Search

Study region: Rangun Khola Watershed, Mahakali River Basin, Far Western Nepal. Study focus: We mapped and examined the status of 1122 springs in a typical mid-hill watershed in the Sudurpaschim Province of Nepal. Land use/cover trajectory analysis, quantification of climate change indices, analysis of spring flow trends and community perception were used to understand the changing dynamics of the mountain springs. New hydrological insights: 73% of the springs show a continuous declining trend in flow, with 2% already dried up. Land fragmentation between 1990 and 2018 due to the conversion to agricultural land causes landscape disturbances in the spring vicinities, affecting natural spring flows. Climate data assessment revealed a significant increase in temperature and frequency of localised high-intensity rainfall. Local climate and land use change are concurrent with drying spring sources and consistent with the local community's perceived manifestation of changes. Moreover, the growing population and haphazard rural road expansion are overexploiting and disturbing the spring resources. Drying spring sources are leading to implications on water availability and accessibility for livelihood activities, exacerbating gendered vulnerability to climate change. However, the government and local households are lagging in taking direct actions to mitigate the problem, increasing the likelihood of critical long-term consequences for the ecosystem and the local economy in Rangun Khola and similar watersheds in the Himalayas.

Groundwater dependent ecosystems (GDEs) are vulnerable to groundwater regime changes. However, their protection is often hampered by challenges in their identification. Within is presented a remote sensing-based GDE potential mapping approach based on the persistency of relevant vegetation parameters during prolonged dry periods as an indicator of potential ‘consistency’ of water supply (e.g. groundwater). The study uses a novel approach to characterising persistency for selected vegetation parameters based on a normalised difference measure and an adaptation of the coefficient of variation statistic. Aggregation of parameters was facilitated through the analytic hierarchy process providing a structured weighting approach to minimise parameter bias. The approach is demonstrated in the semi-arid Flinders Ranges of South Australia where new groundwater resources are being sought to support local domestic and industry needs. Variations in GDE potential were mapped to better target areas where exploration of groundwater should be avoided. Mapping results indicated a high-level of agreement of 77% with an independent springs dataset, along with an 87% agreement with areas coinciding with known phreatophyte species and depths to groundwater. The index-based mapping approach has potential applicability across landscapes, as it normalises for variations in vegetation cover, minimises technical overheads, and employs continental-wide remote sensing data-products.

Study region: The Xilin River Basin in the Inner Mongolia Autonomous Region, China. Study focus: This study aims to identify the components and change patterns of runoff in the basin, and quantitatively assess the impacts of climate change and overgrazing on runoff. Future changes in river basin runoff were simulated under three climate change scenarios. New hydrological insights for the region: This study quantitatively assessed the impacts of climate change and overgrazing on runoff in the Xilin River Basin using the HYPE model and five attribution scenarios. The assessment results are as follows: (1) Monthly runoff in the basin shows typical bimodal characteristics. Snowmelt is the primary contributor to runoff from March to mid-April, while precipitation has the largest contribution in the remaining months. (2) During the growing season, overgrazing has a more significant impact in reducing runoff than climate change, and runoff changes caused by climate change are more complex on a monthly scale than at the annual scale. (3) Among the three development scenarios, SSP126 with low levels of greenhouse gas emissions appeared to be the most suitable for the sustainable and healthy development of the grassland inland river basin.

