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Summary Lake Chad has recently been perceived to be completely desiccated and almost extinct due to insufficient published ground observations. Given the high spatial variability of rainfall in the region, and the fact that extreme climatic conditions (for example, droughts) could be intensifying in the Lake Chad basin (LCB) due to human activities, a spatio-temporal approach to drought analysis becomes essential. This study employed independent component analysis (ICA), a fourth-order cumulant statistics, to decompose standardised precipitation index (SPI), standardised soil moisture index (SSI), and terrestrial water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) into spatial and temporal patterns over the LCB. In addition, this study uses satellite altimetry data to estimate variations in the Lake Chad water levels, and further employs relevant climate teleconnection indices (El-Niño Southern Oscillation-ENSO, Atlantic Multi-decadal Oscillation-AMO, and Atlantic Meridional Mode-AMM) to examine their links to the observed drought temporal patterns over the basin. From the spatio-temporal drought analysis, temporal evolutions of SPI at 12 month aggregation show relatively wet conditions in the last two decades (although with marked alterations) with the 2012–2014 period being the wettest. In addition to the improved rainfall conditions during this period, there was a statistically significant increase of 0.04 m/yr in altimetry water levels observed over Lake Chad between 2008 and 2014, which confirms a shift in the hydrological conditions of the basin. Observed trend in TWS changes during the 2002–2014 period shows a statistically insignificant increase of 3.0 mm/yr at the centre of the basin, coinciding with soil moisture deficit indicated by the temporal evolutions of SSI at all monthly accumulations during the 2002–2003 and 2009–2012 periods. Further, SPI at 3 and 6 month scales indicated fluctuating drought conditions at the extreme south of the basin, coinciding with a statistically insignificant decline in TWS of about 4.5 mm/yr at the southern catchment of the basin. Finally, correlation analyses indicate that ENSO, AMO, and AMM are associated with extreme rainfall conditions in the basin, with AMO showing the strongest association (statistically significant correlation of 0.55) with SPI 12 month aggregation. Therefore, this study provides a framework that will support drought monitoring in the LCB. [ABSTRACT FROM AUTHOR]

Knowledge of soil water percolation below the rooting zone and its responses to the dynamic interactions of different factors are important for the control of non-point source pollution. Based on 3600 scenarios in Hydrus-1D simulation, this study revealed the integrated effects of rainfall characteristics (rainfall amount, maximum rainfall intensity or MRI, time distribution characteristics of rainfall or TDC), antecedent moisture and the season on deep percolation (DP) at a forest site in Taihu Lake Basin, China. Results showed that Hydrus-1D model can well simulate the soil water dynamics at this site. Antecedent moisture had the greatest relative contribution to DP (85.7%), followed by rainfall amount (10.9%) and MRI (3.4%). As the antecedent moisture increased, the relative contribution of the season on DP increased from 0.0% to 16.4%. In comparison, that of MRI decreased from 58.7% to 38.5% and that of rainfall amount followed a bell shape pattern (greatest when the antecedent moisture was 0.26 m 3 m −3 ). The relative contribution of antecedent moisture to DP in summer was the greatest (87.8%), while that of the rainfall was the least. The TDC influenced DP by affecting the responses of DP to other factors. When the rainfall amount was ⩾80 mm and the antecedent moisture content was ⩾0.34 m 3 m −3 , effect of TDC on DP could be observed. The DP of TDC_B (rainfall intensity linearly increased with time) was the lowest, while that of TDC_E (rainfall intensity kept constant with time) was the greatest. Findings of this study have practical significance for investigating the water and pollutant transport in vadose zone. [ABSTRACT FROM AUTHOR]

Summary This paper examines the combination of support vector machines (SVM) and the dual ensemble Kalman filter (EnKF) technique to estimate root zone soil moisture at different soil layers up to 100 cm depth. Multiple experiments are conducted in a data rich environment to construct and validate the SVM model and to explore the effectiveness and robustness of the EnKF technique. It was observed that the performance of SVM relies more on the initial length of training set than other factors (e.g., cost function, regularization parameter, and kernel parameters). The dual EnKF technique proved to be efficient to improve SVM with observed data either at each time step or at a flexible time steps. The EnKF technique can reach its maximum efficiency when the updating ensemble size approaches a certain threshold. It was observed that the SVM model performance for the multi-layer soil moisture estimation can be influenced by the rainfall magnitude (e.g., dry and wet spells). [ABSTRACT FROM AUTHOR]

Summary The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input–output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture. [ABSTRACT FROM AUTHOR]

Summary Shallow saturated subsurface flow is a dominant runoff mechanism on hillslopes of headwater catchments under humid temperate climate. Its timing and magnitude is significantly affected by the presence of preferential pathways. Reliable prediction of runoff from hillslope soils under such conditions remains a challenge. In this study, a quantitative relationship between rainfall, stormflow, and leakage to bedrock for hillslopes, where lateral preferential runoff represents a dominant part of the overall response, was sought. Combined effects of temporal rainfall distribution and initial hillslope saturation (antecedent moisture conditions) on stormflow, leakage to bedrock, and overall water balance were evaluated by conducting simulations with synthetic rainfall episodes. A two-dimensional dual-continuum model was used to analyze hydrological processes at an experimental hillslope site located in a small forested headwater catchment. Long-term seasonal simulations with natural rainfall indicated that leakage to bedrock occurred mostly as saturated flow during major runoff events. The amount of rainfall needed to initiate stormflow appeared as a dynamic hillslope property, depending on temporal rainfall distribution, initial hillslope storage, and the spatial distribution of soil water within the hillslope. No single valued rainfall threshold responsible for triggering stormflow was found. Rainfall–stormflow as well as rainfall–leakage relationships were found highly nonlinear for low initial hillslope saturations. Temporal rainfall distribution affected the amount of rainfall necessary to initiate stormflow more than it did the amounts of stormflow or leakage to bedrock. In spite of a simple hillslope geometry with constant slope and parallel soil–atmosphere and soil–bedrock interfaces considered in the analysis, the applied model predicted a hysteretic behavior of storage–discharge relationship. The results showed a mutual interplay of components of hillslope water balance exposing a nonlinear character of the hillslope response. The study provided a quantitatively coherent insight in the hydraulic functioning of hillslopes where preferential flow constitutes a dominant part of stormflow. [ABSTRACT FROM AUTHOR]

Summary The Advanced Weather Research and Forecasting (WRF) model was used to simulate two warm season events representative of reverse orographic enhancement of warm season precipitation in the Southern Appalachians under weak (9–12 July, 2012) and strong (12–16 May, 2014) synoptic forcing conditions. Reverse orographic enhancement refers to significant enhancement of rainfall intensity (up to one order of magnitude) at low elevations in the inner mountain valleys, but not in the ridges. This is manifest in significant increases of radar reflectivity observations and associated integral quantities (rain rate) at low levels (within 500 m of the surface), as well as changes in the observed microphysical properties of rainfall (raindrop size distribution). Analysis of high-resolution (1.25 km × 1.25 km) WRF simulations shows that the model captures the march of observed rainfall, though not the timing in the case of strong synoptic forcing. For each event, the results show that the space–time variability of rainfall in the inner region is strongly coupled to the development and persistence of organized within-valley low-level moisture convergence that is a necessary precursor to valley fog and low level cloud formation. Microphysical interactions among precipitation from propagating storm systems, and local low-level clouds and fog promote coalescence efficiency through the seeder-feeder mechanism leading to significant enhancement of rainfall intensity near the ground as shown by Wilson and Barros (2014). The simulations support the hypothesis that ridge–valley precipitation gradients, and in particular the reverse orographic enhancement effects in inner mountain valleys, are linked to horizontal heterogeneity in the vertical structure of low level clouds and precipitation promoted through landform controls on moisture convergence. [ABSTRACT FROM AUTHOR]

