Impact of spatial variations of land surface parameters on regional evaporation: a case study with remote sensing data

Most precipitation in watersheds is consumed by evaporation, thus techniques to appraise regional evaporation are important to assess the availability of water resources. Many algorithms to estimate evaporation from remotely sensed spectral data have been developed in the recent past. In addition to differences in the physical parameterization of surface fluxes, these algorithms have different solutions for describing spatial variations of the parameters in the soil–vegetation–atmosphere–transfer (SVAT) continuum. In this study, the necessity to spatially distinguish SVAT parameters for computing surface heat fluxes is analysed for the Naivasha watershed in the Kenyan Rift Valley. Landsat Thematic Mapper (TM) spectral data have been used to first delineate the watershed into 15 hydrological units using surface temperature, normalized difference vegetation index and surface albedo as attributes. Thereafter, semi‐empirical relationships between these TM‐based parameters and other SVAT parameters have been applied to compute the spatial variation of SVAT parameters and the associated evaporation from the different hydrological units. The impact of using watershed‐constant or watershed‐distributed SVAT parameters on the fluxes is analysed. The determination of watershed averaged evaporation with area‐aggregated SVAT parameters is feasible without significant loss of accuracy. Distributed evaporation in heterogeneous watersheds, however, can be investigated only with remote sensing flux algorithms that can account for spatially variable air temperature, surface roughness, surface albedo and the stability correction of the temperature profile due to buoyancy. Erroneous results can be expected if area‐aggregated SVAT parameters are used to calculate local evaporation. As most of the recently developed remote sensing flux algorithms are based on areal constant SVAT parameters, direct applications in watersheds are still limited. Copyright © 2001 John Wiley & Sons, Ltd.     

The application of the land surface model for calculating river runoff in high latitudes

The potentialities of the land surface model as applied to the calculation of river runoff in high latitudes were examined. Three approaches were used to specify input data based on meteorological data and land surface parameters. A method was developed for automated optimization of some model parameters by using direct search of minimum of root-mean-square deviation between the calculated and measured streamflow values. The global data sets are shown to be applicable in principle for hydrological calculations.     

A land surface data assimilation framework using the land information system: Description and applications

The Land Information System (LIS) is an established land surface modeling framework that integrates various community land surface models, ground measurements, satellite-based observations, high performance computing and data management tools. The use of advanced software engineering principles in LIS allows interoperability of individual system components and thus enables assessment and prediction of hydrologic conditions at various spatial and temporal scales. In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms. These capabilities are demonstrated here in a suite of experiments that use the ensemble Kalman filter (EnKF) and assimilation through direct insertion. In a soil moisture experiment, we discuss the impact of differences in modeling approaches on assimilation performance. Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results. In a snow assimilation experiment, we investigate the relative merits of assimilating different types of observations (snow cover area and snow water equivalent). The experiments show that data assimilation enhancements in LIS are uniquely suited to compare the assimilation of various data types into different land surface models within a single framework. The high performance infrastructure provides adequate support for efficient data assimilation integrations of high computational granularity.     

A review of continental scale hydrological models and their suitability for drought forecasting in (sub-Saharan) Africa

The aim of this review is to provide a basis for selecting a suitable hydrological model, or combination of models, for hydrological drought forecasting in Africa at different temporal and spatial scales; for example short and medium range (1–10 days or monthly) forecasts at medium to large river basin scales or seasonal forecasts at the Pan-African scale. Several global hydrological models are currently available with different levels of complexity and data requirements. However, most of these models are likely to fail to properly represent the water balance components that are particularly relevant in arid and semi-arid basins in sub-Saharan Africa. This review critically looks at weaknesses and strengths in the representation of different hydrological processes and fluxes of each model. The major criteria used for assessing the suitability of the models are (1) the representation of the processes that are most relevant for simulating drought conditions, such as interception, evaporation, surface water-groundwater interactions in wetland areas and flood plains and soil moisture dynamics; (2) the capability of the model to be downscaled from a continental scale to a large river basin scale model; and (3) the applicability of the model to be used operationally for drought early warning, given the data availability of the region. This review provides a framework for selecting models for hydrological drought forecasting, conditional on spatial scale, data availability and end-user forecast requirements. Among 16 well known hydrological and land surface models selected for this review, PCR-GLOBWB, GWAVA, HTESSEL, LISFLOOD and SWAT show higher potential and suitability for hydrological drought forecasting in Africa based on the criteria used in this evaluation.     