Accurate estimation of aboveground biomass of grasslands is key to sustainable grassland utilization. However, most satellites cannot provide high temporal and spatial resolution data. Patterns of grassland dynamics associated with variability in climate conditions across spatiotemporal scales are yet to be adequately quantified. A spatiotemporal fusion model offers the opportunity to combine the resolution advantages of different remote sensing data to achieve a high frequency and high precision monitoring of vegetation. We test a flexible spatiotemporal data fusion (FSDAF) methodology to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. The methodology is tested for the semi-arid grassland of the Xilin River Basin, China. Based on NDVI data fusion and field measured aboveground biomass an aboveground biomass estimation model is established for the watershed. Exploring the temporal and spatial changes of biomass and its relationship with environmental factors. The results show that: (1) The FSDAF model performs well (R2 = 0.75) and has clear textural features. (2) The established Support Vector Machine Aboveground Biomass model not only ensured the accuracy of estimation (R2 = 0.78, RMSE = 15.43 g/m2), but also generated spatiotemporal maps of biomass with higher spatial (30 m) and temporal resolution (8 days). (3) The grassland aboveground biomass in this area decreases from southeast to northwest, and the grassland biomass reaches its peak at the end of July. The average biomass of different grasslands decreases in the order of meadow grassland > typical grassland > desert grassland. (4) Aboveground biomass increased linearly with increasing water content, organic carbon and total nitrogen, and was most sensitive to soil water content. During the early growing and rapid growing period, aboveground biomass is mainly affected by both air temperature and precipitation, while the effects of temperature and human activities gradually dominate in the middle and late growing periods. This study helps to improve the spatial and temporal resolution of dynamic monitoring of grassland biomass, and provides a scientific basis for grassland protection and management in arid and semi-arid regions.

Study region: Mond basin, comprising 61 aquifers in southern Iran. Study focus: New indicators and a framework are presented for quantifying the intensity of groundwater stress by considering hydrogeological and anthropogenic factors, including renewable water availability, non-renewable water storage, withdrawal, and return flow. In contrast to existing methods, our proposed indicators consider accumulated historical water stress tolerated by an aquifer during an exploitation period and incorporate groundwater over-extraction from both renewable and non-renewable storages of aquifers. New hydrological insights: We assessed the applicability of existing and newly defined groundwater stress indicators on the Mond aquifers. Our results indicate that the proposed approach is able to inform the degree of groundwater stress and identify vulnerable aquifers with high intensity of groundwater stress. Statistical analysis of long-term mean annual groundwater drawdown and groundwater storage confirmed the results of aquifer classification in terms of intensity of groundwater stress. Finally, the response of aquifers in different stages of water stress was evaluated by scenarios of i) reduced human-induced withdrawal and irrigation return flow and ii) increasing recharge. These scenario analyses indicate that even up to a 50% reduction in human water use or a 50% increase in recharge does not recover the more severely stressed aquifers to sustainable hydrologic conditions.

One of the most critical stages for developing groundwater resources for drinking water use is assessing the water quality. The use of a Water Quality Index (WQI) is considered an effective method of evaluating water quality. The objective of this research was to evaluate the performance of six multiple artificial intelligence techniques, i.e., linear regression (stepwise), support vector regression SVM (linear and polynomial kernels), Gaussian process regression (GPR), Fit binary tree, and artificial neural network ANN (Bayesian) to predict the WQI in Jizan, Southern Saudi Arabia. A total of 145 groundwater samples were collected from shallow dug wells and boreholes tapping the phreatic aquifer. The WQI was calculated from 11 physicochemical parameters (pH, TDS, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, and TH). The spatial distribution results showed that higher values of Cl− and SO42− were recorded in the places close to the coastline, indicating the occurrence of seawater intrusion and salinisation. Seven wells had a WQI of greater than 300, indicating that the water was unfit for consumption. The results showed that the GPR, linear regression (stepwise), and ANN models performed best during the training and testing stages, with a high correlation of 1.00 and low errors. The stepwise fitting model indicated that pH, K+, and NO3− were the most significant variables, while HCO3− was a non-significant variable for the WQI. The GPR, stepwise regression, and ANN models performed best during the training and testing stages, with a high correlation and low errors. In contrast, the SVM and Fit binary tree models performed the worst in the training and testing phases. Based on subset regression analysis, the optimum input combination for WQI model prediction was determined as these eight input combinations with high R2 (0.975–1.00) and high Adj-R2 (0.974–1.00). The resultant WQI model significantly contributes to sustainable groundwater resource management in arid areas and generates improved prediction precision with fewer input parameters.

