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High‐intensity and short‐duration rainfalls can generate sudden and abundant runoff at the base of rocky cliffs that, entraining sediments, may originate debris flows. Two gauge networks have been set up in headwater sites of Dolomites (Northeastern Italian Alps) to monitor rainfall corresponding to the debris‐flow activity occurring there. The rain gauges are positioned both upstream and downstream the initiation areas of debris flows. Other five rain gauges sparse in the area integrate the two networks. In the years 2009–2020, rain gauges recorded rainfalls that triggered 41 debris flows. In most cases, rainfalls show a higher spatial variability along with both distance and altitude. Precipitation data are then compared with rainfalls estimated through a weather radar far about 70 km from there, to verify the possible interchangeability of the two measurement systems for the prediction of debris‐flow occurrence through suitable modeling of triggering discharges. The following results are obtained: (1) raw‐radar images mostly tend to underestimate precipitations recorded by rain gauges; (2) such underestimation entails, on average, a larger one on the simulated discharges and the prediction of debris‐flow occurrences (missed in 65% of the cases). Some methods for the correction on ground truth of raw‐radar images are applied to assess their use for evaluating the triggering discharges. Results show that once corrected using rain gauge data, radar‐derived rainfall estimates produce debris‐flow initiation predictions that more frequently match observations. Therefore, the presence of rain gauges close to the watershed centroids results essential for early warning systems based on triggering discharge modeling. Key Points: In the last decade several debris flows have been triggered in the highly tourist Boite ValleyRain gauge and radar recorded rainfalls have been used to simulate debris‐flow triggering dischargesIn this area radar needs the presence of rain gauges to be used in early warning systems [ABSTRACT FROM AUTHOR]

The discipline of hydrology has long focused on quantifying the water balance, which is frequently used to estimate unknown water fluxes or stores. While technologies for measuring water balance components continue to improve, all components of the balance have substantial uncertainty at the watershed scale. Watershed‐scale evapotranspiration, storage, and groundwater import or export are particularly difficult to measure. Given these uncertainties, analyses based on assumed water balance closure are highly sensitive to uncertainty propagation and errors of omission, where unknown components are assumed negligible. This commentary examines how greater insight may be gained in some cases by keeping the water balance open rather than applying methods that impose water balance closure. An open water balance can facilitate identifying where unknowns such as groundwater import/export are affecting watershed‐scale streamflow. Strategic improvements in monitoring networks can help reduce uncertainties in observable variables and improve our ability to quantify unknown parts of the water balance. Improvements may include greater spatial overlap between measurements of water balance components through coordination between entities responsible for monitoring precipitation, snow, evapotranspiration, groundwater, and streamflow. Measuring quasi‐replicate watersheds can help characterize the range of variability in the water balance, and nested measurements within watersheds can reveal areas of net groundwater import or export. Well‐planned monitoring networks can facilitate progress on critical hydrologic questions about how much water becomes evapotranspiration, how groundwater interacts with surface watersheds at varying spatial and temporal scales, how much humans have altered the water cycle, and how streamflow will respond to future climate change. Plain Language Summary: The water balance is a fundamental concept in hydrology that underlies many tools for predicting streamflow, soil moisture, or groundwater availability. It is often expressed as an equation that relates water inputs, outputs, and storage for a watershed. Inputs can be rainfall, snowmelt, or water imports to the watershed. Outputs include water movement into the atmosphere (evaporation, transpiration, and sublimation), streamflow, and water exports through groundwater or human diversions. Water storage can be in snow or ice, surface water bodies, or underground. Each of these water balance components is difficult to measure, and some are rarely measured. Therefore, researchers often simplify the water balance, assuming that difficult to measure quantities, like groundwater imports/exports or changes in water storage, can be neglected. Such simplifying assumptions lead to missed opportunities for discovering where these unknowns in the water balance are important controls on streamflow. This commentary advocates strategically expanding watershed monitoring networks to coordinate monitoring of different water balance components, monitor multiple similar watersheds within each geographic region, and nest monitoring of tributary streams within larger watersheds. This can accelerate progress in understanding groundwater flow, plant water availability, streamflow generation, and human impacts to the water balance. Key Points: Quantifying the watershed‐scale water balance remains elusive because all components are uncertainImposing water balance closure can lead to missed opportunities for identifying unknowns in the water balanceWe need more strategic quasi‐replicate and nested watershed monitoring to improve water balance understanding [ABSTRACT FROM AUTHOR]

