Flood risk emerges from the interaction of hazard and vulnerability. Over recent decades the notion of risk being the basis for flood management decisions has become widely accepted and operationalised through the use of models and quantified risk analysis providing the evidence for risk-informed decision making. However, it is now abundantly apparent that changes in time, at a range of scales, of pertinent variables that determine risk are not a second order consideration but, instead, fundamentally challenge the conventional approach to flood risk management. The nature of some of these changes, particularly those that operate on extended timescales, are highly uncertain, yet decisions that may have implications for several decades still have to be taken. In this paper we explore how flood risk management may be adapted to address processes of uncertain future change. We identify a range of levels at which change may be incorporated in decision making: in the representation of uncertain non-stationary quantities; in the rules that are used to identify preferred options; in the variety of options that may be contemplated for flood risk management; in the scope of problem definition, which increasingly extends to address multiple hazards and multiple functions of river basins; and in the social and organizational characteristics that promote adaptive capacity. Integrated responses to changing flood risk need to attend to each of these levels of decision making, from the technicalities of non-stationarity, to the promotion of resilient societies.

This study provides a comprehensive evaluation of eight high spatial resolution gridded precipitation products in Adige Basin located in Italy within 45-47.1°N. The Adige Basin is characterized by a complex topography, and independent ground data are available from a network of 101 rain gauges during 2000-2010. The eight products include the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis 3B42 product, three products from CMORPH (the Climate Prediction Center MORPHing technique), i.e., CMORPH_RAW, CMORPH_CRT and CMORPH_BLD, PCDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record), PGF (Global Meteorological Forcing Dataset for land surface modelling developed by Princeton University), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and GSMaP_MVK (Global Satellite Mapping of Precipitation project Moving Vector with Kalman-filter product). All eight products are evaluated against interpolated rain gauge data at the common 0.25° spatial resolution, and additional evaluations at native finer spatial resolution are conducted for CHIRPS (0.05°) and GSMaP_MVK (0.10°). Evaluation is performed at multiple temporal (daily, monthly and annual) and spatial scales (grid and watershed). Evaluation results show that in terms of overall statistical metrics the CHIRPS, TRMM and CMORPH_BLD comparably rank as the top three best performing products, while the PGF performs worst. All eight products underestimate and overestimate the occurrence frequency of daily precipitation for some intensity ranges. All products tend to show higher error in the winter months (December-February) when precipitation is low. Very slight difference can be observed in the evaluation metrics and aspects between at the aggregated 0.25° spatial resolution and at the native finer resolutions (0.05°) for CHIRPS and (0.10°) for GSMaP_MVK products. This study has implications for precipitation product development and the global view of the performance of various precipitation products, and provides valuable guidance when choosing alternative precipitation data for local community.

Precipitation is often the most important input data in hydrological models when simulating streamflow. The Soil and Water Assessment Tool (SWAT), a widely used hydrological model, only makes use of data from one precipitation gauge station that is nearest to the centroid of each subbasin, which is eventually corrected using the elevation band method. This leads in general to inaccurate representation of subbasin precipitation input data, particularly in catchments with complex topography. To investigate the impact of different precipitation inputs on the SWAT model simulations in Alpine catchments, 13years (1998-2010) of daily precipitation data from four datasets including OP (Observed precipitation), IDW (Inverse Distance Weighting data), CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and TRMM (Tropical Rainfall Measuring Mission) has been considered. Both model performances (comparing simulated and measured streamflow data at the catchment outlet) as well as parameter and prediction uncertainties have been quantified. For all three subbasins, the use of elevation bands is fundamental to match the water budget. Streamflow predictions obtained using IDW inputs are better than those obtained using the other datasets in terms of both model performance and prediction uncertainty. Models using the CHIRPS product as input provide satisfactory streamflow estimation, suggesting that this satellite product can be applied to this data-scarce Alpine region. Comparing the performance of SWAT models using different precipitation datasets is therefore important in data-scarce regions. This study has shown that, precipitation is the main source of uncertainty, and different precipitation datasets in SWAT models lead to different best estimate ranges for the calibrated parameters. This has important implications for the interpretation of the simulated hydrological processes.

