Explore the vast range of titles available.

Ecosystem engineering species can affect their environment at multiple spatial scales, from the local scale up to a significant distance, by indirectly affecting the surrounding habitats. Structural changes in the landscape can have important consequences for ecosystem functioning, for example, by increasing retention of limiting resources in the system. Yet, it remains poorly understood how extensive the footprint of ecosystem engineers on the landscape is. Using remote sensing techniques, we reveal that depression storage capacity on intertidal flats is greatly enhanced by engineering by shellfish resulting in intertidal pools. Many organisms use such pools to bridge low water events. This storage capacity was significantly higher both locally within the shellfish reef, but also at extensive spatial scales up to 115 m beyond the physical reef borders. Therefore, the footprint of these ecosystem engineers on the landscape was more than 5 times larger than their actual coverage; the shellfish cover approximately 2% of the total intertidal zone, whereas they influence up to approximately 11% of the area by enhancing water storage capacity. We postulate that increased residence time of water due to higher water storage capacity within engineered landscapes is an important determinant of ecosystem functioning that may extend well beyond the case of shellfish reefs provided here.

Rainfall partition on paved urban surfaces is governed to a great extent by depression storage. This is especially the case for small rainfall events, which are often ignored in urban hydrology. If storage, infiltration and evaporation (important for urban heat island mitigation), rather than storm water run-off, are of interest, high-resolution simulations with exact values for depression storage capacities are required. Terrestrial laser scanners deliver fast, high-resolution surveys of pavement surface morphology. The depression storage capacity can be quantified from 3D points by generating digital elevation models and applying cut-and-fill algorithms in a geographic information system. The method was validated using a test model. It was possible to quantify depressions with a depth of at least 1.4 × 10−3 m and a surface of at least 15 × 10−6 m2 with an uncertainty below 30%. Applying this method, the depression storage capacities for 11 ideal, typical pavement designs were found to vary from 0.07 to 1.4 mm. Realistic urban pavements must also be surveyed, as cracks and puddles from their use history can have a major impact on the depression storage capacities and thus on infiltration, evaporation and, finally, the annual run-off.

The Prairie Pothole Region of North America is characterized by numerous, small, wetland depressions that perform important ecological and hydrological functions. Recent studies have shown that total wetland area in the region is decreasing due to cumulative impacts related to natural and anthropogenic changes. The impact of wetland losses on landscape hydrology is an active area of research and management. Various spatially distributed hydrologic models have been developed to simulate effects of wetland depression storage on peak river flows, frequently using dated geospatial wetland inventories. We describe an innovative method for identifying wetland depressions and quantifying their nested hierarchical bathymetric/topographic structure using high-resolution light detection and ranging (LiDAR) data. This contour tree method allows identified wetland depressions to be quantified based on their dynamic filling-spilling-merging hydrological processes. In addition, wetland depression properties, such as surface area, maximum depth, mean depth, storage volume, etc., can be computed for each component of a depression as well as the compound depression. We successfully applied the proposed method to map wetland depressions in the Little Pipestem Creek watershed in North Dakota. The methods described in this study will provide more realistic and higher resolution data layers for hydrologic modeling and other studies requiring characterization of simple and complex wetland depressions, and help prioritize conservation planning efforts for wetland resources.

This paper proposes a new multi-step approach for sensitivity assessment of surface runoff parameters. The procedure has been tested on a peri-urban basin in southern Italy, interested by intense urbanization. The basin has limited data about land characteristics, and nearby precipitation measurements are not available. Accordingly, rainfall events are defined based on depth-duration-frequency curve valid for the area. The main novelties of the work are to provide a general framework for assessing the influence of runoff parameters (i.e. depression storage and surface roughness) for a basin model in SWMM in relation to rain events of various intensity/duration, and to provide a ranking of crucial parameters significantly affecting peak discharge and total volume of the hydrograph, for an ungauged basin, by means the Fourier Amplitude Sensitivity Test (FAST). Results indicate the dependence on rainfall characteristics of the relative importance of the parameters describing the pervious and impervious areas. Notably, the peak discharge of the shortest considered event is influenced only by the two parameters of the impervious area, while the opposite holds for the longest rain event. The total runoff volume is mostly influenced by the depression storage of impervious areas, with the parameters of pervious areas becoming more influential for longer rain events. Results allow a clear interpretation of the modelled physical processes variability within the basin and their relationship with rainfall/areas features, thus providing useful insights for key parameter definition in other contexts and for other models.

