Advances in Water Resources

ADV measurements of the study cases and bathymetry data of the experimental flume

The list of files and folder are introduced and summarized below. The files with extension .h1d can be opened by open-access Hydrus1D software. (1) LiteratureData: The folder includes two excel files that contain literature values of Rooting depths, Evapotranspiration, and Interception. Literature values were collected from similar sites to Mingtang region. Average values from the literature included in the Topsoil model settings. (a) SimilarSitesET_I.xlsx: Studies chosen for interception and evapotranspiration. (b) SimilarSitesRootDepth.xlsx: Studies chosen for average root depth. (2) Functions: They open and read the Nod_Inf.out and T_Level.out files. (a) openNread2.R: R function reads Nod_Inf.out files and transforms them into a data frame. (b) openNreadT.R: R function reads T_Level.out files and transforms them into a data frame. (3) Output_drawdown: Includes numerical model output data for 9 different cases of water table boundary conditions analyzed in Section 5. (a) Simulations for alpha=1, C_1,C_4,C_7 (b) Simulations for alpha=0.2, C_2,C_5,C_8 (c) Simulations for alpha=0.1, C_3,C_6,C_9 (4) Output_weather: Includes numerical model output data for atmospheric boundary condition cases in Section 6. (a) weather.xlsx: Modified meteorological data (b) Budyko_type.R: Script for Figure 7 and Budyko-type analysis (Figure 9). (c) C_1-C_6 are simulations with weather boundary conditions on homogeneous and layered soil profiles. (5) Example: This folder demonstrates the usage of functions to visualize the simulations results. (a) example.R: the Main folder to run the example, can be used to visualize the simulations file in the above folders

Water footprint and economic water productivity data per purpose (hydroelectricity, irrigation, residential and industrial water supply, flood protection, fishing and recreation) per reservoir (6000+ entries) and (aggregated) per country.

The network data files contain information about the network structure of each simulation, including the volume of the pores, and the adjacency matrix weighted by the throat radius. The drainage, high salinity waterflood, low salinity waterflood, and low salinity waterflood datasets contain the pressure and saturation information at each node of the network.

MATLAB code used to generate the figures in the paper.

Code for generating and analyzing heterogeneous hyporheic exchange scenarios, alongside with preprocessed data generated with that code.

This a file of late time-concentration data that was evaluated for this study.

Excel spreadsheet contains four datasets used in this study. "well_data" contains the results of pump tests used to build the near-surface permeability map. "slip_data" contains the inverted fault slip from GPS inversion for the 2012 Nicoya earthquake. "topo_data_2D" and "topo_data_3D" contain the topography data used as a water table proxy for topographic driven groundwater flow in 2D and 3D, respectively.

A simple Matlab code that runs example 1D realizations of dispersion as presented in the paper, and, the output RMSE (Root Mean Squared Error) data presented in the paper.

The water quality parameters included in this database were nitrate (NO3-), total phosphorus (TP) and soluble reactive phosphorus (SRP). From approximately 10 000 water quality stations present in the French national public database (http://www.naiades.eaufrance.fr/), we selected the stations meeting all the following criteria: i) C station can be paired with a Q station (data from http://www.hydro.eaufrance.fr/) when their catchments share at least 90% surface area; ii) all C catchments are independent; iii) C data contains at least 50 observations after outliers removal (i.e. values over 200 mgN L-1 and 5 gP L-1) over the period 2008-2015; iv) at least 30% of C observations occurred during “major” hydrological events (defined here as Q(t) > 1.5 x Q_baseflow); v) trends on C are non-significant over the period (p-value of Sen’s Slope test > 0.05, following Hipel and McLeod (2005)). Finally, stations where a single concentration value was observed more than 15% of the time were removed from the selection, a situation often seen in P surveys when concentrations are below quantification limits. This resulted in 219 unique catchments with respectively 179, 138 and 107 individual time series for NO3-, TP and SRP.