Summary Pre-storm soil moisture conditions play a crucial role in the analysis of the physical processes involved in conceptual rainfall–runoff modeling and for demonstration of the soil–water–atmosphere interaction. Runoff quantification is of common interest to most hydrologists who study rainfall–runoff modeling. The scientific community prefers hydrological models with a smaller number of parameter requirements and superior performance, because of the increased water resource applications. In order to circumvent the impacts of sudden undesirable jumps (e.g., in the conventional CN model) in runoff estimation, we investigated the integral effects of soil moisture proxies before and after rainfall occurrence so as to reduce the structural inconsistencies of the previous models. Accordingly, these sudden jumps were avoided by incorporating a new expression that varied from storm to storm and with prior rainfall. Similarly, the utilization of additional information to calibrate new parameters reduced the applicability to ungauged watersheds. By persisting with the principle of simplicity and avoiding over-parameterization, a new model was developed and validated using measured data of 1804 storm rainfall–runoff events from 39 South Korean watersheds. The significant results exhibited by the suggested one-parameter model in comparison to those of the other contenders were hydrologically justified using three statistical metrics and scatter plots. [ABSTRACT FROM AUTHOR]

Summary Discriminating moisture sources with precision is an important requirement to better understand the processes involved in extreme rainfall episodes. In a previous contribution by Gangoiti et al. (2011b), an innovative technique was presented to assess surface moisture sources contributing to a target precipitation within a Lagrangian framework. The technique was based in transporting parcels of vapor, representing the target precipitation, across a set of nested grids covering a large area at different resolutions. A mesoscale model estimated the meteorological variables to transport and redistribute the vapor back into its original sources, all of them assumed to be at the surface. The sequence of extreme rainfall events, which occurred over central Europe on August 11–13, 2002, was chosen to put the methodology to test. An important innovation has now been introduced. This new advance allows discriminating not only the terrestrial and oceanic sources but also the evaporation from precipitation occurring below the clouds and falling either on land or on the open sea. It is also able to detect with greater precision the relative importance of remote versus local sources, together with the sequence of evaporation of a rainfall event. After its application to the same episode and targets, our results confirm a similar distribution and strength of surface terrestrial and marine sources. Furthermore, the estimated direct evaporation from precipitation columns contributes to the precipitation episode with a significant amount of moisture which averages around 18% of the total sources, with a main fraction evaporated over land and close to the target regions in central Europe. This contribution adds to the surface sources, and it is consistent with the existence of an important mechanism of positive feedback for the inland transport efficiency of moisture and precipitation, operating at the regional level for this type of episodes. Significant regional differences are found in the contribution to different rainfall targets, with a lower fraction of 14% for the southern target (Upper Austria), and 22% for the northern one (Bohemia). [ABSTRACT FROM AUTHOR]

Summary Groundwater recharge assessment of karst aquifers, at various spatial and temporal scales, is a major scientific topic of current importance, since these aquifers play an essential role for both socio-economic development and fluvial ecosystems. In this study, groundwater recharge was estimated at local and episodic scales in a representative perched karst aquifer in a region of southern Italy with a Mediterranean climate. The research utilized measurements of precipitation, air temperature, soil water content, and water-table depth, obtained in 2008 at the Acqua della Madonna test area (Terminio Mount karst aquifer, Campania region). At this location the aquifer is overlain by ash-fall pyroclastic soils. The Episodic Master Recession (EMR) method, an improved version of the Water Table Fluctuation (WTF) method, was applied to estimate the amount of recharge generated episodically by individual rainfall events. The method also quantifies the amount of precipitation generating each recharge episode, thus permitting calculation of the Recharge to the Precipitation Ratio (RPR) on a storm-by-storm basis. Depending on the seasonally varying air temperature, evapotranspiration, and precipitation patterns, calculated values of RPR varied between 35% and 97% among the individual episodes. A multiple linear correlation of the RPR with both the average intensity of recharging rainfall events and the antecedent soil water content was calculated. Given the relatively easy measurability of precipitation and soil water content, such an empirical model would have great hydrogeological and practical utility. It would facilitate short-term forecasting of recharge in karst aquifers of the Mediterranean region and other aquifers with similar hydrogeological characteristics. By establishing relationships between the RPR and climate-dependent variables such as average storm intensity, it would facilitate prediction of climate-change effects on groundwater recharge. The EMR methodology could further be applied to other aquifers for evaluating the relationship of recharge to various hydrometeorological and hydrogeological processes. [ABSTRACT FROM AUTHOR]

Summary Soil water is recognized as the key factor that controls the organization and functioning of dryland ecosystems. However, in spite of its great importance in ecohydrological processes as well as in modelling applications, most of the studies focus on daily or longer timescales, while its dynamics at shorter timescales are very little known. The main objective of this work was to determine the role of vegetation covers (grassland and tree canopy) in the soil hydrological response using measurements with high temporal resolution in evergreen oak woodland with Mediterranean climate. For this, soil water content was measured with capacitive sensors installed in the soil profile at different depths registering continuously with a high time resolution. Three study areas were monitored for two and half hydrological years. Results obtained revealed that rainwater amounts reaching the soil may temporarily be modified by covers according to precipitation properties and antecedent conditions (from dry to wet) before the rain episode. Rainfall amounts triggering a positive soil hydrological response decreased as initial states became drier, being more accentuated below tree canopies. The frequency of re-wetting cycles and the antecedent states seem to be as important or even more than either the duration or the precipitation amount. Therefore, the role of vegetation was more decisive under drier environmental conditions, where events lower than 6 mm and 2 mm never caused soil hydrological response either below tree canopy or grassland, respectively. This is important because initial conditions were independent of seasonality and because more than half of all rainfall events registered amounts smaller than 5 mm. If changes on precipitation patterns turn out in drier conditions, the predominance of such situations could have important ecohydrological consequences in semiarid ecosystems. [ABSTRACT FROM AUTHOR]

• The PF contributed to the total infiltration was >50% • The control mechanisms of temporal and small-scale spatial characteristics on different types of preferential flow processes were clarified. • Environmental characteristics differed between two types of preferential flow. • The two types of preferential flow affected soil moisture parameters differently. The control mechanisms of temporal and small-scale spatial characteristics on different types of preferential flow (PF) processes during infiltration are still lacking. Two-year high-resolution in situ soil moisture profiles were used to investigate the controls of PF processes and its interaction with moisture and vegetation dynamics, which covers three hillslope positions (upslope–14˚, midslope–9˚, and downslope–4˚) and three vegetation treatments (bare ground, stubble grassland and natural grassland) in a typical semi-arid grassland of Eurasia. Two types of PF were identified. Non-sequential response (NSR) was taken as one indication of PF using the disordered response of soil moisture profiles to rainfall. Fast preferential flow in sequence response (PF-rate) was taken as another indicator when a measured maximum pore water rate higher than saturated hydraulic conductivity. The conditions for the occurrence of the two PFs were inconsistent and different from the studies which showed the higher probability of PFs under dryer conditions and with higher rainfall intensities. Small rainfall events with low rainfall intensity and relatively low soil moisture at most locations showed higher NSR frequency, which further highlight the strong dependence of NSR on hydrophobicity and macropore structure of the topsoil. Whereas a higher rainfall intensity increased PF-rate under wet conditions. Meanwhile, the occurrence of PF-rate was also significantly controlled by vegetation coverage, soil texture and saturated hydraulic conductivity of 5–10 cm soil layer. Furthermore, the frequency of PF was 15.9%–44.5%, and its contribution to the total infiltration was >50%. The water recession rate of NSR and PF was slower and faster than that of matrix flow, respectively. This demonstrates the importance of PF to water replenishment in scarce rain areas and the influence of combinations of spatial–temporal factors on different flow processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • SM2RAIN-NWF is developed through substituting the net water flux model. • Performance of SM2RAIN-NWF algorithm was compared against SM2RAIN. • Results showed the better performance of SM2RAIN-NWF algorithm compared to SM2RAIN. • The quality of soil moisture data impact on the performance of the algorithm. Sparse distribution of rain gauge networks challenges the estimation of rainfall variability over space and time. The SM2RAIN algorithm was developed to estimate rainfall from the knowledge of soil moisture (SM) by inverting the soil‐water balance equation. The algorithm was simplified by neglecting the contribution of evapotranspiration and surface runoff rate during the rainfall event. A recent study developed an analytical model to estimate the net water flux (NWF) from SM data via inversion of analytical Warrick's equation. In this study, the SM2RAIN-NWF algorithm was developed by integrating the SM2RAIN algorithm and the NWF model to improve the accuracy of rainfall estimation. The applicability of the SM2RAIN-NWF algorithm was evaluated based on observed rainfall data in the Lake Urmia basin, Iran. Satellite SM data was obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The algorithm calibrated based on the data from July 3, 2012, to December 31, 2017, was then used to estimate rainfall for two years extending from January 2018 to December 2019. Estimated rainfall through SM2RAIN-NWF algorithm improved compared to SM2RAIN by 14% and 37.4% increase in the average values of correlation coefficient (R) and Nash–Sutcliffe (NS), and 11.5% decrease in the Percentage Root Mean Square Error (PRMSE) over the calibration period. Validating the estimated rainfall showed a considerable improvement in the performance of the SM2RAIN-NWF algorithm compared to the SM2RAIN algorithm by 8.6% and 30.4% increase in the average values of R and NS, and 13.4% decrease in the PRMSE. It was also found that the SM2RAIN-NWF algorithm contributes to the improvement of error indices in rainfall estimation and simulates the rainfall variation trend in a better fashion than the SM2RAIN algorithm. [ABSTRACT FROM AUTHOR]

