood observations and relevant drainage basin characteristics. Therefore, this study evaluates the flood hazard in the Sinai Peninsula through multi-criteria analysis of morphometric characteristics of the drainage patterns, which can be easily inferred from readily available remote sensing data and geographical analysis tools. From a digital elevation model with a spatial resolution of 30 m, 112 catchments are identified, each characterized by twenty morphometric parameters, grouped into three categories: geometry, drainage network and relief. The importance of the morphometric parameters on flooding is assessed with the analytical hierarchy process. Normalized weights for each parameter and category, obtained from pairwise comparison matrices, allow to derive a flood sensitivity index and create a map showing different degrees of flash flood hazard. The results show that basins characterized as highly sensitive to flooding generally have high values for all three morphometric parameter categories: geometry, flow network and relief. About 17% of the Sinai appears to be very highly sensitive to flooding, 39% highly sensitive and 34% moderately sensitive. The very highly flood-prone basins are all located in the southern Sinai mountain ranges, while the highly flood-prone basins are mainly found along the outer edges of the southern Sinai mountain ranges and in some sub-basins of wadi El-Aris. Comparison with case studies reported in other publications and the media shows a strong agreement, indicating that the proposed methodology is reliable and accurate. Based on the obtained flash flood sensitivity map, management plans are proposed to reduce the risk of flash flooding and to protect major cities and roads by installing side channels and culverts to collect and drain the excess water from areas most affected prone to flooding.

The Geba basin is one of the most food-insecure areas of the Tigray regional state in northern Ethiopia due to recurrent drought resulting from erratic distribution of rainfall. Since the beginning of the 1990s, rain-fed agriculture has been supported through small-scale irrigation schemes mainly by surface-water harvesting, but success has been limited. Hence, use of groundwater for irrigation purposes has gained considerable attention. The main purpose of this study is to assess groundwater resources in the Geba basin by means of a MODFLOW modeling approach. The model is calibrated using observed groundwater levels, yielding a clear insight into the groundwater flow systems and reserves. Results show that none of the hydrogeological formations can be considered as aquifers that can be exploited for large-scale groundwater exploitation. However, aquitards can be identified that can support small-scale groundwater abstraction for irrigation needs in regions that are either designated as groundwater discharge areas or where groundwater levels are shallow and can be tapped by hand-dug wells or shallow boreholes.

The Gaza Strip is in a chronic state of water shortage and the coastal aquifer as the only freshwater source is increasingly depleted and polluted, especially by nitrate. Assessment of groundwater vulnerability to pollution is essential for adequate protection and management. In this study, the assessment of the aquifer vulnerability to contamination is derived by applying the DRASTIC procedure, firstly with original default weights and ratings and, secondly, improved by estimating rating values by multiple linear regression of observed log-transformed nitrate concentration in groundwater, with DRASTIC factors extended to land-use. The results are very different because high and low vulnerability areas shift considerably. Subsequently, a geostatistical analysis of the spatial distribution of the nitrate concentration is performed, firstly by ordinary kriging interpolation of the observed nitrate concentration and secondly by regression kriging using DRASTIC factors and land-use as indicators of the spatial variation in nitrate occurrence. These maps differ because the map obtained by regression kriging interpolation shows much more details of environmental factors such as dunes, ridges, soil types and built-up areas that affect the presence of nitrate in groundwater. The results of this study can be used by the Palestinian authorities concerned with sustainable groundwater management in the Gaza Strip.

The Nubian sandstone aquifer is the only water source for domestic use and irrigation in the Kharga oasis, Egypt. In this study, 46 pumping tests are analyzed to estimate the transmissivity of the aquifer and to derive a spatial distribution map by geostatistical analysis and kriging interpolation. The resulting transmissivity values are log-normally distributed and spatially correlated over a distance of about 20 km. Representative values for the transmissivity are a geometric average of about 400 m2/d and a 95% confidence interval of 100&ndash, 1475 m2/d. There is no regional trend in the spatial distribution of the transmissivity, but there are local clusters with higher or lower transmissivity values. The error map indicates that the highest prediction accuracy is obtained along the central north-south traffic route along which most agricultural areas and major well sites are located. This study can contribute to a better understanding of the hydraulic properties of the Nubian sandstone aquifer in the Kharga oasis for an effective management strategy.

A double constraint method is proposed to analyze pumping tests in a confined aquifer. An expression for estimation of aquifer transmissivity is derived by minimizing the sum of squared err...

