Initial Hydrologic Conditions Research Articles

Seasonal Soil Moisture Drought Prediction over Europe Using the North American Multi-Model Ensemble (NMME)

Abstract Droughts diminish crop yields and can lead to severe socioeconomic damages and humanitarian crises (e.g., famine). Hydrologic predictions of soil moisture droughts several months in advance are needed to mitigate the impact of these extreme events. In this study, the performance of a seasonal hydrologic prediction system for soil moisture drought forecasting over Europe is investigated. The prediction system is based on meteorological forecasts of the North American Multi-Model Ensemble (NMME) that are used to drive the mesoscale hydrologic model (mHM). The skill of the NMME-based forecasts is compared against those based on the ensemble streamflow prediction (ESP) approach for the hindcast period of 1983–2009. The NMME-based forecasts exhibit an equitable threat score that is, on average, 69% higher than the ESP-based ones at 6-month lead time. Among the NMME-based forecasts, the full ensemble outperforms the single best-performing model CFSv2, as well as all subensembles. Subensembles, however, could be useful for operational forecasting because they are showing only minor performance losses (less than 1%), but at substantially reduced computational costs (up to 60%). Regardless of the employed forecasting approach, there is considerable variability in the forecasting skill ranging up to 40% in space and time. High skill is observed when forecasts are mainly determined by initial hydrologic conditions. In general, the NMME-based seasonal forecasting system is well suited for a seamless drought prediction system as it outperforms ESP-based forecasts consistently over the entire study domain at all lead times.

Western Mediterranean environmental changes: Evidences from fluvial archives

When dealing with current and past landscape evolution, a key issue addresses responses of geomorphic systems to the large number of influencing variables. Identifying morphodynamic phases and revealing interrelations with specific driving forces are demanding tasks for Quaternary research. In this paper, we present late Pleistocene and Holocene fluvial sedimentation patterns of three Western Mediterranean river catchments, namely Jarama River, Guadalete River and Guadalquivir River that extent along a climatic transect from semi-humid SW-Spain to semi-arid central Spain. These studies are based on extensive fieldwork conducted on 36 exposures and 13 drillings in floodplain positions. Field data is supported by geochemical analyses, while the chronological framework was obtained from the analyses of 70 radiocarbon samples. Results show distinct patterns of fluvial sedimentation as well as soil formation linked to floodplain stability for each river catchment. On regional or catchment scale, pollen stratigraphical correlation and comparison with lacustrine records show that fluvial dynamics have a strong reaction to climatic shifts, with phases of high fragility characterized by catchment erosion and floodplain sedimentation in response to climatic aridification events and phases of climate change in general. The comparison of the examined river systems reveals that periods of supra-regional floodplain sedimentation in several catchments occurred from 8.0 to 7.0, 5.0 to 3.8, 2.2 to 1.5, and around 1.0 as well as 0.4 ka cal. BP, while we found periods of supra-regional soil formation from 13.3 to 12.7, 7.0 to 5.1 (with a short interruption around 6.0 to 5.5 ka), 2.8 to 2.3 ka, 1.4 to 1.2 ka, and 0.8 to 0.5 ka cal. BP. Beside these consistencies we found deviating dynamic patterns that are apparently expressed in terms of differing onset and offset, differing durations, or even the lack of fluvial system response. The main reasons for this can be seen in different regional climate condition and impacts of further influencing factors, or in different levels of sensitivity of the river catchments that may be controlled by initial hydrological conditions, catchment size, or the degree of anthropogenic influence. A larger scale assessment shows that fluvial dynamic patterns are hardly comparable across entire Spain due to strong spatial heterogeneity of physiographic and climatic conditions on the Iberian Peninsula, in particular when areas are influenced by different circulation systems (e.g. regions influenced by the Atlantic Ocean vs. regions influenced by the Mediterranean Sea). However, the consideration of North Atlantic marine records reveals a certain coupling between North Atlantic coolings, atmospheric processes leading to arid climate over large parts of Spain, as well as increased landscape instability including strong fluvial sedimentation activity. Attendant atmospheric conditions are discussed.

A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada

Abstract Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall–Runoff Regime

Abstract Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.

