The effect of lithology on valley width,terrace distribution,and bedload provenance in a tectonically stable catchment with flat‐lying stratigraphy

How rock resistance or erodibility affects fluvial landforms and processes is an outstanding question in geomorphology that has recently garnered attention owing to the recognition that the erosion rates of bedrock channels largely set the pace of landscape evolution. In this work, we evaluate valley width, terrace distribution, and bedload provenance in terms of reach scale variation in lithology in the study reach and discuss the implications for landscape evolution in a catchment with relatively flat‐lying stratigraphy and very little uplift. A reach of the Buffalo National River in Arkansas was partitioned into lithologic reaches and the mechanical and chemical resistance of the main lithologies making up the catchment was measured. Valley width and the spatial distribution of terraces were compared among the different lithologic reaches. The surface grain size and provenance of coarse (2–90 mm) sediment of both modern gravel bars and older terrace deposits that make up the former bedload were measured and defined. The results demonstrate a strong impact of lithology upon valley width, terrace distribution, and bedload provenance and therefore, upon landscape evolution processes. Channel down‐cutting through different lithologies creates variable patterns of resistance across catchments and continents. Particularly in post‐tectonic and non‐tectonic landscapes, the variation in resistance that arises from the exhumation of different rocks in channel longitudinal profiles can impact local base levels, initiating responses that can be propagated through channel networks. The rate at which that response is transmitted through channels is potentially amplified and/or mitigated by differences between the resistance of channel beds and bedload sediment loads. In the study reach, variation in lithologic resistance influences the prevalence of lateral and vertical processes, thus producing a spatial pattern of terraces that reflects rock type rather than climate, regional base level change, or hydrologic variability. Copyright © 2017 John Wiley & Sons, Ltd.     

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

The most popular practice for analysing nonstationarity of flood series is to use a fixed single‐type probability distribution incorporated with the time‐varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time‐varying under changing environments. To allow the investigation of this complex nature, the time‐varying two‐component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta‐heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single‐type distributions, stationary mixture distributions and time‐varying single‐type distributions. The results highlighted the advantages of TTMD with physically‐based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. Copyright © 2016 John Wiley & Sons, Ltd.     

The effect of land cover change on duration and severity of high and low flows

Land cover has been increasingly recognized as an important factor affecting hydrologic processes at the basin and regional level. Therefore, improved understanding of how land cover change affects hydrologic systems is needed for better management of water resources. The objective of this study is to investigate the effects of land cover change on the duration and severity of high and low flows by using the Soil Water Assessment Tool model, Bayesian model averaging and copulas. Two basins dominated by different land cover in the Ohio River basin are used as study area in this study. Two historic land covers from the 1950s and 1990s are considered as input to the Soil Water Assessment Tool model, thereby investigating the hydrologic high and low flow response of different land cover conditions of these two basins. The relationships between the duration and severity of both low and high flow are defined by applying the copula method; changes in the frequency of the duration and severity are investigated. The results show that land cover changes affect both the duration and severity of both high and low flows. An increase in forest area leads to a decrease in the duration and severity during both high and low flows, but its impact is highest during extreme flows. The results also show that the land cover changes have had significant influences on changes in the joint return periods of duration and severity of low and high flows. While this study sheds light on the role of land cover change on severity and duration of high and low flow conditions, more studies using various land cover conditions and climate types are required in order to draw more reliable conclusions in the future. Copyright © 2016 John Wiley & Sons, Ltd.     

Hydrologic response of engineered media in living roofs and bioretention to large rainfalls: experiments and modeling

The hydrologic response of engineered media plays an important role in determining a stormwater control measure's ability to reduce runoff volume, flow rate, timing, and pollutant loads. Five engineered media, typical of living roof and bioretention stormwater control measures, were investigated in laboratory column experiments for their hydrologic responses to steady, large inflow rates. The inflow, medium water content response, and outflow were all measured. The water flow mechanism (uniform flow vs. preferential flow) was investigated by analyzing medium water content response in terms of timing, magnitude, and sequence with depth. Modeling the hydrologic process was conducted in the HYDRUS‐1D software, applying the Richards equation for uniform flow modeling, and a mobile–immobile model for preferential flow modeling. Uniform flow existed in most cases, including all initially dry living roof media with bimodal pore size distributions and one bioretention medium with unimodal pore size distribution. The Richards equation can predict the outflow hydrograph reasonably well for uniform flow conditions when medium hydraulic properties are adequately represented by appropriate functions. Preferential flow was found in two media with bimodal pore size distributions. The occurrence of preferential flow is more likely due to the interaction between the bimodal pore structure and the initial water content rather than the large inflow rate.     

Efficient DEM‐based overland flow routing using integrated recursive algorithms

Two primary concerns in performing watershed overland flow routing are the numerical instability and computational efficiency. The stability of executing an explicit scheme has to be maintained by observing the Courant–Friedrich–Lewy criterion, which is adopted to confirm that the numerical marching speed is larger than the wave celerity. Moreover, there is another criterion of time step devised in previous studies to avoid back‐and‐forth refluxing between adjacent grids. The situation of refluxing usually occurs on flat regions. In light of this, the selection of a small time increment to honor both restrictions simultaneously is believed to decrease the computational efficiency in performing overland flow routing. This study aims at creating a robust algorithm to relax both restrictions. The proposed algorithm was first implemented on a one‐dimensional overland plane to evaluate the accuracy of the numerical result by comparing it with an analytical solution. Then, the algorithm was further applied to a watershed for 2D runoff simulations. The results show that the proposed integrated algorithm can provide an accurate runoff simulation and achieve satisfactory performance in terms of computational speed.     

