Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden-Fletcher-Goldfarb-Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning.

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Abstract. In this paper, we present and analyze a global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and USA. In addition to its global spatial coverage, the collected infiltration curves cover a time span of research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use were gathered along with the infiltration data, which makes the database valuable for the development of pedo-transfer functions for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76 %) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on the land use is available for 76 % of experimental sites with agricultural land use as the dominant type (~40 %). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for use by public domain only and can be copied freely by referencing it. Supplementary data are available at doi:10.1594/PANGAEA.885492. Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend/update the SWIG by uploading new data to it.

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Flood occurs as a result of high intensity rainfall, long-term rainfalls and snowmelt which flows out of the main river channel onto the floodplain areas and damages to buildings, roads, and facilities and causing life losses. This study aims to implement Extreme gradient boosting method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). The input factors include altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), distance from rivers, normalized difference vegetation index (NDVI), rainfall, land use, and lithology. For running the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as nonflood locations. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. To evaluate the efficacy of the algorithms, receiver operating characteristics (ROC) curve were implemented. The results of the current research showed that the RF model and EGB had the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR model with AUC values of 0.97, and 0.953, respectively. 2The results of factor importance by the RF model showed that distance from rivers had an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.

Soil erosion in forestlands affects not only land productivity but also the water body down stream. The Universal Soil Loss Equation (USLE) has been applied broadly for the prediction of soil loss from upland fields. However, there are few reports concerning the prediction of nutrient (P) losses based on the USLE and its versions. The present study was conducted to evaluate the applicability of the deterministic model Modified Universal Soil Loss Equation (MUSLE) to estimation of phosphorus losses in the Kojor forest watershed, northern Iran. The model was tested and calibrated using accurate continuous P loss data collected during seven storm events in 2008. Results of the original model simulations for storm-wise P loss did not match the observed data, while the revised version of the model could imitate the observed values well. The results of the study approved the efficient application of the revised MUSLE in estimating storm-wise P losses in the study area with a high level of agreement of beyond 93%, an acceptable estimation error of some 35%.