A seasonal agricultural drought forecast system for food-insecure regions of East Africa

Abstract. The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this forecast system for a region of equatorial EA (2° S–8° N, 36–46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM forecasts generated by this system on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM forecasts with start-of-season (SOS) (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the forecast was initialized with midseason (i.e., 5 April) SM conditions, the skill of forecasting SM estimates until the end-of-season improved (correlation > 0.5 over several grid cells). We also found these SM forecasts to be more skillful than the ones generated using the Ensemble Streamflow Prediction (ESP) method, which derives its hydrologic forecast skill solely from the knowledge of the initial hydrologic conditions. Finally, we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years (when standardized anomaly of MAM precipitation is below 0). This indicates that this system might be particularity useful for identifying drought events in this region and can support decision-making for mitigation or humanitarian assistance.

On the sources of global land surface hydrologic predictability

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall–runoff regime

Abstract. Skillful seasonal streamflow forecasts obtained from climate and land surface conditions could significantly improve water and energy management. Since climate forecasts are updated on a monthly basis, we evaluate the potential in developing operational monthly streamflow forecasts on a continuous basis throughout the year. Further, basins in the rainfall–runoff regime critically depend on the forecasted precipitation in the upcoming months as opposed to snowmelt regimes where initial hydrological conditions (IHC) play a critical role. The goal of this study is to quantify the role of updated monthly precipitation forecasts and IHC in forecasting 6-month lead monthly streamflow and soil moisture for a rainfall–runoff mechanism dominated basin – Apalachicola River at Chattahoochee, FL. The Variable Infiltration Capacity (VIC) land surface model is implemented with two forcings: (a) updated monthly precipitation forecasts from ECHAM4.5 Atmospheric General Circulation Model (AGCM) forced with sea surface temperature forecasts and (b) daily climatological ensembles. The difference in skill between the above two quantifies the improvements that could be attainable using the AGCM forecasts. Monthly retrospective streamflow forecasts are developed from 1981 to 2010 and streamflow forecasts estimated from the VIC model are also compared with those predicted by using the principal component regression (PCR) model. The mean square error (MSE) in predicting monthly streamflows, using the VIC model, are compared with the MSE of streamflow climatology under ENSO (El Niño Southern Oscilation) conditions as well as under normal years. Results indicate that VIC forecasts obtained using ECHAM4.5 are significantly better than VIC forecasts obtained using climatological ensembles and PCR models over 2–6 month lead time during winter and spring seasons in capturing streamflow variability and reduced mean square errors. However, at 1-month lead time, streamflow utilizing the climatological forcing scheme outperformed ECHAM4.5 based streamflow forecasts during winter and spring, indicating a dominant role of IHCs up to a 1-month lead time. During ENSO years, streamflow forecasts exhibit better skill even up to a six-month lead time. Comparisons of the seasonal soil moisture forecasts, developed using ECHAM4.5 forcings, with seasonal streamflows also show significant skill, up to a 6-month lead time, in the four seasons.

Value of medium range weather forecasts in the improvement of seasonal hydrologic prediction skill

Abstract. We investigated the contribution of medium range weather forecasts with lead times of up to 14 days to seasonal hydrologic prediction skill over the conterminous United States (CONUS). Three different Ensemble Streamflow Prediction (ESP) based experiments were performed for the period 1980–2003 using the Variable Infiltration Capacity (VIC) hydrology model to generate forecasts of monthly runoff and soil moisture (SM) at lead-1 (first month of the forecast period) to lead-3. The first experiment (ESP) used a resampling from the retrospective period 1980–2003 and represented full climatological uncertainty for the entire forecast period. In the second and third experiments, the first 14 days of each ESP ensemble member were replaced by either observations (perfect 14-day forecast) or by a deterministic 14-day weather forecast. We used Spearman rank correlations of forecasts and observations as the forecast skill score. We estimated the potential and actual improvement in baseline skill as the difference between the skill of experiments 2 and 3 relative to ESP, respectively. We found that useful runoff and SM forecast skill at lead-1 to -3 months can be obtained by exploiting medium range weather forecast skill in conjunction with the skill derived by the knowledge of initial hydrologic conditions. Potential improvement in baseline skill by using medium range weather forecasts for runoff [SM] forecasts generally varies from 0 to 0.8 [0 to 0.5] as measured by differences in correlations, with actual improvement generally from 0 to 0.8 of the potential improvement. With some exceptions, most of the improvement in runoff is for lead-1 forecasts, although some improvement in SM was achieved at lead-2.

Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill

Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.

Evaluation of Three Numerical Weather Prediction Models for Short and Medium Range Agrohydrological Applications

The skill and accuracy of the quantitative precipitation forecasts by CCAM, UM and NCEP-MRF models are verified using various statistical scores at the Mgeni catchment in KwaZulu-Natal, South Africa. The CCAM model is capable of identifying a rainfall event, but with a tendency of under-estimating its magnitude. The UM model is capable of distinguishing rainy days from non-rainy days, but with a significant over-estimation of rainfall amount. There is no significant difference between the 1 and 2 day lead time UM forecasts. Statistical comparisons show that there is an acceptable skill in the CCAM forecasts, but the forecast skill of the UM model is low and unreliable. The role of the initial hydrological conditions in affecting the accuracy of CCAM and UM streamflows forecasts was significant. The results show that the under-estimation of the CCAM forecasts was reduced from −44% to −10%, while the over-estimation in the UM forecasts was reduced from 291% to only 59% when the ACRU agrohydrological model was initialised with observed rainfalls up to the previous day at each forecast run within the study period. The combined use of the CCAM and UM models by a “weighted averaging” had little effect in improving the skill as it is overshadowed more by the over-estimation of the UM forecasts than the under-estimation of the CCAM forecasts. Results obtained for a continuous period of 92 days showed that the NCEP-MRF rainfall forecasts were significantly over-predicted. The NCEP-MRF rainfall forecast is found to be totally unskillful, although the skill was seen to slightly increase with decreasing lead time.

Flash flood forecasting combining meteorological ensemble forecasts and uncertainty of initial hydrological conditions

Flood forecasting for fast responding catchments encounters problems, especially in terms of short warning periods and a very limited reliability. Within a new stochastic framework based on rigorous rainfall-runoff modelling and Monte Carlo simulations, we consider uncertainties of two sources: (i) uncertainty from the estimation of initial hydrological conditions, and (ii) the uncertainty of the meteorological rainfall forecast. We avoided the high computational demand of extensive Monte Carlo simulations by using a symbiosis between physically-based hydrological modelling and computationally highly efficient artificial intelligence techniques. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting; see Schmitz et al, 2005, and Cullmann, 2006) employs a physically-based hydrological/hydraulic model of the considered catchment for generating, in a first step, the complete range of realistic possible flood scenarios on the basis of a catchment specific meteorological analysis. The resulting database of corresponding input/output vectors – supplemented by generally available hydrological and meteorological data for characterising the catchment situation prior to a storm event – serves, in a second step, for setting up a set of task-specific artificial neural networks (ANN), which finally portray both the rainfall-runoff process and the hydrodynamic flood wave propagation in the river. We subsequently use this tool for investigating the global uncertainty of flash flood forecasting in a small-to medium-sized catchment on the basis of a comprehensive Monte Carlo analysis. Along these lines, the computationally highly efficient PAI-OFF technique allowed performing an extensive number of runs for obtaining ensembles of predicted stream flow that can be used to evaluate probabilities of exceedance of critical river stages/flows via an integration of both the hydrological uncertainty and the meteorological uncertainty. This approach was then implemented and applied to the Freiberger Mulde catchment in the Ore Mountains in Eastern Germany (with an area of about 3000 km2). The results of the overall ensemble predictions in the form of the ensemble mean values unveiled an astonishing underestimation of the recorded flood peak – most due to the bias of the considered initial hydrological conditions.

