Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields

Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m⁻² and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R² = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m³ ha⁻¹). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.     

Kinematic wave solutions for pollutant transport by runoff over an impervious plane,with instantaneous or finite‐period mixing

Kinematic wave solutions are derived for transport of a conservative non‐point‐source pollutant during a rainfall‐runoff event over an impervious plane. Rainfall is assumed to be steady, uniform and finite in duration. Prior to the start of rainfall, the pollutant is distributed uniformly over the plane. When rainfall occurs, the pollutant is washed off in a particular manner and the mixing of pollutant in the runoff water occurs either instantaneously or in a finite period of time under the assumption that the pollutant is soluble and is mixed completely in the runoff water. Copyright © 2002 John Wiley & Sons, Ltd.     

Evaluation of risk of hydrological droughts by the trivariate Plackett copula in the East River basin (China)

East River, one of the major tributaries of Pearl River, is the major source of water supply for mega-cites within and in the vicinity of the Pearl River Delta, China. The availability and variability of water resources of the East River basin are therefore of practical importance. This study aims to investigate the probabilistic behavior of hydrological droughts in the East River basin using the trivariate Plackett copula. Daily streamflow data for the period of 1975–2009 from 3 hydrological stations in the East River basin are analyzed. Defining hydrological droughts by drought severity, duration, and minimum flow, secondary return periods are computed. Results show that the Plackett copula satisfactorily models bivariate and trivariate probability distributions of correlated drought variables. Results of risk evaluation show an increasing drought risk from the upper to the lower East River basin. This result is important for basin-scale water resources management in the East River basin.     

Debris flow hazard assessment using set pair analysis models: Take Beichuan county as an example

Assessment of debris flow hazards is important for developing measures to mitigate the loss of life and property and to minimize environmental damage. Two modified uncertainty models, Set Pair Analysis （SPA） and modified Set Pair Analysis （mSPA）, were suggested to assess the regional debris flow hazard. A ease study was conducted in seven towns of the Beichuan county, Sichuan Province, China, to test and compare the application of these two models in debris flow hazard assessment. The results showed that mSPA only can fit for value-variables, but not for non value-variable assessment indexes, Furthermore, as for a given assessment index xi, mSPA only considers two cases, namely, when grade value increases with xi and when grade value decreases with xi. Thus, mSPA can not be used for debris flow hazard assessment but SPA is credible for the assessment because there are no limitations when using SPA model to assess the debris flow hazard. Therefore, in this study SPA is proposed for assessing debris flow hazard.     

Analysis of long-term rainfall trends in India

Abstract

The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water. In this work, monthly, seasonal and annual trends of rainfall have been studied using monthly data series of 135 years (1871–2005) for 30 sub-divisions (sub-regions) in India. Half of the sub-divisions showed an increasing trend in annual rainfall, but for only three (Haryana, Punjab and Coastal Karnataka), this trend was statistically significant. Similarly, only one sub-division (Chattisgarh) indicated a significant decreasing trend out of the 15 sub-divisions showing decreasing trend in annual rainfall. In India, the monsoon months of June to September account for more than 80% of the annual rainfall. During June and July, the number of sub-divisions showing increasing rainfall is almost equal to those showing decreasing rainfall. In August, the number of sub-divisions showing an increasing trend exceeds those showing a decreasing trend, whereas in September, the situation is the opposite. The majority of sub-divisions showed very little change in rainfall in non-monsoon months. The five main regions of India showed no significant trend in annual, seasonal and monthly rainfall in most of the months. For the whole of India, no significant trend was detected for annual, seasonal, or monthly rainfall. Annual and monsoon rainfall decreased, while pre-monsoon, post-monsoon and winter rainfall increased at the national scale. Rainfall in June, July and September decreased, whereas in August it increased, at the national scale.

Citation Kumar, V., Jain, S. K. & Singh, Y. (2010) Analysis of long-term rainfall trends in India. Hydrol. Sci. J. 55(4), 484–496.     

An adaptive multilevel correlation analysis: a new algorithm and case study

ABSTRACT

An adaptive multilevel correlation analysis, a kind of data-driven methodology, is proposed. The analysis is done by subdividing the time series into segments such that adjacent segments have significantly different mean values. It is shown that the proposed methodology can provide multilevel information about the correlation between two variables. An integrated coefficient with its significance testing is also proposed to summarize the correlation at each level. Using the adaptive multilevel correlation analysis methodology, the correlation between streamflow and water level is investigated for a case study, and the results indicate that real correlation might be far more complicated than the empirically constructed picture.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR E. Volpi     

Modelling of infiltration using artificial intelligence techniques in semi-arid Iran

ABSTRACT

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.     

Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation

We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.     

Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data

This study aims to model the joint probability distribution of periodic hydrologic data using meta-elliptical copulas. Monthly precipitation data from a gauging station (410120) in Texas, US, was used to illustrate parameter estimation and goodness-of-fit for univariate drought distributions using chi-square test, Kolmogorov–Smirnov test, Cramer-von Mises statistic, Anderson-Darling statistic, modified weighted Watson statistic, and Liao and Shimokawa statistic. Pearson’s classical correlation coefficient r _n , Spearman’s ρ _n, Kendall’s τ, Chi-Plots, and K-Plots were employed to assess the dependence of drought variables. Several meta-elliptical copulas and Gumbel-Hougaard, Ali-Mikhail-Haq, Frank and Clayton copulas were tested to determine the best-fit copula. Based on the root mean square error and the Akaike information criterion, meta-Gaussian and t copulas gave a better fit. A bootstrap version based on Rosenblatt’s transformation was employed to test the goodness-of-fit for meta-Gaussian and t copulas. It was found that none of meta-Gaussian and t copulas considered could be rejected at the given significance level. The meta-Gaussian copula was employed to model the dependence, and these results were found satisfactory.     

Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin,China

Variations in streamflows of five tributaries of the Poyang Lake basin, China, because of the influence of human activities and climate change were evaluated using the Australia Water Balance Model and multivariate regression. Results indicated that multiple regression models were appropriate with precipitation, potential evapotranspiration of the current month, and precipitation of the last month as explanatory variables. The NASH coefficient for the Australia Water Balance Model was larger than 0.842, indicating satisfactory simulation of streamflow of the Poyang Lake basin. Comparison indicated that the sensitivity method could not exclude the benchmark‐period human influence, and the human influence on streamflow changes was overestimated. Generally, contributions of human activities and climate change to streamflow changes were 73.2% and 26.8% respectively. However, human‐induced and climate‐induced influences on streamflow were different in different river basins. Specifically, climate change was found to be the major driving factor for the increase of streamflow within the Rao, Xin, and Gan River basins; however, human activity was the principal driving factor for the increase of streamflow of the Xiu River basin and also for the decrease of streamflow of the Fu River basin. Meanwhile, impacts of human activities and climate change on streamflow variations were distinctly different at different temporal scales. At the annual time scale, the increase of streamflow was largely because of climate change and human activities during the 1970s–1990s and the decrease of streamflow during the 2000s. At the seasonal scale, climate change was the main factor behind the increase of streamflow in the spring and summer season. Human activities increase the streamflow in autumn and winter, but decrease the streamflow in spring. At the monthly scale, different influences of climate change and human activities were detected. Climate change was the main factor behind the decrease of streamflow during May to June and human activities behind the decrease of streamflow during February to May. Results of this study can provide a theoretical basis for basin‐scale water resources management under the influence of climate change and human activities. Copyright © 2016 John Wiley & Sons, Ltd.