Models as multiple working hypotheses: hydrological simulation of tropical alpine wetlands

Tropical alpine grasslands, locally known as páramos, are the water towers of the northern Andes. They are an essential water source for drinking water, irrigation schemes and hydropower plants. But despite their high socio‐economic relevance, their hydrological processes are very poorly understood. Since environmental change, ranging from small scale land‐use changes to global climate change, is expected to have a strong impact on the hydrological behaviour, a better understanding and hydrological prediction are urgently needed. In this paper, we apply a set of nine hydrological models of different complexity to a small, well monitored upland catchment in the Ecuadorian Andes. The models represent different hypotheses on the hydrological functioning of the páramo ecosystem at catchment scale. Interpretation of the results of the model prediction and uncertainty analysis of the model parameters reveals important insights in the evapotranspiration, surface runoff generation and base flow in the páramo. However, problems with boundary conditions, particularly spatial variability of precipitation, pose serious constraints on the differentiation between model representations. Copyright © 2010 John Wiley & Sons, Ltd.     

IHMS—Integrated Hydrological Modelling System. Part 1. Hydrological processes and general structure

A newly Integrated Hydrological Modelling System (IHMS) has been developed to study the impact of changes in climate, land use and water management on groundwater and seawater intrusion (SWI) into coastal areas. The system represents the combination of three models, which can, if required, be run separately. It has been designed to assess the combined impact of climate, land use and groundwater abstraction changes on river, drainage and groundwater flows, groundwater levels and, where appropriate, SWI. The approach is interdisciplinary and reflects an integrated water management approach. The system comprises three packages: the Distributed Catchment Scale Model (DiCaSM), MODFLOW (96 and 2000) and SWI models. In addition to estimating all water balance components, DiCaSM, produces the recharge data that are used as input to the groundwater flow model of the US Geological Survey, MODFLOW. The latter subsequently generates the head distribution and groundwater flows that are used as input to the SWI model, SWI. Thus, any changes in land use, rainfall, water management, abstraction, etc. at the surface are first handled by DiCaSM, then by MODFLOW and finally by the SWI. The three models operate at different spatial and temporal scales and a facility (interface utilities between models) to aggregate/disaggregate input/output data to meet a desired spatial and temporal scale was developed allowing smooth and easy communication between the three models. As MODFLOW and SWI are published and in the public domain, this article focuses on DiCaSM, the newly developed unsaturated zone DiCaSM and equally important the interfacing utilities between the three models. DiCaSM simulates a number of hydrological processes: rainfall interception, evapotranspiration, surface runoff, infiltration, soil water movement in the root zone, plant water uptake, crop growth, stream flow and groundwater recharge. Input requirements include distributed data sets of rainfall, land use, soil types and digital terrain; climate data input can be either distributed or non‐distributed. The model produces distributed and time series output of all water balance components including potential evapotranspiration, actual evapotranspiration, rainfall interception, infiltration, plant water uptake, transpiration, soil water content, soil moisture (SM) deficit, groundwater recharge rate, stream flow and surface runoff. This article focuses on details of the hydrological processes and the various equations used in DiCaSM, as well as the nature of the interface to the MODFLOW and SWI models. Furthermore, the results of preliminary tests of DiCaSM are reported; these include tests related to the ability of the model to predict the SM content of surface and subsurface soil layers, as well as groundwater levels. The latter demonstrates how the groundwater recharge calculated from DiCaSM can be used as input into the groundwater model MODFLOW using aggregation and disaggregation algorithms (built into the interface utility). SWI has also been run successfully with hypothetical examples and was able to reproduce the results of some of the original examples of Bakker and Schaars ( 2005 ). In the subsequent articles, the results of applications to different catchments will be reported. Copyright © 2010 John Wiley & Sons, Ltd.     

Assessing hydrological model predictive uncertainty using stochastically generated geological models

In distributed and coupled surface water–groundwater modelling, the uncertainty from the geological structure is unaccounted for if only one deterministic geological model is used. In the present study, the geological structural uncertainty is represented by multiple, stochastically generated geological models, which are used to develop hydrological model ensembles for the Norsminde catchment in Denmark. The geological models have been constructed using two types of field data, airborne geophysical data and borehole well log data. The use of airborne geophysical data in constructing stochastic geological models and followed by the application of such models to assess hydrological simulation uncertainty for both surface water and groundwater have not been previously studied. The results show that the hydrological ensemble based on geophysical data has a lower level of simulation uncertainty, but the ensemble based on borehole data is able to encapsulate more observation points for stream discharge simulation. The groundwater simulations are in general more sensitive to the changes in the geological structure than the stream discharge simulations, and in the deeper groundwater layers, there are larger variations between simulations within an ensemble than in the upper layers. The relationship between hydrological prediction uncertainties measured as the spread within the hydrological ensembles and the spatial aggregation scale of simulation results has been analysed using a representative elementary scale concept. The results show a clear increase of prediction uncertainty as the spatial scale decreases. Copyright © 2015 John Wiley & Sons, Ltd.     