Summary In the context of evaluating the occurrence of drought events over Europe, soil moisture maps provide an invaluable resource to quantify the effects of rainfall deficits on vegetated lands. Spatially distributed models represent one of the main options, alongside satellite remote sensing, to successfully monitor this quantity over large areas in a cost effective way. This work has the double aim of: (i) intercomparing three soil moisture outputs obtained by different land-surface models (LISFOOD, CLM and TESSEL) through long (at least 6 years of data between 2001 and 2011) in-situ measured datastreams, and (ii) quantifying the added value of combining the estimates of these three models by means of a simple ensemble approach. Generally, the three models return similar soil moisture anomalies over most of Europe, with few notable exceptions during summer in Mediterranean regions. The comparison with in-situ data suggests no substantial differences among the models, with LISFLOOD slightly outperforming the other two in terms of correlation as also supported by a pairwise comparison. The combined soil moisture anomalies obtained via the ensemble-mean approach are characterized by an increase of both the correlation and the accuracy in retrieving extreme events compared to the single models; however, the number of observed extreme events actually captured by the ensemble model does not increase significantly if compared to the single models. Overall, the ensemble model results are skillful, with an all site average skill score of about 0.4. [ABSTRACT FROM AUTHOR]

Summary Characterizing the spatial and temporal variability of small scale runoff responses is essential to distributed hydrologic modeling. To explore the variability of runoff responses, we analyzed surface runoff hydrographs from 12 neighboring hillslopes in central Iowa, USA that were observed for 72 runoff events over a four-year period. These agricultural experimental hillslopes receive various prairie filter strip treatments and drain areas ranging from 0.48 to 3.19 ha. The distances between them vary from tens of meters to about 3 km. We compared the hydrographs from the remaining 11 hillslopes to the hydrograph at the benchmark hillslope (i.e., hillslope B6 with no treatment). The results showed that: (1) for any individual event in which noticeable surface runoff occurred, the hydrographs from these hillslopes had similar shapes but different magnitudes; (2) for any paired hillslopes, the shape similarity persisted, but the scaling factor (the regression slope between two flow series) changed across events; and (3) for any runoff event, no simple relationship exists between the spatial variation of the scaling factor and the slope, slope length, area, and prairie strip width at the footslope of the hillslopes. Interestingly, we found that for 9 out of the 11 paired hillslopes, 40–70% of the temporal variation in the scaling factors can be explained by the antecedent wetness condition and the maximum hourly rain accumulation. These results suggest that the small-scale surface runoff responses are spatially variable but organized linearly, i.e., shape similarity (or linearity) in space is another feature of the small-scale runoff process. This phenomenon seems to result from the spatial vicinity and small-scale spatial variability of rainfall intensity and antecedent soil moisture. [ABSTRACT FROM AUTHOR]

Summary Soil moisture plays a key role in runoff generation processes, and the assimilation of soil moisture observations into rainfall–runoff models is regarded as a way to improve their prediction accuracy. Given the scarcity of in situ measurements, satellite soil moisture observations offer a valuable dataset that can be assimilated into models; however, very few studies have used these coarse resolution products to improve rainfall–runoff model prediction. In this work we evaluate the assimilation of satellite soil moisture into the probability distributed model (PDM) for the purpose of reducing flood prediction uncertainty in an operational context. The surface soil moisture (SSM) and the soil wetness index (SWI) derived from the Advanced Microwave Scanning Radiometer (AMSR-E) are assimilated using an ensemble Kalman filter. Two options for the observed data are considered to remove the systematic differences between SSM/SWI and the model soil moisture prediction: linear regression (LR) and anomaly-based cumulative distribution function (aCDF) matching. In addition to a complete period rescaling scheme (CP), an operationally feasible real-time rescaling scheme (RT) is tested. On average, the discharge prediction uncertainty, expressed as the ensemble mean of the root mean squared difference (MRMSD), is reduced by 25% after assimilation and little overall difference is found between the various approaches. However, when specific flood events are analysed, the level of improvement varies. Our results reveal that efficacy of the soil moisture assimilation for flood prediction is robust with respect to different assumptions regarding the observation error variance. The assimilation performs similarly between the operational RT and the CP schemes, which suggests that short-term training is sufficient to effectively remove observation biases. Regarding the different rescaling techniques used, aCDF matching consistently leads to better assimilation results than LR. Differences between the assimilation of SSM and SWI, however, are not significant. Even though there is improvement in streamflow prediction, the assimilation of soil moisture shows limited capability in error correction when there exists a large bias in the peak flow prediction. Findings of this work imply that proper pre-processing of observed soil moisture is critical for the efficacy of the data assimilation and its performance is affected by the quality of model calibration. [ABSTRACT FROM AUTHOR]

Summary Stemflow focusses water delivery to the forest floor in a relatively small area surrounding the tree bole, with the potential to enhance soil water contents and water recharge relative to more distal sites beneath the canopy. These stemflow fluxes may decrease as a given tree species ages due to changes in branch orientation and bark roughness, suggesting that the relative contribution of stemflow to water recharge near the bole will decline with time. The hypothesis that stemflow fluxes decline with tree age was tested in a chronosequence of red pine ( Pinus resinosa Ait.) stands in a managed forest in southern Ontario, Canada, and stemflow contributions to soil water recharge below 1 m depth were quantified. Throughfall, stemflow and sub-canopy soil water contents (0.1 m and 1.5 m from the tree bole to 1 m depth) in stands ranging from 28 to 80 years in age were studied from late-Spring to Fall in 2012, supplemented by artificial irrigations of stemflow to examine short-term soil wetting at the two distances from the bole. The hypothesized decline in stemflow with increasing tree age was not supported, and canopy cover variations and forest management exerted a greater control on inter-stand differences in stemflow fluxes. Stemflow contributions generally resulted in greater soil water recharge below 1 m depth at 0.1 m from the tree bole compared to the 1.5 m distance. This enhanced recharge was greatest for the youngest stand and differences in recharge between the 0.1 m and 1.5 m distances from the bole were likely not significant for stands between 40 and 80 years of age. Nevertheless, the relative contribution of stemflow to soil water recharge may increase in this managed forest as red pine stands give way to a mixed hardwood-conifer forest, due to greater stemflow fluxes from hardwood species in this landscape. [ABSTRACT FROM AUTHOR]