This chapter introduces the subject matter: parameter estimation for groundwater flow models by the double constraint methodology (DCM). After a brief introduction of forward and inverse modeling, the difference between imaging and calibration is mentioned. Some relevant literature is reviewed, and the contents of the chapters of this book are presented.

This chapter deals with the basic elements of the double constraint methodology (DCM). Since the DCM is an imaging method, while most parameter estimation methods are calibration methods, the difference is introduced in Sect. 3.1 and worked out in much greater depths in Sect. 3.9. Section 3.2 introduces the DCM in its most simple, intuitive form. To avoid presentation of three times the same equation for the three conductivity components in anisotropic media, we introduce the “voxel notation” in Sect. 3.3. Sections 3.4 and 3.5 present the theoretical justifications for the “intuitive DCM” presented in Sect. 3.2, while Sect. 3.6 discusses convergence and termination of the iterations. Section 3.7 presents some different approaches to handling anisotropy. Since the DCM is based on two models—the flux model and the head model—the rules for posing the correct boundary conditions for the two models are dealt with in Sect. 3.8.

This chapter deals with the basic equation governing groundwater flow. In Sect. 2.1, the equations are presented in their full four-dimensional form (three spatial dimensions + time) with emphasis on the parameters. Section 2.2 introduces Calderon’s approach to determination of the spatially heterogeneous hydraulic conductivity field. To avoid negative hydraulic conductivities in the double constraint methodology (DCM), this approach is based on the square root of the hydraulic conductivity (sqrt-conductivity α). Section 2.3 introduces Stefanescu’s α-center method for parameter estimation, while Sect. 2.4 analyzes Calderon’s approach in more depth using inspiration from Stefanesco’s method. Although this chapter sets the scene for the real subject matter of this book—the double constraint methodology (DCM)—its reading may be skipped by readers who want to go directly to the DCM treated in Chap. 3.

This chapter deals with a realistic case study in which a time series of conductivity observations by the double constraint methodology (DCM) is combined with the Kalman filter to determine the observation uncertainty from the spread of the observation noise.

We start in Sect. 4.1 with the specific storage coefficient and the specific yield, which occur in terms with a time derivative. Next, we deal with flow caused by time-dependent driving mechanisms like recharge and varying pumping well rates. In such cases, we can determine a series of time-dependent parameter estimates, from which the mean value and the standard deviation (the spread of the observation noise) can be determined. According to Bayesian theory on conditional probabilities, the uncertainty in a parameter’s mean value will decrease below the spread with increasing length of the time series. Section 4.2 presents the relevant Kalman filter equations, while Sect. 4.3 presents a comprehensive in-depth explanation. This section also presents calibration by the ensemble Kalman filter (EnKF) with hints how the double constraint methodology (DCM) could be applied to mitigate some disadvantages of the EnKF.

This chapter deals with an extension of the double constraint methodology to handle clusters of grid volumes, or zones. In each zone, the conductivities in the grid volume are correlated. The equations presented in this chapter exemplify this “zone interaction” for clusters with the same grid block conductivities. The results of a case study with a limited number of zones are summarized.

This chapter summarizes the double constraint methodology for the estimation of the heterogeneous conductivity fields for groundwater flow modeling. Also, the combination of the double constraint methodology with the Kalman filter to determine the uncertainty of the estimated conductivities is summarized.

Selection of landfill sites for solid waste disposal is one of the biggest problems in urban areas because of impacts on public health. Based on the nature of the study area, appropriate landfill sites should be selected using multi-criteria evaluation techniques. There are three main landfill sites in the Gaza Strip, one in the Rafah Governorate, one in the Middle Governorate, and one in the Gaza Governorate. The Gaza Strip is one of the most densely populated areas in the world having a population density of 4660 persons per square kilometer and, hence, faces enormous difficulties with respect to waste disposal. At present, the available waste disposal sites are insufficient. This study focuses on the selection of new suitable landfill sites in the Gaza Strip using multi-criteria decision analysis (MCDA) with the help of the analytical hierarchy process (AHP) method. To achieve this aim, different thematic layers such as land use, soil type, depth to groundwater, distance from roads, rainfall, and elevation are considered. The results show that only 5.5 % of the total area of the Gaza Strip is highly suitable for landfill sites. The high suitable zone for landfill sites is predominantly located in the southeast of the Khan-Younis and Rafah Governorates. These suitable areas consist of cultivated area or natural resources, with sandy loess soil over loess, clay loam, or loessal sandy soil. The depth to ground water varies from 70 to 100 m from soil surface, the rainfall varies from 200 to 350 mm/year, and the altitudes are between 60 and 80 m above mean sea level. Moreover, these suitable sites are also located within 500 m from the road network. This information can be used by concerned authorities and stakeholders for establishing new solid waste disposal sites in the Gaza Strip.

