Abstract. Accurate representation of the real spatiotemporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.This study attempts to exhaustively evaluate -for the first time -the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25 • grid if necessary). The modified Kling-Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.Results revealed that most SRE products performed better for the humid South (36.4-43.7 • S) and Central Chile (32.18-36.4 • S), in particular at low-and mid-elevation zones (0-1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM-JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all timescales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR, and the real-time PERSIANN-CCS-Adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resource assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.
Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.
Satellite‐based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground‐based observations are limited. However, existing satellite‐based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN‐CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM‐GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN‐CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN‐CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root‐mean‐square errors and mean biases. The systematic biases of the PERSIANN‐CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground‐based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.
The seasonal predictability of daily winter rainfall characteristics relevant to dry-land management was investigated in the Coquimbo region of central northern Chile, with focus on the seasonal rainfall total, daily rainfall frequency, and mean daily rainfall intensity on wet days at the station scale. Three approaches of increasing complexity were tested. First, an index of the simultaneous El Niño–Southern Oscillation (ENSO) was regressed onto May–August (MJJA) observed precipitation; this explained 32% of station-averaged rainfall-amount variability, but performed poorly in a forecasting setting. The second approach used retrospective seasonal forecasts made with three general circulation models (GCMs) to produce downscaled seasonal rainfall statistics by means of canonical correlation analysis (CCA). In the third approach, a nonhomogeneous hidden Markov model (nHMM) driven by the GCM’s seasonal forecasts was used to model stochastic daily rainfall sequences. While the CCA is used as a downscaling method for the seasonal rainfall characteristics themselves, the nHMM has the ability to simulate a large ensemble of daily rainfall sequences at each station from which the rainfall statistics were calculated. Similar cross-validated skill estimates were obtained using both the CCA and nHMM, with the highest correlations with observations found for seasonal rainfall amount and rainfall frequency (up to 0.9 at individual stations). These findings were interpreted using analyses of observed rainfall spatial coherence, and by means of synoptic rainfall states derived from the HMM. The downscaled hindcasts were then tailored to meteorological drought prediction, using the standardized precipitation index (SPI) based on seasonal values, the frequency of substantial rainfall days (>15 mm; FREQ15) and the daily accumulated precipitation deficit. Deterministic hindcasts of SPI showed high hit rates, with high ranked probability skill score for probabilistic hindcasts of FREQ15 obtained via the nHMM.
Infiltration measurements in arid, stony soils are notoriously variable in visually homogeneous areas, and have been reported to be influenced by embedded stone fragments. This study aimed to identify the influence of rock fragment contents, orientation, and position within a small arid watershed on hydraulic conductivity in northern Chile. Two different measurement techniques were used, a single‐ring infiltrometer with constant ponding head and a tension infiltrometer, which were applied at both an undisturbed field site (44 locations along three transects) and on the disturbed <2‐mm soil fraction from the same locations. Variations in saturated and unsaturated hydraulic conductivities were observed when using different calculation methods, adding to the observed variability. For saturated conditions, only small differences in conductivities were observed between two calculation methods, whereas unsaturated hydraulic conductivities calculated by five different methods showed more important variations. Stone fragment content correlated significantly with both saturated and unsaturated conductivities, probably due to a positive correlation between stone content and coarse lacunar pore space. Slope orientations with higher amounts of stone fragments gave higher infiltration rates, as well as transects with steeper slopes and more, but smaller, rock fragments. When evaluating differences in infiltration rates observed along three transects in the watershed, variability could be mainly attributed to stone fragment content influences.
Accelerated melting of glaciers is expected to have a negative effect on the water resources of mountain regions and their adjacent lowlands, with tropical mountain regions being among the most vulnerable. In order to quantify those impacts, it is necessary to understand the changing dynamics of glacial melting, but also to map how glacial meltwater contributes to current and future water use, which often occurs at considerable distance downstream of the terminus of the glacier. While the dynamics of tropical glacial melt are increasingly well understood and documented, major uncertainty remains on how the contribution of tropical glacial meltwater propagates through the hydrological system, and hence how it contributes to various types of human water use in downstream regions. Therefore, in this paper we present a detailed regional mapping of current water demand in regions downstream of the major tropical glaciers. We combine these maps with a regional water balance model to determine the dominant spatiotemporal patterns of the contribution of glacial meltwater to human water use at an unprecedented scale and resolution. We find that the number of users relying continuously on water resources with a high (>25%) long-term average contribution from glacial melt is low (391 000 domestic users, 398 km 2 of irrigated land, and 11 MW of hydropower production), but this reliance increases sharply during drought conditions (up to 3.92 million domestic users, 2096 km 2 of irrigated land, and 732 MW of hydropower production in the driest month of a drought year). A large proportion of domestic and agricultural users are located in rural regions where climate adaptation capacity tends to be low. Therefore, we suggest that adaptation strategies should focus on increasing the natural and artificial water storage and regulation capacity to bridge dry periods.
Determination of the field‐saturated hydraulic conductivity (Kfs) can result in very high variability due to soil heterogeneity, the measurement method, the number of replications, and the Kfs calculation method used. Especially for dryland soils, stoniness can influence infiltration rates significantly. To identify this variability as well as its source, six widely used measurement methods were compared: single‐ring (SR) and double‐ring (DR) infiltrometers, the constant‐head well infiltrometer (CH), the inverse auger hole method (IA), the tension infiltrometer (TI), and the rainfall simulator (RFS). The six methods were applied at three locations in a semiarid part of Chile that showed moderate (15%) to high (55%) stoniness. Additionally, Kfs variations due to different calculation techniques for the same measurement method were thoroughly investigated. Results showed that different calculation techniques sometimes gave significantly different estimates of Kfs when using the same data set, and those relative differences were conserved among measurement locations. The borehole methods (IA and CH) showed high discard rates due to stoniness, making these methods less appropriate. The SR and DR methods gave considerably higher Kfs estimates, while the RFS and TI proved good candidates as reference methods for stony soils, with low failure rates and coefficients of variation.
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