Center for Hydrometeorology and Remote Sensing

Abstract This review surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. We argue that satellite observations not currently used for operational drought monitoring, such as near‐surface air relative humidity data from the Atmospheric Infrared Sounder mission, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi‐indicator drought models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., the Gravity Recovery and Climate Experiment) provide only a decade of data, which may not be sufficient to study droughts from a climate perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple data sets and/or assimilate satellite observations into model simulations to generate long‐term climate data records. Finally, the study identifies a major gap in indicators for describing drought impacts on the carbon and nitrogen cycle, which are fundamental to assessing drought impacts on ecosystems.

Abstract Global warming and the associated rise in extreme temperatures substantially increase the chance of concurrent droughts and heat waves. The 2014 California drought is an archetype of an event characterized by not only low precipitation but also extreme high temperatures. From the raging wildfires, to record low storage levels and snowpack conditions, the impacts of this event can be felt throughout California. Wintertime water shortages worry decision‐makers the most because it is the season to build up water supplies for the rest of the year. Here we show that the traditional univariate risk assessment methods based on precipitation condition may substantially underestimate the risk of extreme events such as the 2014 California drought because of ignoring the effects of temperature. We argue that a multivariate viewpoint is necessary for assessing risk of extreme events, especially in a warming climate. This study discusses a methodology for assessing the risk of concurrent extremes such as droughts and extreme temperatures.

Abstract Reservoirs are fundamental human‐built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast‐informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month‐ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

Abstract This study investigates changes in temporal trends and spatial patterns of precipitation in Beijing over the last six decades. These changes are discussed in the context of rapid urbanization and the growing imbalance between water supply and demand in Beijing. We observed significant decreases in precipitation amounts from 1950 to 2012, with the annual precipitation decreasing by 32% at a decadal rate of 28.5 mm. In particular, precipitation decrease is more pronounced in the summer and warm seasons when water use is at its seasonal peak. We further analyzed hourly precipitation data from 43 rain gauges between 1980 and 2012 to examine the spatiotemporal characteristics of both precipitation amount and intensity across six distinct subregions in Beijing. No significant spatial variations in precipitation changes were identified, but slightly greater amounts of precipitation were noted in the urban areas (plains) than in the surrounding suburbs (mountains), due to the effect of urbanization and topography. Precipitation intensity has increased substantially, especially at the hourly duration, as evidenced by the more frequent occurrence of extreme storms. The observed decreased water availability and the increase in extreme weather events require more integrated water management, particularly given the expectation of a warmer and more variable climate, the continued rapid growth of the Beijing metropolis, and the intensifying conflict between water supply and demand.

Abstract Recent heat waves have been a matter of serious concern for India because of potential impacts on agriculture, food security, and socioeconomic progress. This study examines the trends and variability in frequency, duration, and intensity of hot episodes during three time periods (1951–2013, 1981–2013 and 1998–2013) by defining heat waves based on the percentile of maximum, minimum, and mean temperatures. The study also explores heat waves and their relationships with hydroclimatic variables, such as rainfall, terrestrial water storage, Palmer drought severity index, and sea surface temperature. Results reveal that the number, frequency, and duration of daytime heat waves increased considerably during the post‐1980 dry and hot phase over a large area. The densely populated and agriculturally dominated northern half of India stands out as a key region where the nighttime heat wave metrics reflected the most pronounced amplifications. Despite the recent warming hiatus in India and other parts of the world, we find that both daytime and nighttime extreme measures have undergone substantial changes during or in the year following a dry year since 2002, with the probability distribution functions manifesting a hotter‐than‐normal climate during 1998–2013. This study shows that a few months preceding the 2010 record‐breaking heat wave in Russia, India experienced the largest hot episode in the country's history. Interestingly, both these mega events are comparable in terms of their evolution and amplification. These findings emphasize the importance of planning for strategies in the context of the rising cooccurrence of dry and hot events.

A multicriteria algorithm, the MultiObjective Generalized Sensitivity Analysis (MOGSA), was used to investigate the parameter sensitivity of five different land surface models with increasing levels of complexity in the physical representation of the vegetation (BUCKET, CHASM, BATS 1, Noah, and BATS 2) at five different sites representing crop land/pasture, grassland, rain forest, cropland, and semidesert areas. The methodology allows for the inclusion of parameter interaction and does not require assumptions of independence between parameters, while at the same time allowing for the ranking of several single‐criterion and a global multicriteria sensitivity indices. The analysis required on the order of 50 thousand model runs. The results confirm that parameters with similar “physical meaning” across different model structures behave in different ways depending on the model and the locations. It is also shown that after a certain level an increase in model structure complexity does not necessarily lead to better parameter identifiability, i.e., higher sensitivity, and that a certain level of overparameterization is observed. For the case of the BATS 1 and BATS 2 models, with essentially the same model structure but a more sophisticated vegetation model, paradoxically, the effect on parameter sensitivity is mainly reflected in the sensitivity of the soil‐related parameters.