In the coming years, climate change is predicted to impact irrigation water demand considerably, particularly in semi-arid regions. The aim of this research is to investigate the expected adverse impacts of climate change on water irrigation management in Saudi Arabia. We focus on the influence of climate change on irrigation water requirements in the Al Quassim (97,408 ha) region. Different climate models were used for the intermediate emission SSP2-4.5 and the high emission SSP5-8.5 Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. The FAO-CROPWAT 8.0 model was used to calculate reference evapotranspiration (ETo) using weather data from 13 stations from 1991 to 2020 and for both the SSP2-4.5 and SSP5-8.5 scenarios for the 2040s, 2060s, 2080s, and 2100s. The findings indicated that, for the 2100s, the SSP2-4.5 and SSP5-8.5 scenarios forecast annual average ETo increases of 0.35 mm/d (6%) and 0.7 mm/d (12.0%), respectively. Net irrigation water requirement (NIWR) and growth of irrigation water requirement (GIWR) for the main crops in the Al Quassim region were assessed for the current, SSP2-4.5, and SSP5-8.5 scenarios. For SSP5-8.5, the GIWR for the 2040s, 2060s, 2080s, and 2100s are expected to increase by 2.7, 6.5, 8.5, and 12.4%, respectively, compared to the current scenario (1584.7 million m3). As a result, there will be higher deficits in 2100 under SSP5-8.5 for major crops, with deficits of 15.1%, 10.7%, 8.3%, 13.9%, and 10.7% in the crop areas of wheat, clover, maize, other vegetables, and dates, respectively. Optimal irrigation planning, crop pattern selection, and modern irrigation technologies, combined with the proposed NIWR values, can support water resources management. The findings can assist managers and policymakers in better identifying adaptation strategies for areas with similar climates.

Abstract Australia, the driest inhabited continent, is prone to natural disasters, such as droughts, floods, bushfires, and heatwaves. Strong climate variability causes recurring threats to water supply, agriculture, and the environment. Improving our insight into changes in hydroclimatic patterns is required to provide useful information for society. Previous studies mainly focused on the causes of extreme wet or dry events in specific periods and their impacts on agriculture and ecosystems. An understanding of long‐term spatio‐temporal patterns of wetting and drying in Australia is still lacking. Here we show, based on analyses of Gravity Recovery and Climate Experiment satellite derived terrestrial water storage and extended datasets, that there are four consecutive periods of seesaw wetting and drying between eastern and western Australia in the past five decades. The seesaw phenomenon is characterized by eastern Australia gaining water, while western Australia is losing water, and vice versa. Strong La Niña induced continent‐wide wetting, resets this pattern, leaving each seesaw to last for 11 ± 5 years. We provide one possible mechanism related to vegetation response to climate variability and its feedback on hydrological processes to explain the seesaw pattern. The identified recurring seesaw pattern indicates that society would need to become more adaptive in managing forest, water, and disaster risks in the wake of a next strong La Niña induced continent‐wide wetting in Australia.

Study region: Osun Drainage Basin, Nigeria. Study focus: Estimating spatial and temporal patterns of recharge is important for sustainable groundwater resources management. This is especially true for data poor regions, such as the Basement Complex in Nigeria, which has shallow aquifers, a proliferation of wells and no efficient groundwater monitoring network. This study evaluates the performance of a spatially distributed monthly water balance model (WetSpass-M) in estimating groundwater recharge. The WetSpass-M model has moderate data demands, which allows for comprehensive assessment of recharge. New hydrological insights for the region: 27 % of the rainfall in Osun drainage basin becomes recharge, while the remaining is lost through evapotranspiration (43 %), surface runoff (21 %) and interception (9 %). September is the month with highest recharge, ranging between 0 and 73 mm in the north and 129 up to 213 mm in the south and northeast of the basin. The study revealed the significance of the applied water balance model in understanding the spatial and temporal status of recharge. Therefore, the spatial and temporal patterns of recharge should be taken into consideration in preparing a sustainable groundwater resources management plan for the Osun drainage basin. Artificial recharge might be adopted to store storm water runoff during wet periods to improve the groundwater supply in dry months. Also, monthly groundwater withdrawals should be regulated in relation to spatial and temporal recharge patterns. Keywords: Groundwater, Recharge, WetSpass-M, Groundwater management, Osun basin