Impact of the spatial arrangement of land management practices on surface runoff for small catchments

Predicting the impact of land use changes on the hydrological response is crucial for water resource management. In the particular case of small catchments (1–10 km²), distributed models could provide useful answers regarding the effects of cultivation practices and man‐made works on water fluxes. However, the impacts of specific land use spatial arrangements are difficult to predict because of the prohibitive number of possible cases to consider. Focusing on surface runoff, this article describes a strategy based on a water particle tracking routine to be plugged‐in a distributed model that is designed to determine the spatial arrangements of land management practices that have the greatest impact on volume, peak discharge and lag time at the catchment outlet. A case study is described; the hydrological response of the Roujan catchment (Herault, France) is simulated with the MHYDAS model. The Roujan catchment contains a vineyard in a Mediterranean climate in a landscape in which weeding practices highly influence the partition between soil infiltration and runoff. Results showed that the proposed strategy is much more efficient than a random approach to design the spatial arrangements of the vineyard weeding practices with the greatest impact. Therefore, the proposed strategy may lead to innovative policies for the spatial planning of land management practices. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluating the spatial distribution of water balance in a small watershed,Pennsylvania

A conceptual water‐balance model was modified from a point application to be distributed for evaluating the spatial distribution of watershed water balance based on daily precipitation, temperature and other hydrological parameters. The model was calibrated by comparing simulated daily variation in soil moisture with field observed data and results of another model that simulates the vertical soil moisture flow by numerically solving Richards' equation. The impacts of soil and land use on the hydrological components of the water balance, such as evapotranspiration, soil moisture deficit, runoff and subsurface drainage, were evaluated with the calibrated model in this study. Given the same meteorological conditions and land use, the soil moisture deficit, evapotranspiration and surface runoff increase, and subsurface drainage decreases, as the available water capacity of soil increases. Among various land uses, alfalfa produced high soil moisture deficit and evapotranspiration and lower surface runoff and subsurface drainage, whereas soybeans produced an opposite trend. The simulated distribution of various hydrological components shows the combined effect of soil and land use. Simulated hydrological components compare well with observed data. The study demonstrated that the distributed water balance approach is efficient and has advantages over the use of single average value of hydrological variables and the application at a single point in the traditional practice. Copyright © 2000 John Wiley & Sons, Ltd.     

Multicriteria sensitivity analysis as a diagnostic tool for understanding model behaviour and characterizing model uncertainty

Complex hydrological models are being increasingly used nowadays for many purposes such as studying the impact of climate and land‐use change on water resources. However, building a high‐fidelity model, particularly at large scales, remains a challenging task, due to complexities in model functioning and behaviour and uncertainties in model structure, parameterization, and data. Global sensitivity analysis (GSA), which characterizes how the variation in the model response is attributed to variations in its input factors (e.g., parameters and forcing data), provides an opportunity to enhance the development and application of these complex models. In this paper, we advocate using GSA as an integral part of the modelling process by discussing its capabilities as a tool for diagnosing model structure and detecting potential defects, identifying influential factors, characterizing uncertainty, and selecting calibration parameters. Accordingly, we conduct a comprehensive GSA of a complex land surface–hydrology model, Modélisation Environmentale–Surface et Hydrologie (MESH), which combines the Canadian land surface scheme with a hydrological routing component, WATROUTE. Various GSA experiments are carried out using a new technique, called Variogram Analysis of Response Surfaces, for alternative hydroclimatic conditions in Canada using multiple criteria, various model configurations, and a full set of model parameters. Results from this study reveal that, in addition to different hydroclimatic conditions and SA criteria, model configurations can also have a major impact on the assessment of sensitivity. GSA can identify aspects of the model internal functioning that are counter‐intuitive and thus help the modeller to diagnose possible model deficiencies and make recommendations for improving development and application of the model. As a specific outcome of this work, a list of the most influential parameters for the MESH model is developed. This list, along with some specific recommendations, is expected to assist the wide community of MESH and Canadian land surface scheme users, to enhance their modelling applications.     

Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models?

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions.     