High-resolution real-time satellite-based precipitation estimation datasets can play a more essential role in flood forecasting and risk analysis of infrastructures. This is particularly true for extended deserts or mountainous areas with sparse rain gauges like Iran. However, there are discrepancies between these satellite-based estimations and ground measurements, and it is necessary to apply adjustment methods to reduce systematic bias in these products. In this study, we apply a quantile mapping method with gauge information to reduce the systematic error of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). Due to the availability and quality of the ground-based measurements, we divide Iran into seven climate regions to increase the sample size for generating cumulative probability distributions within each region. The cumulative distribution functions (CDFs) are then employed with a quantile mapping 0.6° × 0.6° filter to adjust the values of PERSIANN-CCS. We use eight years (2009–2016) of historical data to calibrate our method, generating nonparametric cumulative distribution functions of ground-based measurements and satellite estimations for each climate region, as well as two years (2017–2018) of additional data to validate our approach. The results show that the bias correction approach improves PERSIANN-CCS data at aggregated to monthly, seasonal and annual scales for both the calibration and validation periods. The areal average of the annual bias and annual root mean square errors are reduced by 98% and 56% during the calibration and validation periods, respectively. Furthermore, the averages of the bias and root mean square error of the monthly time series decrease by 96% and 26% during the calibration and validation periods, respectively. There are some limitations in bias correction in the Southern region of the Caspian Sea because of shortcomings of the satellite-based products in recognizing orographic clouds.

Abstract This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS). The PERSIANN‐CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN‐CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN‐CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO‐based PERSIANN‐CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO‐based PERSIANN‐CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN‐CCS rainfall estimation is obtained.

Malaria control programs in Africa encounter daunting challenges that hinder progressive steps toward elimination of the disease. These challenges include widespread insecticide resistance in mosquito vectors, increasing outdoor malaria transmission, lack of vector surveillance and control tools suitable for outdoor biting vectors, weakness in malaria surveillance, and an inadequate number of skilled healthcare personnel. Ecological and epidemiological changes induced by environmental modifications resulting from water resource development projects pose additional barriers to malaria control. Cognizant of these challenges, our International Center of Excellence for Malaria Research (ICEMR) works in close collaboration with relevant government ministries and agencies to align its research efforts with the objectives and strategies of the national malaria control and elimination programs for the benefit of local communities. Our overall goal is to assess the impact of water resource development projects, shifting agricultural practices, and vector interventions on Plasmodium falciparum and P. vivax malaria in Kenya and Ethiopia. From 2017 to date, the ICEMR has advanced knowledge of malaria epidemiology, transmission, immunology, and pathogenesis, and developed tools to enhance vector surveillance and control, improved clinical malaria surveillance and diagnostic methods, and strengthened the capacity of local healthcare providers. Research findings from the ICEMR will inform health policy and strategic planning by ministries of health in their quest to sustain malaria control and achieve elimination goals.

A new estimation strategy for estimating the parameters of the Heffernan and Tawn conditional extreme value model is proposed. The technique makes use of empirical Bayes estimation for the conditional likelihood that otherwise does not have a simple closed‐form expression. The approach is tested on simulations from different types of extreme dependence (and independence) structures, as well as for two real data cases consisting of precipitation analysis conditional on extreme temperature in Boulder, Colorado, and Los Angeles, California, USA. The strategy generally has good coverage when informative priors are used for one of the parameters, except for the independence case where the coverage is low until the sample size reaches about 50. Results for the precipitation and temperature data are found to be consistent with the semi‐non‐parametric strategy. The presented model can be potentially applied in a wide variety of science fields, especially in earth, environment and climate sciences. Copyright © 2014 John Wiley & Sons, Ltd.

Food insecurity, recurrent famine, and poverty threaten the health of millions of African residents. Construction of dams and rural irrigation schemes is key to solving these problems. The sub-Saharan Africa International Center of Excellence for Malaria Research addresses major knowledge gaps and challenges in Plasmodium falciparum and Plasmodium vivax malaria control and elimination in malaria-endemic areas of Kenya and Ethiopia where major investments in water resource development are taking place. This article highlights progress of the International Center of Excellence for Malaria Research in malaria vector ecology and behavior, epidemiology, and pathogenesis since its inception in 2017. Studies conducted in four field sites in Kenya and Ethiopia show that dams and irrigation increased the abundance, stability, and productivity of larval habitats, resulting in increased malaria transmission and a greater disease burden. These field studies, together with hydrological and malaria transmission modeling, enhance the ability to predict the impact of water resource development projects on vector larval ecology and malaria risks, thereby facilitating the development of optimal water and environmental management practices in the context of malaria control efforts. Intersectoral collaborations and community engagement are crucial to develop and implement cost-effective malaria control strategies that meet food security needs while controlling malaria burden in local communities.