Summary Five hydrological models with different spatial resolutions and process descriptions were applied to a medium sized catchment in Belgium in order to assess the accuracy and differences of simulated hydrological variables, including peak and low flow extremes and quick and slow runoff subflows. The models varied from the lumped conceptual NAM, PDM and VHM models over the intermediate detailed and distributed WetSpa model to the highly detailed and fully distributed MIKE SHE model. The latter model accounts for the 3D groundwater processes and interacts bi-directionally with a full hydrodynamic MIKE 11 river model. A consistent protocol to model calibration was applied to all models. This protocol uses information on the response behavior of the catchment extracted from the river flow and input time series and explicitly focuses on reproducing the quick and slow runoff subflows, and the extreme high and low flows next to testing the conventional model performance statistics. Also the model predictive capacity under high rainfall intensities, which might become more extreme under future climate change was explicitly verified for the models. The tail behavior of the extreme flow distributions was graphically evaluated as well as the changes in runoff coefficients in relation to the changing rainfall intensities. After such calibration, all tested models succeed to produce high performance for the total runoff and quick and slow runoff subflow dynamics and volumes, peak and low flow extremes and their frequency distributions. Calibration of the lumped parameter models is much less time consuming and produced higher overall model performance in comparison to the more complex distributed models.

This study investigates how extreme flows in the Grote Nete watershed located in the Flanders region of Belgium will respond to climate change and urban growth using the hydrological model WetSpa. Three climate change scenarios (low, mean and high), three urban development scenarios (low, medium and high) and the nine combined climate–urban change scenarios are considered. The results indicate that extreme low flows would decrease noticeably by climate change, while they would be less sensitive to urban development. On the other hand, extreme peak flows are predicted to increase considerably due to both climate change and urban growth. It is concluded that coupling the effects of land use change with climate change may lead to severe increase in the frequency river floods in winter as well as the frequency of extreme river low flows in summer.

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map.

Summary The Geba basin is one of the most water-stressed areas of Ethiopia, with only a short rainy period from mid-June to mid-September. Because rainfall in this region has been consistently erratic in the last decades, both in time and space, rain-fed agriculture has become problematic. Hence, in order to supplement rain-fed agriculture by irrigation, a detailed understanding of local and regional surface water and groundwater resources is important. The main objective of this study is to assess the available water resources in the Geba basin using a spatially distributed water balance model (WetSpass). Relevant input data for the model is prepared in the form of digital maps using remote sensing images, GIS tools, FAO and NASA databases, field reconnaissance and processing of meteorological and hydrological observations. The model produces digital maps of long-term average, seasonal and annual surface runoff, evapotranspiration and groundwater recharge. Results of the model show that 76% of the precipitation in the basin is lost through evapotranspiration, 18% becomes surface runoff and only 6% recharges the groundwater system. Model predictions are verified against river flow observations and are shown to be reliable. Additional maps are derived of accumulated surface runoff, safe yield for groundwater abstraction and water deficit for crop growth. Comparison of existing reservoirs with the accumulated runoff map shows that many reservoirs have failed because their design capacity is much higher than the actual inflow. Comparison of the safe yield map with the crop water deficit map shows that in most areas groundwater can be safely abstracted to supplement the water deficit for crop growth during the wet summer season. However, in the dry winter season the crop water deficit is too high to be supplemented by groundwater abstraction in a sustainable way.

The goal of this paper is to evaluate and compare the consistency of GIS-based heuristic and bivariate landslide susceptibility mapping techniques in the Himalayan region, taking the Kulekhani watershed of central Nepal as an example. For this purpose, a heuristic and two statistical bivariate landslide susceptibility mapping methods are applied, and three separate landslide susceptibility zonation maps are produced. The maps are compared using three approaches: landslide density analysis, success rate analysis, and agreed area analysis. A comparison of the values obtained from landslide density analysis and the curves of success rate analysis indicate that the two bivariate methods produce almost identical results, whereas the map produced with the heuristic method differs significantly from the others. On the other hand, the agreed area analysis highlights significant spatial differences in the maps obtained from the three methods. Although the three approaches evaluate the consistency of susceptibility maps, only the agreed area analysis is capable of spatially comparing them. Hence, this approach proves to be more suitable for spatially and quantitatively evaluating the consistency of various landslide susceptibility zonation maps.