The characteristics of surface runoff and the infiltration properties of urban green land are important to determine the effects of runoff reduction by low-impact development (LID) facilities. In this paper, two typical types of urban green land (lawn and shrub) in Shanghai were selected to study the runoff characteristics under eight rainfall events. The sensitivity of the runoff parameters was analyzed, and then, the optimal parameters were determined using the Stormwater Management Model (SWMM). The results showed that the interception and infiltration capacities of shrub were greater than those of lawn. The rainfall intensity and rainfall pattern were the major factors that influenced the interception and infiltration of rainwater. The threshold value that generates runoff varied across the eight rainfall events ranged from 1.6 to 28.5 mm for lawn and 4.5 to 32.0 mm for shrub. The maximum reduction ratios of runoff and peak flow for shrub were 52 and 57% higher than them for lawn, respectively. The parameters for shrub were more sensitive to runoff and peak flow compared with those for lawn. Under light rainfalls with a short duration, the maximum infiltration rate and depression storage were more sensitive than those under heavy rainfalls with a long duration. Antecedent dry weather period was not found to be a sensitive parameter except for the shrub under light rainfalls. The relative errors of runoff and dynamic mean runoff (60 min) for lawn and shrub were within ± 9.5%. The errors of peak flow ranged between − 21 and 16.6%. The dynamic runoff characteristics and the parameters for lawn and shrub determined in this study can provide references for simulating urban runoff and planning LID areas.

The simulation of overland flow, wherein runoff yield and concentration are influenced by topography, is fundamental to hydrological forecasting. Therefore, critically evaluating the characteristics of overland flow under the influence of topographic depressions—which are one of the most common microtopographic structures—is vital for improving current hydrological models. In this study, we developed a solution for the real-world application of overland flow simulations under the influence of depressions in hydrological models. A relative depression storage–outflow curve (RDOC) was proposed to investigate surface outflow processes. Experiments were conducted based on the variable-controlling approach using three rainfall return periods, four slopes, and four depression rates while ensuring a consistent initial soil moisture content. A homogenized RDOC was achieved based on shape analysis; it was parameterized by the outflow threshold and the reciprocal of the curve index of two outflow stages (B and D). A relative depression storage–outflow function (RDOF) was generated and a complete calculation procedure was applied within a hydrological model. Furthermore, we analyzed the hydrological responses to parameters of different hydrological factors to improve our understanding of the parameter determination of the RDOF.

Secondary dryland salinity is a global land degradation issue. Drylands are often less developed, less well instrumented and less well understood, requiring us to adapt and impose understanding from different hydro-geomorphological settings that are better instrumented and understood. Conceptual models of secondary dryland salinity, from wet and more hydrologically connected landscapes imposed with adjustments for rainfall and streamflow, have led to the pervasive understanding that land clearing alters water balance in favour of increased infiltration and rising groundwater that bring salts to the surface. This paper presents data from an intra-catchment surface flow gauging network run for 6 years and a surface-water–groundwater (SW–GW) interaction site to assess the adequacy of our conceptual understanding of secondary dryland salinity in environments with low gradients and runoff yield. The aim is to (re-)conceptualise pathways of water and salt redistribution in dryland landscapes and to investigate the role that surface water flows and connectivity plays in land degradation from salinity in low-gradient drylands. Based on the long-term end-of-catchment gauge, average annual runoff yield is only 0.14 % of rainfall. The internal gauging network that operated from 2007–2012 found pulses of internal water (also mobilising salt) in years when no flow was recorded at the catchment outlet. Data from a surface-water–groundwater interaction site show top-down recharge of surface water early in the water year that transitions to a bottom-up system of discharge later in the water year. This connection provides a mechanism for the vertical diffusion of salts to the surface waters, followed by evapo-concentration and downstream export when depression storage thresholds are exceeded. Intervention in this landscape by constructing a broad-based channel to address these processes resulted in a 25 % increase in flow volume and a 20 % reduction in salinity by allowing the lower catchment to more effectively support bypassing of the storages in the lower landscape that would otherwise retain water and allow salt to accumulate. Results from this study suggest catchment internal redistribution of relatively fresh runoff onto the valley floor is a major contributor to the development of secondary dryland salinity. Seasonally inundated areas are subject to significant transmission losses and drive processes of vertical salt mobility. These surface flow and connectivity processes are not only acting in isolation to cause secondary salinity but are also interacting with groundwater systems responding to land clearing and processes recognised in the more conventional understanding of hillslope recharge and groundwater discharge. The study landscape appears to have three functional hydrological components: upland, hillslope “flow” landscapes that generate fresh runoff; valley floor “fill” landscapes with high transmission losses and poor flow connectivity controlled by the micro-topography that promotes a surface–groundwater connection and salt movement; and the downstream “flood” landscapes, where flows are recorded only when internal storages (fill landscapes) are exceeded. This work highlights the role of surface water processes as a contributor to land degradation by dryland salinity in low-gradient landscapes.

