Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

A non-linear perturbation model considering catchment wetness and its application in river flow forecasting

A non-linear perturbation model for river flow forecasting is developed, based on consideration of catchment wetness using an antecedent precipitation index (API). Catchment seasonality, of the form accounted for in the linear perturbation model (the LPM), and non-linear behaviour both in the runoff generation mechanism and in the flow routing processes are represented by a constrained non-linear model, the NLPM-API. A total of ten catchments, across a range of climatic conditions and catchment area magnitudes, located in China and in other countries, were selected for testing daily rainfall-runoff forecasting with this model. It was found that the NLPM-API model was significantly more efficient than the original linear perturbation model (the LPM). However, restriction of explicit non-linearity to the runoff generation process, in the simpler LMP-API form of the model, did not produce a significantly lower value of the efficiency in flood forecasting, in terms of the model efficiency index R².     

TOPCAT‐NP: a minimum information requirement model for simulation of flow and nutrient transport from agricultural systems

Future catchment planning requires a good understanding of the impacts of land use and management, especially with regard to nutrient pollution. A range of readily usable tools, including models, can play a critical role in underpinning robust decision‐making. Modelling tools must articulate our process understanding, make links to a range of catchment characteristics and scales and have the capability to reflect future land‐use management changes. Hence, the model application can play an important part in giving confidence to policy makers that positive outcomes will arise from any proposed land‐use changes. Here, a minimum information requirement (MIR) modelling approach is presented that creates simple, parsimonious models based on more complex physically based models, which makes the model more appropriate to catchment‐scale applications. This paper shows three separate MIR models that represent flow, nitrate losses and phosphorus losses. These models are integrated into a single catchment model (TOPCAT‐NP), which has the advantage that certain model components (such as soil type and flow paths) are shared by all three MIR models. The integrated model can simulate a number of land‐use activities that relate to typical land‐use management practices. The modelling process also gives insight into the seasonal and event nature of nutrient losses exhibited at a range of catchment scales. Three case studies are presented to reflect the range of applicability of the model. The three studies show how different runoff and nutrient loss regimes in different soil/geological and global locations can be simulated using the same model. The first case study models intense agricultural land uses in Denmark (Gjern, 114 km²), the second is an intense agricultural area dominated by high superphosphate applications in Australia (Ellen Brook, 66 km²) and the third is a small research‐scale catchment in the UK (Bollington Hall, 2 km²). Copyright © 2007 John Wiley & Sons, Ltd.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

Assessment of hydropower potential using spatial technology and SWAT modelling in the Mat River,southern Mizoram,India

Abstract

In this study, a hydrological model and spatial technologies have been employed to assess water availability in the Mat River basin, southern Mizoram, India. Furthermore, the results obtained from the SWAT (Soil and Water Assessment Tool) model, satellite data and GIS tools were utilized to identify the hydropower potential in the basin. Thirty three sites with hydropower potential were identified within 147 km² of the Mat River basin. A total of 3039, 1127 and 805 kW can be harnessed with 50, 75 and 90% dependability, respectively. The study revealed that the hydropower potential of a river basin can be correctly assessed by employing a digital elevation model, stream network data and a hydrological model, such as the SWAT model, within a GIS framework.

Editor D. Koutsoyiannis     

Temperature and Pressure Dependencies of Thermal Transport Properties of Rocks: Implications for Uncertainties in Thermal Lithosphere Models and new Laboratory Measurements of High-Grade Rocks in the Central Fennoscandian Shield

Measurements on thermal conductivity and diffusivity as functions of temperature (up to 1150 K) and pressure (up to 1000 MPa) are presented for Archaean and Proterozoic mafic high-grade rocks metamorphosed in middle and lower crustal pressures, and situated in eastern Finland, central Fennoscandian Shield. Decrease of 12–20% in conductivity and 40–55% in diffusivity was recorded between room temperature and 1150 K, which can be considered as typical of phonon conductivity. Radiative heat transfer effects were not detected in these samples. Pressure dependencies of the samples are weak if compared to crystalline rocks in general, but relatively typical for mafic rocks.The temperature and pressure dependencies of thermal transport properties (data from literature and the present study) were applied in an uncertainty analysis of lithospheric conductive thermal modellings with random (Monte Carlo) simulations using a 4-layer model representative of shield lithosphere. Model parameters were varied according to predetermined probability functions and standard deviations were calculated for lithospheric temperature and heat flow density after 1500 independent simulations. The results suggest that the variations (uncertainties) in calculated temperature and heat flow density values due to variations in the temperature and pressure dependencies of conductivity are minor in comparison to the effects produced by typical variations in the room temperature value of conductivity, heat production rate or lower boundary condition values.     

Response of vegetation dynamics to climatic variables across a precipitation gradient in the Northeast China Transect

This study investigated the influence of climatic variables on the spatio-temporal variation of vegetation growth using normalized difference vegetation index (NDVI) data and climate data from 2000 to 2013 in the Northeast China Transect. Partial correlation and linear regression methods were applied to quantify the response of the growing season NDVI to climatic variables. Gradient analysis was used to investigate how the response changes across the precipitation gradient over the transect. The results show that, at the spatial scale, NDVI increases with precipitation in grassland, and the spatial sensitivity is 0.001/mm. At the temporal scale, grassland NDVI is less correlated with precipitation in wet areas where precipitation exceeds a threshold of 250 mm. The temporal sensitivity of grassland NDVI to precipitation is 0.0003–0.0006/mm. Positive correlations between NDVI and temperature dominate in forest areas, and forest NDVI is sensitive to temperature by 0.06–0.12/°C.     