A review of the estimation of downward surface shortwave radiation based on satellite data: Methods,progress and problems

The estimation of downward surface shortwave radiation(DSSR) is important for the Earth's energy budget and climate change studies. This review was organised from the perspectives of satellite sensors, algorithms and future trends,retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years. The shortwave radiation reaching the Earth's surface is affected by both atmospheric and land surface parameters. In recent years,studies have given detailed considerations to the factors which affect DSSR. It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces(such as snow-covered areas) on DSSR estimation. This review classified DSSR retrieval methods into four categories:empirical, parameterisation, look-up table and machine-learning methods, and evaluated their advantages, disadvantages and accuracy. Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud, haze, water vapor and other land surface parameters such as albedo of complex terrain and bright surface, organically combine machine learning and other methods, use the new-generation geostationary satellite and polar orbit satellite data to produce highresolution DSSR products, and promote the application of radiation products in hydrological and climate models.     

Determining hydrological regimes in an agriculturally used tropical inland valley wetland in Central Uganda using soil moisture,groundwater, and digital elevation data

Inadequate knowledge exists on the distribution of soil moisture and shallow groundwater in intensively cultivated inland valley wetlands in tropical environments, which are required for determining the hydrological regime. This study investigated the spatial and temporal variability of soil moisture along 4 hydrological positions segmented as riparian zone, valley bottom, fringe, and valley slope in an agriculturally used inland valley wetland in Central Uganda. The determined hydrological regimes of the defined hydrological positions are based on soil moisture deficit calculated from the depth to the groundwater table. For that, the accuracy and reliability of satellite‐derived surface models, SRTM‐30m and TanDEM‐X‐12m, for mapping microscale topography and hydrological regimes are evaluated against a 5‐m digital elevation model (DEM) derived from field measurements. Soil moisture and depth to groundwater table were measured using frequency domain reflectometry sensors and piezometers installed along the hydrological positions, respectively. Results showed that spatial and temporal variability in soil moisture increased significantly (p < .05) towards the riparian zone; however, no significant difference was observed between the valley bottom and riparian zone. The distribution of soil hydrological regimes, saturated, near‐saturated, and nonsaturated regimes does not correlate with the hydrological positions. This is due to high spatial and temporal variability in depth to groundwater and soil moisture content across the valley. Precipitation strongly controlled the temporal variability, whereas microscale topography, soil properties, distance from the stream, anthropogenic factors, and land use controlled the spatial variability in the inland valley. TanDEM‐X DEM reasonably mapped the microscale topography and thus soil hydrological regimes relative to the Shuttle Radar Topography Mission DEM. The findings of the study contribute to improved understanding of the distribution of hydrological regimes in an inland valley wetland, which is required for a better agricultural water management planning.     

Some aspects of the HAPEX-MOBILHY programme: The data base and the modelling strategy

The main thrust of the HAPEX-MOBILHY experiment was towards investigating techniques involved in integrating the turbulent surface fluxes measured at local sites to a larger scale approaching that used in general circulation models.Some aspects of the field data collected at various times and spatial scales are presented. Annual cycle of the soil moisture at many sites is discussed in relation with outputs of a large scale hydrological model. At shorter time scales, the spatial variability of surface energy partition is examined with regard to spatial contrasts in albedo, surface roughness and plant properties related to the two main vegetation classes found in the HAPEX square: A pine forest and the nearby agricultural area.Finally, examples of daily spatial integration with an atmospheric mesoscale model including a comprehensive treatment of land surface processes are presented.     

抚仙湖集水域地表径流入湖水量模拟

采用适用于无资料流域、参数较少的SCS模型计算抚仙湖集水域地表径流量.模型考虑了集水域下垫面条件的空间差异,利用Maplnfo/Arc view软件按照土地利用方式与土壤类型的不同,把集水域划分为若干个水文响应单元,分别计算产流量,较准确地模拟了入湖径流量.通过对梁王河流域和大鲫鱼沟流域实测降雨径流资料的分析与反演,提出了适合该区域的产流计算CN值.在对CN值作坡度修正后再应用到其它无观测数据区域.通过模型计算得到的抚仙湖集水域2005年3月1日-2006年2月28日地表径流量为1.74×108 m3,陆面径流系数为0.395.模型为指导抚仙湖集水域径流观测及入湖污染物负荷的计算提供依据.     