Simple analytical and numerical solutions for confined and unconfined groundwater-surface water interaction in one and two dimensions were developed in the STRIVE package (stream river ecosystem) as part of FEMME (flexible environment for mathematically modelling the environment). Analytical and numerical solutions for interaction between one-dimensional confined and unconfined aquifers and rivers were used to study the effects of a 0.5 m sudden rise in the river water level for 24 h. Furthermore, a two-dimensional groundwater model for an unconfined aquifer was developed and coupled with a one-dimensional hydrodynamic model. This model was applied on a 1 km long reach of the Aa River, Belgium. Two different types of river water level conditions were tested. A MODFLOW model was set up for these different types of water level condition in order to compare the results with the models implemented in STRIVE. The results of the analytical solutions for confined and unconfined aquifers were in good agreement with the numerical results. The results of the two-dimensional groundwater model developed in STRIVE also showed that there is a good agreement with the MODFLOW solutions. It is concluded that the facilities of STRIVE can be used to improve the understanding of groundwater-surface water interaction and to couple the groundwater module with other modules developed for STRIVE. With these new models STRIVE proves to be a powerful example as a development and testing environment for integrated water modeling.

Surface soil moisture (SM) plays a fundamental role in energy and water partitioning in the soil–plant–atmosphere continuum. A reliable and operational algorithm is much needed to retrieve regional surface SM at high spatial and temporal resolutions. Here, we provide an operational framework of estimating surface SM at fine spatial resolutions (using visible/thermal infrared images and concurrent meteorological data) based on a trapezoidal space defined by remotely sensed vegetation cover (Fc) and land surface temperature (LST). Theoretical solutions of the wet and dry edges were derived to achieve a more accurate and effective determination of the Fc/LST space. Subjectivity and uncertainty arising from visual examination of extreme boundaries can consequently be largely reduced. In addition, theoretical derivation of the extreme boundaries allows a per-pixel determination of the VI/LST space such that the assumption of uniform atmospheric forcing over the entire domain is no longer required. The developed approach was tested at the Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) site in central Tibet, China, from August 2010 to August 2011 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra images. Results indicate that the developed trapezoid model reproduced the spatial and temporal patterns of observed surface SM reasonably well, with showing a root-mean-square error of 0.06 m3·m−3 at the site level and 0.03 m3·m−3 at the regional scale. In addition, a case study on 2 September 2010 highlighted the importance of the theoretically calculated wet and dry edges, as they can effectively obviate subjectivity and uncertainties in determining the Fc/LST space arising from visual interpretation of satellite images. Compared with Land Surface Models (LSMs) in Global Land Data Assimilation System-1, the remote sensing-based trapezoid approach gave generally better surface SM estimates, whereas the LSMs showed systematic underestimation. Sensitivity analyses suggested that the trapezoid method is most sensitive to field capacity and temperature but less sensitive to other meteorological observations and parameters.

Study region: The area of study is the northern part of Belgium (Flanders). The seven evaluated Aquifer Thermal Energy Storage (ATES) systems are positioned in key aquifers, which contain major groundwater resources for the region. Study focus: The increasing number of ATES systems leads to concerns by drinking water companies and environmental regulators about the long term impacts of ATES systems on the groundwater quality. This study assesses the influence of ATES on groundwater chemistry by means of a literature review, and a comparison of groundwater quality monitoring data at seven ATES systems with ambient groundwater quality values from 69 monitoring wells. New hydrological insights for the region: The results of the analysis of the hydrochemical data confirm that the small temperature differences (ΔT ≤ 10) at which the ATES systems are operating do not influence the concentrations of the main chemical constituents. Mixing of shallow with deeper groundwater during ATES operation, on the other hand, can alter groundwater quality. The results of this study, however, suggest that the groundwater quality changes are rather small, so that there is no immediate risk for the drinking water supply. However, the installation of ATES systems in the vicinity of public drinking water supply well fields should be handled with care, especially in phreatic aquifers.