Highlights: [•] Local- and field-scale evolution of surface soil moisture for a single rainfall event are described. [•] Reference scaling curves are developed based on sharp front approximation. [•] Spatial heterogeneity is described by log-normal distribution of saturated hydraulic conductivity. [•] Reference scaling curves reduce the need for extensive measurement requirements in the field. [•] Developed model is compared with experimental and numerical results. [ABSTRACT FROM AUTHOR]

Highlights: [•] Small scale rainfall variability affects significantly the basin response. [•] High temporal rainfall correlation generates higher, faster, and more variable responses. [•] Initial soil moisture and clustering of saturated areas significantly affect flood response. [ABSTRACT FROM AUTHOR]

Highlights: [•] The calibrated and tested GRNN model successfully predicts runoff hydrographs. [•] The addition of soil moisture data, along with rainfall, tremendously improves the performance of the model. [•] The use of soil moisture data at different depths allows to preserve the memory of the system. [•] Replacing past values of rainfall with soil moisture data is effective in GRNN modeling. [ABSTRACT FROM AUTHOR]

Summary: Understanding all components of the water balance, especially temporal rainfall patterns, is essential to optimize water use in rain-fed agriculture area. The effect of temporal rainfall patterns on water balance, evapotranspiration (ET), and crop growth was evaluated by considering root water extraction of plants in a rain-fed maize field. Soil water contents at depths of 15, 30, 60, and 90cm were measured daily in 2-year growth seasons. A soil water balance approach was applied to estimate changes in daily soil water storage. For the detailed water partitioning of the water balance and root water extraction, the soil–water-atmosphere-plant (SWAP) model with water and crop modules was applied. Results suggested that the main depths of root water uptake occurred in the top 60cm soil layer. Crop transpiration (T) can reach a level above 40% of the total water consumption during all growth stages, and its reduction was mainly due to the dry condition of soil. The crop yield in 2010 was 1125kgha−1 higher than that in 2011, although the rainfall amount in that year was 132mm less than the rainfall amount in 2011. The water use efficiency (WUE) was also higher in 2010. Therefore, the influence of temporal rainfall patterns was clearly more important than rainfall amounts (water partitioning into evaporation (E), T, and soil water content). Growing season T/ET can be a potential parameter for maize productivity. The field can be irrigated at pivotal growth stages under dry conditions to obtain the optimal effect in improving WUE and increasing grain yield. The SWAP model was a useful tool to analyze water partitioning in the freeze–thaw zone. [Copyright &y& Elsevier]

Summary: In this paper we extend the usefulness of the TOPKAPI model by adding a Green-Ampt infiltration module and make the model and source code freely available on the internet as PyTOPKAPI. Then, we investigate the sensitivity of the PyTOPKAPI hydrological model to systematic bias in the variables rainfall and evapotranspiration, as well as the physically based soil properties that describe the model behaviour. The model sensitivity is assessed in terms of relative changes in the Soil Saturation Index (SSI), which is defined as the percentage of soil pore space filled by water. The volumetric soil moisture content, can be calculated from SSI using location dependent soil properties, if required. The model sensitivity is calculated at 7200 sites in South Africa, for a 2.5 year simulation period with a time-step of three hours. This large spatial extent gives results for a wide array of climates and land properties. Overall, the sensitivity of the model turns out to be a closely linear function of, and the same order of magnitude as (or less than), the forcing/parameter bias. This indicates that the model is robust to errors in forcing/parameters. The results also show that the best estimates of soil water can be obtained by improving estimates of the storage parameters and rainfall forcing. However, the storage parameters must be obtained from static soil property data-sets and we show that there is value in making improvements to the rainfall forcing (in this case TRMM 3B42RT) for places where it is biased relative to observed rainfall. This work is particularly relevant for model application in ungauged basins, where the quality of forcing variables and physical parameters cannot be calibrated. [Copyright &y& Elsevier]

Abstract: Rainfall is regarded as the most important input for the hydrology and nonpoint source (H/NPS) models and uncertainty related to rainfall is generally recognized as a major challenge in watershed modeling. In this paper, we focus on the impact of spatial rainfall variability on H/NPS modeling of a large watershed. The uncertainty introduced by spatial rainfall variability was determined using a number of commonly-used interpolation methods: (1) the Centroid method; (2) the Thiessen Polygon method; (3) the Inverse Distance Weighted (IDW) method; (4) the Dis-Kriging method; and (5) the Co-Kriging method. The Soil and Water Assessment tool (SWAT) was used to quantify the effect of rainfall spatial variability on watershed H/NPS modeling of the Daning watershed in China. Results indicated that these interpolation methods could contribute significant uncertainty in spatial rainfall variability and the carry-magnify effect caused even larger uncertainty in the H/NPS modeling. This uncertainty was magnified from hydrology modeling (stream flow) into NPS modeling (sediment, TP, organic nitrogen (N) and dissolved N). This study further suggested that H/NPS prediction uncertainty relating to spatial rainfall variability was scale-dependent due to the averaging effect of spatial heterogeneity. From a practical point of view, a global interpolation method, such as IDW and Kriging, as well as elevation data derived from a digital elevation model (DEM), should be included into the H/NPS models for reliable predictions in larger watersheds. [Copyright &y& Elsevier]

Summary: Image analysis could be a useful tool for investigating the spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to define apparent soil moisture patterns from vertical planes of Vertisol pit images and (ii) to describe the scaling of apparent soil moisture distribution using fractal parameters. Twelve soil pits (0.70m long×0.60m width×0.30m depth) were excavated on a bare Mazic Pellic Vertisol. Six of them were excavated in April/2011 and six pits were established in May/2011 after 3days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a Kodak™ digital camera. The mean image size was 1600×945 pixels with one physical pixel ≈373μm of the photographed soil pit. Each soil image was analyzed using two fractal scaling exponents, box counting (capacity) dimension (DBC ) and interface fractal dimension (Di ), and three prefractal scaling coefficients, the total number of boxes intercepting the foreground pattern at a unit scale (A), fractal lacunarity at the unit scale (Λ 1) and Shannon entropy at the unit scale (S 1). All the scaling parameters identified significant differences between both sets of spatial patterns. Fractal lacunarity was the best discriminator between apparent soil moisture patterns. Soil image interpretation with fractal exponents and prefractal coefficients can be incorporated within a site-specific agriculture toolbox. While fractal exponents convey information on space filling characteristics of the pattern, prefractal coefficients represent the investigated soil property as seen through a higher resolution microscope. In spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used in connection with traditional soil moisture sampling, which always renders punctual estimates. [ABSTRACT FROM AUTHOR]

Summary: Scale difference between in situ and remotely sensed soil moisture observations and model grid size has been an issue for validation of remote sensing data, soil moisture data assimilation and calibration of hydrologic models. This study aims to link two different scales of soil moisture estimates by upscaling single point measurements to field averages for representing field-scale agricultural areas (∼2ha) located within the Upper Cedar Creek Watershed in northeastern Indiana. Several statistical methods, mainly focusing on cumulative distribution function (CDF) matching, are tested to upscale point measurements to spatially representative soil moisture. These transforming equations are termed observation operators. The CDF matching is found to be the most successful upscaling method followed by the mean relative difference method using temporally stable point measurements. This study also tests the temporal and spatial (horizontal and vertical) transferability of the different observation operators. Results indicate that the two observation operators from the CDF matching approach and the mean relative difference method using a temporally stable location are transferable in space, but not in time. Rainfall characteristic is most likely the dominant factor affecting the success of observation operator transferability. In addition, the CDF matching approach is found to be an effective method to deduce spatial variability (standard deviation) of soil moisture from single point measurements. [Copyright &y& Elsevier]