Soil volumetric water content ( $VWC$ ) is a vital parameter to understand several ecohydrological and environmental processes. Its cost-effective measurement can potentially drive various technological tools to promote data-driven sustainable agriculture through supplemental irrigation solutions, the lack of which has contributed to severe agricultural distress, particularly for smallholder farmers. The cost of commercially available $VWC$ sensors varies over four orders of magnitude. A laboratory study characterizing and testing sensors from this wide range of cost categories, which is a prerequisite to explore their applicability for irrigation management, has not been conducted. Within this context, two low-cost capacitive sensors—SMEC300 and SM100—manufactured by Spectrum Technologies Inc. (Aurora, IL, USA), and two very low-cost resistive sensors—the Soil Hygrometer Detection Module Soil Moisture Sensor (YL100) by Electronicfans and the Generic Soil Moisture Sensor Module (YL69) by KitsGuru—were tested for performance in laboratory conditions. Each sensor was calibrated in different repacked soils, and tested to evaluate accuracy, precision and sensitivity to variations in temperature and salinity. The capacitive sensors were additionally tested for their performance in liquids of known dielectric constants, and a comparative analysis of the calibration equations developed in-house and provided by the manufacturer was carried out. The value for money of the sensors is reflected in their precision performance, i.e., the precision performance largely follows sensor costs. The other aspects of sensor performance do not necessarily follow sensor costs. The low-cost capacitive sensors were more accurate than manufacturer specifications, and could match the performance of the secondary standard sensor, after soil specific calibration. SMEC300 is accurate ( $MAE$ , $RMSE$ , and $RAE$ of 2.12%, 2.88% and 0.28 respectively), precise, and performed well considering its price as well as multi-purpose sensing capabilities. The less-expensive SM100 sensor had a better accuracy ( $MAE$ , $RMSE$ , and $RAE$ of 1.67%, 2.36% and 0.21 respectively) but poorer precision than the SMEC300. However, it was established as a robust, field ready, low-cost sensor due to its more consistent performance in soils (particularly the field soil) and superior performance in fluids. Both the capacitive sensors responded reasonably to variations in temperature and salinity conditions. Though the resistive sensors were less accurate and precise compared to the capacitive sensors, they performed well considering their cost category. The YL100 was more accurate ( $MAE$ , $RMSE$ , and $RAE$ of 3.51%, 5.21% and 0.37 respectively) than YL69 ( $MAE$ , $RMSE$ , and $RAE$ of 4.13%, 5.54%, and 0.41, respectively). However, YL69 outperformed YL100 in terms of precision, and response to temperature and salinity variations, to emerge as a more robust resistive sensor. These very low-cost sensors may be used in combination with more accurate sensors to better characterize the spatiotemporal variability of field scale soil moisture. The laboratory characterization conducted in this study is a prerequisite to estimate the effect of low- and very low-cost sensor measurements on the efficiency of soil moisture based irrigation scheduling systems.

The countries of Central Asia are collectively known as the five ''-stans'': Uzbekistan, Kyrgyzstan, Turkmenistan, Tajikistan and Kazakhstan. In recent times, the Central Asian region has been affected by the shrinkage of the Aral Sea, widespread desertification, soil salinization, biodiversity loss, frequent sand storms, and many other ecological disasters. This paper is a review article based upon the collection, identification and collation of previous studies of environmental changes and regional developments in Central Asia in the past 30 years. Most recent studies have reached a consensus that the temperature rise in Central Asia is occurring faster than the global average. This warming trend will not only result in a higher evaporation in the basin oases, but also to a significant retreat of glaciers in the mountainous areas. Water is the key to sustainable development in the arid and semi-arid regions in Central Asia. The uneven distribution, over consumption, and pollution of water resources in Central Asia have caused severe water supply problems, which have been affecting regional harmony and development for the past 30 years. The widespread and significant land use changes in the 1990s could be used to improve our understanding of natural variability and human interaction in the region. There has been a positive trend of trans-border cooperation among the Central Asian countries in recent years. International attention has grown and research projects have been initiated to provide water and ecosystem protection in Central Asia. However, the agreements that have been reached might not be able to deliver practical action in time to prevent severe ecological disasters. Water management should be based on hydrographic borders and ministries should be able to make timely decisions without political intervention. Fully integrated management of water resources, land use and industrial development is essential in Central Asia. The ecological crisis should provide sufficient motivation to reach a consensus on unified water management throughout the region.