Fluvial loads for total nitrogen, total oxidized nitrogen, total phosphorus, orthophosphate phosphorus, and total suspended solids were estimated for three watersheds of different sizes using a regression-based method and compared with loads computed by a method based on direct hydrologic and water quality observations. Statistically significant differences between the fluvial loads estimated by the two methods were observed in all cases. Using the direct method as the basis for comparison, the regression method was found to underpredict the annual fluvial load for the watersheds analyzed. A representative 'magnitude-of-difference' measure was computed. The larger watershed (area > 104 km2) had a lower magnitude-of-difference compared to the two smaller watersheds (area ∼102 km2). The five constituents analyzed also had statistically significant effect on the magnitude-of-difference, with higher magnitude-of-difference observed for total suspended solids and total phosphorus compared to other parameters. For the watersheds analyzed, estimates for the magnitudes-of-difference obtained by long-term fluvial load analysis (15 years for larger, and 9 and 12 years for two smaller watersheds) may be evaluated by shorter-term comparisons. The time required to obtain a stable magnitude-of-difference was found to vary with the watershed size and the constituent of interest in the study watersheds. Although the results from this study represent the watersheds analyzed and are not likely to be directly applicable for other watersheds, the comparison between fluvial loads estimated by regression and intensive sampling may be applied to assess the applicability of the regression method in other watersheds. [ABSTRACT FROM AUTHOR]

A method for siting water level monitors based on information theory measurements is presented. The first measurement is joint entropy, which evaluates the amount of information content that a monitoring set is able to collect, and the second measurement is total correlation, which evaluates the level of dependency or redundancy among monitors in the set. In order to find the most convenient set of places to put monitors from a large number of potential sites, a multiobjective optimization problem is posed under two different considerations: (1) taking into account the costs of placing new monitors and (2) considering the cost of placing monitors too close to hydraulic structures. In both cases, the joint entropy of the set is maximized and its total correlation is minimized. The costs are considered in terms of information theory units, for which additional terms affecting the objective functions are introduced. The proposed method is applied in a case study of the Delfland region, Netherlands. Results show that total correlation is an effective way to measure multivariate independency and that it must be combined with joint entropy to get results that cover a significant proportion of the total information content of the system. The maximization of joint entropy gives results that cover between 82% and 85% of the total information content. [ABSTRACT FROM AUTHOR]

Recent work by Isham et al. and Rodrìguez-Iturbe et al. has characterized the space-time variability of soil moisture through its analytically derived covariance function which depends on soil properties, vegetation structure, and rainfall patterns typical of a region. This paper uses such characterization to address the strategies and methodologies for the sampling of soil moisture fields. The focus is on the estimation of the long-term mean soil moisture and the daily soil moisture averaged over a given area as a function of the network geometry, number of stations, number of sampling days and landscape heterogeneity. It is found that the spatial geometry of the network has a significant impact on the sampling of the average soil moisture over an area in any particular day, while it is much less relevant for the sampling of the long-term mean daily soil moisture over the region. In the latter case, the length of the record is a commanding factor in what concerns the variance of estimation, specially for soils with shallow rooted vegetation. Spatial vegetation heterogeneity plays an important role on the variance of estimation of the soil moisture, being particularly critical for the sampling of the average soil moisture over an area for a given day. [ABSTRACT FROM AUTHOR]

This study aims to develop a methodology for data collection that accounts for the application of simulation models in decision making for groundwater management. Simulation model reliability is estimated by comparing the effects that a perturbation in the model parameter space has over the model output as well as over the solution of a multiobjective optimization problem. The problem of experimental design for parameter estimation is formulated and solved using a combination of genetic algorithm and gradient-based optimization. Gaussian quadrature and Bayesian decision theory are combined for selecting the best design under parameter uncertainty. The proposed methodology is useful for selecting a robust design that will outperform all other candidate designs under most potential 'true' states of the system. Results also show that the uncertainty analysis is able to identify complex interactions among the model parameters that may affect the performance of the experimental designs as well as the attainability of management objectives. [ABSTRACT FROM AUTHOR]