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful. This paper investigates the use of model-based clustering for SBT classification, and the influence of different clustering approaches on the properties and spatial distribution of the obtained soil classes. We additionally propose a methodology for automated lithostratigraphic mapping of regionally occurring sedimentary units using SBT classification. The methodology is applied to a large CPT dataset, covering a groundwater basin of ~60 km2 with predominantly unconsolidated sandy sediments in northern Belgium. Results show that the model-based approach is superior in detecting the true lithological classes when compared to more frequently applied unsupervised classification approaches or literature classification diagrams. We demonstrate that automated mapping of lithostratigraphic units using advanced SBT classification techniques can provide a large gain in efficiency, compared to more time-consuming manual approaches and yields at least equally accurate results.

Rivers are important ecosystems under continuous anthropogenic stresses. The hyporheic zone is a ubiquitous, reactive interface between the main channel and its surrounding sediments along the river network. We elaborate on the main physical, biological, and biogeochemical drivers and processes within the hyporheic zone that have been studied by multiple scientific disciplines for almost half a century. These previous efforts have shown that the hyporheic zone is a modulator for most metabolic stream processes and serves as a refuge and habitat for a diverse range of aquatic organisms. It also exerts a major control on river water quality by increasing the contact time with reactive environments, which in turn results in retention and transformation of nutrients, trace organic compounds, fine suspended particles, and microplastics, among others. The paper showcases the critical importance of hyporheic zones, both from a scientific and an applied perspective, and their role in ecosystem services to answer the question of the manuscript title. It identifies major research gaps in our understanding of hyporheic processes. In conclusion, we highlight the potential of hyporheic restoration to efficiently manage and reactivate ecosystem functions and services in river corridors.

Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time).

Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modelling. In this regard, hypothesis testing is essential to increase system understanding by refuting alternative conceptual models. Often a stepwise approach, with respect to complexity, is promoted but hypothesis testing of simple groundwater models is rarely applied. We present an approach to model-based Bayesian hypothesis testing in a simple groundwater balance model, which involves optimization of a model in function of both parameter values and conceptual model through trans-dimensional sampling. We apply the methodology to the Wildman River area, Northern Territory, Australia, where we set up 32 different conceptual models. A factorial approach to conceptual model development allows for direct attribution of differences in performance to individual uncertain components of the conceptual model. The method provides a screening tool for prioritizing research efforts while also giving more confidence to the predicted water balance compared to a deterministic water balance solution. We show that the testing of alternative conceptual models can be done efficiently with a simple additive and linear groundwater balance model and is best done relatively early in the groundwater modelling workflow.

Vegetation is often represented in an oversimplified way in hydrological models for urbanised catchments, resulting in a generalised parameterisation of urban green. This is common practice, despite the fact that some studies clearly indicate that both the coverage and influence of urban green is often underestimated. In general vegetated surfaces play an important role as areas of recharge, for the redistribution of precipitation and in the regulation of surface runoff, especially for medium intensity storms. Hence, a more realistic and spatially distributed vegetation parameterisation would be of high value for hydrological modelling in urban catchments. In this paper an Earth observation based methodology is presented as an alternative to quantify the influence of canopy interception storage as well as the influence of seasonal dynamics on the urban water balance. Results indicate that Earth observation based interception storage capacities for the Upper Woluwe catchment (Brussels) are up to 25% higher than values obtained from literature, resulting in an increase of cumulative interception rates with 10% over a two year period. The results seem to vary with the rainfall intensity as well as with seasonal dynamics. In order to prove the general applicability of the proposed approach, these results need further confirmation using multi-year analyses and preferably a validation with ground truthing, which is a challenging future task.

Spaceborne thermal sensors provide important physical parameters for urban studies. However, due to technical constraints, spaceborne thermal sensors yield a trade-off between their spatial and temporal resolution. The aims of this study are (1) to downscale the three originally low spatial resolution (960 m) Moderate Resolution Imaging Spectroradiometer (MODIS/Terra) land surface temperature image products (MOD11_L2, MOD11A1 and MOD11A2) to resolutions of 60, 90, 120, 240 and 480 m; and (2) to propose an improved version of the DisTrad method for downscaling the MODIS/Terra land surface temperature products over urban areas. The proposed improved DisTrad is based on a better parameterization of the original DisTrad residuals in urban areas. The improved resampling technique is based on a regression relationship between the residuals of the temperature estimation and the impervious percentage index. Validation of the improved DisTrad, the original DisTrad, and the uniformly disaggregated MODIS land surface temperature images (UniTrad) are performed by comparative analysis with a time-coincident Landsat 7 ETM+ thermal image. Statistical results indicate that the improved DisTrad method shows a higher correlation (R2 = 0.48) with the observed temperatures than the original DisTrad (R2 = 0.43) and a lower mean absolute error (MAE = 1.88 °C) than the original DisTrad (MAE = 2.07 °C). It is concluded that the improved DisTrad method has a stronger capability to downscale land surface temperatures in urban areas than the original DisTrad.