GIS-based landslide susceptibility maps for the Kankai watershed in east Nepal are developed using the frequency ratio method and the multiple linear regression technique. The maps are derived from comparing observed landslides with possible causative factors: slope angle, slope aspect, slope curvature, relative relief, distance from drainage, land use, geology, distance from faults and mean annual rainfall. The consistency of the maps is evaluated using landslide density analysis, success rate analysis and spatially agreed area approach. The first two analyses produce almost identical quantitative results, whereas the last approach is able to reveal spatial differences between the maps and also to improve predictions in the agreed high landslide-susceptible area.

Potential effects of climate change on streamflow and soil moisture in Vermilion basin, Illinois, are investigated using a spatial distributed hydrological model (WetSpa). The model results show good agreement between observed and simulated discharges at the Leonore gauge station and soil moisture at the Stelle soil moisture recording site. Climate scenarios are derived by statistical downscaling of regional climate predictor variables for two climate scenarios and four time periods. The downscaled local climate variables are used as inputs to the WetSpa model. The results of the hydrological simulations show that streamflow in the Vermilion basin may decrease substantially, especially in summer, as a result of less precipitation but primarily due to increased evapotranspiration. Predictions also indicate that reduced precipitation in summer combined with a substantial increase in evaporative demand may lead to soil moisture deficits in the fall, which could have a negative impact on the natural v...

Hydrological models have been widely used to assess changes in stream discharges by climate change; however, concern might arise on the accurateness of the model predictions under the changed conditions particularly during low flow periods. In this study, two spatially distributed hydrological models MIKE SHE and WetSpa, each representing a different model complexity in terms of process description, data needs, parameter space, degree of calibration, were compared in their estimation of the climate change impact on the flow regimes in a medium-sized catchment in Belgium. The fully integrated, physically based MIKE SHE model, comprising a three-dimensional groundwater flow and river model, was applied to better understand the groundwater flow and groundwater–river interactions under changed climate conditions. Both models were able to capture the flow dynamics very well with high efficiencies and simulated the flow extremes very accurately. The groundwater heads in MIKE SHE and their seasonal variation had a high model performance. The two models simulated similar changes to future flows because of climatic changes. Peak flows were expected to increase or decrease depending, taking the large uncertainty in future climate trends into account. The model structural uncertainties on these high flow predictions were rather limited. Low flows were expected to reduce because of drier conditions by future climate change, indicating elevated low flow risks for Belgium. However, the projected low flow changes differed significantly over the models and even exceeded the uncertainty by the expected climate trends. Smaller impact was predicted by considering the groundwater physics and river interaction in the MIKE SHE model. These changes to the surface water regimes were verified by the changes in groundwater heads. Future groundwater conditions also point towards drier conditions with a small decrease in heads for future summer and autumn periods. Projected variations in winter heads depend on the climate scenario. Copyright © 2012 John Wiley & Sons, Ltd.

Mountainous areas in Nepal are prone to landslides, resulting in an enormous loss of life and property every year. As a first step towards mitigating or controlling such problems, it is necessary to prepare landslide susceptibility maps. Various methodologies have been proposed for landslide susceptibility mapping. This study applies the weight of evidence method to the Tinau watershed in west Nepal. A landslide susceptibility map is prepared on the basis of field observations and available data of geology, land use, topography and hydrology. Predicted susceptibility levels are found to be in good agreement with the locations of past landslides. The results show that about 30 % of the area is highly susceptible to landsliding. The present results provide useful information to the authorities concerning the landslide susceptibility zones and possible improvements for disaster management activities and sustainable development.