Evaluations of subsurface flow for reconstructions of climate change using borehole temperature and isotope data in Kamchatka

Subsurface temperature is affected by heat advection due to groundwater flow and surface temperature changes. To evaluate their effects, it was implemented the measurements of temperature-depth profile (T-D profile) and the continuous monitoring of soil temperature in the southern part of Kamchatka which has not affected by human activity. Additionally, stable isotopic compositions of surface water and groundwater were analyzed. T-D profile and stable isotopic compositions show groundwater flow system is differ from the shallow aquifer to the deep aquifer. In the shallow aquifer, T-D profile suggests the existence of upward groundwater flux. On the other hand, the annual variation of soil temperature is divided into the large variation period (VP) and the stable period (SP) by the magnitude of daily and seasonal variation. VP and SP correspond to the summer and the winter season, respectively, and it considers that the difference between VP and SP is caused by the effect of snow cover. Therefore, the T-D profile is affected by not only upward groundwater flux but also the surface warming particularly in the summer season (VP).     

Predicting return periods of hydrological droughts using the Pearson 3 distribution: a case from rivers in the Canadian prairies

Abstract

The standardized series of monthly and weekly flow sequences, referred to as standardized hydrological index (SHI) series, from five rivers in the Canadian prairies were subjected to return period (T_r) analysis of drought length (L). The SHI series were truncated at drought probability levels q ranging from 0.5 to 0.05 with the intention of deducing drought events and corresponding drought lengths. The values of L were fitted to the Pearson 3, the gamma (2-parameter), the exponential (1-parameter), the Weibull 3 and the Weibull (2-parameter) probability density functions (pdfs). A priori assignment of one week or one month for the location parameter in the Pearson 3 pdf proved logical and also facilitated the rapid estimation of other parameters using either the method of moments or the method of maximum likelihood. The Pearson 3 turns out to be the most suitable pdf to describe and to estimate return periods of drought lengths. At the monthly and weekly time scales, it was inferred that the sample size (T, months or weeks) of SHI series could be treated equivalent to the return period of the largest recorded drought length. At the annual time scale, however, the sample size (T, years) should be modified using either the Hazen or the Gringorten plotting position formula to reflect the actual return period of the largest recorded drought length in years.

Editor D. Koutsoyiannis; Associate editor E. Gargouri     

Estimation of canopy drying time after rainfall using sap flow measurements in an emergent tree in a lowland mixed‐dipterocarp forest in Sarawak,Malaysia

This study emphasizes the importance of canopy drying time (CDT) after rainfall in a lowland tropical rain forest. In this study, we estimate CDT using sap flow velocities measured by a heat‐pulse method in an emergent tree in a lowland mixed‐dipterocarp forest. Estimated CDT (ECDT) for each rain event has been defined as the time from rainfall cessation to the specific time derived from the difference between diurnal courses of sap flow velocities on a rainy day versus bright days. ECDT could be derived for 22 rain events that were grouped into two types, depending on whether rainfall ceased before or after noon. The ECDTs were distributed more widely and with greater values when rainfall ceased before noon (Type 1) than after noon (Type 2). The ECDTs of both Type 1 and Type 2 decreased with increases in net radiation (Rn) and vapour pressure deficit (VPD) after rainfall. This result shows that ECDT is determined mainly by post‐rainfall evaporation rates. The sap flow velocity as a detector of canopy wetness worked out well because of the specific rainfall characteristics at this site. The practical limitations of the method using sap flow velocities are discussed in relation to rainfall characteristics and time lags between transpirations and sap flow velocities. Copyright © 2005 John Wiley & Sons, Ltd.     

Hybrid method to update the design flood shape for spillway operation in a dam: case study of the Huites Dam,Mexico

To design and review the operation of spillways, it is necessary to estimate design hydrographs, considering their peak flow, shape and volume. A hybrid method is proposed that combines the shape of the design hydrograph obtained with the UNAM Institute of Engineering Method (UNAMIIM) with the peak flow and volume calculated from a bivariate method. This hybrid method is applied to historical data of the Huites Dam, Sinaloa, Mexico. The goal is to estimate return periods for the maximum discharge flows (that account for the damage caused downstream) and the maximum levels reached in the dam (measure of the hydrological dam safety) corresponding to a given spillway and its management policy. Therefore, to validate the method, the results obtained by the flood routing of the 50-year hydrograph are compared with those obtained by the flood routing of the three largest historical floods. Both maximum flow and elevation were in the range of values observed within 37.5–75 years corresponding to the length of the historical record.     

Latitudinal variations of CPI values of long-chain n-alkanes in surface soils:Evidence for CPI as a proxy of aridity

Carbon preference index (CPI) of long-chain n-alkanes preserved in surface soil increases gradually from southeastern China to the north margin of Loess Plateau.Along this latitudinal transect,the CPI value correlates to relative humidity,precipitation,and temperature with a negative linear relationship,respectively,whereas the correlation of CPI to temperature is relatively weak.In the Wuyi,Shennongjia,and Tianshan Mountains,CPI values do not change systemically with altitude increasing (or temperature decreasing).However,mean value of CPI for the individual mountain increases in turn from the humid mountain to the arid.These results jointly suggest that aridity (or humidity) is a dominate climate factor in altering soil CPI value.High CPI values of geological records therefore indicate the arid paleoclimate.Though long-chain n-alkanes in soil are derived mainly from leaf wax of terrestrial vascular plants,the regular latitudinal variations of soil CPI might not be caused by the change of vegetation.We speculate that increased long-chain n-alkanes from microbes and/or enhanced biodegradation in the humid climate lead to the decrease of soil CPI.