Quantifying urban runoff during frequent rainfall events is a key element in quality management of urban water due to their high contribution to the annual runoff flow. This explains the growing interest among hydrologists in studying runoff flow on urban surfaces. In this paper, we review most of the experimental approaches as well as the modeling ones conducted in the literature to understand and estimate runoff flow on urban areas. This review highlights the incoherence between our current understanding of the hydrological behavior of urban areas during frequent events and our conception of the loss functions in the urban drainage models. Field studies provided more insight into the determinant processes occurring on the different surface types during frequent events with depression storage being a fundamental element varying between surface types and for the same surface type and infiltration process being relatively important on paved areas especially in their cracks that constitute preferential pathways for rainwater. Analyzing a wide range of urban drainage models showed that these elements along with the temporal evolution of the hydrological behavior of urban surfaces due to seasonal and state conditions are not fully integrated in the models’ structures, which were initially developed for heavy rainfall events. Adapting the assumptions of urban drainage models based on these new factors must improve the performance of hydrological models for frequent rainfall events.

Depression storage capacity (DSC) models found in the literature were developed using statistical regression for relatively large soil surface roughness and slope values resulting in several fitting parameters. In this research, we developed and tested a conceptual model to estimate surface depression storage having small roughness values usually encountered in rainwater harvesting micro-catchments and bare soil in arid regions with only one fitting parameter. Laboratory impermeable surfaces of 30 x 30 cm 2 were constructed with 4 sizes of gravel and mortar resulting in random roughness values ranging from 0.9 to 6.3 mm. A series of laboratory experiments were conducted under 9 slope values using simulated rain. Depression storage for each combination of relative roughness and slope was estimated by the mass balance approach. Analysis of experimental results indicated that the developed linear model between DSC and the square root of the ratio of random roughness (RR) to slope was significant at p < 0.001 and coeficient of determination R2 = 0.90. The developed model predicted depression storage of small relief at higher accuracy compared to other models found in the literature. However, the model is based on small-scale laboratory plots and further testing in the ifeld will provide more insight for practical applications.

Microtopography regulates soil erosion by shaping surface heterogeneity, but the mechanism of loess slope soil loss remains insufficiently quantified. This study combined laboratory rainfall simulations and machine learning to investigate how tillage-induced microtopography modulates soil loss through surface heterogeneity and hydrodynamic processes. Simulations used loess soil (silty loam) with a 5° slope, 60 mm/h rainfall intensity, and 5–30 min rainfall durations (RD). Results indicated that the mean weight diameter (MWD) and aggregate stability index (ASI) of structural, transition, and depositional crusts under micro-terrain decreased by 36~65% and 41~60%, respectively, while the fractal dimension (D) increased by 10~19%. Negative relationships were observed between ASI/MWD and D (R2 = 0.83~0.98). Horizontal cultivation (THC, surface roughness [SR] = 1.76, average depression storage [ADS] = 2.34 × 10−2 m3) delayed runoff connectivity and reduced cumulative soil loss (LS) by 42–58% compared to hoeing cultivation (THE, SR = 1.47, ADS = 3.23 × 10−4 m3). Abrupt hydrodynamic transitions occurred at 10 min RD (THE) and 15 min RD (artificial digging [TAD]), driven by trench connectivity and depression overflow. LS exhibited a significant positive correlation with D and RD and was inversely correlated with ASI, MWD, and SR. A three-hidden-layer BPNN exhibited high predictive accuracy for LS (mean square error = 0.07), verifying applicability in complex scenarios with significant microtopographic heterogeneity and multi-factor coupling. This study demonstrated that surface roughness and depression storage were the dominant microtopographic controls on loess slope soil loss. BPNN provided a reliable tool for soil loss prediction in heterogeneous microtopographic systems. The findings provide critical insights into optimizing tillage-based soil conservation strategies for sloping loess farmlands.