Abstract. We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB_model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D–2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

Geosci. Model Dev. Discuss., https://doi

This study presents the derivation procedure of an integrated closure relation for infiltration and Hortonian overland flow in the Representative Elementary Watershed (REW) framework that contains directlyobservable parameters. A physically-based high resolution model is used to simulate the infiltration flux and discharge for 6 Â 10 5 set of synthetic REWs and rainstorms scenarios. This synthetic data set serves as a surrogate of real-world data to deduce the closure relation. The closure relation performance is evaluated against the results from the high resolution model. The results show that the closure relation is capable of predicting accurate hydrological responses for an independent set of synthetic REWs and rainstroms in terms of the Nash-Sutcliffe index, errors in total discharge volume, and peak discharge, especially in cases where a relatively large amount of runoff is produced with fast responses. For the estimation of parameters in the closure relation, a local method using inverse distance weighted interpolation in the parameter space is superior to the global method based on the multiple regression, resulting in a better reproduction of runoff characteristics.

This study presents an application of a multiple point geostatistics (MPS) to map landforms. MPS uses information at multiple cell locations including morphometric attributes at a target mapping cell, i.e. digital elevation model (DEM) derivatives, and non-morphometric attributes, i.e. landforms at the neighboring cells, to determine the landform. The technique requires a training data set, consisting of a field map of landforms and a DEM. Mapping landforms proceeds in two main steps. First, the number of cells per landform class, associated with a set of observed attributes discretized into classes (e.g. slope class), is retrieved from the training image and stored in a frequency tree, which is a hierarchical database. Second, the algorithm visits the non-mapped cells and assigns to these a realization of a landform class, based on the probability function of landforms conditioned to the observed attributes as retrieved from the frequency tree. The approach was tested using a data set for the Buëch catchment in the French Alps. We used four morphometric attributes extracted from a 37.5-m resolution DEM as well as two non-morphometric attributes observed in the neighborhood. The training data set was taken from multiple locations, covering 10% of the total area. The mapping was performed in a stochastic framework, in which 35 map realizations were generated and used to derive the probabilistic map of landforms. Based on this configuration, the technique yielded a map with 51.2% of correct cells, evaluated against the field map of landforms. The mapping accuracy is relatively high at high elevations, compared to the mid-slope and low-lying areas. Debris slope was mapped with the highest accuracy, while MPS shows a low capability in mapping hogback and glacis. The mapping accuracy is highest for training areas with a size of 7.5-10% of the total area. Reducing the size of the training images resulted in a decreased mapping quality, as the frequency database only represents local characteristics of landforms that are not representative for the remaining area. MPS outperforms a rulebased technique that only uses the morphometric attributes at the target mapping cell in the classification (i.e. one-point statistics technique), by 15% of cell accuracy.

In 2011, a catastrophic flood disaster in Thailand affected not only humans but also took animal lives. Data on livestock losses, including death, loss, and decreased production, were collected in Nakhon Sawan province. The time-series map of the flooded area from August to December 2011 was available online from the Geo-informatics and Space Technology Development Agency. To evaluate the high-density areas of livestock loss, a spatial hot spot analysis was performed. The Getis-Ord Gi statistic with weighted zone of indifference and the Euclidean distance measurement were employed to identify spatial clusters of species that were affected by the flood. The results indicated that the majority of livestock losses were from poultry and swine farms. The density of poultry and swine loss was significantly different between sub-districts with clusters of high-density loss alongside the river, particularly in Chum Saeng and Kao Liew. Using spatial hot spot analysis as a tool to classify and rank the areas with high flood risks provides an informative outline for farmers to be aware of potential flood damage. To avoid unexpected loss from flooding, poultry and swine farms in risk areas should be properly managed, particularly during the flooding season between August and December.