Tropical precipitation anomalies and d‐excess evolution during El Niño 2014‐16

The last 2014‐16 El Niño event was among the three strongest episodes on record. El Niño considerably changes annual and seasonal precipitation across the tropics. Here, we present a unique stable isotope data set of daily precipitation collected in Costa Rica prior to, during, and after El Niño 2014‐16, in combination with Lagrangian moisture source and precipitation anomaly diagnostics. δ²H composition ranged from ‐129.4 to +18.1 (‰) while δ¹⁸O ranged from ‐17.3 to +1.0 (‰). No significant difference was observed among δ¹⁸O (P=0.186) and δ²H (P=0.664) mean annual compositions. However, mean annual d‐excess showed a significant decreasing trend (from +13.3 to +8.7 ‰) (P<0.001) with values ranging from +26.6 to ‐13.9 ‰ prior to and during the El Niño evolution. The latter decrease in d‐excess can be partly explained by an enhanced moisture flux convergence across the southeastern Caribbean Sea coupled with moisture transport from northern South America by means of an increased Caribbean Low Level Jet regime. During 2014‐15, precipitation deficit across the Pacific domain averaged 46% resulting in a very severe drought; while a 94% precipitation surplus was observed in the Caribbean domain. Understanding these regional moisture transport mechanisms during a strong El Niño event may contribute to a) better understanding of precipitation anomalies in the tropics and b) re‐evaluate past stable isotope interpretations of ENSO events in paleoclimatic archives within the Central America region.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Understory and small trees contribute importantly to stemflow of a lower montane cloud forest

Stemflow (Sf) measurements in tropical rain and montane forests dominated by large trees rarely include the understory and small trees. In this study, contributions of lower (1‐ to 2‐m height) and upper (>2‐m height and <5‐cm diameter at breast height [DBH]) woody understory, small trees (5 < DBH < 10 cm), and canopy trees (>10‐cm DBH) to Sf per unit ground area (Sf_a) of a Mexican lower montane cloud forest were quantified for 32 days with rainfall (P) during the 2014 wet season. Rainfall, stemflow yield (Sf_y), vegetation height, density, and basal area were measured. Subsequently, stemflow funneling ratios (SFRs) were calculated, and three common methods to scale up Sf_y from individual trees to the stand level (tree‐Sf_y correlation, P‐Sf_y correlation, and mean‐Sf_y extrapolation) were used to calculate Sf_a. Understory woody plants, small trees, and upper canopy trees represented 96%, 2%, and 2%, respectively, of the total density. Upper canopy trees had the lowest SFRs (1.6 ± 0.5 Standard Error (SE) on average), although the lower understory had the highest (36.1 ± 6.4). Small trees and upper understory presented similar SFRs (22.9 ± 5.4 and 20.2 ± 3.9, respectively). Different Sf scaling methods generally yielded similar results. Overall Sf_a during the study period was 22.7 mm (4.5% of rainfall), to which the understory contributed 70.1% (15.9 mm), small trees 10.6% (2.4 mm), and upper canopy trees 19.3% (4.4 mm). Our results strongly suggest that for humid tropical forests with dense understory of woody plants and small trees, Sf of these groups should be measured to avoid an underestimation of overall Sf at the stand level.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

A methodology for regionalizing 3‐D effective porosity at watershed scale in crystalline aquifers

An innovative approach for regionalizing the 3‐D effective porosity field is presented and applied to two large, overexploited, and deeply weathered crystalline aquifers located in southern India. The method derives from earlier work on regionalizing a 2‐D effective porosity field in that part of an aquifer where the water table fluctuates, which is now extended over the entire aquifer using a 3‐D approach. A method based on geological and geophysical surveys has also been developed for mapping the weathering profile layers (saprolite and fractured layers). The method for regionalizing 3‐D effective porosity combines water table fluctuation and groundwater budget techniques at various cell sizes with the use of satellite‐based data (for groundwater abstraction), the structure of the weathering profile, and geostatistical techniques. The approach is presented in detail for the Kudaliar watershed (983 km²) and tested on the 730 km² Anantapur watershed. At watershed scale, the effective porosity of the aquifer ranges from 0.5% to 2% in Kudaliar and between 0.3% and 1% in Anantapur, which agrees with earlier works. Results show that (a) depending on the geology and on the structure of the weathering profile, the vertical distribution of effective porosity can be very different and that the fractured layers in crystalline aquifers are not necessarily characterized by a rapid decrease in effective porosity and (b) that the lateral variations in effective porosity can be larger than the vertical ones. These variations suggest that within a same weathering profile, the density of open fractures and/or degree of weathering in the fractured zone may significantly vary from a place to another. The proposed method provides information on the spatial distribution of effective porosity that is of prime interest in terms of flux and contaminant transport in crystalline aquifers. Implications for mapping groundwater storage and scarcity are also discussed, which should help in improving groundwater resource management strategies.