In this study, methods were developed to create and evaluate the performance of the Soil and Water Assessment Tool (SWAT) in southern Peru where commonly used input data sources were not available. Soil classes were defined based on regional soil taxonomy and suitability maps combined with soil profiles. Local land cover and remotely sensed satellite data were used to develop a land cover database. Water balance analysis of the reservoir as well as satellite evapotranspiration data were used for model performance assessment. Results showed that these strategies provided reliable predictions of hydrology in this region, with the uncertainty quantified based on the range of inputs. Overall, this semiarid watershed was base flow driven and average annual surface runoff contribution to streamflow was less than 9%. Assessment of water pathways and their uncertainties based on the uncertainty of estimated inputs also showed that 62% of precipitation was removed by evapotranspiration with up to 16% uncertainty. The methods introduced in this study can be applied to other data-scarce watersheds, and findings provide insights on the hydrology of the Peruvian Andes region.

Majes is one of the largest agricultural areas in the Arequipa region (southern Peru). Low seasonal precipitation and increasing water demands for agricultural irrigation, industry, and human consumption have made water supply projections a major concern. Agricultural development is becoming more extensive in this dry, sunny climate where crops can be grown year-round. However, because this type of project usually involves significant perturbations to the regional water cycle, understanding the effects of irrigation on local hydrology is crucial. Based on the watershed-scale Soil and Water Assessment Tool (SWAT), this investigation focuses on the impacts of intensive irrigation on hydrological responses in the Majes region. This study is unique because we allow for crop-field scale input within our regional-scale model to provide information at this smaller scale, which is important to inform local stakeholders and decision makers. Each hydrologic response unit (HRU) was generated to represent an individual crop field, so that management practices could be applied according to real-world scenarios. The management file of each HRU was modified to include different operation schedules for crop rotation, irrigation, harvest, and tillage. The model was calibrated and validated against monthly observed stream discharge during the 2009–2020 period. Additionally, evapotranspiration, irrigation water volume, and daily stream discharge downstream of the local river (Siguas) were used to verify the model performance. A total of 49 sub-basins and 4222 HRUs were created, with 3000 HRUs designated to represent individual crop fields. The simulation results revealed that infiltration from agricultural activities in Majes represents the majority of annual groundwater return flow, which makes a substantial contribution to streamflow downstream of the Siguas River. Simulations also suggested that groundwater flow processes and the interactions between surface and groundwater have a major impact on the water balance of the study area. Additionally, climate variability had a higher impact on surface runoff than on groundwater return flow, illustrating that the groundwater component in the study area is important for future water resources resiliency under expected climate change scenarios. Finally, there is a need to perform a follow-up implementation to provide a guideline for decision-makers to assess future sustainable water resources management under varying climatic conditions for this arid irrigated system.

Seated at the foot of the Misti volcano in an area prone to intense seasonal rains and earthquakes, the city of Arequipa is highly vulnerable to natural disasters. During the rainy season, intense storms create large volumes of runoff that rush through the city's ephemeral streams, known locally as torrenteras. Episodic flows in these torrenteras have caused flooding, damage to bridges, homes, and other infrastructure, and caused many deaths. In recent years, while unprecedented rain events have caused extreme disasters, the city's population has continued to expand into these channels by creating informal or illegal settlements. Currently, detailed hazard maps of flood‐prone areas surrounding the torrenteras are not available to stakeholders in Arequipa. In this study, hydrologic and hydraulic models were combined to assess flash flood hazards, including inundation, velocity hazards, and slope instability hazards. Hydrologic models were created using satellite precipitation data and terrain‐sensitive, gridded climate maps to characterize flow within six torrenteras in Arequipa. These flows were used in conjunction with elevation data and data collected in the field using an online mobile application system to develop a hydraulic model of these flood events. Hydraulic model outputs were used to determine flood hazards related to inundation, velocity affecting human stability, and slope instability in case study areas of the torrenteras. We then discuss how this information can be used by disaster risk management groups, water authorities, planners and municipalities, and community groups.