Summary: The impact of rainfall characteristics on runoff generation and soil erosion are not fully understood despite their importance. In this study, a series of laboratory experiments, systematically considering different rainfall intensities, durations, moving directions, rainfall positions, and no-rainfall intervals, were conducted to investigate the impacts of rainfall characteristics on runoff generation and soil erosion. Significant differences, including hydrograph, sediment graph, soil water content, and infiltration depth (depth of wetting front), were observed. The following conclusions are drawn for the studied rainfall characteristics and soil from this study: (1) when compared with moving upstream rainfall scenarios (MURSs), moving downstream rainfall scenarios (MDRSs) can generally be characterized by hydrographs with a later rise and higher runoff peak for most of the rainfall events; (2) surface sealing changes the infiltration pattern so that MURS generally produce more total runoff than MDRS, and for some rainfall events MDRS generate lower runoff peak than MURS, which is different from what has been widely reported; (3) with the increase in the runoff peak, the erosion peak increases first and then decreases, indicating a switch from transport-limited erosion to detachment-limited erosion; (4) the increase ratio of underground water content for MURS is lower than MDRS; (5) rainfall duration is an important factor in soil crack occurrence. Not only does this study expand the understanding of hydrologic response and erosion, it also provides an important database for the hydrology community. [Copyright &y& Elsevier]

Summary: Knowledge of soil moisture is critical to understanding many of the hydrological processes that are of interest in soil hydrology, meteorology, and ecology research. In the present study, we used rainfall and soil moisture data from 2003 to 2008 measured at grassland and meadow sites in the Qilian Mountains of northwestern China to analyze the response of soil moisture to rainfall event size during the growing season. The responses of soil moisture at the grassland and meadow sites to rainfall event size were similar, although total rainfall, the frequency of large rainfall events, and the vegetation types were different. Soil moisture at depths of 20 and 40cm increased significantly after rainfall events larger than 15 and 20mm, respectively, but the magnitude of these changes varied in response to differences in the duration of the dry interval preceding the rain. Soil moisture at depths from 60 to 80cm in the grassland increased obviously with rainfall events larger than 40mm or after two consecutive large rainfall events, but was not significantly correlated with rainfall event size in the meadow. Soil moisture at depths from 120 to 160cm did not change significantly during the growing season at either site. The soil water storage at depths from 20 to 80cm at both sites increased obviously after rainfall events larger than 20mm. The present results suggest that large rainfall events (>20mm) play a key role in increasing soil water storage in the grassland and meadow ecosystems of these semi-arid mountains. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the results of a comparative diagnostic study of runoff generation processes in two test basins in Oklahoma: The Blue River at Blue and the Illinois River near Tahlequah. This study involves analysis of signatures of spatio-temporal runoff variability, extracted from both observed rainfall–runoff data and from predictions of a distributed, physically based rainfall–runoff model. Analysis of observed data in both basins indicates that event runoff coefficients are systematically higher in the wet season than in the dry season. Model predictions indicate that the transition from high to low runoff coefficients in the Blue River basin is linked to variations of water table depth and surface soil moisture, contributing to a seasonal switching of surface runoff generation mechanisms, from saturation excess to infiltration excess. In the Illinois River basin, however, due to more permeable soils, infiltration excess runoff occurs rarely. The differences in intra-annual patterns of runoff coefficients and runoff generation mechanisms can be partly explained by the seasonality of climate forcing and water table position. Despite the significant differences of runoff generation mechanisms between the two basins, spatial analysis of the model results reveals that in both watersheds, but especially so in the more humid Illinois River basin, saturation excess runoff and subsurface stormflow coexist in competition throughout the year. This competition is quantitatively shown to be controlled by the relative magnitudes of the saturated hydraulic conductivity of the soils and the topographic slope. In addition, the spatial variabilities of runoff generation processes also impact the spatial scaling behavior of runoff ratios, indicating the existence of a threshold watershed size beyond which the variability is averaged out. [ABSTRACT FROM AUTHOR]

Summary: This paper analyses the observed intra-annual variability of the hydrological response in the Blue River basin (Oklahoma, USA) and explores the underlying process controls with the use of the Tsinghua Representative Elementary Watershed (THREW) model, which is a semi-distributed, physically based model. Analysis of rainfall and runoff data at the event scale revealed a seasonal switching pattern of runoff coefficients. Event runoff coefficients in the moderately wet and wet periods (October–April) are consistently higher than those in the dry period (May–September). The switching pattern can be attributed to the higher potential evaporation in the summer season (relative to precipitation), which contributes to a reduction of soil moisture and thus higher initial losses during storm events. This is partly confirmed by the simulation results with the use of the THREW model. However, the results also show that soil drying alone cannot fully explain the sharp drop of the estimated runoff coefficients between spring and summer. This may be indicative that additional abstraction processes may be operative. Three additional abstraction processes are hypothesized as potential explanations, namely, surface depressions, canopy interception and soil cracking. Each of these processes is introduced into the THREW model sequentially, and evaluated in terms of their ability to reproduce various signatures of intra-annual runoff variability, including the switching behavior of event runoff coefficients. Finally, we present simulation results, where all three abstraction processes are included in the THREW model, and the model are recalibrated against the observed data, resulting in improved reproduction of observed streamflows over the baseline results. [ABSTRACT FROM AUTHOR]

Summary: Catchment hydrological models are widely used in climate change impact studies. The considered climate scenarios often involve increases in rainfall intensities. These increases might go beyond the range of historical events used in model calibration and validation. The hydrological impact results of climate change consequently are under these circumstances highly uncertain. This paper presents a method to test the validity of hydrological models for that type of impact analysis. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. The method is demonstrated, based upon the results of three conceptual rainfall–runoff models (NAM, PDM, VHM) for three catchments in Belgium. It is shown how the performance of the model in predicting impacts of rainfall increases can be integrated in the calibration process and how this method can increase the reliability of climate change impact results on peak flows. The paper addresses the importance of the relation between the soil moisture content and the overland flow coefficient, as it controls the generation of peak flows in the considered rainfall–runoff models. [ABSTRACT FROM AUTHOR]

Summary: We test the ability of three box models () to describe soil moisture dynamics in a regularly monitored experimental site in northwestern Italy. The models include increasingly complex representations of leakage and evapotranspiration processes. We force the models with the local rainfall, and we determine model parameters with a least-square minimization procedure. We study separately summer and annual dynamics. When we use the models to reproduce annual dynamics, we include a simple representation of the annual cycle of evapotranspiration. We test the models both in simulation mode, estimating their parameters from the same data used to verify them, and in forecast mode, determining the model parameters from one period, and using them to forecast soil water dynamics in a different period. The results indicate that simple box models can estimate soil water dynamics at midlatitudes, using rainfall as the only meteorological driver. The models displaying the best performances include the representation of leakage and evapotranspiration as nonlinear functions of soil water content. [ABSTRACT FROM AUTHOR]

Summary: An urban energy and water balance model is presented which uses a small number of commonly measured meteorological variables and information about the surface cover. Rates of evaporation-interception for a single layer with multiple surface types (paved, buildings, coniferous trees and/or shrubs, deciduous trees and/or shrubs, irrigated grass, non-irrigated grass and water) are calculated. Below each surface type, except water, there is a single soil layer. At each time step the moisture state of each surface is calculated. Horizontal water movements at the surface and in the soil are incorporated. Particular attention is given to the surface conductance used to model evaporation and its parameters. The model is tested against direct flux measurements carried out over a number of years in Vancouver, Canada and Los Angeles, USA. At all measurement sites the model is able to simulate the net all-wave radiation and turbulent sensible and latent heat well (RMSE=25–47Wm−2, 30–64 and 20–56Wm−2, respectively). The model reproduces the diurnal cycle of the turbulent fluxes but typically underestimates latent heat flux and overestimates sensible heat flux in the day time. The model tracks measured surface wetness and simulates the variations in soil moisture content. It is able to respond correctly to short-term events as well as annual changes. The largest uncertainty relates to the determination of surface conductance. The model has the potential be used for multiple applications; for example, to predict effects of regulation on urban water use, landscaping and planning scenarios, or to assess climate mitigation strategies. [ABSTRACT FROM AUTHOR]