Scientists and stakeholders from precipitation sensor developers, remote sensing algorithm developers, and data users gathered to discuss the state of the science and users’ needs for operational precipitation algorithms and products from current and future meteorological satellites.

A combination of accelerated population growth and severe droughts has created pressure on food security and driven the development of irrigation schemes across sub-Saharan Africa. Irrigation has been associated with increased malaria risk, but risk prediction remains difficult due to the heterogeneity of irrigation and the environment. While investigating transmission dynamics is helpful, malaria models cannot be applied directly in irrigated regions as they typically rely only on rainfall as a source of water to quantify larval habitats. By coupling a hydrologic model with an agent-based malaria model for a sugarcane plantation site in Arjo, Ethiopia, we demonstrated how incorporating hydrologic processes to estimate larval habitats can affect malaria transmission. Using the coupled model, we then examined the impact of an existing irrigation scheme on malaria transmission dynamics. The inclusion of hydrologic processes increased the variability of larval habitat area by around two-fold and resulted in reduction in malaria transmission by 60%. In addition, irrigation increased all habitat types in the dry season by up to 7.4 times. It converted temporary and semi-permanent habitats to permanent habitats during the rainy season, which grew by about 24%. Consequently, malaria transmission was sustained all-year round and intensified during the main transmission season, with the peak shifted forward by around 1 month. Lastly, we evaluated the spatiotemporal distribution of adult vectors under the effect of irrigation by resolving habitat heterogeneity. These findings could help larval source management by identifying transmission hotspots and prioritizing resources for malaria elimination planning.

Accurate and reliable rainfall-runoff modeling is essential for flood forecasting, drought monitoring, and reservoir management. Currently, the benchmark practice in data-driven modeling is to train Long Short-Term Memory (LSTM) models across multiple catchments to learn diverse hydrological responses. However, the recurrent structure and inductive bias of LSTMs tend to emphasize recent inputs, making them less effective at capturing very long-range dependencies. Their sequential, thus not parallel structure also limits scalability for very large-scale datasets. With the rapid growth of Earth observations, a key challenge still remains whether a model can effectively train data from multi-sources, learn long-range temporal dependencies, and scale performance with increasing data availability? In this study, we introduce an Attention-based model that outperforms LSTM with a median Nash-Sutcliffe Efficiency (NSE) of 0.781 and Kling-Gupta Efficiency (KGE) of 0.808 compared to LSTM’s NSE of 0.765 and KGE of 0.760, respectively, without relying on ensemble runs or multiple precipitation datasets. The model also demonstrates improved skills in high-flow events and shows continued performance with additional training data. In addition, the Attention-based model offers enhanced interpretability, by assigning dynamic importance to historical meteorological forcings and captures diverse rainfall–runoff responses. Furthermore, by embedding catchment-specific attributes, the model adapts to different hydrological conditions and allows extraction of generalized knowledge through activation-based analysis. This study highlights the potential of Attention-based models in hydrological modeling, particularly for scalable, data-driven flood prediction at continental to global scales.

Data-driven rainfall–runoff models have advanced rapidly, yet most still rely on lumped meteorological forcings that smooth out critical spatial variability in precipitation, land cover, and soil properties. This simplification can bias in flood peaks, hydrograph timing, and water balance estimates. Here we develop a generalizable deep learning framework that ingests spatially distributed (gridded) meteorological forcings and evaluates it across 921 catchments in North America using the CAMELS-SPAT dataset. We compare two strategies for fusing spatial and temporal information, i.e., a spatial‑to‑temporal architecture and a temporal‑to‑spatial architecture, and assess the added value of gridded catchment attributes and finer spatial resolution. The temporal‑to‑spatial approach consistently offers the best performance, increasing median NSE from 0.60 (lumped) to 0.65 and KGE from 0.63 to 0.69, with improvements in 75% of basins for NSE. Gains are largest in small and dry catchments. Higher‑resolution inputs further improve low‑flow representation and peak timing. Integrated Gradients analysis shows that the model learns spatially coherent hydrologic behavior, highlighting grid cells near the outlet and identifying dominant meteorological drivers of peak events. These results demonstrate the practical value of spatially distributed forcings for data‑driven hydrology and outline a pathway for interpretable spatial modeling across large sample domains.