Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change

Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and -31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources

Stream water age distributions controlled by storage dynamics and nonlinear hydrologic connectivity : Modeling with high-resolution isotope data

Peer reviewedPublisher PD

Strategic Analyses of the National River Linking Project (NRLP) of India, Series 1. India’s water future: scenarios and issues

River basinsEnvironmental flowsDevelopment projectsWater requirementsIrrigated farmingWater demandFood demandGroundwater irrigationIrrigation efficiencyWater harvestingSupplemental irrigationWater productivityWater conservationDrip irrigationSprinkler irrigationRainfed farmingAgricultural policy

Integration of a physically-based hydrological model with spatial soil data and GIS: an application to the Hafren Catchment, Wales

The present research aims to illustrate and evaluate the effect of spatially variable soil data on the modelling of catchment rainfall-runoff transformations, using the hydrological model Topmodel. The soil-topographic wetness index used in Topmodel has always allowed for a spatially variable To - lateral saturated transmissivity - yet very little published research has focussed on the use of spatial soil datasets to derive To. In recent years the availability of soil hydrologic parameters, either from soil classifications and/or from new measurement techniques has increased significantly and, especially with regards to remote sensing, there is still great potential for further advances. It is therefore important that models like Topmodel should be able to incorporate such distributed soil data and assess if its' inclusion may allow a better representation of rainfall-runoff transformation processes. In particular, one of the key issues is the need to use distributed data to predict internal catchment conditions — such as runoff source areas — and not only global volumetric outflows. This aspect is of importance both at the catchment scale, for improved integrated catchment management (i.e. in the presence of land-use changes), and at the GCM modelling scale for the simulation of regional land-atmosphere interactions.With regard to the soil data, particular importance is associated to soil hydraulic parameters such as porosity and saturated conductivities. Traditionally, such data have only been available from measurements on single soil samples. But in recent years, various analytical methods and hydromorphic classification schemes have been developed which allow us to estimate the above parameters or, alternatively, provide qualitative indeces of the soils behaviour in terms of runoff generation. The present research has therefore evaluated the effect of different soil classification schemes with respect to their ability to improve the prediction of soil moisture deficit using TOPMODEL.Given the strengths of GIS in storing and analysing spatial data, the research has also evaluated if and how GIS can be used to better understand the effect of spatial classification schemes applied to the soil input data. Though GIS cannot substitute the theoretical knowledge of the processes occurring, it can certainly provide the spatial functionalities often lacking in hydrological models. It is this spatial perspective that can allow us to visualise synoptically the phenomena being studied, while at the same time exploring, highlighting, and verifying the prominent spatial variables that control the rainfallrunoff transformation processes.The integration of the three different modelling perspectives was pursued to allow the user to carry out a more thorough validation of both data and modelling methods used. Ultimately, it is hoped that this multidisciplinary approach will help to better assess the validity of the adopted methodology within the context of integrated catchment management

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

AOIPS water resources data management system

A geocoded data management system applicable for hydrological applications was designed to demonstrate the utility of the Atmospheric and Oceanographic Information Processing System (AOIPS) for hydrological applications. Within that context, the geocoded hydrology data management system was designed to take advantage of the interactive capability of the AOIPS hardware. Portions of the Water Resource Data Management System which best demonstrate the interactive nature of the hydrology data management system were implemented on the AOIPS. A hydrological case study was prepared using all data supplied for the Bear River watershed located in northwest Utah, southeast Idaho, and western Wyoming