Summary: Under future climate scenarios, rainfall patterns and species composition in California grasslands are predicted to change, potentially impacting soil-moisture dynamics and ecosystem function. The primary objective of this study was to assess the impact of altered rainfall on soil-moisture dynamics in three annual grassland vegetation types. We monitored seasonal changes in soil moisture under three different rainfall regimes in mesocosms planted with: (1) a mixed forb-grass community, (2) an Avena barbata monoculture, and (3) an Erodium botrys monoculture. We applied watering treatments in pulses, followed by dry periods that are representative of natural rainfall patterns in California annual grasslands. While rainfall was the dominant treatment, its impact on hydrological processes varied over the growing season. Surprisingly, there were only small differences in the hydrologic response among the three vegetation types. We found significant temporal variability in evapotranspiration, seepage, and soil-moisture content. Both Water Use Efficiency (WUE) and Rain Use Efficiency (RUE) decreased as annual precipitation totals increased. Results from this investigation suggest that both precipitation and vegetation have a significant interactive effect on soil-moisture dynamics. When combined, seasonal precipitation and grassland vegetation influence near-surface hydrology in ways that cannot be predicted from manipulation of a single variable. [Copyright &y& Elsevier]

Summary: Sufficiently available soil water is a basic requirement in agricultural production. Monitoring soil water dynamics (SWD) in the root zone is an optimal approach for managing a crop’s growth. This study presents a novel sensor system that simultaneously measures volumetric soil water content (VSWC), apparent electrical conductivity (ECa) and soil temperature at two different soil depths (shallow: 16cm; deep: 36cm). For testing its feasibility in the field, two prototypes were installed, one in bare soil and the other in a sugar beet (Beta vulgaris L.) field in the summer of 2010. Following a sequence of rainfall events randomly distributed over the experimental period, we observed distinct responses from the sensors at each monitored depth in both field conditions. In addition to the multi-parameter measurements, the novel sensor design includes a series of technical advantages such as solar-powered operation, wireless communication, and being relatively easy to install/remove. Thus, the developed wireless sensor system is promising for networked applications in precision farming. [Copyright &y& Elsevier]

Summary: This work proposes an ecohydrology-based approach to study the impact of climate change on groundwater recharge in dry areas. It is largely based on a concept that in dry areas, vegetation community can be divided into two different groups, shallow- and deep-rooted vegetation, with the growing-season average of root-zone soil water saturation tending to be at its optimum value for the growth of deep-rooted vegetation. The concept is supported by data sets collected from different dry areas. Analytical results of soil water dynamics developed in previous studies are adapted here for investigating the impact of climate change. Because the conceptual model allows deep-zone soil–water saturation, averaged over growing seasons, to remain fixed during different climate conditions, we can construct a relationship among groundwater recharge, the coverage of deep-rooted vegetation, and climate. As an illustrative example, we apply the developed approach to the Yucca Mountain area. Our estimated recharge value under the current climate and the vegetation coverage is generally consistent with results estimated from other methods or observed from the site. We also evaluate how the recharge will change under several assumed future climate scenarios. The results show that both groundwater recharge and deep-rooted vegetation coverage increase with decreasing rainfall frequency (for a given amount of annual rainfall), with increasing average rainfall depth per rainfall event (for a fixed frequency) and with increasing frequency (for a fixed rainfall depth per rainfall event). The latter indicates a relatively large degree of buffering effects of vegetation on changes in groundwater recharge. [ABSTRACT FROM AUTHOR]

Summary: Information on soil moisture is vital to describe various hydrological processes. Soil moisture parameters are normally measured using buried sensors in the soil. Alternatively, spatial and temporal characteristics of surface soil moisture are estimated through satellites. Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) is one of such satellites that estimate surface soil moisture in an operational context. These estimates need validation prior to use in various hydrological and water management applications. Such validations are normally carried out using field measurements of soil moisture. This is not technically feasible in vast river basins such as the Indus Basin and for pixel sizes of 25km×25km with non-homogeneous soils and land use. Therefore, AMSR-E data interpreted with Njoku model and posted by the National Snow and Ice Data Center (NSIDC) for the Indus Basin is evaluated by comparing it against auxiliary spatial data. The auxiliary data exists of (i) land use, (ii) rainfall from the Tropical Rainfall Measuring Mission (TRMM) satellite, (iii) seasonality of vegetation from SPOT-Vegetation and (iv) saturated water content (θsat ) inferred from soil maps. A strong relationship was observed between rainfall and surface soil moisture in the land use class “rainfed”. Spearman’s rank correlation coefficient (rs ) between the soil moisture and rainfall ranged from 0.14 to 0.55 with a mean of 0.36. For irrigated land uses, rs ranged from −0.04 to 0.52 with a mean of 0.29 due to control of soil moisture by irrigation water supply. The temporal analysis of soil moisture data with vegetation time series showed resemblance with growth phenology. Higher Pearson’s correlation coefficient (r) between the soil moisture and vegetation development was found for time lags of a few weeks. The daily maximum values estimated by AMSR-E ranged from 0.08 to 0.38cm3 cm−3. The maximum values were near, but below θsat limits for different soil types. AMSR-E captured the flooding processes during July and August 2010 by showing the soil moisture values to approximate the saturated soil moisture content for areas that are reported to be flooded. This suggests that the absolute AMSR-E soil moisture data from NSIDC are accurate in the upper range of land wetness. It is concluded that AMSR-E surface soil moisture data exhibits spatio-temporal behavior, and the trends agree with auxiliary spatial data sets. [Copyright &y& Elsevier]

Summary: The sensitivities of runoff generation to rainfall variability and initial wetness conditions were examined for a major flash flood event that occurred during August 29, 2003 on the upper Tagliamento river basin in the eastern Italian Alps. The Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) distributed hydrologic model was used to simulate the hydrologic response over a range of sub-basins. The model was calibrated for the single flash flood event based on the observed hydrograph at the outlet of the basin examined (Fella basin) and was validated based on the available observed hydrographs at interior points of the basin. A series of hydrologic simulations were performed for different initial soil moisture conditions and rainfall forcing resolutions in order to evaluate the sensitivity of runoff generation to those variables. Evaluation of the results suggests that both initial wetness and rainfall resolution affect significantly the simulated peak flow and runoff volume during the flash flood event. Sensitivity to initial wetness exhibits a scale dependence with the sensitivity increasing with basin scale. The bias introduced to the basin-averaged rainfall due to aggregation had a significant effect on runoff generation for all basin scales, while the effect of variability smoothing was important only for the larger scale basins. Finally, the sensitivity of the flood hydrograph to rainfall aggregation was shown to be more important for drier initial wetness states. [Copyright &y& Elsevier]

Summary: Soil moisture is an important variable in the hydrological cycle and plays a vital role in agronomy, meteorology, and hydrology. It regulates the exchange of water and heat between land surface and atmosphere and thus plays an important role in the development of weather patterns. It is difficult to obtain a comprehensive spatio-temporal map of soil moisture because of expensive installation of soil moisture measuring instruments. In this paper, a model to estimate soil moisture (ms ) using Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) backscatter (σ°) and Normalized Difference Vegetation Index (NDVI) is developed for the Southern United States. Soil moisture data from Soil and Climate Analysis Network (SCAN) stations is used to calibrate and validate the model. The estimated values of ms compare well with the ground measurements of soil moisture. The model works well for various landcovers but works best for low density vegetated areas (closed shrubland). All the soil moisture estimates in this landcover have an absolute error of less than 8%. The model performance deteriorates with increase in vegetation density (crops and forest). Overall, the model performance is satisfactory for all landcover types with RMSE less than 6.3% and absolute error of 10% or less for 90% of the estimates. Estimation of soil moisture over a large area with low error provides another use of TRMMPR data. [Copyright &y& Elsevier]