Phase 2 of the Distributed Model Intercomparison Project (DMIP 2) was formulated primarily as a mechanism to help guide the US National Weather Service (NWS) as it expands its use of spatially distributed watershed models for operational river, flash flood, and water resources forecasting. The overall purpose of DMIP 2 was to test many distributed models with operational quality data with a view towards meeting NWS operational forecasting needs. At the same time, DMIP 2 was formulated as an experiment that could be leveraged by the broader scientific community as a platform for testing, evaluating, and improving the science of spatially distributed models. This paper presents the key results of the DMIP 2 experiments conducted for the Oklahoma region, which included comparison of lumped and distributed model simulations generated with uncalibrated and calibrated parameters, water balance tests, routing and soil moisture tests, and simulations at interior locations. Simulations from 14 independent groups and 16 models are analyzed. As in DMIP 1, the participant simulations were evaluated against observed hourly streamflow data and compared with simulations generated by the NWS operational lumped model. A wide range of statistical measures are used to evaluate model performance on both run-period and event basis. A noteworthy improvement in DMIP 2 was the combined use of two lumped models to form the benchmark for event improvement statistics, where improvement was measured in terms of runoff volume, peak flow, and peak timing for between 20 and 40 events in each basin. Results indicate that in general, those spatially distributed models that are calibrated to perform well for basin outlet simulations also, in general, perform well at interior points whose drainage areas cover a wide range of scales. Two of the models were able to provide reasonable estimates of soil moisture versus depth over a wide geographic domain and through a period containing two severe droughts. In several parent and interior basins, a few uncalibrated spatially distributed models were able to achieve better goodness-of-fit statistics than other calibrated distributed models, highlighting the strength of those model structures combined with their a priori parameters. In general, calibration solely at basin outlets alone was not able to greatly improve relative model performance beyond that established by using uncalibrated a priori parameters. Further, results from the experiment for returning DMIP 1 participants reinforce the need for stationary data for model calibration: in some cases, the improvements gained by distributed models compared to lumped were not realized when the models were calibrated using inconsistent precipitation data from DMIP 1. Event-average improvement of distributed models over the combined lumped benchmark was measured in terms of runoff volume, peak flow, and peak timing for between 20 and 40 events. The percentage of model-basin pairs having positive distributed model improvement at basin outlets and interior points was 18%, 24%, and 28% respectively, for these quantities. These values correspond to 14%, 33%, and 22% respectively, in DMIP 1. While there may not seem to be much gain compared to DMIP 1 results, the DMIP 2 values were based on more precipitation–runoff events, more model-basin combinations (148 versus 51), more interior ungauged points (9 versus 3), and a benchmark comprised of two lumped model simulations. In addition, we propose a set of statistical measures that can be used to guide the calibration of distributed and lumped models for operational forecasting.

Calibration of a distributed hydrologic model (WetSpa) for modeling river flows is performed using automatic parameter optimization. The main purpose of this research is to provide more confidence in the uncertainty analysis of the model parameters and predictions. A Box-Cox transformation and an autoregressive integrated moving average (ARIMA) time series model are used to transform the correlated and nonstationary model residuals to white noise disturbances, which can be minimized by ordinary least squares optimization. The WetSpa model is applied to the Illinois River basin, with a spatial resolution of 30 m and 1 h time step for a 10-year simulation period (1996–2006). The model is calibrated using river flow records (1996–2002) and validated using the remaining flow data (2002–2006). The results show that simple calibration of the model is inaccurate, as the residuals exhibit heteroscedasticity, which results in inaccurate estimates of the model parameters and large prediction uncertainty. The model calibration is improved when the calibration is combined with a Box-Cox transformation of the discharge and ARIMA modeling of the residuals, which considerably enhances the confidence of the model parameter estimates and of the model predictions

Trifa is the most productive agricultural plain of north-eastern Morocco. The development of agricultural activities during the last few decades has been mainly based on imported water for irrigation. However, irrigation requirements have become so large that groundwater is used as a secondary source to supply the agricultural and domestic water needs, causing a depletion of the groundwater resources, especially during dry periods. A hydrological and a hydrogeological model for the Trifa plain have been developed, which yield information on relevant parameters such as groundwater recharge, and estimate the amount of pumped groundwater needed to meet the irrigation needs. The models (MODFLOW and WetSpass) provide insight into the status and evolution of the groundwater reserves. The results of the study are useful to predict the sustainability of the groundwater resources in the Trifa plain and to evaluate possible management actions. A reduction in groundwater abstraction by at least 25% may be necessary to achieve sustainable conditions.