Application of a land surface model for simulating rainfall streamflow hydrograph: 2. Comparison with hydrological models

Rainfall runoff hydrographs for 12 river basins ∼10³ km² in area, simulated using land surface model SWAP, are compared with analogous hydrographs obtained using hydrological models that took part in the International Model Parameter Estimation Experiment project and demonstrated the best results. All models were calibrated against data on daily river runoff from each basin over a 20-year period (1960–1979). Optimized model parameters were used to simulate runoff hydrographs for the following 19 years (1980–1998). The comparison of the modeled hydrographs for 12 basins in different calculational periods demonstrated that the SWAP model can simulate river runoff with an accuracy comparable with that of hydrological models.     

Effects of reduced land cover detail on hydrological model response

Problems related to scale continue to be at the forefront of research in hydrology. Past research into issues of scale has focused mainly on digital elevation model grid size, the appropriate number and size of sub‐areas for subdividing a watershed, parameter transferability between watersheds and appropriate scales for linking hydrological and general circulation models. Much less attention has been given to the effects of scale on the representation of land cover and hydrological model response. Recent studies with respect to changes in land cover and hydrologic response have tended to focus on the issue of land cover maturity and the conversion of land through agricultural and forestry practices. The focus of this study is to examine the impact of the level of detail at which land cover is represented in modelling the hydrological response of Wolf Creek Basin in northwest Canada. A grid‐based land cover map with a spatial resolution of 30 m is coarsened or smoothed using several common grid‐based methods of aggregating categorical data, including: pixel thinning, modal smoothing and modal aggregation. A majority rule method based on polygons is also applied to the 30 m base cover. The SLURP hydrologic model is calibrated for the base cover and used as a reference for comparing simulations for the coarsened or ‘generalized’ land cover maps. Results of the simulations are compared to examine the sensitivity of hydrologic response to generalized land cover information. Comparisons of the SLURP model runs for Wolf Creek suggest that reducing the level of detail of land cover information generally has a limited effect on hydrologic response at the outlet. However, results for averages of water balance components across the basin suggest that the local variability of hydrologic response is affected in general. Copyright © 2007 John Wiley & Sons, Ltd.     

Spatial scale dependency issues in the application of the Modified Universal Soil Loss Equation (MUSLE)

ABSTRACT

The MUSLE is used within hydrological models to estimate sediment yields from catchments of various sizes, but the spatial scale dependency issues associated with estimating the MUSLE parameters have not been adequately addressed. In the absence of detailed observed data on both hydrological response and sediment yield, some analytical approaches and hypothetical examples are presented to identify the key issues. The results suggest that methods used to estimate both the erosivity and topographic factors are scale dependent, particularly if a lumped or semi-distributed modelling approach is used. The conclusion is that spatial scale dependencies will add to the uncertainties inherent in the use of the MUSLE if not carefully understood and appropriately addressed. One suggested approach is to apply the erosivity equation to a fixed (small) representative area and then scale up to the total catchment, an approach that recognizes the variability of averaged parameters across different spatial scales.     

Multi-model averaging for continuous streamflow prediction in ungauged basins

ABSTRACT

This paper assesses the possibility of using multi-model averaging techniques for continuous streamflow prediction in ungauged basins. Three hydrological models were calibrated on the Nash-Sutcliffe Efficiency metric and were used as members of four multi-model averaging schemes. Model weights were estimated through optimization on the donor catchments. The averaging methods were tested on 267 catchments in the province of Québec, Canada, in a leave-one-out cross-validation approach. It was found that the best hydrological model was practically always better than the others used individually or in a multi-model framework, thus no averaging scheme performed statistically better than the best single member. It was also found that the robustness and adaptability of the models were highly influential on the models’ performance in cross-verification. The results show that multi-model averaging techniques are not necessarily suited for regionalization applications, and that models selected in such studies must be chosen carefully to be as robust as possible on the study site.

Editor M.C. Acreman; Associate editor S. Grimaldi     

Quantitative comparison of spatial fields for hydrological model assessment––some promising approaches

The current practice for assessing spatial predictions from distributed hydrological models is simplistic, with visual inspection and occasional point observations generally used for model assessment. With the increasing availability of spatial observations from remote sensing and intensive field studies, the current methods for assessing the spatial component of model predictions need to advance. This paper emphasises the role that spatial field comparisons can play in model assessment. A review of the current methods used in hydrology, and other disciplines where spatial field comparisons are widely used, reveals some promising methods for quantitatively comparing spatial fields. These promising approaches––segmentation, importance maps, fuzzy comparison and multiscale comparison––are for local comparison of spatial fields. They address some of the weaknesses with the current approaches to spatial field comparison used in hydrological modelling and, in doing so, emulate some aspects of human visual comparison. The potential of these approaches for assessing spatial predictions and understanding model performance is illustrated with a simple example.