The dimensionless Leaf Area Index (LAI) is widely used to characterize vegetation cover. With recent remote sensing developments LAI is available for large areas, although not continuous. However, in practice, continuous spatial-temporal LAI datasets are required for many environmental models. We investigate the relationship between LAI and climatic variable rainfall and Growing Degree Days (GDD) on the basis of data of a cold semi-arid region in Southwest Iran. For this purpose, monthly rainfall and temperature data were collected from ground stations between 2003 and 2015; LAI data were obtained from MODIS for the same period. The best relationship for predicting the monthly LAI values was selected from a set of single- and two-variable candidate models by considering their statistical goodness of fit (correlation coefficients, Nash-Sutcliffe coefficients, Root Mean Square Error and mean absolute error). Although various forms of linear and nonlinear relationships were tested, none showed a statistically meaningful relationship between LAI and rainfall for the study area. However, a two-variable nonlinear function was selected based on an iterative procedure linking rainfall and GDD to the expected LAI. By taking advantage of map algebra tools, this relationship can be used to predict missing LAI data for time series simulations. It is also concluded that the relationship between MODIS LAI and modeled LAI on basis of climatic variables shows a higher correlation for the wet season than for dry season.

Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standard techniques and relatively cheap imagery. The number, surface area, nearest distance, and dynamics of isolated temporary and permanent wetlands were determined for the Western Cape, South Africa. Open water bodies (wetlands) were mapped from seven Landsat images (acquired during 1987 – 2002) using supervised maximum likelihood classification. The number of wetlands fluctuated over time. Most wetlands were detected in the winter of 2000 and 2002, probably related to road constructions. Imagery acquired in summer contained fewer wetlands than in winter. Most wetlands identified from Landsat images were smaller than one hectare. The average distance to the nearest wetland was larger in summer. In comparison to temporary wetlands, fewer, but larger permanent wetlands were detected. In addition, classification of non-vegetated wetlands on an Envisat ASAR radar image (acquired in June 2005) was evaluated. The number of detected small wetlands was lower for radar imagery than optical imagery (acquired in June 2002), probably because of deterioration of the spatial information content due the extensive pre-processing requirements of the radar image. Both optical and radar classifications allow to assess wetland characteristics that potentially influence plant and animal metacommunity structure. Envisat imagery, however, was less suitable than Landsat imagery for the extraction of detailed ecological information, as only large wetlands can be detected. This study has indicated that ecologically relevant data can be generated for the larger wetlands through relatively cheap imagery and standard techniques, despite the relatively low resolution of Landsat and Envisat imagery. For the characterisation of very small wetlands, high spatial resolution optical or radar images are needed. This study exemplifies the benefits of integrating remote sensing and ecology and hence stimulates interdisciplinary research of isolated wetlands.

The amount and intensity of runoff on catchment scale are strongly determinedby the presence of impervious land-cover types, which are the predominant cover types inurbanized areas. This paper examines the impact of different methods for estimatingimpervious surface cover on the prediction of peak discharges, as determined by a fullydistributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe Rivercatchment in the southeastern part of Brussels. The study shows that detailed informationon the spatial distribution of impervious surfaces, as obtained from remotely sensed data,produces substantially different estimates of peak discharges than traditional approachesbased on expert judgment of average imperviousness for different types of urban land use.The study also demonstrates that sub-pixel estimation of imperviousness may be a usefulalternative for more expensive high-resolution mapping for rainfall-runoff modelling atcatchment scale.