The paper presents the methodology used to estimate the base and thickness of the Trifa aquifer, north-east Morocco, from very limited drilling and geoelectrical sounding data. The methodology is applicable where the aquifer is a single horizon with considerable lateral variation. The procedure is based on geostatistical techniques: kriging of apparent resistivities, calibration of the resistivities with information from drilling, co-kriging of resistivities and estimation of the thickness of the aquifer by inverting the apparent resistivity formula. Calculated and measured substratum levels are obtained by subtracting the estimated and measured thickness from the topography at the drilling and geo-electrical sounding locations. By co-kriging the estimated substratum levels with the measured values in the drilling locations, a map of the substratum over the plain was prepared.

A case study of slope stability mapping is presented for the A Luoi district situated in the mountainous western part of Thua Thien-Hue Province in Central Vietnam, where slope failures occur frequently and seriously affect local living conditions. The methodology is based on the infinite slope stability model, which calculates a safety factor as the ratio between shear strength and shear stress. The triggering mechanism for slope instability considered in the analysis is the maximum daily precipitation recorded in a 28-year period (1976–2003) taking into account runoff and infiltration predicted with a hydrological model. All necessary physical parameters are derived from topography, soil texture, and land use, in GIS-raster grid format with pixel size of 30 by 30 m. Results of the analysis are compared with a slope failure inventory map of 2001, showing that more than 86.9 % of the existing slope failures are well predicted by the physically based slope stability model. It can be concluded that the larger part of the study area is prone to landsliding. The resulting slope stability map is useful for further research and land-use planning, but for precise prediction of future slope failures, more effort is needed with respect to spatial variation of causative factors and analysis techniques.

Summary The objective of this paper is to investigate the effects of hydrological model structure and calibration on climate change impact results in hydrology. The uncertainty in the hydrological impact results is assessed by the relative change in runoff volumes and peak and low flow extremes from historical and future climate conditions. The effect of the hydrological model structure is examined through the use of five hydrological models with different spatial resolutions and process descriptions. These were applied to a medium sized catchment in Belgium. The models vary from the lumped conceptual NAM, PDM and VHM models over the intermediate detailed and distributed WetSpa model to the fully distributed MIKE SHE model. The latter model accounts for the 3D groundwater processes and interacts bi-directionally with a full hydrodynamic MIKE 11 river model. After careful and manual calibration of these models, accounting for the accuracy of the peak and low flow extremes and runoff subflows, and the changes in these extremes for changing rainfall conditions, the five models respond in a similar way to the climate scenarios over Belgium. Future projections on peak flows are highly uncertain with expected increases as well as decreases depending on the climate scenario. The projections on future low flows are more uniform; low flows decrease (up to 60%) for all models and for all climate scenarios. However, the uncertainties in the impact projections are high, mainly in the dry season. With respect to the model structural uncertainty, the PDM model simulates significantly higher runoff peak flows under future wet scenarios, which is explained by its specific model structure. For the low flow extremes, the MIKE SHE model projects significantly lower low flows in dry scenario conditions in comparison to the other models, probably due to its large difference in process descriptions for the groundwater component, the groundwater–river interactions. The effect of the model calibration was tested by comparing the manual calibration approach with automatic calibrations of the VHM model based on different objective functions. The calibration approach did not significantly alter the model results for peak flow, but the low flow projections were again highly influenced. Model choice as well as calibration strategy hence have a critical impact on low flows, more than on peak flows. These results highlight the high uncertainty in low flow modelling, especially in a climate change context.

Summary An analytical solution is presented for delineating the catchment zone of a well located near a groundwater divide in a recharged aquifer. The analytical solution is derived for an infinitely wide, homogeneous, isotropic and uniformly thick semi-confined aquifer. It is shown that the shape and extent of the well catchment zone only depend on the distance to the groundwater divide and on the ratio of the well pumping and groundwater recharge rates. The catchment zone of a pumping well extends beyond the groundwater divide, such that the total area affected at the other side of the groundwater divide equals one half of the well-catchment area, and consists of a part from which groundwater is captured by the well and another part from which groundwater is flowing to the other side but bypassing the well. The amount of groundwater transfer involved equals half of the pumping rate. In case of an injection well, the catchment zone comprises the whole aquifer when the well is sufficiently close to the groundwater divide, and otherwise consist of an infinite but laterally bounded zone in the direction away from the groundwater divide. In both cases, the aquifer at the other side of the groundwater divide gains half of the well injection rate either directly by flow from the injection well or indirectly by capturing groundwater recharge from the other side of the groundwater divide.