Summary: Through a systematic modelling analysis for focus catchments in Costa Rica (131km2), Ecuador (10km2), Chile (0.35km2) and Argentina (12.9km2), the hypothesis is tested that, as the size of the hydrological event increases, the effect of forest cover on the peak discharge becomes less important. For each focus catchment, a 1000-year synthetic rainfall time series was generated, representative of the current climate. This time series was used to run SHETRAN hydrological models for each catchment with two contrasting land use scenarios (generally with and without a forest cover). The corresponding maximum daily discharges for the contrasting scenarios were then compared to show the extent to which the two responses converged as the size of the peak discharge increased. For a given forest catchment discharge there could be a range of larger non-forest catchment discharges, depending on antecedent soil moisture content. The simulations show consistently for the rainfall dominated sites that the width of this range either remains constant or narrows as discharge increases, indicating either relative or absolute convergence of the responses. The pattern is more difficult to distinguish for a snowmelt regime but a relative convergence of response still appears possible. The results therefore support the test hypothesis. However, the pattern is complicated by factors such as catchment scale, soil depth, antecedent moisture content and land management. Forests may also still offer significant flood mitigation benefits for moderate (and more frequent) rainfall events and they protect against soil erosion and sediment transport for a wide range of events. [ABSTRACT FROM AUTHOR]

Summary: New hydro-pedotransfer functions (HPTF) for flat grasslands are presented to estimate both annual capillary rise from the groundwater into the root zone and actual evapotranspiration on a regional scale. Based on easily available site information, soil water components such as percolation rate and therefore groundwater recharge can also be evaluated. This information may be obtained without detailed knowledge of soil hydraulic properties and daily weather data. Rather, the only data needed is soil texture class, groundwater depth, summer rainfall and potential evapotranspiration (ET 0) according to the FAO guideline. The basic idea is to evaluate the increase of actual evapotranspiration (=gain, G) caused by capillary rise from groundwater compared to identical site conditions, but without groundwater influence. This gain (G) represents an effective parameter to express both the soil and climate dependent effective capillary rise for a given grassland site. To develop hydro-pedotransfer functions expressing gain, we first used the numerical simulation program SWAP in order to calculate water balances for a broad spectrum of soils, groundwater depths, and climate conditions. Secondly we analyzed this data statistically in order to obtain simple equations for predicting G without using a numerical model. The new hydro-pedotransfer functions were developed and tested for different climate regions in Germany. [ABSTRACT FROM AUTHOR]

Summary: Variation in soil moisture content throughout soil profiles during several sequential rainfalls represents the internal hydrological response on a hillslope scale. A multiplex TDR system has been operating on a mountainous hillslope to obtain the time series of soil moisture along two transects in the study area. The soil moisture modeling conducted in this study highlights our understanding of the inter-relationships between soil moistures at identical spatial locations, but at different depths. A sequential procedure was used for the time series modeling to delineate an appropriate model for application to all monitoring points. The feedback relationship of soil wetness between two different depths was expressed with the proposed vector autoregressive model. Based on the successful modeling of 31 coupled soil water histories, the vertical distributions of the stochastic model throughout the study area were obtained. The distribution of the delineated models implied a spatial distribution of the hydrological processes, such as vertical infiltration for the upper soil layers and some of the lower soil layers (38 out of 62 models), lateral redistribution and subsurface flow over bedrock mostly for the lower soil layers (24 out of 62 models) on the steep hillslope. With the use of the resultant models, applications were proposed to improve the data acquisition system, i.e. gap filling for missing data and limited prediction for an ungauged location. [ABSTRACT FROM AUTHOR]

Summary: There is evidence that, under certain conditions, rapid preferential recharge via the fracture network can occur in Chalk aquifers. This has potentially important implications for contaminant migration through the Chalk unsaturated zone, CUZ, and for groundwater flooding in Chalk catchments. In the case of groundwater flooding, deficiencies in modelling aquifer response have been attributed to inadequate representation of flow processes in the CUZ. In this paper we consider two complementary approaches for assessing controls on preferential recharge to Chalk aquifers: an empirical approach and a physically-based modelling approach. We show that the main controls on preferential recharge to Chalk aquifers are the characteristics of rainfall events, in terms of duration and intensity, the physical properties of the near surface, and the antecedent soil moisture in the near surface. We demonstrate a number of deficiencies when past models of the CUZ are applied to the problem of simulating preferential recharge, notably that the assumption of instantaneous equilibrium between fractures and matrix is not valid, particularly during extreme recharge events. In order to simulate preferential recharge, fractures and matrix must be modelled as separate but interacting domains. This was achieved using a dual continua model. The model was computationally demanding, but was able to reproduce observed behaviour, including apparently hysteretic soil moisture characteristic relationships in the near surface, and rapid preferential recharge fluxes in response to high intensity rainfall events. [ABSTRACT FROM AUTHOR]

Summary: A simple conceptual model for local infiltration into a two-layered soil profile with the upper layer much more permeable than the subsoil is proposed. It is a reformulation of a conceptual model of [J. Hydrol. 273, 58–73] involving coupled ordinary differential equations that was developed for infiltration and soil water content distribution in a two-layered soil under any rainfall pattern. The simpler model framework proposed here is restricted to the estimate of infiltration rate alone in cases where the upper layer is more permeable than the lower one. First, the maximum value, K 1 h , of the saturated hydraulic conductivity of the upper soil, K 1 s , that yields the time to ponding while the dynamic wetting front is entirely within this homogeneous layer is defined by a conceptual approach. Then, parameterized forms for the time when the wetting front reaches the interface, for time to ponding associated with K 1 s ⩾ K 1 h , and for the suction head at the interface as a function of time are determined. With these functional forms, the current model utilizes algebraic equations for ponding time and soil infiltration capacity, thus providing substantial savings in computational effort for describing infiltration in two-layered soils. When compared to the detailed conceptual model, the proposed model represents the infiltration rate accurately, with less than 10% average error on the dynamic cumulative infiltration depth. This simplified model offers potential for upscaling to field-scale infiltration over layered soils exhibiting spatial heterogeneity. [Copyright &y& Elsevier]

Summary: Soil infiltrability, or infiltration capacity, is the rate at which water penetrates into the soil at a rate directly controlled by soil factors alone. The infiltrability is of great importance in understanding and managing hydrological processes, crop water supply, irrigation, and soil erosion. The infiltration dynamics measured with the run off-on-out (ROOO) method follows the changes in infiltrability during the infiltration process under rainfall conditions. In this study, the ROOO method was used to quantitatively measure the soil infiltrability under three rainfall intensities (20, 40 and 60mm/h) and three antecedent soil moisture contents (2.6%, 10.4% and 19.5%, equivalent to 7.5% (air-dry), 30% and 60% of field capacity, respectively, of a clay loam soil. The soil infiltrability determined by the ROOO method decreased with increase in initial soil water content, due to the lower hydraulic gradient at the wetting front. Surface seal formation due to raindrop impact had a significant influence on soil infiltrability and was related to the soil water content. The faster wetting rate of drier soil under higher rainfall intensities, or when using the double-ring infiltrometer for comparison, caused severe aggregate breakdown to promote surface sealing, and significant decrease in soil infiltrability. Cumulative infiltration increased rapidly at the beginning of the infiltration process and then increased approximately linearly with time as the infiltration rate approached to constant. The rate of increase in cumulative infiltration was less under higher initial soil water contents, especially in the initial rainfall stage. Moreover, lower rainfall intensity resulted in higher infiltration rates and greater cumulative infiltration. The soil infiltrability processes measured with the ROOO method were fitted better by Kostiakov, Horton and Philip infiltration models than those measured by the double-ring infiltrometer method. The ROOO method provided reliable data for the entire infiltration process without the limitations of conventional rainfall simulation during the initial phase or of the drawbacks of the double-ring infiltrometer method. The results will have introductory meanings to further studies along this line. [Copyright &y& Elsevier]