A retrospective assessment of National Centers for Environmental Prediction climate model–based ensemble hydrologic forecasting in the western United States

We assess the potential forecast skill of a climate model–based approach for seasonal ensemble hydrologic and streamflow forecasting for the western United States. By using climate model ensemble forecasts and ensembles formed via the resampling of observations, we distinguish hydrologic forecast skill resulting from the predictable evolution of initial hydrologic conditions from that derived from the climate model forecasts. Monthly climate model ensembles of precipitation and temperature produced by the National Centers for Environmental prediction global spectral model (GSM) are downscaled for use as forcings of the variable infiltration capacity (VIC) hydrologic model. VIC then simulates ensembles of streamflow and spatially distributed hydrologic variables such as snowpack, soil moisture, and runoff. The regional averages of the ensemble forcings and derived hydrologic variables were evaluated over five regions: the Pacific Northwest, California, the Great Basin, the Colorado River basin, and the upper Rio Grande River basin. The skill assessment focuses on a retrospective 21‐year period (1979–1999) during which GSM retrospective forecast ensembles (termed hindcasts), created using similar procedures to GSM real‐time forecasts, are available. The observational verification data set for the hindcasts was a retrospective hydroclimatology at 1/8°–1/4° consisting of gridded observations of temperature and precipitation and gridded hydrologic simulation results (for hydrologic variables and streamflow) based on the observed meteorological inputs. The GSM hindcast skill was assessed relative to that of a naive ensemble climatology forecast and to that of ensemble streamflow prediction (ESP) hindcasts, a forecast baseline sharing the same initial condition information as the GSM‐based hindcasts. We found that the unconditional (all years) GSM hindcasts for regionally averaged variables provided practically no skill improvement over the ESP hindcasts and did not lead to improved regional hydrologic variable or streamflow forecasts. GSM‐based conditional (strong El Niño–Southern Oscillation (ENSO) years only) hindcasts, however, had higher skill in a number of hindcast months for surface air temperature, with mixed results (better and worse) for precipitation, depending on location and season. Consequently, for California and to a lesser extent the Pacific Northwest and Great Basin, hydrologic hindcast skill in winter and fall increased enough under the strong ENSO composite that streamflow hindcasts were measurably better than with ESP. The opposite was found, however, for the Colorado and upper Rio Grande River basins, where the ENSO teleconnection is somewhat weaker.

A Local Forecast of Land Surface Wetness Conditions Derived from Seasonal Climate Predictions

An ensemble local hydrologic forecast derived from the seasonal forecasts of the International Research Institute for Climate Prediction (IRI) is presented. Three-month seasonal forecasts were used to resample historical meteorological conditions and generate ensemble forcing datasets for a TOPMODEL-based hydrology model. Eleven retrospective forecasts were run at Florida and New York sites. Forecast skill was assessed for mean area modeled water table depth (WTD) and compared with WTD simulated with observed data. Hydrology model forecast skill was evident at the Florida site. Persistence of initial hydrologic conditions and local skill of the IRI seasonal forecast contributed to this local hydrologic forecast skill. At the New York site, there was no persistence of initial hydrologic conditions and local skill of the IRI seasonal forecast was poor; these factors precluded local hydrologic forecast skill at this site.

A HYDROLOGICAL MODEL STUDY OF A FORESTED AND A CUTOVER SLOPE

ABSTRACT The problem of analysing and predicting the effect of vegetation removal on the hydrological regime or soil water potential on steep slopes was approached through an experimental model set up in a greenhouse. A tilting soil bin positioned at different slope angles simulated a soil layer overlying impervious bedrock, and a forest cover was modelled by a number of small trees. The response of the soil water potential to simulated rain storms was studied first for the forested and later for the cutover slope. Transpiration in the greenhouse was measured by means of phytometers. The results showed that a forest cover accelerates the soil moisture depletion in a shallow soil mantle regardless of the steepness of the slope. For a storm of given intensity and duration, the recharge phase for the clear-cut slope was therefore much shorter than for the forested one. The slope angle was only responsible for the distribution of recharge and the initial hydrological conditions when drainage had ceased. During the drainage phase the flow in the slope was two-dimensional, whereas it was essentially one-dimensional (i.e. vertical upwards) after field capacity had been reached. At this point the matric suction increased exponentially with time for suctions within the tensiometer range. Within this same range water loss from any point in the soil took place at a constant rate. This result is consistent with field observations.