Summary: A method to estimate antecedent soil moisture conditions is proposed to improve the accuracy of flash flood forecasts at ungauged locations. The method combines two indexes: a ‘climatic’ temporal index calculated in each cell using an uncalibrated soil moisture accounting (SMA) scheme and a spatial ‘statistical’ index giving the “average saturation state” usually encountered before a flood. The proposed method was carried out on two different models: an event-based distributed model, and a REGRESSION model. Simulation results were analysed for 562 individual events, issued from 160 catchments located in the South of France. The presented method improved the efficiency of both models. This indicates that the both proposed indexes give relevant information for estimating antecedent soil moisture conditions at ungauged locations. Furthermore, a contingency statistical analysis based on a 2-year flood threshold showed that alerts were better forecasted by the distributed model. These encouraging results suggest that further efforts should be made for the development of the method and its operational application. [ABSTRACT FROM AUTHOR]

Summary: This study conducts a broad sensitivity analysis, taking into account the influence of initial soil moisture content in two soil layers, layer depths, event properties, and two infiltration models. A distributed hydrology and soil erosion model (LISEM) is used. Using the terrain data from the Catsop research catchment and two different rainfall events, the sensitivity of discharge is investigated for a range of pre-event soil moisture contents (0.1–0.40cm3 cm−3) in two-layers for a two-layer Green–Ampt as well as Richards infiltration model. The sensitivity of the predicted discharge to the initial condition of soil moisture appears to depend highly on all factors: infiltration model, event properties, topsoil/subsoil depth configuration and the level of the initial condition itself. There are interaction effects between all the factors. However, the effect of the different infiltration models is most pronounced. The Green–Ampt model shows less sensitivity to moisture content variation of both top and subsoil. Top/subsoil depth configuration rarely influences the results of the Green–Ampt model. The Richards model shows a highly variable discharge – initial soil moisture relation with changing rainfall intensity and topsoil/subsoil depth configurations. Two methods of sensitivity analysis, relative sensitivity and One factor-At-a Time sensitivity, have been used. The two methods gave comparable results. Depending on the other parameter values, 1% changes in topsoil moisture content resulted into 0.8–1.81% and 0.03–3.5% changes in total discharge predicted by the Green–Ampt and Richards models, respectively. [ABSTRACT FROM AUTHOR]

Summary: Hillslope threshold response to storm rainfall is poorly understood. Basic questions regarding the type, location, and flow dynamics of lateral, subsurface flow remain unanswered, even at our most intensively studied field sites. Here we apply a forensic approach where we combined irrigation and excavation experiments at the well studied Maimai hillslope to determine the typology and morphology of the primary lateral subsurface flowpaths, and the control of bedrock permeability and topography on these flowpaths. The experiments showed that downslope flow is concentrated at the soil bedrock interface, with flowpath locations controlled by small features in the bedrock topography. Lateral subsurface flow is characterized by high velocities, several orders of magnitude greater than predicted by Darcy’s Law using measured hydraulic conductivities at the site. We found the bedrock to be moderately permeable, and showed that vertical percolation of water into the bedrock is a potentially large component of the hillslope water balance. Our results suggest that it is the properties of the bedrock (topography and permeability) that control subsurface flow at Maimai, and the soil profile plays a less significant role than previously thought. A companion paper incorporates these findings into a conceptual model of hydrological processes at the site to explore the generalities of whole-hillslope threshold response to storm rainfall. [ABSTRACT FROM AUTHOR]

Summary: Streamflow sources across various hydrologic conditions were examined in a 5.1ha temperate humid forested catchment (Laurentians, Canada). In that system, the relationship between rainfall and runoff is nonlinear, thus hinting towards complex processes involving critical, transient source areas and a changing catchment internal state of connectivity. Multiyear daily stream chemistry data were broken down into several hydrologic scenarios reflecting different conditions with respect to stream discharge and antecedent catchment wetness. End-member mixing analysis and mass balance calculations were performed to: (1) compare the dimensionality of the mixing spaces (i.e. the number of streamflow sources) obtained under 64 different hydrologic scenarios; (2) screen independently sampled end-members (i.e. the nature of sources) to assess catchment connectivity from a spatial perspective; and (3) estimate the relative contributions of end-members to streamflow to characterize hydrological connectivity from a volumetric standpoint. Mixing space dimensionality did not vary significantly among the tested hydrologic scenarios, as three end-members were generally required to account for most of the variance in stream geochemistry. Differences were significant in the ability of the tested end-members to fit in mixing spaces; for instance, throughfall and organic soil water end-members better fitted in mixing spaces associated with high rather than low discharges. The relative contributions of end-members to streamflow were highly variable in time. Scenarios involving low discharges and dry antecedent conditions were mostly associated with baseflow, while scenarios involving high discharges and wet antecedent conditions were associated with increased proportions of throughfall and organic soil water from downstream downslope and downstream upslope areas. These results suggest a cautious evaluation of the predictive power of one single mixing space with regards to the nature of streamflow sources across hydrologic conditions. [Copyright &y& Elsevier]

Summary: Streams in intensively used agricultural catchments are frequently characterised by high transport of suspended solids during rainfall events. Due to a high variability in runoff, the sediment concentration relationship during and between different events and various hysteresis effects, instantaneous sediment concentrations and event loads are difficult to calculate. We tested the applicability of turbidity measurements for calculating instantaneous sediment concentrations and loads in a small agricultural catchment in Austria. We calibrated quasi-continuous turbidity measurements using additional water sampling and employed these calibrated sediment concentrations as benchmark sediment concentrations. Four different methods to calculate instantaneous sediment concentrations were tested on 19 events. A generalized rating curve approach resulted in a considerable bias for both event specific sediment concentrations and total sediments loads. Fitting of event-specific rating curves still misrepresented instantaneous sediment concentrations for the different events, but gave load estimations that were in a range of 5% of the benchmark values. Two approaches accounting explicitly for hysteresis exhibited the best fit and provided load estimations that were in a range of 0–1% deviation to the benchmark sediment concentrations. Nevertheless, several limitations to the hysteresis model approach were identified. Testing the various hysteresis effects against other event parameters such as total rainfall amount, maximum rainfall intensity and initial soil water content revealed interactions to these parameters that could predefine parameter values of the hysteresis model approach. [Copyright &y& Elsevier]

Abstract: Flash floods are the most destructive natural hazards that occur in the Mediterranean region. Rainfall–runoff models can be very useful for flash flood forecasting and prediction. Event-based models are very popular for operational purposes, but there is a need to reduce the uncertainties related to the initial moisture conditions estimation prior to a flood event. This paper aims to compare several soil moisture indicators: local Time Domain Reflectometry (TDR) measurements of soil moisture, modelled soil moisture through the Interaction-Sol–Biosphère–Atmosphère (ISBA) component of the SIM model (Météo-France), antecedent precipitation and base flow. A modelling approach based on the Soil Conservation Service–Curve Number method (SCS–CN) is used to simulate the flood events in a small headwater catchment in the Cevennes region (France). The model involves two parameters: one for the runoff production, S, and one for the routing component, K. The S parameter can be interpreted as the maximal water retention capacity, and acts as the initial condition of the model, depending on the antecedent moisture conditions. The model was calibrated from a 20-flood sample, and led to a median Nash value of 0.9. The local TDR measurements in the deepest layers of soil (80–140cm) were found to be the best predictors for the S parameter. TDR measurements averaged over the whole soil profile, outputs of the SIM model, and the logarithm of base flow also proved to be good predictors, whereas antecedent precipitations were found to be less efficient. The good correlations observed between the TDR predictors and the S calibrated values indicate that monitoring soil moisture could help setting the initial conditions for simplified event-based models in small basins. [Copyright &y& Elsevier]